1269 lines
53 KiB
HTML
1269 lines
53 KiB
HTML
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<meta name="description" content="Reduce VRAM usage when training MoE model adapters by quantizing expert weights on load">
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<title>MoE Expert Quantization – Axolotl</title>
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<nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../docs/fsdp_qlora.html">Advanced Features</a></li><li class="breadcrumb-item"><a href="../docs/expert_quantization.html">MoE Expert Quantization</a></li></ol></nav>
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Reduce VRAM usage when training MoE model adapters by quantizing expert weights on load
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|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
|
||
</header>
|
||
|
||
|
||
<p>Transformers v5 changed MoE expert layers from <code>nn.Linear</code> to fused <code>nn.Parameter</code> (3D+ tensors).
|
||
This means <code>bitsandbytes</code> can no longer quantize them during model loading, resulting in all expert
|
||
weights being loaded in full bf16 precision and causing massive VRAM usage.</p>
|
||
<p><code>quantize_moe_experts</code> solves this by quantizing expert weights during model loading.
|
||
It intercepts the weight loading process, quantizes each expert tensor on the fly, and
|
||
immediately frees the original bf16 tensor from VRAM. This dramatically reduces peak memory.
|
||
For example, GLM-4.7-Flash QLoRA drops from ~127GiB to ~23GiB reserved memory.</p>
|
||
<section id="usage" class="level2">
|
||
<h2 class="anchored" data-anchor-id="usage">Usage</h2>
|
||
<p>Enable expert quantization in your Axolotl config:</p>
|
||
<div class="code-copy-outer-scaffold"><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="fu">quantize_moe_experts</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<p>This works with both 4-bit (QLoRA) and 8-bit (LoRA) quantization.</p>
|
||
<section id="expert-lora-targeting" class="level3">
|
||
<h3 class="anchored" data-anchor-id="expert-lora-targeting">Expert LoRA targeting</h3>
|
||
<p>You can optionally apply LoRA adapters directly to expert weights using <code>lora_target_parameters</code>:</p>
|
||
<div class="code-copy-outer-scaffold"><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">lora_target_parameters</span><span class="kw">:</span></span>
|
||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> mlp.experts.gate_up_proj</span></span>
|
||
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> mlp.experts.down_proj</span></span>
|
||
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co"> # - mlp.gate.weight # router</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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<div class="callout callout-style-default callout-note callout-titled">
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||
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|
||
<div class="callout-title-container flex-fill">
|
||
Note
|
||
</div>
|
||
</div>
|
||
<div class="callout-body-container callout-body">
|
||
<p><code>lora_dropout</code> must be <code>0</code> when using <code>lora_target_parameters</code>.</p>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<section id="requirements" class="level2">
|
||
<h2 class="anchored" data-anchor-id="requirements">Requirements</h2>
|
||
<ul>
|
||
<li>Requires (<code>adapter: lora</code> and <code>load_in_8bit: true</code>) or (<code>adapter: qlora</code> and <code>load_in_4bit: true</code>)</li>
|
||
<li>CUDA GPUs only (not tested with ROCm or other backends)</li>
|
||
<li>FSDP2 compatible for distributed training</li>
|
||
</ul>
|
||
</section>
|
||
<section id="limitations" class="level2">
|
||
<h2 class="anchored" data-anchor-id="limitations">Limitations</h2>
|
||
<ul>
|
||
<li><code>cpu_ram_efficient_loading</code> hangs / takes long time with FSDP2 + QLoRA.</li>
|
||
<li>Total model parameter count may display incorrectly (trainable param count is correct).</li>
|
||
<li>FSDP LoRA (8-bit) may have a large initial VRAM spike at the first 1-2 steps, which then drops. QLoRA does not exhibit this.</li>
|
||
<li>FSDP2 may use more VRAM per GPU than single GPU training due to not all layers being properly sharded across ranks.</li>
|
||
<li>Model loading takes longer due to on-demand quantization, even on consecutive runs.</li>
|
||
<li>DeepSpeed has not been tested.</li>
|
||
</ul>
|
||
</section>
|
||
<section id="implementation-details" class="level2">
|
||
<h2 class="anchored" data-anchor-id="implementation-details">Implementation details</h2>
|
||
<p>The quantization is applied by patching transformers to intercept weight loading.
|
||
When a 3D+ CUDA tensor with “expert” in its name is detected:</p>
|
||
<ul>
|
||
<li><strong>4-bit mode:</strong> Uses bitsandbytes NF4 parametrization (configurable via <code>bnb_4bit_quant_type</code>).</li>
|
||
<li><strong>8-bit mode:</strong> Uses a custom row-wise int8 parametrization with bitsandbytes dequantization.</li>
|
||
</ul>
|
||
<p>The original bf16 tensor is freed immediately after quantization. Multiple sub-patches are applied to
|
||
transformers, PEFT and accelerate FSDP2 to support these parametrized expert modules.</p>
|
||
<p>For full implementation details, see <a href="https://github.com/axolotl-ai-cloud/axolotl/pull/3439">PR #3439</a>.</p>
|
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|
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el,
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const ref = bibliorefs[i];
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const citeInfo = findCites(ref);
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tippyHover(citeInfo.el, function() {
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var popup = window.document.createElement('div');
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citeInfo.cites.forEach(function(cite) {
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var citeDiv = window.document.createElement('div');
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citeDiv.classList.add('hanging-indent');
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citeDiv.classList.add('csl-entry');
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var biblioDiv = window.document.getElementById('ref-' + cite);
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if (biblioDiv) {
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citeDiv.innerHTML = biblioDiv.innerHTML;
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popup.appendChild(citeDiv);
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return popup.innerHTML;
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});
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</script>
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</div> <!-- /content -->
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||
</body></html> |