Training a model with PyTorch for ROCm

Training a model with PyTorch for ROCm#

2025-03-13

8 min read time

Applies to Linux

PyTorch is an open-source machine learning framework that is widely used for model training with GPU-optimized components for transformer-based models.

The PyTorch for ROCm training Docker (rocm/pytorch-training:v25.4) 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 training workloads:

Software component

Version

ROCm

6.3.0

PyTorch

2.7.0a0+git637433

Python

3.10

Transformer Engine

1.11

Flash Attention

3.0.0

hipBLASLt

git258a2162

Triton

3.1

Supported models#

The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.

  • Llama 3.1 8B

  • Llama 3.1 70B

  • Llama 2 70B

  • FLUX.1-dev

Note

Only these models are supported in the following steps.

Some models, such as Llama 3, require an external license agreement through a third party (for example, Meta).

Performance measurements#

To evaluate performance, the Performance results with AMD ROCm software page provides reference throughput and latency measurements for training popular AI models.

Note

The performance data presented in Performance results with AMD ROCm software should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.

System validation#

If you have already validated your system settings, including NUMA auto-balancing, skip this step. Otherwise, complete the system validation and optimization steps to set up your system before starting training.

Environment setup#

This Docker image is optimized for specific model configurations outlined below. Performance can vary for other training workloads, as AMD doesn’t validate configurations and run conditions outside those described.

Download the Docker image#

  1. Use the following command to pull the Docker image from Docker Hub.

    docker pull rocm/pytorch-training:v25.4

  2. Run the Docker container.

    docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME:$HOME -v  $HOME/.ssh:/root/.ssh --shm-size 64G --name training_env rocm/pytorch-training:v25.4

  3. Use these commands if you exit the training_env container and need to return to it.

    docker start training_env
docker exec -it training_env bash

  4. In the Docker container, clone the ROCm/MAD repository and navigate to the benchmark scripts directory /workspace/MAD/scripts/pytorch_train.

    git clone https://github.com/ROCm/MAD
cd MAD/scripts/pytorch_train

Prepare training datasets and dependencies#

The following benchmarking examples require downloading models and datasets from Hugging Face. To ensure successful access to gated repos, set your HF_TOKEN.

export HF_TOKEN=$your_personal_hugging_face_access_token

Run the setup script to install libraries and datasets needed for benchmarking.

./pytorch_benchmark_setup.sh

pytorch_benchmark_setup.sh installs the following libraries:

Library

Benchmark model

Reference

accelerate

Llama 3.1 8B, FLUX

Hugging Face Accelerate

datasets

Llama 3.1 8B, 70B, FLUX

Hugging Face Datasets 3.2.0

torchdata

Llama 3.1 70B

TorchData

tomli

Llama 3.1 70B

Tomli

tiktoken

Llama 3.1 70B

tiktoken

blobfile

Llama 3.1 70B

blobfile

tabulate

Llama 3.1 70B

tabulate

wandb

Llama 3.1 70B

Weights & Biases

sentencepiece

Llama 3.1 70B, FLUX

SentencePiece 0.2.0

tensorboard

Llama 3.1 70 B, FLUX

TensorBoard 2.18.0

csvkit

FLUX

csvkit 2.0.1

deepspeed

FLUX

DeepSpeed 0.16.2

diffusers

FLUX

Hugging Face Diffusers 0.31.0

GitPython

FLUX

GitPython 3.1.44

opencv-python-headless

FLUX

opencv-python-headless 4.10.0.84

peft

FLUX

PEFT 0.14.0

protobuf

FLUX

Protocol Buffers 5.29.2

pytest

FLUX

PyTest 8.3.4

python-dotenv

FLUX

python-dotenv 1.0.1

seaborn

FLUX

Seaborn 0.13.2

transformers

FLUX

Transformers 4.47.0

pytorch_benchmark_setup.sh downloads the following models from Hugging Face:

Along with the following datasets:

Getting started#

The prebuilt PyTorch with ROCm training environment allows users to quickly validate system performance, conduct training benchmarks, and achieve superior performance for models like Llama 3.1 and Llama 2. This container should not be expected to provide generalized performance across all training workloads. You can expect the container to perform in the model configurations described in the following section, but other configurations are not validated by AMD.

Use the following instructions to set up the environment, configure the script to train models, and reproduce the benchmark results on MI325X and MI300X accelerators with the AMD PyTorch training Docker image.

Once your environment is set up, use the following commands and examples to start benchmarking.

Pretraining#

To start the pretraining benchmark, use the following command with the appropriate options. See the following list of options and their descriptions.

./pytorch_benchmark_report.sh -t $training_mode -m $model_repo -p $datatype -s $sequence_length

Options and available models#

Name

Options

Description

$training_mode

pretrain

Benchmark pretraining

finetune_fw

Benchmark full weight fine-tuning (Llama 3.1 70B with BF16)

finetune_lora

Benchmark LoRA fine-tuning (Llama 3.1 70B with BF16)

HF_finetune_lora

Benchmark LoRA fine-tuning with Hugging Face PEFT (Llama 2 70B with BF16)

$datatype

FP8 or BF16

Only Llama 3.1 8B supports FP8 precision.

$model_repo

Llama-3.1-8B

Llama 3.1 8B

Llama-3.1-70B

Llama 3.1 70B

Llama-2-70B

Llama 2 70B

Flux

FLUX.1 [dev]

$sequence_length

Sequence length for the language model.

Between 2048 and 8192. 8192 by default.

Note

Occasionally, downloading the Flux dataset might fail. In the event of this error, manually download it from Hugging Face at black-forest-labs/FLUX.1-dev and save it to /workspace/FluxBenchmark. This ensures that the test script can access the required dataset.

Fine-tuning#

To start the fine-tuning benchmark, use the following command. It will run the benchmarking example of Llama 3.1 70B with the WikiText dataset using the AMD fork of torchtune.

./pytorch_benchmark_report.sh -t {finetune_fw, finetune_lora} -p BF16 -m Llama-3.1-70B

Use the following command to run the benchmarking example of Llama 2 70B with the UltraChat 200k dataset using Hugging Face PEFT.

./pytorch_benchmark_report.sh -t HF_finetune_lora -p BF16 -m Llama-2-70B

Benchmarking examples#

Here are some examples of how to use the command.

  • Example 1: Llama 3.1 70B with BF16 precision with torchtitan.

    ./pytorch_benchmark_report.sh -t pretrain -p BF16 -m Llama-3.1-70B -s 8192

  • Example 2: Llama 3.1 8B with FP8 precision using Transformer Engine (TE) and Hugging Face Accelerator.

    ./pytorch_benchmark_report.sh -t pretrain -p FP8 -m Llama-3.1-70B -s 8192

  • Example 3: FLUX.1-dev with BF16 precision with FluxBenchmark.

    ./pytorch_benchmark_report.sh -t pretrain -p BF16 -m Flux

  • Example 4: Torchtune full weight fine-tuning with Llama 3.1 70B

    ./pytorch_benchmark_report.sh -t finetune_fw -p BF16 -m Llama-3.1-70B

  • Example 5: Torchtune LoRA fine-tuning with Llama 3.1 70B

    ./pytorch_benchmark_report.sh -t finetune_lora -p BF16 -m Llama-3.1-70B

  • Example 6: Hugging Face PEFT LoRA fine-tuning with Llama 2 70B

    ./pytorch_benchmark_report.sh -t HF_finetune_lora -p BF16 -m Llama-2-70B

Previous versions#

This table lists previous versions of the ROCm PyTorch training Docker image for training performance validation. For detailed information about available models for benchmarking, see the version-specific documentation.

Image version

ROCm version

PyTorch version

Resources

v25.3

6.3.0

2.7.0a0+git637433