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A comprehensive guide for using Axolotl on distributed systems with AMD GPUs
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< p > This guide provides step-by-step instructions for installing and configuring Axolotl on a High-Performance Computing (HPC) environment equipped with AMD GPUs.< / p >
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< h2 class = "anchored" data-anchor-id = "setup" > Setup< / h2 >
< section id = "install-python" class = "level3" >
< h3 class = "anchored" data-anchor-id = "install-python" > 1. Install Python< / h3 >
< p > We recommend using Miniforge, a minimal conda-based Python distribution:< / p >
< div class = "sourceCode" id = "cb1" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb1-1" > < a href = "#cb1-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "ex" > curl< / span > < span class = "at" > -L< / span > < span class = "at" > -O< / span > < span class = "st" > "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-< / span > < span class = "va" > $(< / span > < span class = "fu" > uname< / span > < span class = "va" > )< / span > < span class = "st" > -< / span > < span class = "va" > $(< / span > < span class = "fu" > uname< / span > < span class = "at" > -m< / span > < span class = "va" > )< / span > < span class = "st" > .sh"< / span > < / span >
< span id = "cb1-2" > < a href = "#cb1-2" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "fu" > bash< / span > Miniforge3-< span class = "va" > $(< / span > < span class = "fu" > uname< / span > < span class = "va" > )< / span > -< span class = "va" > $(< / span > < span class = "fu" > uname< / span > < span class = "at" > -m< / span > < span class = "va" > )< / span > .sh< / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< / section >
< section id = "configure-python-environment" class = "level3" >
< h3 class = "anchored" data-anchor-id = "configure-python-environment" > 2. Configure Python Environment< / h3 >
< p > Add Python to your PATH and ensure it’ < / p >
< div class = "sourceCode" id = "cb2" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb2-1" > < a href = "#cb2-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "bu" > echo< / span > < span class = "st" > 'export PATH=~/miniforge3/bin:$PATH'< / span > < span class = "op" > > > < / span > ~/.bashrc< / span >
< span id = "cb2-2" > < a href = "#cb2-2" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "bu" > echo< / span > < span class = "st" > 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi'< / span > < span class = "op" > > > < / span > ~/.bash_profile< / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< / section >
< section id = "load-amd-gpu-software" class = "level3" >
< h3 class = "anchored" data-anchor-id = "load-amd-gpu-software" > 3. Load AMD GPU Software< / h3 >
< p > Load the ROCm module:< / p >
< div class = "sourceCode" id = "cb3" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb3-1" > < a href = "#cb3-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "ex" > module< / span > load rocm/5.7.1< / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< p > Note: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.< / p >
< / section >
< section id = "install-pytorch" class = "level3" >
< h3 class = "anchored" data-anchor-id = "install-pytorch" > 4. Install PyTorch< / h3 >
< p > Install PyTorch with ROCm support:< / p >
< div class = "sourceCode" id = "cb4" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb4-1" > < a href = "#cb4-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "ex" > pip< / span > install < span class = "at" > -U< / span > torch torchvision torchaudio < span class = "at" > --index-url< / span > https://download.pytorch.org/whl/rocm5.7 < span class = "at" > --force-reinstall< / span > < / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< / section >
< section id = "install-flash-attention" class = "level3" >
< h3 class = "anchored" data-anchor-id = "install-flash-attention" > 5. Install Flash Attention< / h3 >
< p > Clone and install the Flash Attention repository:< / p >
< div class = "sourceCode" id = "cb5" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb5-1" > < a href = "#cb5-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "fu" > git< / span > clone < span class = "at" > --recursive< / span > https://github.com/ROCmSoftwarePlatform/flash-attention.git< / span >
< span id = "cb5-2" > < a href = "#cb5-2" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "bu" > export< / span > < span class = "va" > GPU_ARCHS< / span > < span class = "op" > =< / span > < span class = "st" > "gfx90a"< / span > < / span >
< span id = "cb5-3" > < a href = "#cb5-3" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "bu" > cd< / span > flash-attention< / span >
< span id = "cb5-4" > < a href = "#cb5-4" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "bu" > export< / span > < span class = "va" > PYTHON_SITE_PACKAGES< / span > < span class = "op" > =< / span > < span class = "va" > $(< / span > < span class = "ex" > python< / span > < span class = "at" > -c< / span > < span class = "st" > 'import site; print(site.getsitepackages()[0])'< / span > < span class = "va" > )< / span > < / span >
< span id = "cb5-5" > < a href = "#cb5-5" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "fu" > patch< / span > < span class = "st" > "< / span > < span class = "va" > ${PYTHON_SITE_PACKAGES}< / span > < span class = "st" > /torch/utils/hipify/hipify_python.py"< / span > hipify_patch.patch< / span >
< span id = "cb5-6" > < a href = "#cb5-6" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "ex" > pip< / span > install .< / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< / section >
< section id = "install-axolotl" class = "level3" >
< h3 class = "anchored" data-anchor-id = "install-axolotl" > 6. Install Axolotl< / h3 >
< p > Clone and install Axolotl:< / p >
< div class = "sourceCode" id = "cb6" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb6-1" > < a href = "#cb6-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "fu" > git< / span > clone https://github.com/axolotl-ai-cloud/axolotl< / span >
< span id = "cb6-2" > < a href = "#cb6-2" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "bu" > cd< / span > axolotl< / span >
< span id = "cb6-3" > < a href = "#cb6-3" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "ex" > pip< / span > install packaging ninja< / span >
< span id = "cb6-4" > < a href = "#cb6-4" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "ex" > pip< / span > install < span class = "at" > -e< / span > .< / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< / section >
< section id = "apply-xformers-workaround" class = "level3" >
< h3 class = "anchored" data-anchor-id = "apply-xformers-workaround" > 7. Apply xformers Workaround< / h3 >
< p > 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 < code > False< / code > 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.< / p >
< / section >
< section id = "prepare-job-submission-script" class = "level3" >
< h3 class = "anchored" data-anchor-id = "prepare-job-submission-script" > 8. Prepare Job Submission Script< / h3 >
< p > Create a script for job submission using your HPC’ 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< / p >
< div class = "sourceCode" id = "cb7" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb7-1" > < a href = "#cb7-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "bu" > export< / span > < span class = "va" > TRANSFORMERS_OFFLINE< / span > < span class = "op" > =< / span > 1< / span >
< span id = "cb7-2" > < a href = "#cb7-2" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "bu" > export< / span > < span class = "va" > HF_DATASETS_OFFLINE< / span > < span class = "op" > =< / span > 1< / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< / section >
< section id = "download-base-model" class = "level3" >
< h3 class = "anchored" data-anchor-id = "download-base-model" > 9. Download Base Model< / h3 >
< p > Download a base model using the Hugging Face CLI:< / p >
< div class = "sourceCode" id = "cb8" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb8-1" > < a href = "#cb8-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "ex" > huggingface-cli< / span > download meta-llama/Meta-Llama-3.1-8B < span class = "at" > --local-dir< / span > ~/hfdata/llama3.1-8B< / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< / section >
< section id = "create-axolotl-configuration" class = "level3" >
< h3 class = "anchored" data-anchor-id = "create-axolotl-configuration" > 10. Create Axolotl Configuration< / h3 >
< p > Create an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.< / p >
< p > Note: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.< / p >
< / section >
< section id = "preprocess-data" class = "level3" >
< h3 class = "anchored" data-anchor-id = "preprocess-data" > 11. Preprocess Data< / h3 >
< p > Run preprocessing on the login node:< / p >
< div class = "sourceCode" id = "cb9" > < pre class = "sourceCode bash code-with-copy" > < code class = "sourceCode bash" > < span id = "cb9-1" > < a href = "#cb9-1" aria-hidden = "true" tabindex = "-1" > < / a > < span class = "va" > CUDA_VISIBLE_DEVICES< / span > < span class = "op" > =< / span > < span class = "st" > ""< / span > < span class = "ex" > python< / span > < span class = "at" > -m< / span > axolotl.cli.preprocess /path/to/your/config.yaml< / span > < / code > < button title = "Copy to Clipboard" class = "code-copy-button" > < i class = "bi" > < / i > < / button > < / pre > < / div >
< / section >
< section id = "train" class = "level3" >
< h3 class = "anchored" data-anchor-id = "train" > 12. Train< / h3 >
< p > You are now ready to submit your previously prepared job script. 🚂< / p >
< / section >
< / section >
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