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docs: Add JAX MaxText benchmark v25.7 (#5182)

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Peter Park
2025-09-08 21:42:56 -04:00
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parent 94476f34ca
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2
.wordlist.txt
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@@ -293,6 +293,7 @@ Multicore
Multithreaded Multithreaded
MyEnvironment MyEnvironment
MyST MyST
NANOO
NBIO NBIO
NBIOs NBIOs
NCCL NCCL
@@ -742,6 +743,7 @@ logits
lossy lossy
macOS macOS
matchers matchers
maxtext
megatron megatron
microarchitecture microarchitecture
migraphx migraphx

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docs/data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml Normal file
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@@ -0,0 +1,72 @@
dockers:
- pull_tag: rocm/jax-training:maxtext-v25.7
docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7/images/sha256-45f4c727d4019a63fc47313d3a5f5a5105569539294ddfd2d742218212ae9025
components:
ROCm: 6.4.1
JAX: 0.5.0
Python: 3.10.12
Transformer Engine: 2.1.0+90d703dd
hipBLASLt: 1.x.x
- pull_tag: rocm/jax-training:maxtext-v25.7-jax060
docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7/images/sha256-45f4c727d4019a63fc47313d3a5f5a5105569539294ddfd2d742218212ae9025
components:
ROCm: 6.4.1
JAX: 0.6.0
Python: 3.10.12
Transformer Engine: 2.1.0+90d703dd
hipBLASLt: 1.1.0-499ece1c21
model_groups:
- group: Meta Llama
tag: llama
models:
- model: Llama 3.3 70B
mad_tag: jax_maxtext_train_llama-3.3-70b
model_repo: Llama-3.3-70B
precision: bf16
doc_options: ["single-node"]
- model: Llama 3.1 8B
mad_tag: jax_maxtext_train_llama-3.1-8b
model_repo: Llama-3.1-8B
precision: bf16
doc_options: ["single-node"]
- model: Llama 3.1 70B
mad_tag: jax_maxtext_train_llama-3.1-70b
model_repo: Llama-3.1-70B
precision: bf16
doc_options: ["single-node"]
- model: Llama 3 8B
mad_tag: jax_maxtext_train_llama-3-8b
multinode_training_script: llama3_8b_multinode.sh
doc_options: ["multi-node"]
- model: Llama 3 70B
mad_tag: jax_maxtext_train_llama-3-70b
multinode_training_script: llama3_70b_multinode.sh
doc_options: ["multi-node"]
- model: Llama 2 7B
mad_tag: jax_maxtext_train_llama-2-7b
model_repo: Llama-2-7B
precision: bf16
multinode_training_script: llama2_7b_multinode.sh
doc_options: ["single-node", "multi-node"]
- model: Llama 2 70B
mad_tag: jax_maxtext_train_llama-2-70b
model_repo: Llama-2-70B
precision: bf16
multinode_training_script: llama2_70b_multinode.sh
doc_options: ["single-node", "multi-node"]
- group: DeepSeek
tag: deepseek
models:
- model: DeepSeek-V2-Lite (16B)
mad_tag: jax_maxtext_train_deepseek-v2-lite-16b
model_repo: DeepSeek-V2-lite
precision: bf16
doc_options: ["single-node"]
- group: Mistral AI
tag: mistral
models:
- model: Mixtral 8x7B
mad_tag: jax_maxtext_train_mixtral-8x7b
model_repo: Mixtral-8x7B
precision: bf16
doc_options: ["single-node"]

383
docs/how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
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@@ -2,9 +2,9 @@
:description: How to train a model using JAX MaxText for ROCm. :description: How to train a model using JAX MaxText for ROCm.
:keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker :keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
************************************** ******************************************
Training a model with MaxText for ROCm Training a model with JAX MaxText for ROCm
************************************** ******************************************
MaxText is a high-performance, open-source framework built on the Google JAX 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 machine learning library to train LLMs at scale. The MaxText framework for
@@ -12,70 +12,108 @@ ROCm is an optimized fork of the upstream
`<https://github.com/AI-Hypercomputer/maxtext>`__ enabling efficient AI workloads `<https://github.com/AI-Hypercomputer/maxtext>`__ enabling efficient AI workloads
on AMD MI300X series accelerators. on AMD MI300X series accelerators.
The MaxText for ROCm training Docker (``rocm/jax-training:maxtext-v25.5``) image 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 accelerators,
including essential components like JAX, XLA, ROCm libraries, and MaxText utilities. including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
It includes the following software components: It includes the following software components:
+--------------------------+--------------------------------+ .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
| Software component | Version |
+==========================+================================+
| ROCm | 6.3.4 |
+--------------------------+--------------------------------+
| JAX | 0.4.35 |
+--------------------------+--------------------------------+
| Python | 3.10.12 |
+--------------------------+--------------------------------+
| Transformer Engine | 1.12.0.dev0+b8b92dc |
+--------------------------+--------------------------------+
| hipBLASLt | 0.13.0-ae9c477a |
+--------------------------+--------------------------------+
Supported features and models {% set dockers = data.dockers %}
============================= .. tab-set::
MaxText provides the following key features to train large language models efficiently: {% for docker in dockers %}
{% set jax_version = docker.components["JAX"] %}
.. tab-item:: JAX {{ jax_version }}
:sync: {{ docker.pull_tag }}
.. list-table::
:header-rows: 1
* - Software component
- Version
{% for component_name, component_version in docker.components.items() %}
* - {{ component_name }}
- {{ component_version }}
{% endfor %}
{% if jax_version == "0.6.0" %}
.. note::
Shardy is a new config in JAX 0.6.0. You might get related errors if it's
not configured correctly. For now you can turn it off by setting
``shardy=False`` during the training run. You can also follow the `migration
guide <https://docs.jax.dev/en/latest/shardy_jax_migration.html>`__ to enable
it.
The provided multi-node training scripts in this documentation are
not currently supported with JAX 0.6.0. For multi-node training, use the JAX 0.5.0
Docker image.
{% endif %}
{% endfor %}
MaxText with on ROCm provides the following key features to train large language models efficiently:
- Transformer Engine (TE) - Transformer Engine (TE)
- Flash Attention (FA) 3 - Flash Attention (FA) 3 -- with or without sequence input packing
- GEMM tuning - GEMM tuning
- Multi-node support - Multi-node support
.. _amd-maxtext-model-support: - NANOO FP8 quantization support
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators. .. _amd-maxtext-model-support-v257:
* Llama 3.3 70B Supported models
================
* Llama 3.1 8B The following models are pre-optimized for performance on AMD Instinct MI300
series accelerators. Some instructions, commands, and available training
configurations in this documentation might vary by model -- select one to get
started.
* Llama 3.1 70B .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
* Llama 3 8B {% set model_groups = data.model_groups %}
.. raw:: html
* Llama 3 70B <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
{% for model_group in model_groups %}
<div class="col-4 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
* Llama 2 7B <div class="row mt-1">
<div class="col-2 me-2 model-param-head">Model variant</div>
* Llama 2 70B <div class="row col-10">
{% for model_group in model_groups %}
* DeepSeek-V2-Lite {% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
</div>
</div>
</div>
.. note:: .. note::
Some models, such as Llama 3, require an external license agreement through Some models, such as Llama 3, require an external license agreement through
a third party (for example, Meta). a third party (for example, Meta).
Unsupported features
--------------------
Currently, MaxText's default packed input format is not supported. Using this format
with the current Docker image results in incorrect attention calculations
across different input sequences. Support for packed input format is planned for a future release.
System validation System validation
================= =================
@@ -98,14 +136,14 @@ This Docker image is optimized for specific model configurations outlined
as follows. Performance can vary for other training workloads, as AMD as follows. Performance can vary for other training workloads, as AMD
doesn’t validate configurations and run conditions outside those described. doesn’t validate configurations and run conditions outside those described.
.. _amd-maxtext-multi-node-setup: .. _amd-maxtext-multi-node-setup-v257:
Multi-node setup Multi-node setup
---------------- ----------------
For multi-node environments, ensure you have all the necessary packages for 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 your network device, such as, RDMA. If you're not using a multi-node setup
with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`. with RDMA, skip ahead to :ref:`amd-maxtext-get-started-v257`.
1. Install the following packages to build and install the RDMA driver. 1. Install the following packages to build and install the RDMA driver.
@@ -180,196 +218,203 @@ with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
# If using Mellanox NIC # If using Mellanox NIC
export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9 export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9
.. _amd-maxtext-download-docker: .. _amd-maxtext-get-started-v257:
Pull the Docker image Benchmarking
--------------------- ============
1. Use the following command to pull the Docker image from Docker Hub. Once the setup is complete, choose between two options to reproduce the
benchmark results:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
.. _vllm-benchmark-mad:
{% set dockers = data.dockers %}
{% set model_groups = data.model_groups %}
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{model.mad_tag}}
.. tab-set::
{% if model.mad_tag and "single-node" in model.doc_options %}
.. 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 .. code-block:: shell
docker pull rocm/jax-training:maxtext-v25.5 git clone https://github.com/ROCm/MAD
cd MAD
pip install -r requirements.txt
2. Use the following command to launch the Docker container. Note that the benchmarking scripts 2. Use this command to run the performance benchmark test on the {{ model.model }} model
used in the :ref:`following section <amd-maxtext-get-started>` automatically launch the Docker container using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
and execute the benchmark.
.. code-block:: shell .. code-block:: shell
docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME/.ssh:/root/.ssh --shm-size 128G --name maxtext_training rocm/jax-training:maxtext-v25.5 export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
madengine run \
--tags {{model.mad_tag}} \
--keep-model-dir \
--live-output \
--timeout 28800
.. _amd-maxtext-get-started: MAD launches a Docker container with the name
``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
model are collected in the following path: ``~/MAD/perf.csv/``.
{% endif %}
Getting started .. tab-item:: Standalone benchmarking
===============
The following examples demonstrate how to get started with single node .. rubric:: Download the Docker image and required scripts
and multi-node training using the benchmarking scripts provided at
`<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__.
.. important:: Run the JAX MaxText benchmark tool independently by starting the
Docker container as shown in the following snippet.
The provided scripts launch a Docker container and execute a benchmark. Ensure you run these commands outside of any existing Docker container. .. tab-set::
{% for docker in dockers %}
{% set jax_version = docker.components["JAX"] %}
Before running any benchmarks, ensure the ``$HF_HOME`` environment variable is .. tab-item:: JAX {{ jax_version }}
set correctly and points to your Hugging Face cache directory. Refer to the :sync: {{ docker.pull_tag }}
README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__
for more detailed instructions.
Single node training benchmarking examples
------------------------------------------
* Example 1: Single node training with Llama 2 7B
Download the benchmarking script:
.. code-block:: shell .. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_7b.sh docker pull {{ docker.pull_tag }}
{% endfor %}
Run the single node training benchmark: {% if model.model_repo and "single-node" in model.doc_options %}
.. rubric:: Single node training
1. Set up environment variables.
.. code-block:: shell .. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_7b.sh export MAD_SECRETS_HFTOKEN=<Your Hugging Face token>
export HF_HOME=<Location of saved/cached Hugging Face models>
* Example 2: Single node training with Llama 2 70B ``MAD_SECRETS_HFTOKEN`` is your Hugging Face access token to access models, tokenizers, and data.
See `User access tokens <https://huggingface.co/docs/hub/en/security-tokens>`__.
Download the benchmarking script: ``HF_HOME`` is where ``huggingface_hub`` will store local data. See `huggingface_hub CLI <https://huggingface.co/docs/huggingface_hub/main/en/guides/cli#huggingface-cli-download>`__.
If you already have downloaded or cached Hugging Face artifacts, set this variable to that path.
Downloaded files typically get cached to ``~/.cache/huggingface``.
2. Launch the Docker container.
.. tab-set::
{% for docker in dockers %}
{% set jax_version = docker.components["JAX"] %}
.. tab-item:: JAX {{ jax_version }}
:sync: {{ docker.pull_tag }}
.. code-block:: shell .. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_70b.sh 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 \
-v $HF_HOME:/hf_cache \
-e HF_HOME=/hf_cache \
-e MAD_SECRETS_HFTOKEN=$MAD_SECRETS_HFTOKEN
--shm-size 64G \
--name training_env \
{{ docker.pull_tag }}
{% endfor %}
Run the single node training benchmark: 3. In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``MAD/scripts/jax-maxtext``.
.. code-block:: shell .. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_70b.sh git clone https://github.com/ROCm/MAD
cd MAD/scripts/jax-maxtext
* Example 3: Single node training with Llama 3 8B 4. Run the setup scripts to install libraries and datasets needed
for benchmarking.
Download the benchmarking script:
.. code-block:: shell .. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_8b.sh ./jax-maxtext_benchmark_setup.sh -m {{ model.model_repo }}
Run the single node training benchmark: 5. To run the training benchmark without quantization, use the following command:
.. code-block:: shell .. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_8b.sh ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }}
* Example 4: Single node training with Llama 3 70B For quantized training, use the following command:
Download the benchmarking script:
.. code-block:: shell .. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_70b.sh ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }} -q nanoo_fp8
Run the single node training benchmark: .. important::
.. code-block:: shell Quantized training is not supported with the JAX 0.6.0 Docker image; support
will be added in a future release. For quantized training, use the JAX 0.5.0
Docker image: ``rocm/jax-training:maxtext-v25.7``.
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_70b.sh {% endif %}
{% if model.multinode_training_script and "multi-node" in model.doc_options %}
.. rubric:: Multi-node training
* Example 5: Single node training with Llama 3.3 70B The following examples use SLURM to run on multiple nodes.
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3.3_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3.3_70b.sh
* Example 6: Single node training with DeepSeek V2 16B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/deepseek_v2_16b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./deepseek_v2_16b.sh
.. note:: .. note::
The reported TFLOP/s by MaxText for DeepSeek is not accurate. Use The following scripts will launch the Docker container and run the
the tokens/s as a performance indicator. benchmark. Run them outside of any Docker container.
Multi-node training benchmarking examples 1. Make sure ``$HF_HOME`` is set before running the test. See
----------------------------------------- `ROCm benchmarking <https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/readme.md>`__
for more details on downloading the Llama models before running the
benchmark.
The following examples use SLURM for running on multiple nodes -- the commands might need to be adjusted for your 2. To run multi-node training for {{ model.model }},
own cluster setup. use the
`multi-node training script <https://github.com/ROCm/MAD/blob/develop/scripts/jax-maxtext/gpu-rocm/{{ model.multinode_training_script }}>`__
under the ``scripts/jax-maxtext/gpu-rocm/`` directory.
* Example 1: Multi-node training with Llama 2 7B 3. Run the multi-node training benchmark script.
Download the benchmarking script:
.. code-block:: shell .. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_7b_multinode.sh sbatch -N <num_nodes> {{ model.multinode_training_script }}
Run the multi-node training benchmark. For example: {% else %}
.. rubric:: Multi-node training
.. code-block:: shell For multi-node training examples, choose a model from :ref:`amd-maxtext-model-support-v257`
with an available `multi-node training script <https://github.com/ROCm/MAD/tree/develop/scripts/jax-maxtext/gpu-rocm>`__.
{% endif %}
{% endfor %}
{% endfor %}
sbatch -N <num_nodes> llama2_7b_multinode.sh Further reading
===============
* Example 2: Multi-node training with Llama 2 70B - See the ROCm/maxtext benchmarking README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/readme.md>`__.
Download the benchmarking script: - 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>`__.
.. code-block:: shell - 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>`_.
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_70b_multinode.sh - 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>`_.
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama2_70b_multinode.sh
* Example 3: Multi-node training with Llama 3 8B model
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_8b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama3_8b_multinode.sh
* Example 4: Multi-node training with Llama 3 70B model
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_70b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama3_70b_multinode.sh
Previous versions Previous versions
================= =================

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docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history.rst
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@@ -17,12 +17,21 @@ previous releases of the ``ROCm/jax-training`` Docker image on `Docker Hub <http
- Components - Components
- Resources - Resources
* - 25.5 (latest) * - 25.7 (latest)
-
* ROCm 6.4.1
* JAX 0.6.0, 0.5.0
-
* :doc:`Documentation <../jax-maxtext>`
* `Docker Hub (JAX 0.6.0) <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7-jax060/images/sha256-7352212ae033a76dca2b9dceffc23c1b5f1a61a7a560082cf747a9bf1acfc9ce>`__
* `Docker Hub (JAX 0.5.0) <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7/images/sha256-45f4c727d4019a63fc47313d3a5f5a5105569539294ddfd2d742218212ae9025>`__
* - 25.5
- -
* ROCm 6.3.4 * ROCm 6.3.4
* JAX 0.4.35 * JAX 0.4.35
- -
* :doc:`Documentation <../jax-maxtext>` * :doc:`Documentation <jax-maxtext-v25.5>`
* `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.5/images/sha256-4e0516358a227cae8f552fb866ec07e2edcf244756f02e7b40212abfbab5217b>`__ * `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.5/images/sha256-4e0516358a227cae8f552fb866ec07e2edcf244756f02e7b40212abfbab5217b>`__
* - 25.4 * - 25.4

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@@ -51,7 +51,7 @@ MaxText provides the following key features to train large language models effic
- Multi-node support - Multi-node support
.. _amd-maxtext-model-support: .. _amd-maxtext-model-support-v254:
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 accelerators.

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.. meta::
:description: How to train a model using JAX MaxText for ROCm.
:keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
**************************************
Training a model with MaxText for ROCm
**************************************
.. caution::
This documentation does not reflect the latest version of ROCm JAX MaxText
training performance documentation. See :doc:`../jax-maxtext` for the latest version.
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.
The MaxText for ROCm training Docker (``rocm/jax-training:maxtext-v25.5``) image
provides a prebuilt environment for training on AMD Instinct MI300X and MI325X accelerators,
including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
It includes the following software components:
+--------------------------+--------------------------------+
| Software component | Version |
+==========================+================================+
| ROCm | 6.3.4 |
+--------------------------+--------------------------------+
| JAX | 0.4.35 |
+--------------------------+--------------------------------+
| Python | 3.10.12 |
+--------------------------+--------------------------------+
| Transformer Engine | 1.12.0.dev0+b8b92dc |
+--------------------------+--------------------------------+
| hipBLASLt | 0.13.0-ae9c477a |
+--------------------------+--------------------------------+
Supported features and models
=============================
MaxText provides the following key features to train large language models efficiently:
- Transformer Engine (TE)
- Flash Attention (FA) 3
- GEMM tuning
- Multi-node support
.. _amd-maxtext-model-support-v255:
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
* Llama 3.3 70B
* Llama 3.1 8B
* Llama 3.1 70B
* Llama 3 8B
* Llama 3 70B
* Llama 2 7B
* Llama 2 70B
* DeepSeek-V2-Lite
.. note::
Some models, such as Llama 3, require an external license agreement through
a third party (for example, Meta).
Unsupported features
--------------------
Currently, MaxText's default packed input format is not supported. Using this format
with the current Docker image results in incorrect attention calculations
across different input sequences. Support for packed input format is planned for a future release.
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.
Environment setup
=================
This Docker image is optimized for specific model configurations outlined
as follows. Performance can vary for other training workloads, as AMD
doesn’t validate configurations and run conditions outside those described.
.. _amd-maxtext-multi-node-setup-v255:
Multi-node setup
----------------
For multi-node environments, ensure you have all the necessary packages for
your network device, such as, RDMA. If you're not using a multi-node setup
with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
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>` 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
.. _amd-maxtext-download-docker-v255:
Pull the Docker image
---------------------
1. Use the following command to pull the Docker image from Docker Hub.
.. code-block:: shell
docker pull rocm/jax-training:maxtext-v25.5
2. Use the following command to launch the Docker container. Note that the benchmarking scripts
used in the :ref:`following section <amd-maxtext-get-started>` automatically launch the Docker container
and execute the benchmark.
.. code-block:: shell
docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME/.ssh:/root/.ssh --shm-size 128G --name maxtext_training rocm/jax-training:maxtext-v25.5
.. _amd-maxtext-get-started-v255:
Getting started
===============
The following examples demonstrate how to get started with single node
and multi-node training using the benchmarking scripts provided at
`<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__.
.. important::
The provided scripts launch a Docker container and execute a benchmark. Ensure you run these commands outside of any existing Docker container.
Before running any benchmarks, ensure the ``$HF_HOME`` environment variable is
set correctly and points to your Hugging Face cache directory. Refer to the
README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__
for more detailed instructions.
Single node training benchmarking examples
------------------------------------------
* Example 1: Single node training with Llama 2 7B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_7b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_7b.sh
* Example 2: Single node training with Llama 2 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_70b.sh
* Example 3: Single node training with Llama 3 8B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_8b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_8b.sh
* Example 4: Single node training with Llama 3 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_70b.sh
* Example 5: Single node training with Llama 3.3 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3.3_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3.3_70b.sh
* Example 6: Single node training with DeepSeek V2 16B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/deepseek_v2_16b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./deepseek_v2_16b.sh
.. note::
The reported TFLOP/s by MaxText for DeepSeek is not accurate. Use
the tokens/s as a performance indicator.
Multi-node training benchmarking examples
-----------------------------------------
The following examples use SLURM for running on multiple nodes -- the commands might need to be adjusted for your
own cluster setup.
* Example 1: Multi-node training with Llama 2 7B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_7b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama2_7b_multinode.sh
* Example 2: Multi-node training with Llama 2 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_70b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama2_70b_multinode.sh
* Example 3: Multi-node training with Llama 3 8B model
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_8b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama3_8b_multinode.sh
* Example 4: Multi-node training with Llama 3 70B model
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_70b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama3_70b_multinode.sh
Previous versions
=================
See :doc:`jax-maxtext-history` to find documentation for previous releases
of the ``ROCm/jax-training`` Docker image.