# Training with AMD GPUs on HPC Systems

A comprehensive guide for using Axolotl on distributed systems with AMD GPUs

This guide provides step-by-step instructions for installing and configuring Axolotl on a High-Performance Computing (HPC) environment equipped with AMD GPUs.

## Setup

### 1. Install Python

We recommend using Miniforge, a minimal conda-based Python distribution:

    curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
    bash Miniforge3-$(uname)-$(uname -m).sh

### 2. Configure Python Environment

Add Python to your PATH and ensure it’s available at login:

    echo 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc
    echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile

### 3. Load AMD GPU Software

Load the ROCm module:

    module load rocm/5.7.1

Note: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.

### 4. Install PyTorch

Install PyTorch with ROCm support:

    pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall

### 5. Install Flash Attention

Clone and install the Flash Attention repository:

    git clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git
    export GPU_ARCHS="gfx90a"
    cd flash-attention
    export PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')
    patch "${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py" hipify_patch.patch
    pip install --no-build-isolation .

### 6. Install Axolotl

Clone and install Axolotl:

    git clone https://github.com/axolotl-ai-cloud/axolotl
    cd axolotl
    pip install packaging ninja
    pip install --no-build-isolation -e .

### 7. Apply xformers Workaround

xformers appears to be incompatible with ROCm. Apply the following workarounds: - Edit $HOME/packages/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py modifying the code to always return `False` for SwiGLU availability from xformers. - Edit $HOME/miniforge3/lib/python3.10/site-packages/xformers/ops/swiglu_op.py replacing the “SwiGLU” function with a pass statement.

### 8. Prepare Job Submission Script

Create a script for job submission using your HPC’s particular software (e.g. Slurm, PBS). Include necessary environment setup and the command to run Axolotl training. If the compute node(s) do(es) not have internet access, it is recommended to include

    export TRANSFORMERS_OFFLINE=1
    export HF_DATASETS_OFFLINE=1

### 9. Download Base Model

Download a base model using the Hugging Face CLI:

    huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B

### 10. Create Axolotl Configuration

Create an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.

Note: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.

### 11. Preprocess Data

Run preprocessing on the login node:

    CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess /path/to/your/config.yaml

### 12. Train

You are now ready to submit your previously prepared job script. 🚂