Hugging Face's logo

Spaces:
muellerzr
/
llm-conf
Running

App Files Community
llm-conf / index.html
muellerzr's picture
muellerzr
Update
264a231
raw
history blame contribute delete
60.7 kB
 <!DOCTYPE html>
<html lang="en"><head>
<script src="llm_conf_files/libs/clipboard/clipboard.min.js"></script>
<script src="llm_conf_files/libs/quarto-html/tabby.min.js"></script>
<script src="llm_conf_files/libs/quarto-html/popper.min.js"></script>
<script src="llm_conf_files/libs/quarto-html/tippy.umd.min.js"></script>
<link href="llm_conf_files/libs/quarto-html/tippy.css" rel="stylesheet">
<link href="llm_conf_files/libs/quarto-html/light-border.css" rel="stylesheet">
<link href="llm_conf_files/libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
<link href="llm_conf_files/libs/quarto-html/quarto-syntax-highlighting-dark.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
<meta name="generator" content="quarto-99.9.9">
<title>Scaling Model Training with More Compute, How Do They Do It?</title>
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
<link rel="stylesheet" href="llm_conf_files/libs/revealjs/dist/reset.css">
<link rel="stylesheet" href="llm_conf_files/libs/revealjs/dist/reveal.css">
<style>
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
div.columns{display: flex; gap: min(4vw, 1.5em);}
div.column{flex: auto; overflow-x: auto;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
ul.task-list li input[type="checkbox"] {
width: 0.8em;
margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */
vertical-align: middle;
}
/* CSS for syntax highlighting */
pre > code.sourceCode { white-space: pre; position: relative; }
pre > code.sourceCode > span { line-height: 1.25; }
pre > code.sourceCode > span:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode > span { color: inherit; text-decoration: inherit; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
pre > code.sourceCode { white-space: pre-wrap; }
pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
}
pre.numberSource code
{ counter-reset: source-line 0; }
pre.numberSource code > span
{ position: relative; left: -4em; counter-increment: source-line; }
pre.numberSource code > span > a:first-child::before
{ content: counter(source-line);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
}
pre.numberSource { margin-left: 3em; padding-left: 4px; }
div.sourceCode
{ color: #f8f8f2; }
@media screen {
pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
}
code span { color: #f8f8f2; } /* Normal */
code span.al { color: #f07178; background-color: #2a0f15; font-weight: bold; } /* Alert */
code span.an { color: #d4d0ab; } /* Annotation */
code span.at { color: #00e0e0; } /* Attribute */
code span.bn { color: #d4d0ab; } /* BaseN */
code span.bu { color: #abe338; } /* BuiltIn */
code span.cf { color: #ffa07a; font-weight: bold; } /* ControlFlow */
code span.ch { color: #abe338; } /* Char */
code span.cn { color: #ffd700; } /* Constant */
code span.co { color: #f8f8f2; font-style: italic; } /* Comment */
code span.cv { color: #ffd700; } /* CommentVar */
code span.do { color: #f8f8f2; } /* Documentation */
code span.dt { color: #ffa07a; } /* DataType */
code span.dv { color: #d4d0ab; } /* DecVal */
code span.er { color: #f07178; text-decoration: underline; } /* Error */
code span.ex { color: #00e0e0; font-weight: bold; } /* Extension */
code span.fl { color: #d4d0ab; } /* Float */
code span.fu { color: #ffa07a; } /* Function */
code span.im { color: #abe338; } /* Import */
code span.in { color: #d4d0ab; } /* Information */
code span.kw { color: #ffa07a; font-weight: bold; } /* Keyword */
code span.op { color: #ffa07a; } /* Operator */
code span.ot { color: #00e0e0; } /* Other */
code span.pp { color: #dcc6e0; } /* Preprocessor */
code span.re { color: #00e0e0; background-color: #f8f8f2; } /* RegionMarker */
code span.sc { color: #abe338; } /* SpecialChar */
code span.ss { color: #abe338; } /* SpecialString */
code span.st { color: #abe338; } /* String */
code span.va { color: #00e0e0; } /* Variable */
code span.vs { color: #abe338; } /* VerbatimString */
code span.wa { color: #dcc6e0; } /* Warning */
</style>
<link rel="stylesheet" href="llm_conf_files/libs/revealjs/dist/theme/quarto.css">
<link href="llm_conf_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.css" rel="stylesheet">
<link href="llm_conf_files/libs/revealjs/plugin/reveal-menu/menu.css" rel="stylesheet">
<link href="llm_conf_files/libs/revealjs/plugin/reveal-menu/quarto-menu.css" rel="stylesheet">
<link href="llm_conf_files/libs/revealjs/plugin/quarto-support/footer.css" rel="stylesheet">
<style type="text/css">
.callout {
margin-top: 1em;
margin-bottom: 1em;
border-radius: .25rem;
}
.callout.callout-style-simple {
padding: 0em 0.5em;
border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
display: flex;
}
.callout.callout-style-default {
border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
}
.callout .callout-body-container {
flex-grow: 1;
}
.callout.callout-style-simple .callout-body {
font-size: 1rem;
font-weight: 400;
}
.callout.callout-style-default .callout-body {
font-size: 0.9rem;
font-weight: 400;
}
.callout.callout-titled.callout-style-simple .callout-body {
margin-top: 0.2em;
}
.callout:not(.callout-titled) .callout-body {
display: flex;
}
.callout:not(.no-icon).callout-titled.callout-style-simple .callout-content {
padding-left: 1.6em;
}
.callout.callout-titled .callout-header {
padding-top: 0.2em;
margin-bottom: -0.2em;
}
.callout.callout-titled .callout-title p {
margin-top: 0.5em;
margin-bottom: 0.5em;
}
.callout.callout-titled.callout-style-simple .callout-content p {
margin-top: 0;
}
.callout.callout-titled.callout-style-default .callout-content p {
margin-top: 0.7em;
}
.callout.callout-style-simple div.callout-title {
border-bottom: none;
font-size: .9rem;
font-weight: 600;
opacity: 75%;
}
.callout.callout-style-default div.callout-title {
border-bottom: none;
font-weight: 600;
opacity: 85%;
font-size: 0.9rem;
padding-left: 0.5em;
padding-right: 0.5em;
}
.callout.callout-style-default div.callout-content {
padding-left: 0.5em;
padding-right: 0.5em;
}
.callout.callout-style-simple .callout-icon::before {
height: 1rem;
width: 1rem;
display: inline-block;
content: "";
background-repeat: no-repeat;
background-size: 1rem 1rem;
}
.callout.callout-style-default .callout-icon::before {
height: 0.9rem;
width: 0.9rem;
display: inline-block;
content: "";
background-repeat: no-repeat;
background-size: 0.9rem 0.9rem;
}
.callout-title {
display: flex
}
.callout-icon::before {
margin-top: 1rem;
padding-right: .5rem;
}
.callout.no-icon::before {
display: none !important;
}
.callout.callout-titled .callout-body > .callout-content > :last-child {
padding-bottom: 0.5rem;
margin-bottom: 0;
}
.callout.callout-titled .callout-icon::before {
margin-top: .5rem;
padding-right: .5rem;
}
.callout:not(.callout-titled) .callout-icon::before {
margin-top: 1rem;
padding-right: .5rem;
}
/* Callout Types */
div.callout-note {
border-left-color: #4582ec !important;
}
div.callout-note .callout-icon::before {
background-image: url('data:image/png;base64,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');
}
div.callout-note.callout-style-default .callout-title {
background-color: #dae6fb
}
div.callout-important {
border-left-color: #d9534f !important;
}
div.callout-important .callout-icon::before {
background-image: url('data:image/png;base64,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');
}
div.callout-important.callout-style-default .callout-title {
background-color: #f7dddc
}
div.callout-warning {
border-left-color: #f0ad4e !important;
}
div.callout-warning .callout-icon::before {
background-image: url('data:image/png;base64,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');
}
div.callout-warning.callout-style-default .callout-title {
background-color: #fcefdc
}
div.callout-tip {
border-left-color: #02b875 !important;
}
div.callout-tip .callout-icon::before {
background-image: url('data:image/png;base64,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');
}
div.callout-tip.callout-style-default .callout-title {
background-color: #ccf1e3
}
div.callout-caution {
border-left-color: #fd7e14 !important;
}
div.callout-caution .callout-icon::before {
background-image: url('data:image/png;base64,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');
}
div.callout-caution.callout-style-default .callout-title {
background-color: #ffe5d0
}
</style>
<style type="text/css">
.reveal div.sourceCode {
margin: 0;
overflow: auto;
}
.reveal div.hanging-indent {
margin-left: 1em;
text-indent: -1em;
}
.reveal .slide:not(.center) {
height: 100%;
}
.reveal .slide.scrollable {
overflow-y: auto;
}
.reveal .footnotes {
height: 100%;
overflow-y: auto;
}
.reveal .slide .absolute {
position: absolute;
display: block;
}
.reveal .footnotes ol {
counter-reset: ol;
list-style-type: none;
margin-left: 0;
}
.reveal .footnotes ol li:before {
counter-increment: ol;
content: counter(ol) ". ";
}
.reveal .footnotes ol li > p:first-child {
display: inline-block;
}
.reveal .slide ul,
.reveal .slide ol {
margin-bottom: 0.5em;
}
.reveal .slide ul li,
.reveal .slide ol li {
margin-top: 0.4em;
margin-bottom: 0.2em;
}
.reveal .slide ul[role="tablist"] li {
margin-bottom: 0;
}
.reveal .slide ul li > *:first-child,
.reveal .slide ol li > *:first-child {
margin-block-start: 0;
}
.reveal .slide ul li > *:last-child,
.reveal .slide ol li > *:last-child {
margin-block-end: 0;
}
.reveal .slide .columns:nth-child(3) {
margin-block-start: 0.8em;
}
.reveal blockquote {
box-shadow: none;
}
.reveal .tippy-content>* {
margin-top: 0.2em;
margin-bottom: 0.7em;
}
.reveal .tippy-content>*:last-child {
margin-bottom: 0.2em;
}
.reveal .slide > img.stretch.quarto-figure-center,
.reveal .slide > img.r-stretch.quarto-figure-center {
display: block;
margin-left: auto;
margin-right: auto;
}
.reveal .slide > img.stretch.quarto-figure-left,
.reveal .slide > img.r-stretch.quarto-figure-left {
display: block;
margin-left: 0;
margin-right: auto;
}
.reveal .slide > img.stretch.quarto-figure-right,
.reveal .slide > img.r-stretch.quarto-figure-right {
display: block;
margin-left: auto;
margin-right: 0;
}
</style>
<script src="llm_conf_files/libs/quarto-diagram/mermaid.min.js"></script>
<script src="llm_conf_files/libs/quarto-diagram/mermaid-init.js"></script>
<link href="llm_conf_files/libs/quarto-diagram/mermaid.css" rel="stylesheet">
</head>
<body class="quarto-dark">
<div class="reveal">
<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>Distributed Data Parallelism (DDP):
<ul>
<li>A full copy of the model exists on each device, but data is chunked between each GPU</li>
</ul></li>
<li>Fully Sharded Data Parallelism (FSDP) &amp; DeepSpeed (DS):
<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>
<style type="text/css">
span.MJX_Assistive_MathML {
position:absolute!important;
clip: rect(1px, 1px, 1px, 1px);
padding: 1px 0 0 0!important;
border: 0!important;
height: 1px!important;
width: 1px!important;
overflow: hidden!important;
display:block!important;
}</style></aside>
</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>
<style type="text/css">
span.MJX_Assistive_MathML {
position:absolute!important;
clip: rect(1px, 1px, 1px, 1px);
padding: 1px 0 0 0!important;
border: 0!important;
height: 1px!important;
width: 1px!important;
overflow: hidden!important;
display:block!important;
}</style></aside></li>
</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>
<style type="text/css">
span.MJX_Assistive_MathML {
position:absolute!important;
clip: rect(1px, 1px, 1px, 1px);
padding: 1px 0 0 0!important;
border: 0!important;
height: 1px!important;
width: 1px!important;
overflow: hidden!important;
display:block!important;
}</style></aside>
</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>
<div class="footer footer-default">
</div>
</section></section>
</div>
</div>
<script>window.backupDefine = window.define; window.define = undefined;</script>
<script src="llm_conf_files/libs/revealjs/dist/reveal.js"></script>
<!-- reveal.js plugins -->
<script src="llm_conf_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js"></script>
<script src="llm_conf_files/libs/revealjs/plugin/pdf-export/pdfexport.js"></script>
<script src="llm_conf_files/libs/revealjs/plugin/reveal-menu/menu.js"></script>
<script src="llm_conf_files/libs/revealjs/plugin/reveal-menu/quarto-menu.js"></script>
<script src="llm_conf_files/libs/revealjs/plugin/quarto-support/support.js"></script>
<script src="llm_conf_files/libs/revealjs/plugin/notes/notes.js"></script>
<script src="llm_conf_files/libs/revealjs/plugin/search/search.js"></script>
<script src="llm_conf_files/libs/revealjs/plugin/zoom/zoom.js"></script>
<script src="llm_conf_files/libs/revealjs/plugin/math/math.js"></script>
<script>window.define = window.backupDefine; window.backupDefine = undefined;</script>
<script>
// Full list of configuration options available at:
// https://revealjs.com/config/
Reveal.initialize({
'controlsAuto': true,
'previewLinksAuto': false,
'pdfSeparateFragments': false,
'autoAnimateEasing': "ease",
'autoAnimateDuration': 1,
'autoAnimateUnmatched': true,
'menu': {"side":"left","useTextContentForMissingTitles":true,"markers":false,"loadIcons":false,"custom":[{"title":"Tools","icon":"<i class=\"fas fa-gear\"></i>","content":"<ul class=\"slide-menu-items\">\n<li class=\"slide-tool-item active\" data-item=\"0\"><a href=\"#\" onclick=\"RevealMenuToolHandlers.fullscreen(event)\"><kbd>f</kbd> Fullscreen</a></li>\n<li class=\"slide-tool-item\" data-item=\"1\"><a href=\"#\" onclick=\"RevealMenuToolHandlers.speakerMode(event)\"><kbd>s</kbd> Speaker View</a></li>\n<li class=\"slide-tool-item\" data-item=\"2\"><a href=\"#\" onclick=\"RevealMenuToolHandlers.overview(event)\"><kbd>o</kbd> Slide Overview</a></li>\n<li class=\"slide-tool-item\" data-item=\"3\"><a href=\"#\" onclick=\"RevealMenuToolHandlers.togglePdfExport(event)\"><kbd>e</kbd> PDF Export Mode</a></li>\n<li class=\"slide-tool-item\" data-item=\"4\"><a href=\"#\" onclick=\"RevealMenuToolHandlers.keyboardHelp(event)\"><kbd>?</kbd> Keyboard Help</a></li>\n</ul>"}],"openButton":true},
'smaller': false,
// Display controls in the bottom right corner
controls: false,
// Help the user learn the controls by providing hints, for example by
// bouncing the down arrow when they first encounter a vertical slide
controlsTutorial: false,
// Determines where controls appear, "edges" or "bottom-right"
controlsLayout: 'edges',
// Visibility rule for backwards navigation arrows; "faded", "hidden"
// or "visible"
controlsBackArrows: 'faded',
// Display a presentation progress bar
progress: true,
// Display the page number of the current slide
slideNumber: false,
// 'all', 'print', or 'speaker'
showSlideNumber: 'all',
// Add the current slide number to the URL hash so that reloading the
// page/copying the URL will return you to the same slide
hash: true,
// Start with 1 for the hash rather than 0
hashOneBasedIndex: false,
// Flags if we should monitor the hash and change slides accordingly
respondToHashChanges: true,
// Push each slide change to the browser history
history: true,
// Enable keyboard shortcuts for navigation
keyboard: true,
// Enable the slide overview mode
overview: true,
// Disables the default reveal.js slide layout (scaling and centering)
// so that you can use custom CSS layout
disableLayout: false,
// Vertical centering of slides
center: false,
// Enables touch navigation on devices with touch input
touch: true,
// Loop the presentation
loop: false,
// Change the presentation direction to be RTL
rtl: false,
// see https://revealjs.com/vertical-slides/#navigation-mode
navigationMode: 'linear',
// Randomizes the order of slides each time the presentation loads
shuffle: false,
// Turns fragments on and off globally
fragments: true,
// Flags whether to include the current fragment in the URL,
// so that reloading brings you to the same fragment position
fragmentInURL: false,
// Flags if the presentation is running in an embedded mode,
// i.e. contained within a limited portion of the screen
embedded: false,
// Flags if we should show a help overlay when the questionmark
// key is pressed
help: true,
// Flags if it should be possible to pause the presentation (blackout)
pause: true,
// Flags if speaker notes should be visible to all viewers
showNotes: false,
// Global override for autoplaying embedded media (null/true/false)
autoPlayMedia: null,
// Global override for preloading lazy-loaded iframes (null/true/false)
preloadIframes: null,
// Number of milliseconds between automatically proceeding to the
// next slide, disabled when set to 0, this value can be overwritten
// by using a data-autoslide attribute on your slides
autoSlide: 0,
// Stop auto-sliding after user input
autoSlideStoppable: true,
// Use this method for navigation when auto-sliding
autoSlideMethod: null,
// Specify the average time in seconds that you think you will spend
// presenting each slide. This is used to show a pacing timer in the
// speaker view
defaultTiming: null,
// Enable slide navigation via mouse wheel
mouseWheel: false,
// The display mode that will be used to show slides
display: 'block',
// Hide cursor if inactive
hideInactiveCursor: true,
// Time before the cursor is hidden (in ms)
hideCursorTime: 5000,
// Opens links in an iframe preview overlay
previewLinks: false,
// Transition style (none/fade/slide/convex/concave/zoom)
transition: 'none',
// Transition speed (default/fast/slow)
transitionSpeed: 'default',
// Transition style for full page slide backgrounds
// (none/fade/slide/convex/concave/zoom)
backgroundTransition: 'none',
// Number of slides away from the current that are visible
viewDistance: 3,
// Number of slides away from the current that are visible on mobile
// devices. It is advisable to set this to a lower number than
// viewDistance in order to save resources.
mobileViewDistance: 2,
// The "normal" size of the presentation, aspect ratio will be preserved
// when the presentation is scaled to fit different resolutions. Can be
// specified using percentage units.
width: 1050,
height: 700,
// Factor of the display size that should remain empty around the content
margin: 0.1,
math: {
mathjax: 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js',
config: 'TeX-AMS_HTML-full',
tex2jax: {
inlineMath: [['\\(','\\)']],
displayMath: [['\\[','\\]']],
balanceBraces: true,
processEscapes: false,
processRefs: true,
processEnvironments: true,
preview: 'TeX',
skipTags: ['script','noscript','style','textarea','pre','code'],
ignoreClass: 'tex2jax_ignore',
processClass: 'tex2jax_process'
},
},
// reveal.js plugins
plugins: [QuartoLineHighlight, PdfExport, RevealMenu, QuartoSupport,
RevealMath,
RevealNotes,
RevealSearch,
RevealZoom
]
});
</script>
<script id="quarto-html-after-body" type="application/javascript">
window.document.addEventListener("DOMContentLoaded", function (event) {
const toggleBodyColorMode = (bsSheetEl) => {
const mode = bsSheetEl.getAttribute("data-mode");
const bodyEl = window.document.querySelector("body");
if (mode === "dark") {
bodyEl.classList.add("quarto-dark");
bodyEl.classList.remove("quarto-light");
} else {
bodyEl.classList.add("quarto-light");
bodyEl.classList.remove("quarto-dark");
}
}
const toggleBodyColorPrimary = () => {
const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");
if (bsSheetEl) {
toggleBodyColorMode(bsSheetEl);
}
}
toggleBodyColorPrimary();
const tabsets = window.document.querySelectorAll(".panel-tabset-tabby")
tabsets.forEach(function(tabset) {
const tabby = new Tabby('#' + tabset.id);
});
const isCodeAnnotation = (el) => {
for (const clz of el.classList) {
if (clz.startsWith('code-annotation-')) {
return true;
}
}
return false;
}
const clipboard = new window.ClipboardJS('.code-copy-button', {
text: function(trigger) {
const codeEl = trigger.previousElementSibling.cloneNode(true);
for (const childEl of codeEl.children) {
if (isCodeAnnotation(childEl)) {
childEl.remove();
}
}
return codeEl.innerText;
}
});
clipboard.on('success', function(e) {
// button target
const button = e.trigger;
// don't keep focus
button.blur();
// flash "checked"
button.classList.add('code-copy-button-checked');
var currentTitle = button.getAttribute("title");
button.setAttribute("title", "Copied!");
let tooltip;
if (window.bootstrap) {
button.setAttribute("data-bs-toggle", "tooltip");
button.setAttribute("data-bs-placement", "left");
button.setAttribute("data-bs-title", "Copied!");
tooltip = new bootstrap.Tooltip(button,
{ trigger: "manual",
customClass: "code-copy-button-tooltip",
offset: [0, -8]});
tooltip.show();
}
setTimeout(function() {
if (tooltip) {
tooltip.hide();
button.removeAttribute("data-bs-title");
button.removeAttribute("data-bs-toggle");
button.removeAttribute("data-bs-placement");
}
button.setAttribute("title", currentTitle);
button.classList.remove('code-copy-button-checked');
}, 1000);
// clear code selection
e.clearSelection();
});
function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
const config = {
allowHTML: true,
maxWidth: 500,
delay: 100,
arrow: false,
appendTo: function(el) {
return el.closest('section.slide') || el.parentElement;
},
interactive: true,
interactiveBorder: 10,
theme: 'light-border',
placement: 'bottom-start',
};
if (contentFn) {
config.content = contentFn;
}
if (onTriggerFn) {
config.onTrigger = onTriggerFn;
}
if (onUntriggerFn) {
config.onUntrigger = onUntriggerFn;
}
config['offset'] = [0,0];
config['maxWidth'] = 700;
window.tippy(el, config);
}
const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
for (var i=0; i<noterefs.length; i++) {
const ref = noterefs[i];
tippyHover(ref, function() {
// use id or data attribute instead here
let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
try { href = new URL(href).hash; } catch {}
const id = href.replace(/^#\/?/, "");
const note = window.document.getElementById(id);
return note.innerHTML;
});
}
const findCites = (el) => {
const parentEl = el.parentElement;
if (parentEl) {
const cites = parentEl.dataset.cites;
if (cites) {
return {
el,
cites: cites.split(' ')
};
} else {
return findCites(el.parentElement)
}
} else {
return undefined;
}
};
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
for (var i=0; i<bibliorefs.length; i++) {
const ref = bibliorefs[i];
const citeInfo = findCites(ref);
if (citeInfo) {
tippyHover(citeInfo.el, function() {
var popup = window.document.createElement('div');
citeInfo.cites.forEach(function(cite) {
var citeDiv = window.document.createElement('div');
citeDiv.classList.add('hanging-indent');
citeDiv.classList.add('csl-entry');
var biblioDiv = window.document.getElementById('ref-' + cite);
if (biblioDiv) {
citeDiv.innerHTML = biblioDiv.innerHTML;
}
popup.appendChild(citeDiv);
});
return popup.innerHTML;
});
}
}
});
</script>
</body></html>