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<title>Accelerate, Three Powerful Sublibraries for PyTorch</title>
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<div class="slides">
<section id="title-slide" class="quarto-title-block center">
<h1 class="title">Accelerate, Three Powerful Sublibraries for PyTorch</h1>
<div class="quarto-title-authors">
<div class="quarto-title-author">
<div class="quarto-title-author-name">
Zachary Mueller
</div>
</div>
</div>
</section>
<section id="what-is-accelerate" class="slide level2">
<h2>What is 🤗 Accelerate?</h2>
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<p>
</p><pre class="mermaid mermaid-js" data-tooltip-selector="#mermaid-tooltip-1">graph LR
A{"🤗 Accelerate#32;"}
A --&gt; B["Launching&lt;br&gt;Interface#32;"]
A --&gt; C["Training Library#32;"]
A --&gt; D["Big Model&lt;br&gt;Inference#32;"]
</pre>
<div id="mermaid-tooltip-1" class="mermaidTooltip">
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<p></p>
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</section>
<section>
<section id="a-launching-interface" class="title-slide slide level1 center">
<h1>A Launching Interface</h1>
<p>Can’t I just use <code>python do_the_thing.py</code>?</p>
</section>
<section id="a-launching-interface-1" class="slide level2">
<h2>A Launching Interface</h2>
<p>Launching scripts in different environments is complicated:</p>
<ul>
<li><div class="sourceCode" id="cb1"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1"></a><span class="ex">python</span> script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div></li>
<li><div class="sourceCode" id="cb2"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1"></a><span class="ex">torchrun</span> <span class="at">--nnodes</span><span class="op">=</span>1 <span class="at">--nproc_per_node</span><span class="op">=</span>2 script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div></li>
<li><div class="sourceCode" id="cb3"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1"></a><span class="ex">deepspeed</span> <span class="at">--num_gpus</span><span class="op">=</span>2 script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div></li>
</ul>
<p>And more!</p>
</section>
<section id="a-launching-interface-2" class="slide level2">
<h2>A Launching Interface</h2>
<p>But it doesn’t have to be:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1"></a><span class="ex">accelerate</span> launch script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>A single command to launch with <code>DeepSpeed</code>, Fully Sharded Data Parallelism, across single and multi CPUs and GPUs, and to train on TPUs<sup>1</sup> too!</p>
<aside><ol class="aside-footnotes"><li id="fn1"><p>Without needing to modify your code and create a <code>_mp_fn</code></p></li></ol></aside></section>
<section id="a-launching-interface-3" class="slide level2">
<h2>A Launching Interface</h2>
<p>Generate a device-specific configuration through <code>accelerate config</code></p>
<img data-src="images/CLI.gif" class="r-stretch"></section>
<section id="a-launching-interface-4" class="slide level2">
<h2>A Launching Interface</h2>
<p>Or don’t. <code>accelerate config</code> doesn’t <em>have</em> to be done!</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1"></a><span class="ex">torchrun</span> <span class="at">--nnodes</span><span class="op">=</span>1 <span class="at">--nproc_per_node</span><span class="op">=</span>2 script.py</span>
<span id="cb5-2"><a href="#cb5-2"></a><span class="ex">accelerate</span> launch <span class="at">--multi_gpu</span> <span class="at">--nproc_per_node</span><span class="op">=</span>2 script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>A quick default configuration can be made too:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1"></a><span class="ex">accelerate</span> config default</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="a-launching-interface-5" class="slide level2">
<h2>A Launching Interface</h2>
<p>With the <code>notebook_launcher</code> it’s also possible to launch code directly from your Jupyter environment too!</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1"></a><span class="im">from</span> accelerate <span class="im">import</span> notebook_launcher</span>
<span id="cb7-2"><a href="#cb7-2"></a>notebook_launcher(</span>
<span id="cb7-3"><a href="#cb7-3"></a> training_loop_function, </span>
<span id="cb7-4"><a href="#cb7-4"></a> args, </span>
<span id="cb7-5"><a href="#cb7-5"></a> num_processes<span class="op">=</span><span class="dv">2</span></span>
<span id="cb7-6"><a href="#cb7-6"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="sourceCode" id="cb8"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1"></a>Launching training on <span class="dv">2</span> GPUs.</span>
<span id="cb8-2"><a href="#cb8-2"></a>epoch <span class="dv">0</span>: <span class="fl">88.12</span></span>
<span id="cb8-3"><a href="#cb8-3"></a>epoch <span class="dv">1</span>: <span class="fl">91.73</span></span>
<span id="cb8-4"><a href="#cb8-4"></a>epoch <span class="dv">2</span>: <span class="fl">92.58</span></span>
<span id="cb8-5"><a href="#cb8-5"></a>epoch <span class="dv">3</span>: <span class="fl">93.90</span></span>
<span id="cb8-6"><a href="#cb8-6"></a>epoch <span class="dv">4</span>: <span class="fl">94.71</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section></section>
<section>
<section id="a-training-library" class="title-slide slide level1 center">
<h1>A Training Library</h1>
<p>Okay, will <code>accelerate launch</code> make <code>do_the_thing.py</code> use all my GPUs magically?</p>
</section>
<section id="a-training-library-1" class="slide level2">
<h2>A Training Library</h2>
<ul>
<li>Just showed that its possible using <code>accelerate launch</code> to <em>launch</em> a python script in various distributed environments</li>
<li>This does <em>not</em> mean that the script will just “use” that code and still run on the new compute efficiently.</li>
<li>Training on different computes often means <em>many</em> lines of code changed for each specific compute.</li>
<li>🤗 <code>accelerate</code> solves this by ensuring the same code can be ran on a CPU or GPU, multiples, and on TPUs!</li>
</ul>
</section>
<section id="a-training-library-2" class="slide level2">
<h2>A Training Library</h2>
<div class="sourceCode" id="cb9"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1"></a><span class="cf">for</span> batch <span class="kw">in</span> dataloader:</span>
<span id="cb9-2"><a href="#cb9-2"></a> optimizer.zero_grad()</span>
<span id="cb9-3"><a href="#cb9-3"></a> inputs, targets <span class="op">=</span> batch</span>
<span id="cb9-4"><a href="#cb9-4"></a> inputs <span class="op">=</span> inputs.to(device)</span>
<span id="cb9-5"><a href="#cb9-5"></a> targets <span class="op">=</span> targets.to(device)</span>
<span id="cb9-6"><a href="#cb9-6"></a> outputs <span class="op">=</span> model(inputs)</span>
<span id="cb9-7"><a href="#cb9-7"></a> loss <span class="op">=</span> loss_function(outputs, targets)</span>
<span id="cb9-8"><a href="#cb9-8"></a> loss.backward()</span>
<span id="cb9-9"><a href="#cb9-9"></a> optimizer.step()</span>
<span id="cb9-10"><a href="#cb9-10"></a> scheduler.step()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="a-training-library-3" class="slide level2 smaller">
<h2>A Training Library</h2>
<div class="columns">
<div class="column" style="width:43%;">
<p><br><br><br></p>
<div class="sourceCode" id="cb10" data-code-line-numbers="5-6,9"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1"></a><span class="co"># For alignment purposes</span></span>
<span id="cb10-2"><a href="#cb10-2"></a><span class="cf">for</span> batch <span class="kw">in</span> dataloader:</span>
<span id="cb10-3"><a href="#cb10-3"></a> optimizer.zero_grad()</span>
<span id="cb10-4"><a href="#cb10-4"></a> inputs, targets <span class="op">=</span> batch</span>
<span id="cb10-5"><a href="#cb10-5"></a> inputs <span class="op">=</span> inputs.to(device)</span>
<span id="cb10-6"><a href="#cb10-6"></a> targets <span class="op">=</span> targets.to(device)</span>
<span id="cb10-7"><a href="#cb10-7"></a> outputs <span class="op">=</span> model(inputs)</span>
<span id="cb10-8"><a href="#cb10-8"></a> loss <span class="op">=</span> loss_function(outputs, targets)</span>
<span id="cb10-9"><a href="#cb10-9"></a> loss.backward()</span>
<span id="cb10-10"><a href="#cb10-10"></a> optimizer.step()</span>
<span id="cb10-11"><a href="#cb10-11"></a> scheduler.step()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div><div class="column" style="width:57%;">
<div class="sourceCode" id="cb11" data-code-line-numbers="1-7,12-13,16"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1"></a><span class="im">from</span> accelerate <span class="im">import</span> Accelerator</span>
<span id="cb11-2"><a href="#cb11-2"></a>accelerator <span class="op">=</span> Accelerator()</span>
<span id="cb11-3"><a href="#cb11-3"></a>dataloader, model, optimizer scheduler <span class="op">=</span> (</span>
<span id="cb11-4"><a href="#cb11-4"></a> accelerator.prepare(</span>
<span id="cb11-5"><a href="#cb11-5"></a> dataloader, model, optimizer, scheduler</span>
<span id="cb11-6"><a href="#cb11-6"></a> )</span>
<span id="cb11-7"><a href="#cb11-7"></a>)</span>
<span id="cb11-8"><a href="#cb11-8"></a></span>
<span id="cb11-9"><a href="#cb11-9"></a><span class="cf">for</span> batch <span class="kw">in</span> dataloader:</span>
<span id="cb11-10"><a href="#cb11-10"></a> optimizer.zero_grad()</span>
<span id="cb11-11"><a href="#cb11-11"></a> inputs, targets <span class="op">=</span> batch</span>
<span id="cb11-12"><a href="#cb11-12"></a> <span class="co"># inputs = inputs.to(device)</span></span>
<span id="cb11-13"><a href="#cb11-13"></a> <span class="co"># targets = targets.to(device)</span></span>
<span id="cb11-14"><a href="#cb11-14"></a> outputs <span class="op">=</span> model(inputs)</span>
<span id="cb11-15"><a href="#cb11-15"></a> loss <span class="op">=</span> loss_function(outputs, targets)</span>
<span id="cb11-16"><a href="#cb11-16"></a> accelerator.backward(loss) <span class="co"># loss.backward()</span></span>
<span id="cb11-17"><a href="#cb11-17"></a> optimizer.step()</span>
<span id="cb11-18"><a href="#cb11-18"></a> scheduler.step()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</section>
<section id="a-training-library-4" class="slide level2">
<h2>A Training Library</h2>
<p>What all happened in <code>Accelerator.prepare</code>?</p>
<div>
<ol type="1">
<li class="fragment"><code>Accelerator</code> looked at the configuration</li>
<li class="fragment">The <code>dataloader</code> was converted into one that can dispatch each batch onto a seperate GPU</li>
<li class="fragment">The <code>model</code> was wrapped with the appropriate DDP wrapper from either <code>torch.distributed</code> or <code>torch_xla</code></li>
<li class="fragment">The <code>optimizer</code> and <code>scheduler</code> were both converted into an <code>AcceleratedOptimizer</code> and <code>AcceleratedScheduler</code> which knows how to handle any distributed scenario</li>
</ol>
</div>
</section>
<section id="lets-bring-in-fastai" class="slide level2">
<h2>Let’s bring in <code>fastai</code></h2>
<p>To utilize the <code>notebook_launcher</code> and <code>accelerate</code> at once it requires a few steps:</p>
<ol type="1">
<li>Migrate the <code>DataLoaders</code> creation to inside the <code>train</code> function</li>
<li>Use the <code>distrib_ctx</code> context manager fastai provides</li>
<li>Train!</li>
</ol>
</section>
<section id="lets-bring-fastai" class="slide level2">
<h2>Let’s bring <code>fastai</code></h2>
<p>Here it is in code, based on the <a href="https://docs.fast.ai/examples/distributed_app_examples.html">distributed app examples</a></p>
<div class="sourceCode" id="cb12"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1"></a><span class="im">from</span> fastai.vision.<span class="bu">all</span> <span class="im">import</span> <span class="op">*</span></span>
<span id="cb12-2"><a href="#cb12-2"></a><span class="im">from</span> fastai.distributed <span class="im">import</span> <span class="op">*</span></span>
<span id="cb12-3"><a href="#cb12-3"></a></span>
<span id="cb12-4"><a href="#cb12-4"></a>path <span class="op">=</span> untar_data(URLs.PETS)<span class="op">/</span><span class="st">'images'</span></span>
<span id="cb12-5"><a href="#cb12-5"></a></span>
<span id="cb12-6"><a href="#cb12-6"></a><span class="kw">def</span> train():</span>
<span id="cb12-7"><a href="#cb12-7"></a> dls <span class="op">=</span> ImageDataLoaders.from_name_func(</span>
<span id="cb12-8"><a href="#cb12-8"></a> path, get_image_files(path), valid_pct<span class="op">=</span><span class="fl">0.2</span>,</span>
<span id="cb12-9"><a href="#cb12-9"></a> label_func<span class="op">=</span><span class="kw">lambda</span> x: x[<span class="dv">0</span>].isupper(), item_tfms<span class="op">=</span>Resize(<span class="dv">224</span>))</span>
<span id="cb12-10"><a href="#cb12-10"></a> learn <span class="op">=</span> vision_learner(dls, resnet34, metrics<span class="op">=</span>error_rate).to_fp16()</span>
<span id="cb12-11"><a href="#cb12-11"></a> <span class="cf">with</span> learn.distrib_ctx(in_notebook<span class="op">=</span><span class="va">True</span>, sync_bn<span class="op">=</span><span class="va">False</span>):</span>
<span id="cb12-12"><a href="#cb12-12"></a> learn.fine_tune(<span class="dv">1</span>)</span>
<span id="cb12-13"><a href="#cb12-13"></a></span>
<span id="cb12-14"><a href="#cb12-14"></a>notebook_launcher(train, num_processes<span class="op">=</span><span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="lets-bring-fastai-1" class="slide level2">
<h2>Let’s bring <code>fastai</code></h2>
<p>Here it is in code, based on the <a href="https://docs.fast.ai/examples/distributed_app_examples.html">distributed app examples</a></p>
<div class="sourceCode" id="cb13" data-code-line-numbers="1,5,10,13"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1"></a><span class="im">from</span> fastai.vision.<span class="bu">all</span> <span class="im">import</span> <span class="op">*</span></span>
<span id="cb13-2"><a href="#cb13-2"></a><span class="im">from</span> fastai.distributed <span class="im">import</span> <span class="op">*</span></span>
<span id="cb13-3"><a href="#cb13-3"></a></span>
<span id="cb13-4"><a href="#cb13-4"></a>path <span class="op">=</span> untar_data(URLs.PETS)<span class="op">/</span><span class="st">'images'</span></span>
<span id="cb13-5"><a href="#cb13-5"></a></span>
<span id="cb13-6"><a href="#cb13-6"></a><span class="kw">def</span> train():</span>
<span id="cb13-7"><a href="#cb13-7"></a> dls <span class="op">=</span> ImageDataLoaders.from_name_func(</span>
<span id="cb13-8"><a href="#cb13-8"></a> path, get_image_files(path), valid_pct<span class="op">=</span><span class="fl">0.2</span>,</span>
<span id="cb13-9"><a href="#cb13-9"></a> label_func<span class="op">=</span><span class="kw">lambda</span> x: x[<span class="dv">0</span>].isupper(), item_tfms<span class="op">=</span>Resize(<span class="dv">224</span>))</span>
<span id="cb13-10"><a href="#cb13-10"></a> learn <span class="op">=</span> vision_learner(dls, resnet34, metrics<span class="op">=</span>error_rate).to_fp16()</span>
<span id="cb13-11"><a href="#cb13-11"></a> <span class="cf">with</span> learn.distrib_ctx(in_notebook<span class="op">=</span><span class="va">True</span>, sync_bn<span class="op">=</span><span class="va">False</span>):</span>
<span id="cb13-12"><a href="#cb13-12"></a> learn.fine_tune(<span class="dv">1</span>)</span>
<span id="cb13-13"><a href="#cb13-13"></a></span>
<span id="cb13-14"><a href="#cb13-14"></a>notebook_launcher(train, num_processes<span class="op">=</span><span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="lets-bring-fastai-2" class="slide level2">
<h2>Let’s bring <code>fastai</code></h2>
<p>The key important parts to remember are:</p>
<ul>
<li><strong>No</strong> code should <em>touch</em> the GPU before calling <code>notebook_launcher</code></li>
<li>Generally it’s recommended to let fastai handle gradient accumulation and mixed precision in this case, so use their in-house Callbacks</li>
<li>Use the <code>notebook_launcher</code> to run the training function after everything is complete.</li>
</ul>
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