1014 lines
45 KiB
HTML
1014 lines
45 KiB
HTML
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<h2 id="toc-title">On this page</h2>
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<li><a href="#when-to-use-sequence-parallelism" id="toc-when-to-use-sequence-parallelism" class="nav-link active" data-scroll-target="#when-to-use-sequence-parallelism">When to Use Sequence Parallelism</a></li>
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<li><a href="#sample-packing-with-sequence-parallelism" id="toc-sample-packing-with-sequence-parallelism" class="nav-link" data-scroll-target="#sample-packing-with-sequence-parallelism">Sample Packing with Sequence Parallelism</a></li>
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<li><a href="#effect-on-batch-size" id="toc-effect-on-batch-size" class="nav-link" data-scroll-target="#effect-on-batch-size">Effect on Batch Size</a></li>
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<div class="quarto-title">
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<h1 class="title">Sequence Parallelism</h1>
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</div>
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<div>
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<div class="description">
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Train with long sequences split across multiple GPUs.
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</header>
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<p>Sequence parallelism is a technique that splits sequences across multiple GPUs,
|
||
allowing you to train with very long sequences that wouldn’t fit on a single GPU. Each
|
||
GPU processes a different portion of the sequence, and the results are aggregated
|
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through a ring communication pattern.</p>
|
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<section id="when-to-use-sequence-parallelism" class="level2">
|
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<h2 class="anchored" data-anchor-id="when-to-use-sequence-parallelism">When to Use Sequence Parallelism</h2>
|
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<p>Use sequence parallelism when:</p>
|
||
<ul>
|
||
<li>You need to train with sequence lengths that don’t fit into a single GPU’s memory</li>
|
||
<li>You have multiple GPUs available</li>
|
||
<li>You’re experiencing OOM (Out Of Memory) errors with long sequences</li>
|
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</ul>
|
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</section>
|
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<section id="configuration" class="level2">
|
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<h2 class="anchored" data-anchor-id="configuration">Configuration</h2>
|
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<p>To enable sequence parallelism, add the following to your configuration file:</p>
|
||
<div class="sourceCode" id="cb1"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Set to a divisor (> 1) of the number of GPUs available</span></span>
|
||
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">sequence_parallel_degree</span><span class="kw">:</span><span class="at"> </span><span class="dv">4</span><span class="co"> # Split sequences across 4 GPUs</span></span>
|
||
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Optional; strides across the key dimension. Larger values use more memory but should make training faster.</span></span>
|
||
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">heads_k_stride</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span></span>
|
||
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Optional; one of "varlen_llama3" or "batch_ring". Defaults to</span></span>
|
||
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="co"># "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.</span></span>
|
||
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">ring_attn_func</span><span class="kw">:</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
<p>The <code>sequence_parallel_degree</code> should be a divisor of the total number of GPUs. For example:</p>
|
||
<ul>
|
||
<li>With 8 GPUs, valid values would be 2, 4, or 8</li>
|
||
<li>With 4 GPUs, valid values would be 2 or 4</li>
|
||
</ul>
|
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</section>
|
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<section id="implementation-details" class="level2">
|
||
<h2 class="anchored" data-anchor-id="implementation-details">Implementation Details</h2>
|
||
<p>When sequence parallelism is enabled:</p>
|
||
<ol type="1">
|
||
<li>Each sequence is divided into equal chunks across the GPUs in a sequence parallel group</li>
|
||
<li>The data collator handles the chunking of input_ids, attention_mask, labels, and position_ids</li>
|
||
<li>Position IDs are adjusted to maintain proper relative positions</li>
|
||
<li>The trainer uses special ring communication patterns for attention operations</li>
|
||
</ol>
|
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</section>
|
||
<section id="requirements" class="level2">
|
||
<h2 class="anchored" data-anchor-id="requirements">Requirements</h2>
|
||
<p>To use sequence parallelism, you need:</p>
|
||
<ul>
|
||
<li>Multiple GPUs (at least 2)</li>
|
||
<li>The <code>ring-flash-attn</code> package. Install with:
|
||
<ul>
|
||
<li><code>pip install axolotl[ring-flash-attn]</code> (preferred)</li>
|
||
<li><code>pip install ring-flash-attn>=0.1.4</code></li>
|
||
</ul></li>
|
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</ul>
|
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</section>
|
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<section id="limitations" class="level2">
|
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<h2 class="anchored" data-anchor-id="limitations">Limitations</h2>
|
||
<ul>
|
||
<li>Flash attention must be enabled for this to work (<code>flash_attention: true</code> in config YAML)</li>
|
||
<li>May have a small performance overhead due to communication between GPUs</li>
|
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</ul>
|
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</section>
|
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<section id="example" class="level2">
|
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<h2 class="anchored" data-anchor-id="example">Example</h2>
|
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<div class="sourceCode" id="cb2"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">base_model</span><span class="kw">:</span><span class="at"> meta-llama/Llama-3-8B-Instruct</span></span>
|
||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">sequence_len</span><span class="kw">:</span><span class="at"> </span><span class="dv">8192</span></span>
|
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<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
|
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<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co">...</span></span>
|
||
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="co">sequence_parallel_degree: 4 # Split each sequence into 4 parts, one per GPU</span></span>
|
||
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Optional; strides across the key dimension. Larger values use more memory but should make training faster.</span></span>
|
||
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="co">heads_k_stride: 1</span></span>
|
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<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Optional; one of "varlen_llama3" or "batch_ring". Defaults to</span></span>
|
||
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a><span class="co"># "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.</span></span>
|
||
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a><span class="co">ring_attn_func:</span></span>
|
||
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a><span class="co">...</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||
<p>This will train the Llama 3 8B model with 8K context length, with each sequence split
|
||
into 2 subsequences of length 4096 across 2 GPUs.</p>
|
||
</section>
|
||
<section id="sample-packing-with-sequence-parallelism" class="level2">
|
||
<h2 class="anchored" data-anchor-id="sample-packing-with-sequence-parallelism">Sample Packing with Sequence Parallelism</h2>
|
||
<p>Sequence parallelism is compatible with Axolotl’s sample packing functionality. When using both features together:</p>
|
||
<ol type="1">
|
||
<li>Samples are first packed together</li>
|
||
<li>The packed sequences are then divided across GPUs in the sequence parallel group</li>
|
||
<li>Position IDs are automatically adjusted to maintain proper relative positions</li>
|
||
</ol>
|
||
</section>
|
||
<section id="effect-on-batch-size" class="level2">
|
||
<h2 class="anchored" data-anchor-id="effect-on-batch-size">Effect on Batch Size</h2>
|
||
<p>When using sequence parallelism, your effective global batch size is <strong>divided</strong> by the <code>sequence_parallel_degree</code>. This happens because:</p>
|
||
<ul>
|
||
<li>Each group of <code>sequence_parallel_degree</code> GPUs works on the same batch (just different parts of each sequence)</li>
|
||
<li>The number of batches processed per step decreases</li>
|
||
</ul>
|
||
<p>For example:
|
||
- With 8 GPUs and no sequence parallelism: 8 different batches processed per step
|
||
- With 8 GPUs and <code>sequence_parallel_degree=4</code>: Only 2 different batches processed per step (each split across 4 GPUs)
|
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- If your per-GPU <code>micro_batch_size</code> is 2, the global batch size decreases from 16 to 4</p>
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for (const child of el.children) {
|
||
stripColumnClz(child);
|
||
}
|
||
}
|
||
}
|
||
stripColumnClz(note)
|
||
if (id === null || id.startsWith('sec-')) {
|
||
// Special case sections, only their first couple elements
|
||
const container = document.createElement("div");
|
||
if (note.children && note.children.length > 2) {
|
||
container.appendChild(note.children[0].cloneNode(true));
|
||
for (let i = 1; i < note.children.length; i++) {
|
||
const child = note.children[i];
|
||
if (child.tagName === "P" && child.innerText === "") {
|
||
continue;
|
||
} else {
|
||
container.appendChild(child.cloneNode(true));
|
||
break;
|
||
}
|
||
}
|
||
if (window.Quarto?.typesetMath) {
|
||
window.Quarto.typesetMath(container);
|
||
}
|
||
return container.innerHTML
|
||
} else {
|
||
if (window.Quarto?.typesetMath) {
|
||
window.Quarto.typesetMath(note);
|
||
}
|
||
return note.innerHTML;
|
||
}
|
||
} else {
|
||
// Remove any anchor links if they are present
|
||
const anchorLink = note.querySelector('a.anchorjs-link');
|
||
if (anchorLink) {
|
||
anchorLink.remove();
|
||
}
|
||
if (window.Quarto?.typesetMath) {
|
||
window.Quarto.typesetMath(note);
|
||
}
|
||
if (note.classList.contains("callout")) {
|
||
return note.outerHTML;
|
||
} else {
|
||
return note.innerHTML;
|
||
}
|
||
}
|
||
}
|
||
for (var i=0; i<xrefs.length; i++) {
|
||
const xref = xrefs[i];
|
||
tippyHover(xref, undefined, function(instance) {
|
||
instance.disable();
|
||
let url = xref.getAttribute('href');
|
||
let hash = undefined;
|
||
if (url.startsWith('#')) {
|
||
hash = url;
|
||
} else {
|
||
try { hash = new URL(url).hash; } catch {}
|
||
}
|
||
if (hash) {
|
||
const id = hash.replace(/^#\/?/, "");
|
||
const note = window.document.getElementById(id);
|
||
if (note !== null) {
|
||
try {
|
||
const html = processXRef(id, note.cloneNode(true));
|
||
instance.setContent(html);
|
||
} finally {
|
||
instance.enable();
|
||
instance.show();
|
||
}
|
||
} else {
|
||
// See if we can fetch this
|
||
fetch(url.split('#')[0])
|
||
.then(res => res.text())
|
||
.then(html => {
|
||
const parser = new DOMParser();
|
||
const htmlDoc = parser.parseFromString(html, "text/html");
|
||
const note = htmlDoc.getElementById(id);
|
||
if (note !== null) {
|
||
const html = processXRef(id, note);
|
||
instance.setContent(html);
|
||
}
|
||
}).finally(() => {
|
||
instance.enable();
|
||
instance.show();
|
||
});
|
||
}
|
||
} else {
|
||
// See if we can fetch a full url (with no hash to target)
|
||
// This is a special case and we should probably do some content thinning / targeting
|
||
fetch(url)
|
||
.then(res => res.text())
|
||
.then(html => {
|
||
const parser = new DOMParser();
|
||
const htmlDoc = parser.parseFromString(html, "text/html");
|
||
const note = htmlDoc.querySelector('main.content');
|
||
if (note !== null) {
|
||
// This should only happen for chapter cross references
|
||
// (since there is no id in the URL)
|
||
// remove the first header
|
||
if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
|
||
note.children[0].remove();
|
||
}
|
||
const html = processXRef(null, note);
|
||
instance.setContent(html);
|
||
}
|
||
}).finally(() => {
|
||
instance.enable();
|
||
instance.show();
|
||
});
|
||
}
|
||
}, function(instance) {
|
||
});
|
||
}
|
||
let selectedAnnoteEl;
|
||
const selectorForAnnotation = ( cell, annotation) => {
|
||
let cellAttr = 'data-code-cell="' + cell + '"';
|
||
let lineAttr = 'data-code-annotation="' + annotation + '"';
|
||
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
|
||
return selector;
|
||
}
|
||
const selectCodeLines = (annoteEl) => {
|
||
const doc = window.document;
|
||
const targetCell = annoteEl.getAttribute("data-target-cell");
|
||
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
|
||
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
|
||
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
|
||
const lineIds = lines.map((line) => {
|
||
return targetCell + "-" + line;
|
||
})
|
||
let top = null;
|
||
let height = null;
|
||
let parent = null;
|
||
if (lineIds.length > 0) {
|
||
//compute the position of the single el (top and bottom and make a div)
|
||
const el = window.document.getElementById(lineIds[0]);
|
||
top = el.offsetTop;
|
||
height = el.offsetHeight;
|
||
parent = el.parentElement.parentElement;
|
||
if (lineIds.length > 1) {
|
||
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
|
||
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
|
||
height = bottom - top;
|
||
}
|
||
if (top !== null && height !== null && parent !== null) {
|
||
// cook up a div (if necessary) and position it
|
||
let div = window.document.getElementById("code-annotation-line-highlight");
|
||
if (div === null) {
|
||
div = window.document.createElement("div");
|
||
div.setAttribute("id", "code-annotation-line-highlight");
|
||
div.style.position = 'absolute';
|
||
parent.appendChild(div);
|
||
}
|
||
div.style.top = top - 2 + "px";
|
||
div.style.height = height + 4 + "px";
|
||
div.style.left = 0;
|
||
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
|
||
if (gutterDiv === null) {
|
||
gutterDiv = window.document.createElement("div");
|
||
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
|
||
gutterDiv.style.position = 'absolute';
|
||
const codeCell = window.document.getElementById(targetCell);
|
||
const gutter = codeCell.querySelector('.code-annotation-gutter');
|
||
gutter.appendChild(gutterDiv);
|
||
}
|
||
gutterDiv.style.top = top - 2 + "px";
|
||
gutterDiv.style.height = height + 4 + "px";
|
||
}
|
||
selectedAnnoteEl = annoteEl;
|
||
}
|
||
};
|
||
const unselectCodeLines = () => {
|
||
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
|
||
elementsIds.forEach((elId) => {
|
||
const div = window.document.getElementById(elId);
|
||
if (div) {
|
||
div.remove();
|
||
}
|
||
});
|
||
selectedAnnoteEl = undefined;
|
||
};
|
||
// Handle positioning of the toggle
|
||
window.addEventListener(
|
||
"resize",
|
||
throttle(() => {
|
||
elRect = undefined;
|
||
if (selectedAnnoteEl) {
|
||
selectCodeLines(selectedAnnoteEl);
|
||
}
|
||
}, 10)
|
||
);
|
||
function throttle(fn, ms) {
|
||
let throttle = false;
|
||
let timer;
|
||
return (...args) => {
|
||
if(!throttle) { // first call gets through
|
||
fn.apply(this, args);
|
||
throttle = true;
|
||
} else { // all the others get throttled
|
||
if(timer) clearTimeout(timer); // cancel #2
|
||
timer = setTimeout(() => {
|
||
fn.apply(this, args);
|
||
timer = throttle = false;
|
||
}, ms);
|
||
}
|
||
};
|
||
}
|
||
// Attach click handler to the DT
|
||
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
|
||
for (const annoteDlNode of annoteDls) {
|
||
annoteDlNode.addEventListener('click', (event) => {
|
||
const clickedEl = event.target;
|
||
if (clickedEl !== selectedAnnoteEl) {
|
||
unselectCodeLines();
|
||
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
||
if (activeEl) {
|
||
activeEl.classList.remove('code-annotation-active');
|
||
}
|
||
selectCodeLines(clickedEl);
|
||
clickedEl.classList.add('code-annotation-active');
|
||
} else {
|
||
// Unselect the line
|
||
unselectCodeLines();
|
||
clickedEl.classList.remove('code-annotation-active');
|
||
}
|
||
});
|
||
}
|
||
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>
|
||
</div> <!-- /content -->
|
||
|
||
|
||
|
||
|
||
</body></html> |