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update docs for 0.20.0rc2 Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
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<li class="toctree-l1"><a class="reference internal" href="../key-features.html">Key Features</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../torch.html">PyTorch Backend</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../installation/grace-hopper.html">Installing on Grace Hopper</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api/index.html">API Introduction</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_customize.html">Generate text with customization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
|
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</ul>
|
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</details></li>
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<li class="toctree-l1"><a class="reference internal" href="../examples/customization.html">LLM Common Customizations</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/llm_api_examples.html">LLM Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_customize.html">Generate text with customization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_quantization.html">Generation with Quantization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference.html">Generate text</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
|
||
</ul>
|
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</details></li>
|
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<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/trtllm_serve_examples.html">Online Serving Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<section id="expert-parallelism-in-tensorrt-llm">
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<span id="expert-parallelism"></span><h1>Expert Parallelism in TensorRT-LLM<a class="headerlink" href="#expert-parallelism-in-tensorrt-llm" title="Link to this heading">#</a></h1>
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<section id="mixture-of-experts-moe">
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<h2>Mixture of Experts (MoE)<a class="headerlink" href="#mixture-of-experts-moe" title="Link to this heading">#</a></h2>
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<p>Mixture of Experts (MoE) architectures have been used widely recently, such as <a class="reference external" href="https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/blob/main/config.json">Mistral Mixtral 8x7B</a>. Specifically, MOE’s structure supports multiple parallel Feedforward Neural Network (FFN) layers (called experts) to replace the single FFN layer in the dense model. When tokens arrive, the router layer selects the TopK experts for each token. The corresponding hidden state of the token is then dispatched to the selected TopK experts, respectively. As a result, there are multiple tokens’ hidden states that are dispatched to each expert.</p>
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<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/media/moe_structure.png?raw=true" alt="moe_structure" width="500" height="auto">
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<p><sub>the MOE structure in Switch Transformer: <a class="reference external" href="https://arxiv.org/pdf/2101.03961.pdf">https://arxiv.org/pdf/2101.03961.pdf</a> </sub></p>
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<section id="tensor-parallel-vs-expert-parallel">
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<h2>Tensor Parallel vs Expert Parallel<a class="headerlink" href="#tensor-parallel-vs-expert-parallel" title="Link to this heading">#</a></h2>
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<p>Parallelism on multi-GPUs is necessary if the MoE model can not be accommodated by a single GPU’s memory. We have supported two kinds of parallel patterns for MoE structure, Tensor Parallel (default pattern), Expert Parallel, and a hybrid of the two.</p>
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<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/media/tp_ep.png?raw=true" alt="tensor parallel vs expert parallel" width="500" height="auto">
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<p>Tensor Parallel evenly splits each expert’s weight and distributes them to different GPUs, which means each GPU holds partial weight of all experts, While Expert Parallel evenly distributes some of the experts’ full weight to different GPUs, which means each GPU holds part of the experts’ full weight. As a result, each GPU rank in the Tensor Parallel group receives all tokens’ hidden states for all experts, then computes using the partial weights, while for Expert Parallel, each GPU rank only receives part of tokens’ hidden states for experts on this rank, then computes using the full weights.</p>
|
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<p>When both Tensor Parallel and Expert Parallel are enabled, each GPU handles a portion of the expert weights matrices (as in EP mode) and these weights are further sliced across multiple GPUs (as in TP mode). This hybrid approach aims to balance the workload more evenly across GPUs, enhancing efficiency and reducing the likelihood of bottlenecks associated with EP mode alone.</p>
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<section id="how-to-enable">
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<h2>How to Enable<a class="headerlink" href="#how-to-enable" title="Link to this heading">#</a></h2>
|
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<p>The default parallel pattern is Tensor Parallel. You can enable Expert Parallel or hybrid parallel by setting <code class="docutils literal notranslate"><span class="pre">--moe_tp_size</span></code> and <code class="docutils literal notranslate"><span class="pre">--moe_ep_size</span></code> when calling <code class="docutils literal notranslate"><span class="pre">convert_coneckpoint.py</span></code>. If only <code class="docutils literal notranslate"><span class="pre">--moe_tp_size</span></code> is provided, TRT-LLM will use Tensor Parallel for the MoE model; if only <code class="docutils literal notranslate"><span class="pre">--moe_ep_size</span></code> is provided, TRT-LLM will use Expert Parallel; if both are provided, the hybrid parallel will be used.</p>
|
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<p>Ensure the product of <code class="docutils literal notranslate"><span class="pre">moe_tp_size</span></code> and <code class="docutils literal notranslate"><span class="pre">moe_ep_size</span></code> is equal to <code class="docutils literal notranslate"><span class="pre">tp_size</span></code>, since the total number of MoE parallelism across all GPUs must match the total number of parallelism in other parts of the model.</p>
|
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<p>The other parameters related to the MoE structure, such as <code class="docutils literal notranslate"><span class="pre">num_experts_per_tok</span></code> (TopK in previous context) and <code class="docutils literal notranslate"><span class="pre">num_local_experts,</span></code> can be found in the model’s configuration file, such as the one for <a class="reference external" href="https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/blob/main/config.json">Mixtral 8x7B model</a>.
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