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<p><a href="./">English</a></p>
<h1 id="_1">🔮 投机解码</h1>
<p>本项目基于 PaddlePaddle 实现了高效的 <strong>投机解码（Speculative Decoding）</strong> 推理框架，支持多 Token 预测（Multi-token Proposing, MTP），用于加速大语言模型（LLM）的生成，显著降低时延并提升吞吐量。</p>
<h2 id="_2">✅ 投机解码方法支持</h2>
<h3 id="_3">✅ 支持列表</h3>
<ul>
<li>
<p><strong>Ngram</strong></p>
</li>
<li>
<p><strong>MTP (Multi-Token Prediction)</strong></p>
</li>
<li>✅ 已支持：TP 切分</li>
<li>✅ 已支持：共享前缀</li>
<li>✅ 已支持：单机 TP 切分 + PD 分离</li>
<li>⏳ 即将支持：EP + DP + PD 分离</li>
<li>⏳ 即将支持：兼容 Chunk Prefill</li>
<li>
<p>⏳ 即将支持：多层 MTP layer</p>
</li>
<li>
<p><strong>混合MTP、Ngram方法解码(Hybrid-MTP-with-Ngram)</strong></p>
</li>
<li>方法概述：混合MTP与Ngram方法，先使用MTP产出N个草稿Token，再使用Ngram匹配补充草稿Token。</li>
<li>使用场景：适合在需要更多草稿Token时使用，兼顾MTP生成能力与Ngram匹配的高效性。</li>
</ul>
<hr />
<h3 id="_4">⏳ 规划中</h3>
<ul>
<li>Draft Model</li>
<li>Eagle</li>
<li>Hydra</li>
<li>Medusa</li>
<li>...</li>
</ul>
<h2 id="_5">⚙️ 高效投机解码框架设计</h2>
<ul>
<li><strong>Attention机制</strong>：采用 <a href="https://flashinfer.ai/2024/02/02/cascade-inference.html">Cascade Append Attention</a> 的 Attention 机制，支持变长查询统一处理，一次前向推理即可完成所有验证。此外，我们对 Kernel 实现进行了深度定制，以最大化 Tensor Core 的利用率，并在高并发场景下仍然保持高吞吐。</li>
<li><strong>虚拟填充机制</strong>：采用虚拟填充快速定位输出 Token 的批次 ID，避免了高开销的数据拷贝与切片操作。</li>
<li><strong>并行采样与验证</strong>：我们开发了多个融合 Cuda Kernel，用于同时执行采样与验证操作。该 Kernel 支持对每个 batch 样本进行并行处理，避免了显式循环的开销。</li>
<li><strong>高效 DraftModel/MTP 框架</strong>：开发多个融合 Cuda Kernel，统一完成模型类方法的前后处理，相比传统的循环、切片方法，性能高效且易维护</li>
</ul>
<h2 id="_6">🔧 参数说明</h2>
<ul>
<li><code>method</code>: 解码策略，可选值为 <code>"mtp"</code> 或 <code>"ngram"</code></li>
<li><code>num_speculative_tokens</code>: 每轮预测的 Token 数，最大支持 5（当前 MTP 仅支持 1）</li>
<li><code>model</code>: 若选择 MTP，则需指定 MTP 模型路径</li>
<li><code>quantization</code>: 模型量化方式，推荐使用 <code>wint8</code></li>
<li><code>batch_size</code>: 当前支持最大值为 256</li>
</ul>
<h2 id="multi-token-predictionmtp">🚀 使用 Multi-Token-Prediction(MTP) 解码</h2>
<p>详见论文：<a href="https://arxiv.org/pdf/2412.19437">DeepSeek-V3</a></p>
<h3 id="tp">TP 并行部署</h3>
<blockquote>
<p>使用 4×H100，量化方式选择 WINT4
配置文件：<code>benchmarks/yaml/eb45t-32k-wint4-mtp-h100-tp4.yaml</code></p>
</blockquote>
<pre><code>python -m fastdeploy.entrypoints.openai.api_server \
--model ${path_to_main_model} \
--tensor-parallel-size 4 \
--config ${path_to_FastDeploy}benchmarks/yaml/eb45t-32k-wint4-mtp-h100-tp4.yaml \
--speculative-config '{&quot;method&quot;: &quot;mtp&quot;, &quot;num_speculative_tokens&quot;: 1, &quot;model&quot;: &quot;${path_to_mtp_model}&quot;}'
</code></pre>
<h3 id="pd-1p1d">PD 分离式部署（1P1D）</h3>
<blockquote>
<p>在8×H100上部署1P1D，P、D节点 分别使用 4×H100；量化方式选择 WINT4
与常规 PD 分离部署一致，仅需替换配置文件并新增 speculative_config
详情请参考<a href="../disaggregated/">PD分离式部署</a>。
- P 节点（Prefill）</p>
<p>配置文件： <code>benchmarks/yaml/eb45t-32k-wint4-mtp-tp4-prefill.yaml</code></p>
</blockquote>
<pre><code>export FD_LOG_DIR=&quot;log_prefill&quot;
rm -rf ${FD_LOG_DIR}
export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m fastdeploy.entrypoints.openai.api_server \
--model ${path_to_main_model} \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--cache-queue-port 8183 \
--workers 2 \
--tensor-parallel-size 4 \
--quantization wint4 \
--splitwise-role &quot;prefill&quot; \
--scheduler-name &quot;splitwise&quot; \
--scheduler-host &quot;127.0.0.1&quot; \
--scheduler-port 6379 \
--scheduler-ttl 9000 \
--scheduler-topic mtp \
--config ${path_to_FastDeploy}/benchmarks/yaml/eb45t-32k-wint4-mtp-tp4-prefill.yaml \
--scheduler-password &quot;scheduler_mtp&quot; \
--speculative-config '{&quot;method&quot;: &quot;mtp&quot;, &quot;num_speculative_tokens&quot;: 1, &quot;model&quot;: &quot;&quot;${path_to_mtp_model}&quot;}' &amp;
</code></pre>
<ul>
<li>D 节点（Decode）</li>
</ul>
<blockquote>
<p>配置文件： <code>benchmarks/yaml/eb45t-32k-wint4-mtp-tp4-decode.yaml</code></p>
</blockquote>
<pre><code>export FD_LOG_DIR=&quot;log_prefill&quot;
rm -rf ${FD_LOG_DIR}
export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m fastdeploy.entrypoints.openai.api_server \
--model ${path_to_main_model} \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--cache-queue-port 8183 \
--workers 2 \
--tensor-parallel-size 4 \
--quantization wint4 \
--splitwise-role &quot;prefill&quot; \
--scheduler-name &quot;splitwise&quot; \
--scheduler-host &quot;127.0.0.1&quot; \
--scheduler-port 6379 \
--scheduler-ttl 9000 \
--scheduler-topic mtp \
--config ${path_to_FastDeploy}/benchmarks/yaml/eb45t-32k-wint4-mtp-tp4-prefill.yaml \
--scheduler-password &quot;scheduler_mtp&quot; \
--speculative-config '{&quot;method&quot;: &quot;mtp&quot;, &quot;num_speculative_tokens&quot;: 1, &quot;model&quot;: &quot;&quot;${path_to_mtp_model}&quot;}' &amp;
</code></pre>
<h2 id="mtpngram">使用混合MTP、Ngram方法解码</h2>
<p>在启动服务时，只需改动 --speculative-config 即可。例如使用MTP产出两个DraftToken，再额外拼接三个Ngram匹配的DraftToken</p>
<pre><code>--speculative-config '{&quot;method&quot;: &quot;mtp&quot;, &quot;num_model_steps&quot;: 2, &quot;mtp_strategy&quot;: &quot;with_ngram&quot; ,&quot;num_speculative_tokens&quot;: 5, &quot;model&quot;: &quot;'$model_path'/mtp&quot;}'
</code></pre>
<h2 id="ngram">🧠 使用 Ngram 解码</h2>
<p>该算法通过 n-gram 窗口从 prompt 和已生成的 Token 中进行匹配生成草稿 Token，适合输入和输出有很大 overlap 的场景，如代码续写、文档查询等。</p>
<blockquote>
<p>使用 4×H100；量化方式选择 WINT4
配置文件：benchmarks/yaml/eb45t-32k-wint4-mtp-h100-tp4.yaml</p>
</blockquote>
<pre><code>python -m fastdeploy.entrypoints.openai.api_server \
--model ${path_to_main_model} \
--tensor-parallel-size 4 \
--config ${path_to_FastDeploy}benchmarks/yaml/eb45t-32k-wint4-mtp-h100-tp4.yaml \
--speculative-config '{&quot;method&quot;: &quot;ngram&quot;, &quot;num_speculative_tokens&quot;: 1, &quot;model&quot;: &quot;${mtp_model_path}&quot;}'
</code></pre>
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