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<li><a href="#install-python" id="toc-install-python" class="nav-link" data-scroll-target="#install-python">1. Install Python</a></li>
<li><a href="#configure-python-environment" id="toc-configure-python-environment" class="nav-link" data-scroll-target="#configure-python-environment">2. Configure Python Environment</a></li>
<li><a href="#load-amd-gpu-software" id="toc-load-amd-gpu-software" class="nav-link" data-scroll-target="#load-amd-gpu-software">3. Load AMD GPU Software</a></li>
<li><a href="#install-pytorch" id="toc-install-pytorch" class="nav-link" data-scroll-target="#install-pytorch">4. Install PyTorch</a></li>
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<h1 class="title">Training with AMD GPUs on HPC Systems</h1>
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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>
<section id="setup" class="level2">
<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 its available at login:</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">&gt;&gt;</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">&gt;&gt;</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 class="at">--no-build-isolation</span> .</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">--no-build-isolation</span> <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 HPCs particular software (e.g.&nbsp;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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