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<title>Accelerate, Three Powerful Sublibraries for PyTorch</title>
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<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="who-am-i" class="slide level2">
<h2>Who am I?</h2>
<ul>
<li>Zachary Mueller</li>
<li>Deep Learning Software Engineer at 🤗</li>
<li>API design geek</li>
</ul>
</section>
<section id="what-is-accelerate" class="slide level2">
<h2>What is 🤗 Accelerate?</h2>
<div class="cell" data-reveal="true" data-fig-height="6">
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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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<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="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="a-training-library-mixed-precision" class="slide level2">
<h2>A Training Library, Mixed Precision</h2>
<p>🤗 <code>accelerate</code> also supports <em>automatic mixed precision</em>.</p>
<p>Through a single flag to the <code>Accelerator</code> object when calling <code>accelerator.backward()</code> the mixed precision of your choosing (such as <code>bf16</code> or <code>fp16</code>) will be applied:</p>
<div class="sourceCode" id="cb12" data-code-line-numbers="2,9"><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> accelerate <span class="im">import</span> Accelerator</span>
<span id="cb12-2"><a href="#cb12-2"></a>accelerator <span class="op">=</span> Accelerator(mixed_precision<span class="op">=</span><span class="st">"fp16"</span>)</span>
<span id="cb12-3"><a href="#cb12-3"></a>...</span>
<span id="cb12-4"><a href="#cb12-4"></a><span class="cf">for</span> batch <span class="kw">in</span> dataloader:</span>
<span id="cb12-5"><a href="#cb12-5"></a> optimizer.zero_grad()</span>
<span id="cb12-6"><a href="#cb12-6"></a> inputs, targets <span class="op">=</span> batch</span>
<span id="cb12-7"><a href="#cb12-7"></a> outputs <span class="op">=</span> model(inputs)</span>
<span id="cb12-8"><a href="#cb12-8"></a> loss <span class="op">=</span> loss_function(outputs, targets)</span>
<span id="cb12-9"><a href="#cb12-9"></a> accelerator.backward(loss)</span>
<span id="cb12-10"><a href="#cb12-10"></a> optimizer.step()</span>
<span id="cb12-11"><a href="#cb12-11"></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-gradient-accumulation" class="slide level2">
<h2>A Training Library, Gradient Accumulation</h2>
<p>Gradient accumulation in distributed setups often need extra care to ensure gradients are aligned when they need to be and the backward pass is computationally efficient.</p>
<p>🤗 <code>accelerate</code> can just easily handle this for you:</p>
<div class="sourceCode" id="cb13" data-code-line-numbers="2,5"><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> accelerate <span class="im">import</span> Accelerator</span>
<span id="cb13-2"><a href="#cb13-2"></a>accelerator <span class="op">=</span> Accelerator(gradient_accumulation_steps<span class="op">=</span><span class="dv">4</span>)</span>
<span id="cb13-3"><a href="#cb13-3"></a>...</span>
<span id="cb13-4"><a href="#cb13-4"></a><span class="cf">for</span> batch <span class="kw">in</span> dataloader:</span>
<span id="cb13-5"><a href="#cb13-5"></a> <span class="cf">with</span> accelerator.accumulate(model):</span>
<span id="cb13-6"><a href="#cb13-6"></a> optimizer.zero_grad()</span>
<span id="cb13-7"><a href="#cb13-7"></a> inputs, targets <span class="op">=</span> batch</span>
<span id="cb13-8"><a href="#cb13-8"></a> outputs <span class="op">=</span> model(inputs)</span>
<span id="cb13-9"><a href="#cb13-9"></a> loss <span class="op">=</span> loss_function(outputs, targets)</span>
<span id="cb13-10"><a href="#cb13-10"></a> accelerator.backward(loss)</span>
<span id="cb13-11"><a href="#cb13-11"></a> optimizer.step()</span>
<span id="cb13-12"><a href="#cb13-12"></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-gradient-accumulation-1" class="slide level2">
<h2>A Training Library, Gradient Accumulation</h2>
<div class="sourceCode" id="cb14" data-code-line-numbers="5-7,10,11,12,15"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1"></a>ddp_model, dataloader <span class="op">=</span> accelerator.prepare(model, dataloader)</span>
<span id="cb14-2"><a href="#cb14-2"></a></span>
<span id="cb14-3"><a href="#cb14-3"></a><span class="cf">for</span> index, batch <span class="kw">in</span> <span class="bu">enumerate</span>(dataloader):</span>
<span id="cb14-4"><a href="#cb14-4"></a> inputs, targets <span class="op">=</span> batch</span>
<span id="cb14-5"><a href="#cb14-5"></a> <span class="cf">if</span> index <span class="op">!=</span> (<span class="bu">len</span>(dataloader)<span class="op">-</span><span class="dv">1</span>) <span class="kw">or</span> (index <span class="op">%</span> <span class="dv">4</span>) <span class="op">!=</span> <span class="dv">0</span>:</span>
<span id="cb14-6"><a href="#cb14-6"></a> <span class="co"># Gradients don't sync</span></span>
<span id="cb14-7"><a href="#cb14-7"></a> <span class="cf">with</span> accelerator.no_sync(model):</span>
<span id="cb14-8"><a href="#cb14-8"></a> outputs <span class="op">=</span> ddp_model(inputs)</span>
<span id="cb14-9"><a href="#cb14-9"></a> loss <span class="op">=</span> loss_func(outputs, targets)</span>
<span id="cb14-10"><a href="#cb14-10"></a> accelerator.backward(loss)</span>
<span id="cb14-11"><a href="#cb14-11"></a> <span class="cf">else</span>:</span>
<span id="cb14-12"><a href="#cb14-12"></a> <span class="co"># Gradients finally sync</span></span>
<span id="cb14-13"><a href="#cb14-13"></a> outputs <span class="op">=</span> ddp_model(inputs)</span>
<span id="cb14-14"><a href="#cb14-14"></a> loss <span class="op">=</span> loss_func(outputs)</span>
<span id="cb14-15"><a href="#cb14-15"></a> accelerator.backward(loss)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section></section>
<section>
<section id="big-model-inference" class="title-slide slide level1 center">
<h1>Big Model Inference</h1>
<p>Stable Diffusion taking the world by storm</p>
</section>
<section id="bigger-models-higher-compute" class="slide level2">
<h2>Bigger Models == Higher Compute</h2>
<p>As more large models were being released, Hugging Face quickly realized there must be a way to continue our decentralization of Machine Learning and have the day-to-day programmer be able to leverage these big models.</p>
<p>Born out of this effort by Sylvain Gugger:</p>
<p>🤗 Accelerate: Big Model Inference.</p>
</section>
<section id="the-basic-premise" class="slide level2">
<h2>The Basic Premise</h2>
<div>
<ul>
<li class="fragment"><p>In PyTorch, there exists the <code>meta</code> device.</p></li>
<li class="fragment"><p>Super small footprint to load in huge models quickly by not loading in their weights immediatly.</p></li>
<li class="fragment"><p>As an input gets passed through each layer, we can load and unload <em>parts</em> of the PyTorch model quickly so that only a small portion of the big model is loaded in at a single time.</p></li>
<li class="fragment"><p>The end result? Stable Diffusion v1 can be ran on &lt; 800mb of vRAM</p></li>
</ul>
</div>
</section>
<section id="the-code" class="slide level2">
<h2>The Code</h2>
<p>Generally you start with something like so:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1"></a><span class="im">import</span> torch</span>
<span id="cb15-2"><a href="#cb15-2"></a></span>
<span id="cb15-3"><a href="#cb15-3"></a>my_model <span class="op">=</span> ModelClass(...)</span>
<span id="cb15-4"><a href="#cb15-4"></a>state_dict <span class="op">=</span> torch.load(checkpoint_file)</span>
<span id="cb15-5"><a href="#cb15-5"></a>my_model.load_state_dict(state_dict)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>But this has issues:</p>
<ol type="1">
<li>The full version of the model is loaded at <code>3</code></li>
<li>Another version of the model is loaded into memory at <code>4</code></li>
</ol>
<p>If a 6 <em>billion</em> parameter model is being loaded, each model class has a dictionary of 24GB so 48GB of vRAM is needed</p>
</section>
<section id="empty-model-weights" class="slide level2">
<h2>Empty Model Weights</h2>
<p>We can fix step 1 by loading in an empty model skeleton at first:</p>
<div class="sourceCode" id="cb16" data-code-line-numbers="1,3-4"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1"></a><span class="im">from</span> accelerate <span class="im">import</span> init_empty_weights</span>
<span id="cb16-2"><a href="#cb16-2"></a></span>
<span id="cb16-3"><a href="#cb16-3"></a><span class="cf">with</span> init_empty_weights():</span>
<span id="cb16-4"><a href="#cb16-4"></a> my_model <span class="op">=</span> ModelClass(...)</span>
<span id="cb16-5"><a href="#cb16-5"></a>state_dict <span class="op">=</span> torch.load(checkpoint_file)</span>
<span id="cb16-6"><a href="#cb16-6"></a>my_model.load_state_dict(state_dict)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="callout callout-important callout-captioned callout-style-default">
<div class="callout-body">
<div class="callout-caption">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<p><strong>This code will not run</strong></p>
</div>
<div class="callout-content">
<p>It is likely that just calling <code>my_model(x)</code> will fail as not all tensor operations are supported on the <code>meta</code> device.</p>
</div>
</div>
</div>
</section>
<section id="sharded-checkpoints---the-concept" class="slide level2">
<h2>Sharded Checkpoints - The Concept</h2>
<p>The next step is to have “Sharded Checkpoints” saved for your model.</p>
<p>Basically smaller chunks of your model weights stored that can be brought in at any particular time.</p>
<p>This reduces the amount of memory step 2 takes in since we can just load in a “chunk” of the model at a time, then swap it out for a new chunk through PyTorch hooks</p>
</section>
<section id="sharded-checkpoints---the-code" class="slide level2">
<h2>Sharded Checkpoints - The Code</h2>
<div class="sourceCode" id="cb17" data-code-line-numbers="1,6-8"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1"></a><span class="im">from</span> accelerate <span class="im">import</span> init_empty_weights, load_checkpoint_and_dispatch</span>
<span id="cb17-2"><a href="#cb17-2"></a></span>
<span id="cb17-3"><a href="#cb17-3"></a><span class="cf">with</span> init_empty_weights():</span>
<span id="cb17-4"><a href="#cb17-4"></a> my_model <span class="op">=</span> ModelClass(...)</span>
<span id="cb17-5"><a href="#cb17-5"></a></span>
<span id="cb17-6"><a href="#cb17-6"></a>my_model <span class="op">=</span> load_checkpoint_and_dispatch(</span>
<span id="cb17-7"><a href="#cb17-7"></a> my_model, <span class="st">"sharded-weights"</span>, device_map<span class="op">=</span><span class="st">"auto"</span></span>
<span id="cb17-8"><a href="#cb17-8"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p><code>device_map="auto"</code> will tell 🤗 Accelerate that it should determine where to put each layer of the model:</p>
<ol type="1">
<li>Maximum space on the GPU(s)</li>
<li>Maximum space on the CPU(s)</li>
<li>Utilize disk space through memory-mapped tensors</li>
</ol>
</section>
<section id="big-model-inference-put-together" class="slide level2">
<h2>Big Model Inference Put Together</h2>
<div class="sourceCode" id="cb18"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1"></a><span class="im">from</span> accelerate <span class="im">import</span> init_empty_weights, load_checkpoint_and_dispatch</span>
<span id="cb18-2"><a href="#cb18-2"></a></span>
<span id="cb18-3"><a href="#cb18-3"></a><span class="cf">with</span> init_empty_weights():</span>
<span id="cb18-4"><a href="#cb18-4"></a> my_model <span class="op">=</span> ModelClass(...)</span>
<span id="cb18-5"><a href="#cb18-5"></a></span>
<span id="cb18-6"><a href="#cb18-6"></a>my_model <span class="op">=</span> load_checkpoint_and_dispatch(</span>
<span id="cb18-7"><a href="#cb18-7"></a> my_model, <span class="st">"sharded-weights"</span>, device_map<span class="op">=</span><span class="st">"auto"</span></span>
<span id="cb18-8"><a href="#cb18-8"></a>)</span>
<span id="cb18-9"><a href="#cb18-9"></a>my_model.<span class="bu">eval</span>()</span>
<span id="cb18-10"><a href="#cb18-10"></a></span>
<span id="cb18-11"><a href="#cb18-11"></a><span class="cf">for</span> batch <span class="kw">in</span> dataloader:</span>
<span id="cb18-12"><a href="#cb18-12"></a> output <span class="op">=</span> my_model(batch)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="is-there-an-easier-way" class="slide level2">
<h2>Is there an easier way?</h2>
<p>The <code>transformers</code> library combined with the Hub makes all this code wrapping much easier for you with the <code>pipeline</code></p>
<div class="sourceCode" id="cb19"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1"></a><span class="im">import</span> torch</span>
<span id="cb19-2"><a href="#cb19-2"></a><span class="im">from</span> transformers <span class="im">import</span> pipeline</span>
<span id="cb19-3"><a href="#cb19-3"></a>pipe <span class="op">=</span> pipeline(</span>
<span id="cb19-4"><a href="#cb19-4"></a> task<span class="op">=</span><span class="st">"text-generation"</span>,</span>
<span id="cb19-5"><a href="#cb19-5"></a> model<span class="op">=</span><span class="st">"EleutherAI/gpt-j-6B"</span>,</span>
<span id="cb19-6"><a href="#cb19-6"></a> device_map<span class="op">=</span><span class="st">"auto"</span>,</span>
<span id="cb19-7"><a href="#cb19-7"></a> torch_dtype<span class="op">=</span>torch.float16</span>
<span id="cb19-8"><a href="#cb19-8"></a>)</span>
<span id="cb19-9"><a href="#cb19-9"></a></span>
<span id="cb19-10"><a href="#cb19-10"></a>text <span class="op">=</span> pipe(<span class="st">"This is some generated text, I think"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section></section>
<section>
<section id="what-about-stable-diffusion" class="title-slide slide level1 center">
<h1>What about Stable Diffusion?</h1>
<p>A demo with <code>diffusers</code> &amp; Weights and Biases</p>
</section>
<section id="some-handy-resources" class="slide level2">
<h2>Some Handy Resources</h2>
<ul>
<li><a href="https://hf.co/docs/accelerate">🤗 Accelerate documentation</a></li>
<li><a href="https://huggingface.co/docs/accelerate/basic_tutorials/launch">Launching distributed code</a></li>
<li><a href="https://huggingface.co/docs/accelerate/basic_tutorials/notebook">Distributed code and Jupyter Notebooks</a></li>
<li><a href="https://huggingface.co/docs/accelerate/basic_tutorials/migration">Migrating to 🤗 Accelerate easily</a></li>
<li><a href="https://huggingface.co/docs/accelerate/usage_guides/big_modeling">Big Model Inference tutorial</a></li>
<li><a href="https://huggingface.co/docs/accelerate/usage_guides/deepspeed">DeepSpeed and 🤗 Accelerate</a></li>
<li><a href="https://huggingface.co/docs/accelerate/usage_guides/fsdp">Fully Sharded Data Parallelism and 🤗 Accelerate</a></li>
</ul>
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