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  <title>Scaling Model Training with More Compute, How Do They Do It?</title>
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<body class="quarto-dark">
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    <div class="slides">

<section id="title-slide" class="quarto-title-block center">
  <h1 class="title">Scaling Model Training with More Compute, How Do They Do It?</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>API design geek</li>
</ul>
</section>
<section id="understanding-gpu-usage" class="slide level2">
<h2>Understanding GPU Usage</h2>
<ul>
<li>We can somewhat estimate the memory usage in vanilla full-fine-tuning of models</li>
<li>Requires certain assumptions (that I’ll be covering):
<ul>
<li>Adam optimizer</li>
<li>Batch size of 1</li>
</ul></li>
</ul>
</section>
<section id="understanding-gpu-usage-1" class="slide level2">
<h2>Understanding GPU Usage</h2>
<p>General estimate (<code>bert-base-cased</code>, 108M params):</p>
<ul>
<li>Each parameter is 4 bytes</li>
<li>Backward ~= 2x the model size</li>
<li>The optimizer step ~= 4x the model size (1x model, 1x gradients, 2x optimizer):</li>
</ul>
<div style="font-size: 50%;">
<table>
<thead>
<tr class="header">
<th>dtype</th>
<th style="text-align: left;">Model</th>
<th style="text-align: center;">Gradients</th>
<th style="text-align: center;">Backward pass</th>
<th style="text-align: center;">Optimizer step</th>
<th style="text-align: center;">Highest</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>float32</td>
<td style="text-align: left;">413.18 MB</td>
<td style="text-align: center;">413.18 MB</td>
<td style="text-align: center;">826.36 MB</td>
<td style="text-align: center;">1.61 GB</td>
<td style="text-align: center;">1.61 GB</td>
</tr>
<tr class="even">
<td>float16</td>
<td style="text-align: left;">413.18 MB*</td>
<td style="text-align: center;">619.77 MB</td>
<td style="text-align: center;">826.36 MB</td>
<td style="text-align: center;">826.36 MB</td>
<td style="text-align: center;">826.36 MB</td>
</tr>
</tbody>
</table>
<p>*All estimations were based off the <a href="https://huggingface.co/spaces/hf-accelerate/model-memory-usage">Model Estimator Tool</a></p>
</div>
</section>
<section id="understanding-gpu-usage-2" class="slide level2">
<h2>Understanding GPU Usage</h2>
<p>This works fine for small models, we have cards with anywhere from 12-24GB of GPU memory (on the GPU-poor side).</p>
<p>But what happens as we scale?</p>
<p>Here’s <code>llama-3-8B</code> (8.03B parameters)</p>
<div style="font-size: 50%;">
<table>
<thead>
<tr class="header">
<th>dtype</th>
<th style="text-align: left;">Model</th>
<th style="text-align: center;">Gradients</th>
<th style="text-align: center;">Backward pass</th>
<th style="text-align: center;">Optimizer step</th>
<th style="text-align: center;">Highest</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>float32</td>
<td style="text-align: left;">28.21 GB</td>
<td style="text-align: center;">28.21 GB</td>
<td style="text-align: center;">56.43 GB</td>
<td style="text-align: center;">112.84 GB</td>
<td style="text-align: center;">112.84 GB</td>
</tr>
<tr class="even">
<td>float16</td>
<td style="text-align: left;">28.21 GB*</td>
<td style="text-align: center;">42.32 GB</td>
<td style="text-align: center;">56.43 GB</td>
<td style="text-align: center;">56.43 GB</td>
<td style="text-align: center;">56.43 GB</td>
</tr>
</tbody>
</table>
</div>
<p>Well, <em>I</em> don’t have 56GB of GPU memory in a single card, let alone 112GB.</p>
<p>What can we do?</p>
</section>
<section>
<section id="distributed-training" class="title-slide slide level1 center">
<h1>Distributed Training</h1>

</section>
<section id="kinds-of-training" class="slide level2">
<h2>Kinds of Training</h2>
<ul>
<li>Single GPU:
<ul>
<li>No distributed techniques at play</li>
</ul></li>
<li>DDP:
<ul>
<li>A full copy of the model exists on each device, but data is chunked between each GPU</li>
</ul></li>
<li>FSDP &amp; DeepSpeed:
<ul>
<li>Split chunks of the model and optimizer states across GPUs, allowing for training bigger models on smaller (multiple) GPUs</li>
</ul></li>
</ul>
</section></section>
<section>
<section id="fully-sharded-data-parallelism" class="title-slide slide level1 center">
<h1>Fully Sharded Data Parallelism</h1>

</section>
<section id="fully-sharded-data-parallelism-1" class="slide level2">
<h2>Fully Sharded Data Parallelism</h2>

<img data-src="fsdp.png" id="fig-539a35d47e664c97a50115a146a7f1bd-1" class="r-stretch quarto-figure-center"><aside class="notes">
<ul>
<li>Take the model and split it across <code>n</code> GPUs</li>
<li>Each GPU computes the shard’s gradients</li>
<li>At the end, all gradients are synchronized and the final full model gradient is calculated</li>
<li>The backward pass can then be performed</li>
</ul>
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</section>
<section id="fsdp-getting-parameter-specific" class="slide level2">
<h2>FSDP: Getting parameter specific</h2>
<ul>
<li>Different parameters can dicatate how much memory is needed for total GPU training across multiple GPUs</li>
<li>These include how model weights are sharded, gradients, and more.</li>
<li>I’ll cover some important ones I needed when doing a Full-Fine-Tune of Llama-3-8B <em>without PEFT</em> on 2x4090’s</li>
</ul>
</section>
<section id="sharding_strategy" class="slide level2">
<h2><code>sharding_strategy</code></h2>
<ul>
<li>Dictates the level of divving resources to perform
<ul>
<li><code>FULL_SHARD</code>: Includes optimizer states, gradients, and parameters</li>
<li><code>SHARD_GRAD_OP</code>: Includes optimizer states and gradients</li>
<li><code>NO_SHARD</code>: Normal DDP</li>
<li><code>HYBRID_SHARD</code>: Includes optimizer states, gradients, and parameters but each node has the full model</li>
</ul>
<aside class="notes">
<pre><code>FULL_SHARD:
  Parameters, Gradients, Optimizer States: All are sharded.
  Parameters Handling: Unshard before forward pass, reshard after forward pass, unshard before backward pass, reshard after backward pass.
  Gradients Handling: Synchronize and shard after backward pass.
  Optimizer States: Updated locally per rank.</code></pre>
<p>SHARD_GRAD_OP: Gradients and Optimizer States: Sharded during computation. Parameters: Unshard before forward pass, remain unsharded during forward pass, reshard after backward pass. Inside no_sync(): Parameters are not resharded after backward computation. Optimizer States: Updated locally per rank.</p>
<p>NO_SHARD: Parameters, Gradients, Optimizer States: Not sharded, replicated across ranks. Gradients Handling: Synchronized via all-reduce after backward pass. Optimizer States: Updated locally per rank.</p>
<p>HYBRID_SHARD: Parameters, Gradients, Optimizer States: Combines FULL_SHARD within a node and replicates parameters across nodes. Communication: Expensive operations like all-gathers and reduce-scatters are limited to within a node, enhancing performance for medium-sized models.</p>
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</ul>
</section>
<section id="auto_wrap_policy" class="slide level2">
<h2><code>auto_wrap_policy</code>:</h2>
<ul>
<li>How the model should be split</li>
<li>Can be either <code>TRANSFORMER_BASED_WRAP</code> or <code>SIZE_BASED_WRAP</code></li>
<li><code>TRANSFORMER</code>/<code>fsdp_transformers_layer_cls_to_wrap</code>:
<ul>
<li>Need to declare the layer</li>
<li>Generally <code>transformers</code> has good defaults</li>
</ul></li>
<li><code>SIZE</code>/<code>fsdp_min_num_param</code>:
<ul>
<li>Number of total parameters in a shard</li>
</ul></li>
</ul>
</section>
<section id="offload_params" class="slide level2">
<h2><code>offload_params</code>:</h2>
<ul>
<li>Offloads the parameters and gradients to the CPU if they can’t fit into memory</li>
<li>Allows you to train much larger models locally, but will be much slower</li>
</ul>
<blockquote>
<p>Case: FFT of Llama-3-8B with <code>fsdp_offload_params</code> on 2x4090 GPUs was 72hrs, vs ~an hour or two when using 1xH100</p>
</blockquote>
</section>
<section id="cpu_ram_efficient_loading-and-sync_module_states" class="slide level2">
<h2><code>cpu_ram_efficient_loading</code> and <code>sync_module_states</code></h2>
<ul>
<li>Uses the idea behind big model inference/the <code>meta</code> device to load in the model to the GPU in a low-ram scenario</li>
<li>Rather than needing <code>model_size</code> * <code>n_gpus</code> RAM, we can load the model on a single node and then send the weights directly to each shard when the time is right via <code>sync_module_states</code></li>
</ul>
</section></section>
<section>
<section id="tying-this-to-accelerate" class="title-slide slide level1 center">
<h1>Tying this to 🤗 Accelerate</h1>

</section>
<section id="tying-this-to-accelerate-1" class="slide level2">
<h2>Tying this to 🤗 Accelerate</h2>
<ul>
<li>So far we’ve covered the theory, but how do we put it into practice</li>
<li>By using a library that’s at the heart of the entire open-source ecosystem</li>
</ul>
<div style="font-size: 60%;padding-left:10%;padding-top:0%;">
<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>
</div>
<p>Are you using it and you don’t even know?</p>
</section>
<section id="what-is-accelerate" class="slide level2">
<h2>What is 🤗 Accelerate?</h2>
<div class="cell" data-reveal="true" data-fig-height="6">
<div class="cell-output-display">
<div>
<div>
<pre class="mermaid mermaid-js">graph LR
    A(("🤗 Accelerate#32;"))
    A --&gt; B["CLI Interface#32;"]
    A --&gt; C["Training Library#32;"]
    A --&gt; D["Big Model&lt;br&gt;Inference#32;"]
</pre>
</div>
</div>
</div>
</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 estimate-memory</code>
<ul>
<li>How to guess vRAM requirements</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>
<ul>
<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">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="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">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="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">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>How can we make this better?</p>
</section>
<section id="accelerate-launch" class="slide level2">
<h2><code>accelerate launch</code></h2>
<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">accelerate</span> launch script.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></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: 50%;padding-left:10%;">
<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="cb6" data-filename="ddp_config.yaml"><pre class="sourceCode numberSource yaml number-lines code-with-copy"><code class="sourceCode yaml"><span id="cb6-1"><a href="#cb6-1"></a><span class="fu">compute_environment</span><span class="kw">:</span><span class="at"> LOCAL_MACHINE</span></span>
<span id="cb6-2"><a href="#cb6-2"></a><span class="fu">distributed_type</span><span class="kw">:</span><span class="at"> MULTI_GPU</span></span>
<span id="cb6-3"><a href="#cb6-3"></a><span class="fu">main_training_function</span><span class="kw">:</span><span class="at"> main</span></span>
<span id="cb6-4"><a href="#cb6-4"></a><span class="fu">mixed_precision</span><span class="kw">:</span><span class="at"> bf16</span></span>
<span id="cb6-5"><a href="#cb6-5"></a><span class="fu">num_machines</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span></span>
<span id="cb6-6"><a href="#cb6-6"></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="cb7" data-filename="fsdp_config.yaml"><pre class="sourceCode numberSource yaml number-lines code-with-copy"><code class="sourceCode yaml"><span id="cb7-1"><a href="#cb7-1"></a><span class="fu">compute_environment</span><span class="kw">:</span><span class="at"> LOCAL_MACHINE</span></span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="fu">distributed_type</span><span class="kw">:</span><span class="at"> FSDP</span></span>
<span id="cb7-3"><a href="#cb7-3"></a><span class="fu">fsdp_config</span><span class="kw">:</span></span>
<span id="cb7-4"><a href="#cb7-4"></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="cb7-5"><a href="#cb7-5"></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="cb7-6"><a href="#cb7-6"></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="cb7-7"><a href="#cb7-7"></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="cb7-8"><a href="#cb7-8"></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="cb7-9"><a href="#cb7-9"></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="cb7-10"><a href="#cb7-10"></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="cb7-11"><a href="#cb7-11"></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="cb7-12"><a href="#cb7-12"></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="cb7-13"><a href="#cb7-13"></a><span class="fu">main_training_function</span><span class="kw">:</span><span class="at"> main</span></span>
<span id="cb7-14"><a href="#cb7-14"></a><span class="fu">mixed_precision</span><span class="kw">:</span><span class="at"> bf16</span></span>
<span id="cb7-15"><a href="#cb7-15"></a><span class="fu">num_machines</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span></span>
<span id="cb7-16"><a href="#cb7-16"></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>
<section>
<section id="a-training-library" class="title-slide slide level1 center">
<h1>A Training Library</h1>

</section>
<section id="a-training-library-the-code" class="slide level2">
<h2>A Training Library: The Code</h2>
<div class="columns" style="font-size: 50%;">
<div class="column">
<p><br><br><br></p>
<div class="sourceCode" id="cb8" data-code-line-numbers="5-6,9"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1"></a><span class="co"># For alignment purposes</span></span>
<span id="cb8-2"><a href="#cb8-2"></a><span class="cf">for</span> batch <span class="kw">in</span> dataloader:</span>
<span id="cb8-3"><a href="#cb8-3"></a>    optimizer.zero_grad()</span>
<span id="cb8-4"><a href="#cb8-4"></a>    inputs, targets <span class="op">=</span> batch</span>
<span id="cb8-5"><a href="#cb8-5"></a>    inputs <span class="op">=</span> inputs.to(device)</span>
<span id="cb8-6"><a href="#cb8-6"></a>    targets <span class="op">=</span> targets.to(device)</span>
<span id="cb8-7"><a href="#cb8-7"></a>    outputs <span class="op">=</span> model(inputs)</span>
<span id="cb8-8"><a href="#cb8-8"></a>    loss <span class="op">=</span> loss_function(outputs, targets)</span>
<span id="cb8-9"><a href="#cb8-9"></a>    loss.backward()</span>
<span id="cb8-10"><a href="#cb8-10"></a>    optimizer.step()</span>
<span id="cb8-11"><a href="#cb8-11"></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">
<div class="sourceCode" id="cb9" 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="cb9-1"><a href="#cb9-1"></a><span class="im">from</span> accelerate <span class="im">import</span> Accelerator</span>
<span id="cb9-2"><a href="#cb9-2"></a>accelerator <span class="op">=</span> Accelerator()</span>
<span id="cb9-3"><a href="#cb9-3"></a>dataloader, model, optimizer scheduler <span class="op">=</span> (</span>
<span id="cb9-4"><a href="#cb9-4"></a>    accelerator.prepare(</span>
<span id="cb9-5"><a href="#cb9-5"></a>        dataloader, model, optimizer, scheduler</span>
<span id="cb9-6"><a href="#cb9-6"></a>    )</span>
<span id="cb9-7"><a href="#cb9-7"></a>)</span>
<span id="cb9-8"><a href="#cb9-8"></a></span>
<span id="cb9-9"><a href="#cb9-9"></a><span class="cf">for</span> batch <span class="kw">in</span> dataloader:</span>
<span id="cb9-10"><a href="#cb9-10"></a>    optimizer.zero_grad()</span>
<span id="cb9-11"><a href="#cb9-11"></a>    inputs, targets <span class="op">=</span> batch</span>
<span id="cb9-12"><a href="#cb9-12"></a>    <span class="co"># inputs = inputs.to(device)</span></span>
<span id="cb9-13"><a href="#cb9-13"></a>    <span class="co"># targets = targets.to(device)</span></span>
<span id="cb9-14"><a href="#cb9-14"></a>    outputs <span class="op">=</span> model(inputs)</span>
<span id="cb9-15"><a href="#cb9-15"></a>    loss <span class="op">=</span> loss_function(outputs, targets)</span>
<span id="cb9-16"><a href="#cb9-16"></a>    accelerator.backward(loss) <span class="co"># loss.backward()</span></span>
<span id="cb9-17"><a href="#cb9-17"></a>    optimizer.step()</span>
<span id="cb9-18"><a href="#cb9-18"></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-how-scaling-works" class="slide level2">
<h2>A Training Library: How Scaling Works</h2>
<ul>
<li>Accelerate’s DataLoaders and schedulers work off of a sharding mindset</li>
<li>Rather than repeating the same data across <code>n</code> nodes, we instead split it</li>
<li>Speeds up training linearly</li>
<li>Given a batch size of 16 on a single GPU, to recreate this across 8 GPUs you would use a batch size of 2</li>
<li>This also means the scheduler will be stepped <code>n</code> GPUs at a time per “global step”</li>
</ul>
</section>
<section id="a-training-library-mixed-precision" class="slide level2">
<h2>A Training Library: Mixed Precision</h2>
<ul>
<li>This may be a bit different than your “normal” idea of mixed precision.</li>
<li>We do <strong>not</strong> convert the model weights to BF16/FP16</li>
<li>Instead we <strong>wrap the forward pass</strong> with <code>autocast</code> to convert the gradients automatically</li>
<li>This preserves the original precision of the weights, which leads to stable training and better fine-tuning later on.</li>
<li><strong>If you use <code>.bf16()</code> weights, you are STUCK in bf16 perminantly</strong></li>
</ul>
</section>
<section id="a-training-library-mixed-precision-1" class="slide level2">
<h2>A Training Library: Mixed Precision</h2>
<ul>
<li>Let’s tie that back up to the model estimator with neat tools like NVIDIA’s TransformerEngine</li>
</ul>
<div style="font-size: 60%;">
<table style="width:100%;">
<colgroup>
<col style="width: 14%">
<col style="width: 14%">
<col style="width: 14%">
<col style="width: 14%">
<col style="width: 14%">
<col style="width: 14%">
<col style="width: 14%">
</colgroup>
<thead>
<tr class="header">
<th>Optimization Level</th>
<th>Computation (GEMM)</th>
<th>Comm</th>
<th>Weight</th>
<th>Master Weight</th>
<th>Weight Gradient</th>
<th>Optimizer States</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>FP16 AMP</td>
<td>FP16</td>
<td>FP32</td>
<td>FP32</td>
<td>N/A</td>
<td>FP32</td>
<td>FP32+FP32</td>
</tr>
<tr class="even">
<td>Nvidia TE</td>
<td>FP8</td>
<td>FP32</td>
<td>FP32</td>
<td>N/A</td>
<td>FP32</td>
<td>FP32+FP32</td>
</tr>
<tr class="odd">
<td>MS-AMP O1</td>
<td>FP8</td>
<td>FP8</td>
<td>FP16</td>
<td>N/A</td>
<td>FP8</td>
<td>FP32+FP32</td>
</tr>
<tr class="even">
<td>MS-AMP O2</td>
<td>FP8</td>
<td>FP8</td>
<td>FP16</td>
<td>N/A</td>
<td>FP8</td>
<td>FP8+FP16</td>
</tr>
<tr class="odd">
<td>MS-AMP O3</td>
<td>FP8</td>
<td>FP8</td>
<td>FP8</td>
<td>FP16</td>
<td>FP8</td>
<td>FP8+FP16</td>
</tr>
</tbody>
</table>
</div>
<aside class="notes">
<p>What is actually happening: * Linear Layers and other certain compatible layers are wrapped in a special version that allows for FP8 computation * The general forward pass is wrapped around BF16 * This means that the most memory saved is done during the gradients of the model, <em>not</em> the model itself. * With tools like <code>MS-AMP</code> we can convert more chunks into lower precision, but again like before stable training occurs when the models weights are in full precision and the backprop happens in full precision too.</p>
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</section>
<section id="deepspeed-vs-fully-sharded-data-parallelism" class="slide level2">
<h2>DeepSpeed vs Fully Sharded Data Parallelism</h2>
<ul>
<li>Extremely similar, however mostly used different naming conventions for items and slight tweaks in the implementation</li>
</ul>
<div style="font-size: 50%;">
<table style="width:100%;">
<colgroup>
<col style="width: 16%">
<col style="width: 16%">
<col style="width: 16%">
<col style="width: 16%">
<col style="width: 16%">
<col style="width: 16%">
</colgroup>
<thead>
<tr class="header">
<th>Framework</th>
<th>Model Loading (<code>torch_dtype</code>)</th>
<th>Mixed Precision</th>
<th>Preparation (Local)</th>
<th>Training</th>
<th>Optimizer (Local)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>FSDP</td>
<td>bf16</td>
<td>default (none)</td>
<td>bf16</td>
<td>bf16</td>
<td>bf16</td>
</tr>
<tr class="even">
<td>FSDP</td>
<td>bf16</td>
<td>bf16</td>
<td>fp32</td>
<td>bf16</td>
<td>fp32</td>
</tr>
<tr class="odd">
<td>DeepSpeed</td>
<td>bf16</td>
<td>bf16</td>
<td>fp32</td>
<td>bf16</td>
<td>fp32</td>
</tr>
</tbody>
</table>
</div>
<p>To learn more, check out the <a href="https://huggingface.co/docs/accelerate/concept_guides/fsdp_and_deepspeed">documentation</a> or join my office hours</p>
</section>
<section id="key-takeaways" class="slide level2">
<h2>Key Takeaways:</h2>
<ul>
<li>You can scale out training with <code>accelerate</code>, FSDP, and DeepSpeed across multiple GPUs to train bigger models</li>
<li>Techniques like <code>FP8</code> can help speed up training some and reduce computational overhead</li>
<li>Comes at a cost of end-precision and locking model weights for futher fine-tunes if not careful</li>
</ul>
</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>
<li><a href="https://huggingface.co/docs/accelerate/concept_guides/fsdp_and_deepspeed">FSDP vs DeepSpeed In-Depth</a></li>
</ul>
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