1350 lines
61 KiB
HTML
1350 lines
61 KiB
HTML
<!DOCTYPE html>
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<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
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<meta name="generator" content="quarto-1.8.27">
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<meta name="description" content="A guide to the performance and memory optimizations available in Axolotl.">
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<title>Optimizations Guide – Axolotl</title>
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code{white-space: pre-wrap;}
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span.smallcaps{font-variant: small-caps;}
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div.columns{display: flex; gap: min(4vw, 1.5em);}
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<div class="navbar-brand-container mx-auto">
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|
||
<li><a href="#quantization-aware-training-qat" id="toc-quantization-aware-training-qat" class="nav-link" data-scroll-target="#quantization-aware-training-qat">Quantization Aware Training (QAT)</a></li>
|
||
<li><a href="#gptq" id="toc-gptq" class="nav-link" data-scroll-target="#gptq">GPTQ</a></li>
|
||
<li><a href="#moe-expert-quantization" id="toc-moe-expert-quantization" class="nav-link" data-scroll-target="#moe-expert-quantization">MoE Expert Quantization</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
<!-- main -->
|
||
<main class="content" id="quarto-document-content">
|
||
|
||
<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../docs/multimodal.html">How To Guides</a></li><li class="breadcrumb-item"><a href="../docs/optimizations.html">Optimizations Guide</a></li></ol></nav>
|
||
<div class="quarto-title">
|
||
<h1 class="title">Optimizations Guide</h1>
|
||
</div>
|
||
|
||
<div>
|
||
<div class="description">
|
||
A guide to the performance and memory optimizations available in Axolotl.
|
||
</div>
|
||
</div>
|
||
|
||
|
||
<div class="quarto-title-meta">
|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
|
||
</header>
|
||
|
||
|
||
<p>Axolotl includes numerous optimizations to speed up training, reduce memory usage, and handle large models.</p>
|
||
<p>This guide provides a high-level overview and directs you to the detailed documentation for each feature.</p>
|
||
<section id="speed-optimizations" class="level2">
|
||
<h2 class="anchored" data-anchor-id="speed-optimizations">Speed Optimizations</h2>
|
||
<p>These optimizations focus on increasing training throughput and reducing total training time.</p>
|
||
<section id="sample-packing" class="level3">
|
||
<h3 class="anchored" data-anchor-id="sample-packing">Sample Packing</h3>
|
||
<p>Improves GPU utilization by combining multiple short sequences into a single packed sequence for training. This requires enabling one of the <a href="#attention-implementations">attention</a> implementations below.</p>
|
||
<ul>
|
||
<li><strong>Config:</strong> <code>sample_packing: true</code></li>
|
||
<li><strong>Learn more:</strong> <a href="../docs/multipack.html">Sample Packing</a></li>
|
||
</ul>
|
||
</section>
|
||
<section id="attention-implementations" class="level3">
|
||
<h3 class="anchored" data-anchor-id="attention-implementations">Attention Implementations</h3>
|
||
<p>Using an optimized attention implementation is critical for training speed.</p>
|
||
<ul>
|
||
<li><strong><a href="https://github.com/Dao-AILab/flash-attention">Flash Attention 2</a></strong>: <code>flash_attention: true</code>. <strong>(Recommended)</strong> The industry standard for fast attention on modern GPUs. Requires Ampere or higher. For AMD, check <a href="https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#amd-rocm-support">AMD Support</a>.</li>
|
||
<li><strong><a href="https://pytorch.org/blog/flexattention/">Flex Attention</a></strong>: <code>flex_attention: true</code>.</li>
|
||
<li><strong><a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html">SDP Attention</a></strong>: <code>sdp_attention: true</code>. PyTorch’s native implementation.</li>
|
||
<li><strong><a href="https://github.com/facebookresearch/xformers">Xformers</a></strong>: <code>xformers_attention: true</code>. Works with FP16.</li>
|
||
</ul>
|
||
<p><em>Note: You should only enable one attention backend.</em></p>
|
||
</section>
|
||
<section id="lora-optimizations" class="level3">
|
||
<h3 class="anchored" data-anchor-id="lora-optimizations">LoRA Optimizations</h3>
|
||
<p>Leverages optimized kernels to accelerate LoRA training and reduce memory usage.</p>
|
||
<ul>
|
||
<li><strong>Learn more:</strong> <a href="../docs/lora_optims.html">LoRA Optimizations Documentation</a></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<section id="memory-optimizations" class="level2">
|
||
<h2 class="anchored" data-anchor-id="memory-optimizations">Memory Optimizations</h2>
|
||
<p>These techniques help you fit larger models or use bigger batch sizes on your existing hardware.</p>
|
||
<section id="parameter-efficient-finetuning-lora-qlora" class="level3">
|
||
<h3 class="anchored" data-anchor-id="parameter-efficient-finetuning-lora-qlora">Parameter Efficient Finetuning (LoRA & QLoRA)</h3>
|
||
<p>Drastically reduces memory by training a small set of “adapter” parameters instead of the full model. This is the most common and effective memory-saving technique.</p>
|
||
<ul>
|
||
<li>Examples: Find configs with <code>lora</code> or <code>qlora</code> in the <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-3">examples directory</a>.</li>
|
||
<li>Config Reference: See <code>adapter</code>, <code>load_in_4bit</code>, and <code>load_in_8bit</code> in the <a href="../docs/config-reference.html">Configuration Reference</a>.</li>
|
||
</ul>
|
||
</section>
|
||
<section id="gradient-checkpointing-activation-offloading" class="level3">
|
||
<h3 class="anchored" data-anchor-id="gradient-checkpointing-activation-offloading">Gradient Checkpointing & Activation Offloading</h3>
|
||
<p>These techniques save VRAM by changing how activations are handled.</p>
|
||
<ul>
|
||
<li>Gradient Checkpointing: re-computes activations during the backward pass, trading compute time for VRAM.</li>
|
||
<li>Activation Offloading: moves activations to CPU RAM or disk, trading I/O overhead for VRAM.</li>
|
||
<li>Learn more: <a href="../docs/gradient_checkpointing.html">Gradient Checkpointing and Offloading Docs</a></li>
|
||
</ul>
|
||
</section>
|
||
<section id="cut-cross-entropy-cce" class="level3">
|
||
<h3 class="anchored" data-anchor-id="cut-cross-entropy-cce">Cut Cross Entropy (CCE)</h3>
|
||
<p>Reduces VRAM usage by using an optimized cross-entropy loss calculation.</p>
|
||
<ul>
|
||
<li><strong>Learn more:</strong> <a href="../docs/custom_integrations.html#cut-cross-entropy">Custom Integrations - CCE</a></li>
|
||
</ul>
|
||
</section>
|
||
<section id="liger-kernels" class="level3">
|
||
<h3 class="anchored" data-anchor-id="liger-kernels">Liger Kernels</h3>
|
||
<p>Provides efficient Triton kernels to improve training speed and reduce memory usage.</p>
|
||
<ul>
|
||
<li><strong>Learn more:</strong> <a href="../docs/custom_integrations.html#liger-kernels">Custom Integrations - Liger Kernels</a></li>
|
||
</ul>
|
||
</section>
|
||
<section id="expert-kernels" class="level3">
|
||
<h3 class="anchored" data-anchor-id="expert-kernels">Expert Kernels</h3>
|
||
<p>Optimized kernel implementations for Mixture of Experts (MoE) model training.</p>
|
||
<ul>
|
||
<li><p><strong>ScatterMoE</strong>: Triton-based MoE kernels with fused LoRA support.</p></li>
|
||
<li><p><strong>SonicMoE</strong>: CUTLASS-based MoE kernels for NVIDIA Hopper and Blackwell GPUs.</p></li>
|
||
<li><p><strong>Learn more:</strong> <a href="../docs/custom_integrations.html#kernels-integration">Custom Integrations - Kernels Integration</a></p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<section id="long-context-models" class="level2">
|
||
<h2 class="anchored" data-anchor-id="long-context-models">Long Context Models</h2>
|
||
<p>Techniques to train models on sequences longer than their original context window.</p>
|
||
<section id="rope-scaling" class="level3">
|
||
<h3 class="anchored" data-anchor-id="rope-scaling">RoPE Scaling</h3>
|
||
<p>Extends a model’s context window by interpolating its Rotary Position Embeddings.</p>
|
||
<ul>
|
||
<li><strong>Config:</strong> Pass the <code>rope_scaling</code> config under the <code>overrides_of_model_config:</code>. To learn how to set RoPE, check the respective model config.</li>
|
||
</ul>
|
||
</section>
|
||
<section id="sequence-parallelism" class="level3">
|
||
<h3 class="anchored" data-anchor-id="sequence-parallelism">Sequence Parallelism</h3>
|
||
<p>Splits long sequences across multiple GPUs, enabling training with sequence lengths that would not fit on a single device.</p>
|
||
<ul>
|
||
<li><strong>Learn more:</strong> <a href="../docs/sequence_parallelism.html">Sequence Parallelism Documentation</a></li>
|
||
</ul>
|
||
</section>
|
||
<section id="artic-long-sequence-training-alst" class="level3">
|
||
<h3 class="anchored" data-anchor-id="artic-long-sequence-training-alst">Artic Long Sequence Training (ALST)</h3>
|
||
<p>ALST is a recipe that combines several techniques to train long-context models efficiently. It typically involves:</p>
|
||
<ul>
|
||
<li><p>TiledMLP to reduce memory usage in MLP layers.</p></li>
|
||
<li><p>Tiled Loss functions (like <a href="#cut-cross-entropy-(cce)%20or%20%5BLiger%5D(#liger-kernels)">CCE</a>.</p></li>
|
||
<li><p>Activation Offloading to CPU.</p></li>
|
||
<li><p>Example: <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst">ALST Example Configuration</a></p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<section id="large-models-distributed-training" class="level2">
|
||
<h2 class="anchored" data-anchor-id="large-models-distributed-training">Large Models (Distributed Training)</h2>
|
||
<p>To train models that don’t fit on a single GPU, you’ll need to use a distributed training strategy like FSDP or DeepSpeed. These frameworks shard the model weights, gradients, and optimizer states across multiple GPUs and nodes.</p>
|
||
<ul>
|
||
<li><strong>Learn more:</strong> <a href="../docs/multi-gpu.html">Multi-GPU Guide</a></li>
|
||
<li><strong>Learn more:</strong> <a href="../docs/multi-node.html">Multi-Node Guide</a></li>
|
||
</ul>
|
||
<section id="n-d-parallelism-beta" class="level3">
|
||
<h3 class="anchored" data-anchor-id="n-d-parallelism-beta">N-D Parallelism (Beta)</h3>
|
||
<p>For advanced scaling, Axolotl allows you to compose different parallelism techniques (e.g., Data, Tensor, Sequence Parallelism). This is a powerful approach to train an extremely large model by overcoming multiple bottlenecks at once.</p>
|
||
<ul>
|
||
<li><strong>Learn more:</strong> <a href="../docs/nd_parallelism.html">N-D Parallelism Guide</a></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<section id="quantization" class="level2">
|
||
<h2 class="anchored" data-anchor-id="quantization">Quantization</h2>
|
||
<p>Techniques to reduce the precision of model weights for memory savings.</p>
|
||
<section id="bit-training-qlora" class="level3">
|
||
<h3 class="anchored" data-anchor-id="bit-training-qlora">4-bit Training (QLoRA)</h3>
|
||
<p>The recommended approach for quantization-based training. It loads the base model in 4-bit using <code>bitsandbytes</code> and then trains QLoRA adapters. See <a href="#adapter-finetuning-lora-qlora">Adapter Finetuning</a> for details.</p>
|
||
</section>
|
||
<section id="fp8-training" class="level3">
|
||
<h3 class="anchored" data-anchor-id="fp8-training">FP8 Training</h3>
|
||
<p>Enables training with 8-bit floating point precision on supported hardware (e.g., NVIDIA Hopper series GPUs) for significant speed and memory gains.</p>
|
||
<ul>
|
||
<li><strong>Example:</strong> <a href="https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/llama-3/3b-fp8-fsdp2.yaml">Llama 3 FP8 FSDP Example</a></li>
|
||
</ul>
|
||
</section>
|
||
<section id="quantization-aware-training-qat" class="level3">
|
||
<h3 class="anchored" data-anchor-id="quantization-aware-training-qat">Quantization Aware Training (QAT)</h3>
|
||
<p>Simulates quantization effects during training, helping the model adapt and potentially improving the final accuracy of the quantized model.</p>
|
||
<ul>
|
||
<li><strong>Learn more:</strong> <a href="../docs/qat.html">QAT Documentation</a></li>
|
||
</ul>
|
||
</section>
|
||
<section id="gptq" class="level3">
|
||
<h3 class="anchored" data-anchor-id="gptq">GPTQ</h3>
|
||
<p>Allows you to finetune LoRA adapters on top of a model that has already been quantized using the GPTQ method.</p>
|
||
<ul>
|
||
<li><strong>Example:</strong> <a href="https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/llama-2/gptq-lora.yml">GPTQ LoRA Example</a></li>
|
||
</ul>
|
||
</section>
|
||
<section id="moe-expert-quantization" class="level3">
|
||
<h3 class="anchored" data-anchor-id="moe-expert-quantization">MoE Expert Quantization</h3>
|
||
<p>Quantizes MoE expert weights on load to reduce VRAM when training MoE models with adapters. Required for Transformers v5+ MoE models where experts use fused <code>nn.Parameter</code> tensors.</p>
|
||
<ul>
|
||
<li><strong>Config:</strong> <code>quantize_moe_experts: true</code></li>
|
||
<li><strong>Learn more:</strong> <a href="../docs/expert_quantization.html">MoE Expert Quantization</a></li>
|
||
</ul>
|
||
|
||
|
||
</section>
|
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|
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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> |