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<h1 id="_1">在线量化</h1>
<p>在线量化是指推理引擎在加载 BF16 权重后对权重做量化，而不是加载离线量化好的低精度权重。FastDeploy 支持将 BF16 在线量化到多种精度，包括：INT4, INT8 和 FP8.</p>
<h2 id="1-wint8-wint4">1. WINT8 &amp; WINT4</h2>
<p>仅将权重在线量化为 INT8 或 INT4，推理时即时地将权重反量化为 BF16 后与激活进行计算。
- <strong>量化粒度</strong>：仅支持 channel-wise 粒度的量化；
- <strong>支持硬件</strong>：GPU，XPU
- <strong>支持结构</strong>：MoE 结构，Dense Linear</p>
<h3 id="wint8wint4">启动WINT8或WINT4推理服务</h3>
<pre><code>python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-Paddle \
--port 8180 --engine-worker-queue-port 8181 \
--cache-queue-port 8182 --metrics-port 8182 \
--tensor-parallel-size 8 \
--quantization wint8 \
--max-model-len 32768 \
--max-num-seqs 32
</code></pre>
<ul>
<li>通过指定 <code>--model baidu/ERNIE-4.5-300B-A47B-Paddle</code> 可自动从AIStudio下载模型。FastDeploy依赖Paddle格式的模型，更多说明参考<a href="../../supported_models/">支持模型列表</a>。</li>
<li>通过设置 <code>--quantization</code> 为 <code>wint8</code> 或 <code>wint4</code> 选择在线 INT8/INT4 量化。</li>
<li>部署 ERNIE-4.5-300B-A47B-Paddle WINT8 最少需要 80G <em>8卡, WINT4 则需要 80GB</em> 4卡。</li>
<li>更多部署教程请参考<a href="../../get_started/ernie-4.5/">get_started</a>.</li>
</ul>
<h2 id="2-block-wise-fp8">2. Block-wise FP8</h2>
<p>加载 BF16 模型，将权重以 128X128 block-wise 的粒度在线量化为 FP8 数值类型。推理时，激活会动态、即时地做 token-wise FP8 量化。</p>
<ul>
<li><strong>FP8规格</strong>：float8_e4m3fn</li>
<li><strong>支持硬件</strong>：Hopper GPU 架构</li>
<li><strong>支持结构</strong>：MoE 结构，Dense Linear</li>
</ul>
<h3 id="block-wise-fp8">启动Block-wise FP8推理服务</h3>
<pre><code>python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-Paddle \
--port 8180 --engine-worker-queue-port 8181 \
--cache-queue-port 8182 --metrics-port 8182 \
--tensor-parallel-size 8 \
--quantization block_wise_fp8 \
--max-model-len 32768 \
--max-num-seqs 32
</code></pre>
<ul>
<li>通过指定 <code>--model baidu/ERNIE-4.5-300B-A47B-Paddle</code> 可自动从AIStudio下载模型。FastDeploy依赖Paddle格式的模型，更多说明参考<a href="../../supported_models/">支持模型列表</a>。</li>
<li>通过设置 <code>--quantization</code> 为 <code>block_wise_fp8</code> 选择在线 Block-wise FP8 量化。</li>
<li>部署 ERNIE-4.5-300B-A47B-Paddle Block-wise FP8 最少需要 80G * 8卡。</li>
<li>更多部署教程请参考<a href="../../get_started/ernie-4.5/">get_started</a></li>
</ul>
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