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<!DOCTYPE html>
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<title>Hugging Face Accelerate: Making device-agnostic ML training and inference easy at scale</title>
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<body class="quarto-dark">
<div class="reveal">
<div class="slides">
<section id="title-slide" class="quarto-title-block center">
<h1 class="title">Hugging Face Accelerate: Making device-agnostic ML training and inference easy at scale</h1>
<div class="quarto-title-authors">
</div>
</section>
<section id="who-am-i" class="slide level2">
<h2>Who am I?</h2>
<ul>
<li>Zachary Mueller</li>
<li>Technical Lead for the 🤗 Accelerate project</li>
<li>Maintain the <code>transformers</code> Trainer</li>
<li>API design geek</li>
</ul>
</section>
<section id="what-is-accelerate" class="slide level2">
<h2>What is 🤗 Accelerate?</h2>
<ul>
<li>A training framework</li>
<li>An inference framework</li>
<li>A command-line interface</li>
</ul>
</section>
<section id="a-training-framework" class="slide level2">
<h2>A Training Framework</h2>
<ul>
<li>Powered by PyTorch</li>
<li>Change a few lines of code, gain device <em>and</em> hardware-agnostic capabilities</li>
<li>Low-code, with minimal magic aimed at easy hackability and use without high-level abstractions</li>
<li>We handle the intracies so you don’t have to</li>
</ul>
</section>
<section id="a-training-framework-1" class="slide level2">
<h2>A Training Framework</h2>
<div style="font-size: 70%;">
<ul>
<li>Support for any hardware-accelerator on the market:
<ul>
<li>CPU, GPU, TPU, XPU, NPU, MLU</li>
</ul></li>
<li>Automatic mixed-precision training <em>safely</em> in whatever fashion you may choose:
<ul>
<li>FP16, BF16, FP8 (through either <code>TransformerEngine</code> or <code>MS-AMP</code>)</li>
</ul></li>
<li>Automatic and efficient gradient accumulation</li>
<li>Support for quantization through <code>bitsandbytes</code></li>
<li>Support your favorite experiment trackers (<code>aim</code>, <code>clearml</code>, <code>comet_ml</code>, <code>dvc-lite</code>, <code>ml-flow</code>, <code>tensorboard</code>, <code>wandb</code>)</li>
<li>Easy to configure plugin or YAML-level API for setting up advanced frameworks like <code>FSDP</code>, <code>DeepSpeed</code>, and <code>Megatron-LM</code></li>
</ul>
</div>
</section>
<section id="low-code" class="slide level2">
<h2>Low-Code</h2>
<div style="font-size: 70%;">
<ul>
<li>Biggest friction with “wrapper” libraries is control of your code</li>
<li>By being minimally intrusive, your code just “works” while still giving you complete control</li>
</ul>
</div>
<div style="font-size: 60%;padding-left:15%;padding-top:0%;padding-right:20%">
<div class="sourceCode" id="cb1"><pre class="sourceCode numberSource diff number-lines code-with-copy"><code class="sourceCode diff"><span id="cb1-1"><a href="#cb1-1"></a> import torch</span>
<span id="cb1-2"><a href="#cb1-2"></a> import torch.nn.functional as F</span>
<span id="cb1-3"><a href="#cb1-3"></a> from datasets import load_dataset</span>
<span id="cb1-4"><a href="#cb1-4"></a><span class="va">+ from accelerate import Accelerator</span></span>
<span id="cb1-5"><a href="#cb1-5"></a></span>
<span id="cb1-6"><a href="#cb1-6"></a><span class="va">+ accelerator = Accelerator()</span></span>
<span id="cb1-7"><a href="#cb1-7"></a><span class="st">- device = 'cpu'</span></span>
<span id="cb1-8"><a href="#cb1-8"></a><span class="va">+ device = accelerator.device</span></span>
<span id="cb1-9"><a href="#cb1-9"></a></span>
<span id="cb1-10"><a href="#cb1-10"></a> model = torch.nn.Transformer().to(device)</span>
<span id="cb1-11"><a href="#cb1-11"></a> optimizer = torch.optim.Adam(model.parameters())</span>
<span id="cb1-12"><a href="#cb1-12"></a> dataset = load_dataset('my_dataset')</span>
<span id="cb1-13"><a href="#cb1-13"></a> data = torch.utils.data.DataLoader(dataset, shuffle=True)</span>
<span id="cb1-14"><a href="#cb1-14"></a></span>
<span id="cb1-15"><a href="#cb1-15"></a><span class="va">+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)</span></span>
<span id="cb1-16"><a href="#cb1-16"></a></span>
<span id="cb1-17"><a href="#cb1-17"></a> model.train()</span>
<span id="cb1-18"><a href="#cb1-18"></a> for epoch in range(10):</span>
<span id="cb1-19"><a href="#cb1-19"></a> for source, targets in dataloader:</span>
<span id="cb1-20"><a href="#cb1-20"></a> source, targets = source.to(device), targets.to(device)</span>
<span id="cb1-21"><a href="#cb1-21"></a> optimizer.zero_grad()</span>
<span id="cb1-22"><a href="#cb1-22"></a> output = model(source)</span>
<span id="cb1-23"><a href="#cb1-23"></a> loss = F.cross_entropy(output, targets)</span>
<span id="cb1-24"><a href="#cb1-24"></a><span class="st">- loss.backward()</span></span>
<span id="cb1-25"><a href="#cb1-25"></a><span class="va">+ accelerator.backward(loss)</span></span>
<span id="cb1-26"><a href="#cb1-26"></a> optimizer.step()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="easy-to-integrate" class="slide level2">
<h2>Easy to integrate</h2>
<div style="font-size: 70%;">
<ul>
<li>Due to the low-code nature, it’s trivial to integrate into existing PyTorch frameworks:
<ol type="1">
<li>Create an <code>Accelerator</code></li>
</ol></li>
</ul>
</div>
<div style="font-size: 60%;padding-left:15%;padding-top:0%;padding-right:20%">
<div class="sourceCode" id="cb2"><pre class="sourceCode numberSource diff number-lines code-with-copy"><code class="sourceCode diff"><span id="cb2-1"><a href="#cb2-1"></a> import torch</span>
<span id="cb2-2"><a href="#cb2-2"></a> import torch.nn.functional as F</span>
<span id="cb2-3"><a href="#cb2-3"></a> from datasets import load_dataset</span>
<span id="cb2-4"><a href="#cb2-4"></a><span class="va">+ from accelerate import Accelerator</span></span>
<span id="cb2-5"><a href="#cb2-5"></a></span>
<span id="cb2-6"><a href="#cb2-6"></a><span class="va">+ accelerator = Accelerator()</span></span>
<span id="cb2-7"><a href="#cb2-7"></a> device = 'cpu'</span>
<span id="cb2-8"><a href="#cb2-8"></a></span>
<span id="cb2-9"><a href="#cb2-9"></a> model = torch.nn.Transformer().to(device)</span>
<span id="cb2-10"><a href="#cb2-10"></a> optimizer = torch.optim.Adam(model.parameters())</span>
<span id="cb2-11"><a href="#cb2-11"></a> dataset = load_dataset('my_dataset')</span>
<span id="cb2-12"><a href="#cb2-12"></a> data = torch.utils.data.DataLoader(dataset, shuffle=True)</span>
<span id="cb2-13"><a href="#cb2-13"></a></span>
<span id="cb2-14"><a href="#cb2-14"></a> model.train()</span>
<span id="cb2-15"><a href="#cb2-15"></a> for epoch in range(10):</span>
<span id="cb2-16"><a href="#cb2-16"></a> for source, targets in dataloader:</span>
<span id="cb2-17"><a href="#cb2-17"></a> source, targets = source.to(device), targets.to(device)</span>
<span id="cb2-18"><a href="#cb2-18"></a> optimizer.zero_grad()</span>
<span id="cb2-19"><a href="#cb2-19"></a> output = model(source)</span>
<span id="cb2-20"><a href="#cb2-20"></a> loss = F.cross_entropy(output, targets)</span>
<span id="cb2-21"><a href="#cb2-21"></a> loss.backward()</span>
<span id="cb2-22"><a href="#cb2-22"></a> optimizer.step()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="easy-to-integrate-1" class="slide level2">
<h2>Easy to integrate</h2>
<div style="font-size: 70%;">
<ul>
<li>Due to the low-code nature, it’s trivial to integrate into existing PyTorch frameworks:
<ol start="2" type="1">
<li>Wrap your PyTorch objects with <code>accelerator.prepare</code> and remove device-placements</li>
</ol></li>
</ul>
</div>
<div style="font-size: 60%;padding-left:15%;padding-top:0%;padding-right:20%">
<div class="sourceCode" id="cb3"><pre class="sourceCode numberSource diff number-lines code-with-copy"><code class="sourceCode diff"><span id="cb3-1"><a href="#cb3-1"></a> import torch</span>
<span id="cb3-2"><a href="#cb3-2"></a> import torch.nn.functional as F</span>
<span id="cb3-3"><a href="#cb3-3"></a> from datasets import load_dataset</span>
<span id="cb3-4"><a href="#cb3-4"></a> from accelerate import Accelerator</span>
<span id="cb3-5"><a href="#cb3-5"></a></span>
<span id="cb3-6"><a href="#cb3-6"></a> accelerator = Accelerator()</span>
<span id="cb3-7"><a href="#cb3-7"></a><span class="st">- device = 'cpu'</span></span>
<span id="cb3-8"><a href="#cb3-8"></a></span>
<span id="cb3-9"><a href="#cb3-9"></a> model = torch.nn.Transformer().to(device)</span>
<span id="cb3-10"><a href="#cb3-10"></a> optimizer = torch.optim.Adam(model.parameters())</span>
<span id="cb3-11"><a href="#cb3-11"></a> dataset = load_dataset('my_dataset')</span>
<span id="cb3-12"><a href="#cb3-12"></a> data = torch.utils.data.DataLoader(dataset, shuffle=True)</span>
<span id="cb3-13"><a href="#cb3-13"></a></span>
<span id="cb3-14"><a href="#cb3-14"></a><span class="va">+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)</span></span>
<span id="cb3-15"><a href="#cb3-15"></a></span>
<span id="cb3-16"><a href="#cb3-16"></a> model.train()</span>
<span id="cb3-17"><a href="#cb3-17"></a> for epoch in range(10):</span>
<span id="cb3-18"><a href="#cb3-18"></a> for source, targets in dataloader:</span>
<span id="cb3-19"><a href="#cb3-19"></a> source, targets = source.to(device), targets.to(device)</span>
<span id="cb3-20"><a href="#cb3-20"></a> optimizer.zero_grad()</span>
<span id="cb3-21"><a href="#cb3-21"></a> output = model(source)</span>
<span id="cb3-22"><a href="#cb3-22"></a> loss = F.cross_entropy(output, targets)</span>
<span id="cb3-23"><a href="#cb3-23"></a> loss.backward()</span>
<span id="cb3-24"><a href="#cb3-24"></a> optimizer.step()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="easy-to-integrate-2" class="slide level2">
<h2>Easy to integrate</h2>
<div style="font-size: 70%;">
<ul>
<li>Due to the low-code nature, it’s trivial to integrate into existing PyTorch frameworks:
<ol start="3" type="1">
<li>Use <code>accelerator.backward</code> for the backward pass</li>
</ol></li>
</ul>
</div>
<div style="font-size: 60%;padding-left:15%;padding-top:0%;padding-right:20%">
<div class="sourceCode" id="cb4"><pre class="sourceCode numberSource diff number-lines code-with-copy"><code class="sourceCode diff"><span id="cb4-1"><a href="#cb4-1"></a> import torch</span>
<span id="cb4-2"><a href="#cb4-2"></a> import torch.nn.functional as F</span>
<span id="cb4-3"><a href="#cb4-3"></a> from datasets import load_dataset</span>
<span id="cb4-4"><a href="#cb4-4"></a> from accelerate import Accelerator</span>
<span id="cb4-5"><a href="#cb4-5"></a></span>
<span id="cb4-6"><a href="#cb4-6"></a> accelerator = Accelerator()</span>
<span id="cb4-7"><a href="#cb4-7"></a></span>
<span id="cb4-8"><a href="#cb4-8"></a> model = torch.nn.Transformer().to(device)</span>
<span id="cb4-9"><a href="#cb4-9"></a> optimizer = torch.optim.Adam(model.parameters())</span>
<span id="cb4-10"><a href="#cb4-10"></a> dataset = load_dataset('my_dataset')</span>
<span id="cb4-11"><a href="#cb4-11"></a> data = torch.utils.data.DataLoader(dataset, shuffle=True)</span>
<span id="cb4-12"><a href="#cb4-12"></a></span>
<span id="cb4-13"><a href="#cb4-13"></a> model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)</span>
<span id="cb4-14"><a href="#cb4-14"></a></span>
<span id="cb4-15"><a href="#cb4-15"></a> model.train()</span>
<span id="cb4-16"><a href="#cb4-16"></a> for epoch in range(10):</span>
<span id="cb4-17"><a href="#cb4-17"></a> for source, targets in dataloader:</span>
<span id="cb4-18"><a href="#cb4-18"></a> source, targets = source.to(device), targets.to(device)</span>
<span id="cb4-19"><a href="#cb4-19"></a> optimizer.zero_grad()</span>
<span id="cb4-20"><a href="#cb4-20"></a> output = model(source)</span>
<span id="cb4-21"><a href="#cb4-21"></a> loss = F.cross_entropy(output, targets)</span>
<span id="cb4-22"><a href="#cb4-22"></a><span class="st">- loss.backward()</span></span>
<span id="cb4-23"><a href="#cb4-23"></a><span class="va">+ accelerator.backward(loss)</span></span>
<span id="cb4-24"><a href="#cb4-24"></a> optimizer.step()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="but-what-about-inference" class="slide level2">
<h2>But what about inference?</h2>
<ul>
<li>🤗 Accelerate is not just for training, and has helped make the GPU-Poor take control of the narrative</li>
<li>Using tools like Big Model Inference, users with <em>tiny</em> compute can run large models locally</li>
<li>Started with the boom of stable diffusion, and now has scaled to having the ability to run huge LLMs locally with a single graphics card</li>
</ul>
</section>
<section id="how-does-it-work" class="slide level2">
<h2>How does it work?</h2>
<ul>
<li>PyTorch introduced <code>device="meta"</code></li>
<li>🤗 Accelerate introduced <code>device_map="auto"</code></li>
</ul>
<div style="padding-left:15%;padding-right:20%">
<video id="video_shortcode_videojs_video1" width="800" height="400" class="video-js vjs-default-skin " controls="" preload="auto" data-setup="{}" title=""><source src="big_model_visualization.mp4"></video>
</div>
</section>
<section id="a-cli-interface" class="slide level2">
<h2>A CLI Interface</h2>
<ul>
<li><code>accelerate config</code>
<ul>
<li>Configure the environment</li>
</ul></li>
<li><code>accelerate launch</code>
<ul>
<li>How to run your script</li>
</ul></li>
</ul>
</section>
<section id="launching-distributed-training-is-hard" class="slide level2">
<h2>Launching distributed training is hard</h2>
<div style="padding-top:0%;padding-left:10%;padding-right:15%;padding-bottom:0%">
<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">python</span> script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div style="padding-left:50%;padding-bottom:0%;padding-top:0%;">
<p>vs.</p>
</div>
<p><br></p>
<div style="padding-top:0%;padding-left:10%;padding-right:15%;padding-bottom:0%">
<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">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>
</div>
<div style="padding-left:50%;padding-bottom:0%;padding-top:0%;">
<p>vs.</p>
</div>
<p><br></p>
<div style="padding-top:0%;padding-left:10%;padding-right:15%;padding-bottom:0%">
<div class="sourceCode" id="cb7"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb7-1"><a href="#cb7-1"></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>
<p><br></p>
</div>
<p>How can we make this better?</p>
</section>
<section id="accelerate-launch" class="slide level2">
<h2><code>accelerate launch</code></h2>
<div style="padding-top:0%;padding-left:5%;padding-right:10%;padding-bottom:0%">
<div class="sourceCode" id="cb8"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb8-1"><a href="#cb8-1"></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><br></p>
<div class="sourceCode" id="cb9"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb9-1"><a href="#cb9-1"></a><span class="ex">accelerate</span> launch <span class="at">--multi_gpu</span> <span class="at">--num_processes</span> 2 script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p><br></p>
<div class="sourceCode" id="cb10"><pre class="sourceCode numberSource bash number-lines code-with-copy"><code class="sourceCode bash"><span id="cb10-1"><a href="#cb10-1"></a><span class="ex">accelerate</span> launch <span class="dt">\</span></span>
<span id="cb10-2"><a href="#cb10-2"></a> <span class="at">--multi_gpu</span> <span class="dt">\ </span></span>
<span id="cb10-3"><a href="#cb10-3"></a> <span class="ex">--use_deepspeed</span> <span class="dt">\</span></span>
<span id="cb10-4"><a href="#cb10-4"></a> <span class="at">--num_processes</span> 2 <span class="dt">\</span></span>
<span id="cb10-5"><a href="#cb10-5"></a> script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="accelerate-config" class="slide level2">
<h2><code>accelerate config</code></h2>
<ul>
<li>Rely on <code>config.yaml</code> files</li>
<li>Choose to either running <code>accelerate config</code> or write your own:</li>
</ul>
<div class="columns" style="font-size: 60%;padding-left:5%;padding-right:5%">
<div class="column" style="width:40%;">
<div class="code-with-filename">
<div class="code-with-filename-file">
<pre><strong>ddp_config.yaml</strong></pre>
</div>
<div class="sourceCode" id="cb11"><pre class="sourceCode numberSource yaml number-lines code-with-copy"><code class="sourceCode yaml"><span id="cb11-1"><a href="#cb11-1"></a><span class="fu">compute_environment</span><span class="kw">:</span><span class="at"> LOCAL_MACHINE</span></span>
<span id="cb11-2"><a href="#cb11-2"></a><span class="fu">distributed_type</span><span class="kw">:</span><span class="at"> MULTI_GPU</span></span>
<span id="cb11-3"><a href="#cb11-3"></a><span class="fu">main_training_function</span><span class="kw">:</span><span class="at"> main</span></span>
<span id="cb11-4"><a href="#cb11-4"></a><span class="fu">mixed_precision</span><span class="kw">:</span><span class="at"> bf16</span></span>
<span id="cb11-5"><a href="#cb11-5"></a><span class="fu">num_machines</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span></span>
<span id="cb11-6"><a href="#cb11-6"></a><span class="fu">num_processes</span><span class="kw">:</span><span class="at"> </span><span class="dv">8</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div><div class="column" style="width:40%;">
<div class="code-with-filename">
<div class="code-with-filename-file">
<pre><strong>fsdp_config.yaml</strong></pre>
</div>
<div class="sourceCode" id="cb12"><pre class="sourceCode numberSource yaml number-lines code-with-copy"><code class="sourceCode yaml"><span id="cb12-1"><a href="#cb12-1"></a><span class="fu">compute_environment</span><span class="kw">:</span><span class="at"> LOCAL_MACHINE</span></span>
<span id="cb12-2"><a href="#cb12-2"></a><span class="fu">distributed_type</span><span class="kw">:</span><span class="at"> FSDP</span></span>
<span id="cb12-3"><a href="#cb12-3"></a><span class="fu">fsdp_config</span><span class="kw">:</span></span>
<span id="cb12-4"><a href="#cb12-4"></a><span class="at"> </span><span class="fu">fsdp_auto_wrap_policy</span><span class="kw">:</span><span class="at"> TRANSFORMER_BASED_WRAP</span></span>
<span id="cb12-5"><a href="#cb12-5"></a><span class="at"> </span><span class="fu">fsdp_backward_prefetch</span><span class="kw">:</span><span class="at"> BACKWARD_PRE</span></span>
<span id="cb12-6"><a href="#cb12-6"></a><span class="at"> </span><span class="fu">fsdp_cpu_ram_efficient_loading</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
<span id="cb12-7"><a href="#cb12-7"></a><span class="at"> </span><span class="fu">fsdp_forward_prefetch</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb12-8"><a href="#cb12-8"></a><span class="at"> </span><span class="fu">fsdp_offload_params</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb12-9"><a href="#cb12-9"></a><span class="at"> </span><span class="fu">fsdp_sharding_strategy</span><span class="kw">:</span><span class="at"> FULL_SHARD</span></span>
<span id="cb12-10"><a href="#cb12-10"></a><span class="at"> </span><span class="fu">fsdp_state_dict_type</span><span class="kw">:</span><span class="at"> SHARDED_STATE_DICT</span></span>
<span id="cb12-11"><a href="#cb12-11"></a><span class="at"> </span><span class="fu">fsdp_sync_module_states</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
<span id="cb12-12"><a href="#cb12-12"></a><span class="at"> </span><span class="fu">fsdp_use_orig_params</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb12-13"><a href="#cb12-13"></a><span class="fu">main_training_function</span><span class="kw">:</span><span class="at"> main</span></span>
<span id="cb12-14"><a href="#cb12-14"></a><span class="fu">mixed_precision</span><span class="kw">:</span><span class="at"> bf16</span></span>
<span id="cb12-15"><a href="#cb12-15"></a><span class="fu">num_machines</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span></span>
<span id="cb12-16"><a href="#cb12-16"></a><span class="fu">num_processes</span><span class="kw">:</span><span class="at"> </span><span class="dv">8</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</section>
<section id="now-that-youre-up-to-speed-whats-new" class="title-slide slide level1 center">
<h1>Now that you’re up to speed, what’s new?</h1>
</section>
<section>
<section id="weve-had-a-busy-last-year-and-so-has-the-ml-community" class="title-slide slide level1 center">
<h1>We’ve had a busy last year, and so has the ML Community!</h1>
</section>
<section id="new-training-techniques" class="slide level2">
<h2>New training techniques</h2>
<ul>
<li>Quantization has taken the field by storm</li>
<li>New ideas such as FSDP + QLoRA to train huge models on tiny compute!</li>
<li>New precision backends as we train natively on smaller precision</li>
<li>Optimizing futher how much we can push on a single machine through efficient RAM and timing techniques</li>
</ul>
</section>
<section id="larger-compute-landscape" class="slide level2">
<h2>Larger compute landscape</h2>
<ul>
<li>As we search for alternatives to NVIDIA, new compilers rise:
<ul>
<li>XPU (Intel)</li>
<li>NPU (Intel)</li>
<li>MLU (Cambricon)</li>
</ul></li>
</ul>
<p>All of which are supported by 🤗 Accelerate</p>
</section>
<section id="lower-abstractions" class="slide level2">
<h2>Lower abstractions</h2>
<ul>
<li>While the <code>Accelerator</code> was great, needed better abstractions focused on controlling behaviors</li>
<li>Introduced the <code>PartialState</code></li>
</ul>
<div style="padding-left:10%;padding-top:0%;padding-right:15%">
<div class="sourceCode" id="cb13"><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> PartialState</span>
<span id="cb13-2"><a href="#cb13-2"></a></span>
<span id="cb13-3"><a href="#cb13-3"></a><span class="cf">if</span> PartialState().is_main_process:</span>
<span id="cb13-4"><a href="#cb13-4"></a> <span class="co"># Run on only 1 device</span></span>
<span id="cb13-5"><a href="#cb13-5"></a></span>
<span id="cb13-6"><a href="#cb13-6"></a><span class="cf">with</span> PartialState().main_process_first:</span>
<span id="cb13-7"><a href="#cb13-7"></a> <span class="co"># Useful for dataset processing</span></span>
<span id="cb13-8"><a href="#cb13-8"></a></span>
<span id="cb13-9"><a href="#cb13-9"></a><span class="co"># Device-agnostic without the bulk of the `Accelerator`</span></span>
<span id="cb13-10"><a href="#cb13-10"></a>device <span class="op">=</span> PartialState().device</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="faster-and-better-inference-alternatives" class="slide level2">
<h2>Faster and better inference alternatives</h2>
<div style="font-size:70%">
<ul>
<li><code>PiPPy</code> gives us efficient pipeline-parallelism in distributed environments to increase throughput while keeping a simple torch-bound API</li>
<li>Rather than having to wait for each GPU, every GPU can be busy in parallel</li>
<li>Will be critical as larger LLMs take hold and more than one computer is needed</li>
</ul>
</div>
<div style="font-size:60%;padding-left:19%;padding-top:0%;padding-right:24%;">
<div class="sourceCode" id="cb14"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1"></a><span class="im">import</span> torch</span>
<span id="cb14-2"><a href="#cb14-2"></a><span class="im">from</span> transformers <span class="im">import</span> AutoModelForSequenceClassification</span>
<span id="cb14-3"><a href="#cb14-3"></a></span>
<span id="cb14-4"><a href="#cb14-4"></a><span class="im">from</span> accelerate <span class="im">import</span> PartialState, prepare_pippy</span>
<span id="cb14-5"><a href="#cb14-5"></a></span>
<span id="cb14-6"><a href="#cb14-6"></a>model <span class="op">=</span> AutoModelForSequenceClassification.from_pretrained(<span class="st">"gpt2"</span>)</span>
<span id="cb14-7"><a href="#cb14-7"></a>model.<span class="bu">eval</span>()</span>
<span id="cb14-8"><a href="#cb14-8"></a></span>
<span id="cb14-9"><a href="#cb14-9"></a><span class="bu">input</span> <span class="op">=</span> torch.randint(</span>
<span id="cb14-10"><a href="#cb14-10"></a> low<span class="op">=</span><span class="dv">0</span>,</span>
<span id="cb14-11"><a href="#cb14-11"></a> high<span class="op">=</span>model.config.vocab_size,</span>
<span id="cb14-12"><a href="#cb14-12"></a> size<span class="op">=</span>(<span class="dv">2</span>, <span class="dv">1024</span>), <span class="co"># bs x seq_len</span></span>
<span id="cb14-13"><a href="#cb14-13"></a> device<span class="op">=</span><span class="st">"cpu"</span>,</span>
<span id="cb14-14"><a href="#cb14-14"></a>)</span>
<span id="cb14-15"><a href="#cb14-15"></a></span>
<span id="cb14-16"><a href="#cb14-16"></a>model <span class="op">=</span> prepare_pippy(model, split_points<span class="op">=</span><span class="st">"auto"</span>, example_args<span class="op">=</span>(<span class="bu">input</span>,))</span>
<span id="cb14-17"><a href="#cb14-17"></a></span>
<span id="cb14-18"><a href="#cb14-18"></a><span class="cf">with</span> torch.no_grad():</span>
<span id="cb14-19"><a href="#cb14-19"></a> output <span class="op">=</span> model(<span class="bu">input</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section></section>
<section>
<section id="adoption-accelerate-in-the-ecosystem" class="title-slide slide level1 center">
<h1>Adoption: Accelerate in the ecosystem</h1>
</section>
<section id="accelerate-in-the-ecosystem" class="slide level2">
<h2>Accelerate in the Ecosystem</h2>
<ul>
<li>Many of the frameworks you use daily already rely on 🤗 Accelerate!
<ul>
<li>Nearly all of 🤗</li>
<li><code>axolotl</code></li>
<li><code>fastai</code></li>
<li><code>FastChat</code></li>
<li><code>lucidrains</code></li>
<li><code>kornia</code></li>
</ul></li>
</ul>
</section>
<section id="accelerate-in-the-ecosystem-1" class="slide level2">
<h2>Accelerate in the Ecosystem</h2>
<div style="font-size: 70%;">
<ul>
<li>Started as a way to isolate out distributed code on TPU and <code>DistributedDataParallelism</code></li>
</ul>
</div>
<div style="padding-left: 30%">
<p><img data-src="sylvain_tweet.JPG" style="width:70.0%"></p>
</div>
</section>
<section id="accelerate-in-the-ecosystem-2" class="slide level2">
<h2>Accelerate in the Ecosystem</h2>
<div style="font-size: 70%;">
<ul>
<li>Now is the backbone of some of the largest PyTorch training frameworks in the ecosystem</li>
</ul>
</div>
<div style="padding-left: 30%;">
<p><img data-src="hf_trainer.JPG" style="width:70.0%"></p>
</div>
</section></section>
<section id="whats-next" class="title-slide slide level1 center">
<h1>What’s next?</h1>
</section>
<section id="elevating-the-community" class="title-slide slide level1 center">
<h1>Elevating the community</h1>
<ul>
<li>Now that more advanced training techniques are reachable (FSDP, DeepSpeed, etc), we need to focus on educating the community on how to use it best</li>
<li>Goes beyond how to use the <code>Trainer</code> or <code>Accelerator</code>, but how to use <em>what</em> where</li>
<li>Keep Accelerate as a tool for the community to utilize when new techniques come out and play with, to push new ideas to scale quickly</li>
</ul>
</section>
<section id="soon" class="title-slide slide level1 center">
<h1>1.0.0: Soon!</h1>
<ul>
<li>Tried and battle-tested by over 7M users/month | 110M+ total downloads</li>
<li>As we’ve been stable for over a year now, we’re near ready to release 1.0.0</li>
</ul>
</section>
<section id="thanks-for-joining" class="title-slide slide level1 center">
<h1>Thanks for joining!</h1>
<div style="font-size: 70%;">
<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>
<li><a href="https://huggingface.co/docs/accelerate/concept_guides/fsdp_and_deepspeed">FSDP vs DeepSpeed In-Depth</a></li>
</ul>
</div>
<div class="footer footer-default">
</div>
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