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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 has-children"><a class="reference internal" href="../llm-api-examples/index.html">LLM Examples Introduction</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="../llm-api-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="../llm-api-examples/llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-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="../llm-api-examples/llm_inference_customize.html">Generate text with customization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_logits_processor.html">Control generated text using logits post processor</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-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="../llm-api-examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-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="../llm-api-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="../llm-api-examples/llm_inference.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_auto_parallel.html">Automatic Parallelism with LLM</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="../llm-api-examples/customization.html">Common Customizations</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-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="../llm-api-examples/llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-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="../llm-api-examples/llm_inference_customize.html">Generate text with customization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_logits_processor.html">Control generated text using logits post processor</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-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="../llm-api-examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-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="../llm-api-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="../llm-api-examples/llm_inference.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Model Definition API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.models.html">Models</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.quantization.html">Quantization</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.runtime.html">Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-build.html">trtllm-build</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/add-model.html">Adding a Model</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced</span></p>
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<ul class="current nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
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<li class="toctree-l1"><a class="reference internal" href="lora.html">Run gpt-2b + LoRA using GptManager / cpp runtime</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Disaggregated-Service (experimental)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../performance/perf-benchmarking.html">Benchmarking</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../performance/performance-tuning-guide/index.html">Performance Tuning Guide</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="../performance/performance-tuning-guide/benchmarking-default-performance.html">Benchmarking Default Performance</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-build-time-flags.html">Useful Build-Time Flags</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/tuning-max-batch-size-and-max-num-tokens.html">Tuning Max Batch Size and Max Num Tokens</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/deciding-model-sharding-strategy.html">Deciding Model Sharding Strategy</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/fp8-quantization.html">FP8 Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-runtime-flags.html">Useful Runtime Options</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="../performance/perf-analysis.html">Performance Analysis</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../reference/troubleshooting.html">Troubleshooting</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../reference/precision.html">Numerical Precision</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../reference/memory.html">Memory Usage of TensorRT-LLM</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Blogs</span></p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="../blogs/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../blogs/H200launch.html">H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../blogs/Falcon180B-H200.html">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../blogs/quantization-in-TRT-LLM.html">Speed up inference with SOTA quantization techniques in TRT-LLM</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../blogs/XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Disaggregated-Service (experimental)</span></li>
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<section id="disaggregated-service-experimental">
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<span id="disaggregated-service"></span><h1>Disaggregated-Service (experimental)<a class="headerlink" href="#disaggregated-service-experimental" title="Link to this heading">#</a></h1>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Note:
|
||
This feature is currently experimental, and the related API is subjected to change in future versions.</p>
|
||
</div>
|
||
<p>Currently TRT-LLM supports <code class="docutils literal notranslate"><span class="pre">disaggregated-service</span></code>, where the context and generation phases of a request can run on different executors. TRT-LLM’s disaggregated service relies on the executor API, please make sure to read the <a class="reference internal" href="executor.html"><span class="std std-doc">executor page</span></a> before reading the document.</p>
|
||
<p>For more information on disaggregated service in LLM inference, one can refer to papers such as <a class="reference external" href="https://arxiv.org/abs/2401.09670">DistServe</a>, <a class="reference external" href="https://arxiv.org/abs/2311.18677">SplitWise</a>.</p>
|
||
<section id="usage">
|
||
<h2>Usage<a class="headerlink" href="#usage" title="Link to this heading">#</a></h2>
|
||
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">RequestType</span>
|
||
<span class="p">{</span>
|
||
<span class="w"> </span><span class="n">REQUEST_TYPE_CONTEXT_AND_GENERATION</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span>
|
||
<span class="w"> </span><span class="n">REQUEST_TYPE_CONTEXT_ONLY</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span>
|
||
<span class="w"> </span><span class="n">REQUEST_TYPE_GENERATION_ONLY</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span>
|
||
<span class="p">};</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>The TRT-LLM executor can execute three types of requests: <code class="docutils literal notranslate"><span class="pre">REQUEST_TYPE_CONTEXT_AND_GENERATION</span></code>, <code class="docutils literal notranslate"><span class="pre">REQUEST_TYPE_CONTEXT_ONLY</span></code>, and <code class="docutils literal notranslate"><span class="pre">REQUEST_TYPE_GENERATION_ONLY</span></code>. An executor instance could execute the context phase of the context-only request or the generation phase of the generation-only request. When the executor completes the context phase of a context-only request, it maintains the corresponding kvCache, which will be requested by the executor for the subsequent generation-only request.</p>
|
||
<p>Note that the environment variable <code class="docutils literal notranslate"><span class="pre">TRTLLM_USE_MPI_KVCACHE=1</span></code> should be set for <code class="docutils literal notranslate"><span class="pre">disaggregated-service</span></code>.</p>
|
||
<p>Here are some key APIs to use disaggregated service:</p>
|
||
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">Request</span><span class="w"> </span><span class="n">request</span><span class="p">{...};</span>
|
||
|
||
<span class="n">request</span><span class="p">.</span><span class="n">setRequestType</span><span class="p">(</span><span class="n">tensorrt_llm</span><span class="o">::</span><span class="n">executor</span><span class="o">::</span><span class="n">RequestType</span><span class="o">::</span><span class="n">REQUEST_TYPE_CONTEXT_ONLY</span><span class="p">);</span>
|
||
|
||
<span class="k">auto</span><span class="w"> </span><span class="n">contextRequestId</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">contextExecutor</span><span class="p">.</span><span class="n">enqueueRequest</span><span class="p">(</span><span class="n">request</span><span class="p">);</span>
|
||
|
||
<span class="k">auto</span><span class="w"> </span><span class="n">contextResponses</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">contextExecutor</span><span class="p">.</span><span class="n">awaitResponses</span><span class="p">(</span><span class="n">contextRequestId</span><span class="p">);</span>
|
||
|
||
<span class="k">auto</span><span class="w"> </span><span class="n">contextPhaseParams</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">contextResponses</span><span class="p">.</span><span class="n">back</span><span class="p">().</span><span class="n">getResult</span><span class="p">().</span><span class="n">contextPhaseParams</span><span class="p">.</span><span class="n">value</span><span class="p">();</span>
|
||
|
||
<span class="n">request</span><span class="p">.</span><span class="n">setContextPhaseParams</span><span class="p">(</span><span class="n">contextPhaseParams</span><span class="p">);</span>
|
||
|
||
<span class="n">request</span><span class="p">.</span><span class="n">setRequestType</span><span class="p">(</span><span class="n">tensorrt_llm</span><span class="o">::</span><span class="n">executor</span><span class="o">::</span><span class="n">RequestType</span><span class="o">::</span><span class="n">REQUEST_TYPE_GENERATION_ONLY</span><span class="p">);</span>
|
||
|
||
<span class="k">auto</span><span class="w"> </span><span class="n">generationRequestId</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">generationExecutor</span><span class="p">.</span><span class="n">enqueueRequest</span><span class="p">(</span><span class="n">request</span><span class="p">);</span>
|
||
|
||
<span class="k">auto</span><span class="w"> </span><span class="n">genResponses</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">generationExecutor</span><span class="p">.</span><span class="n">awaitResponses</span><span class="p">(</span><span class="n">generationRequestId</span><span class="p">);</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>The generationExecutor will require data such as kvCache from the corresponding contextExecutor based on the <code class="docutils literal notranslate"><span class="pre">contextPhaseParams</span></code> attached to the request, so please make sure that the corresponding contextExecutor is not shut down before getting the generationExecutor’s response.</p>
|
||
<p>In the code example above, the <code class="docutils literal notranslate"><span class="pre">contextRequestId</span></code> assigned by the contextExecutor and the <code class="docutils literal notranslate"><span class="pre">generationRequestId</span></code> assigned by the generationExecutor are independent, it is the user’s responsibility to manage the mapping of the <code class="docutils literal notranslate"><span class="pre">requestId</span></code> for context-only requests to the <code class="docutils literal notranslate"><span class="pre">requestId</span></code> for generation-only requests. The <code class="docutils literal notranslate"><span class="pre">contextResponses</span></code> contains the first output token generated by the context phase, and the <code class="docutils literal notranslate"><span class="pre">genResponses</span></code> also contains the first output token generated by the contextExecutor, so all output tokens can be obtained from generationExecutor’s responses.</p>
|
||
<p><img alt="disaggregated-service usage" src="../_images/disaggregated-service_usage.png" /></p>
|
||
<p>An <code class="docutils literal notranslate"><span class="pre">orchestrator</span></code> is required in <code class="docutils literal notranslate"><span class="pre">disaggregated-service</span></code> to manage multiple executor instances and route requests to different executors, TRT-LLM provides class <code class="docutils literal notranslate"><span class="pre">DisaggExecutorOrchestrator</span></code> in <code class="docutils literal notranslate"><span class="pre">cpp/include/tensorrt_llm/executor/disaggServerUtil.h</span></code> to launch multiple executor instances, however, <code class="docutils literal notranslate"><span class="pre">DisaggExecutorOrchestrator</span></code> only routes requests to executors in a simple round-robin policy, users need to implement their own orchestrator for disaggregated-service based on their usage scenario.</p>
|
||
<p>TRT-LLM currently implements kvCache transfer using <code class="docutils literal notranslate"><span class="pre">CUDA-aware</span> <span class="pre">MPI</span></code>, and all executor processes involved need to hold the same MPI world communicator. Therefore, TRT-LLM only supports launching multiple executors using <code class="docutils literal notranslate"><span class="pre">MPI</span></code>, and the <code class="docutils literal notranslate"><span class="pre">CommunicationMode</span></code> of the executors must be set to <code class="docutils literal notranslate"><span class="pre">kLEADER</span></code> or <code class="docutils literal notranslate"><span class="pre">kORCHESTRATOR</span></code> with <code class="docutils literal notranslate"><span class="pre">SpawnProcesses=false</span></code> for <code class="docutils literal notranslate"><span class="pre">disaggregated-service</span></code>, TRT-LLM will relax this restriction in future version to manage executors with greater ease.</p>
|
||
</section>
|
||
<section id="example">
|
||
<h2>Example<a class="headerlink" href="#example" title="Link to this heading">#</a></h2>
|
||
<p>Please refer to <code class="docutils literal notranslate"><span class="pre">examples/cpp/executor/executorExampleDisaggregated.cpp</span></code></p>
|
||
</section>
|
||
<section id="benchmarks">
|
||
<h2>Benchmarks<a class="headerlink" href="#benchmarks" title="Link to this heading">#</a></h2>
|
||
<p>Please refer to <code class="docutils literal notranslate"><span class="pre">benchmarks/cpp/disaggServerBenchmark.cpp</span></code> and <code class="docutils literal notranslate"><span class="pre">benchmarks/cpp/README.md</span></code></p>
|
||
</section>
|
||
<section id="troubleshooting-and-faq">
|
||
<h2>Troubleshooting and FAQ<a class="headerlink" href="#troubleshooting-and-faq" title="Link to this heading">#</a></h2>
|
||
<section id="general-faqs">
|
||
<h3>General FAQs<a class="headerlink" href="#general-faqs" title="Link to this heading">#</a></h3>
|
||
<p><em>Q. What are the limitations of disaggregated-service in TRT-LLM?</em></p>
|
||
<p>A. Currently, only <code class="docutils literal notranslate"><span class="pre">decoder-only</span> <span class="pre">engine</span></code> and <code class="docutils literal notranslate"><span class="pre">beamWidth=1</span></code> are supported, and the kvCache at each layer of the model is required to be homogeneous, with the same data type and the same number of attention headers.</p>
|
||
<p><em>Q. Is the engine used by disaggregated-service different from other engines?</em></p>
|
||
<p>A. No. There are no special requirements for the arguments to build engine.</p>
|
||
<p><em>Q. Do the engines used by the context executor and generation executor need to be the same?</em></p>
|
||
<p>A. No. The engines used by context executor and generation executor can be different, and their parallelism can be heterogeneous, i.e., TP,PP can be different, and TRT-LLM will handle the heterogeneity of kvCache.</p>
|
||
<p><em>Q. Does TRT-LLM support running multiple context executor instances and generation executor instances?</em></p>
|
||
<p>A. Yes. TRT-LLM supports running multiple context executors and generation executors at the same time, and each executor can use different engine, but it is the user’s responsibility to route requests to different executors and manage <code class="docutils literal notranslate"><span class="pre">requestId</span></code>.</p>
|
||
<p><em>Q. Can an executor handle both context-only requests and generation-only requests?</em></p>
|
||
<p>A. Yes, but it’s not recommended, TRT-LLM does not implement proper scheduling for the case where the executor handles mixed context-only requests and generation-only requests, it’s better to run context-only requests and generation-only requests on different executors.</p>
|
||
<p><em>Q. Does disaggregated-service in TRT-LLM support multi-gpu and multi-node?</em></p>
|
||
<p>A. Yes, it’s recommended that different executor use different GPUs . We support context-only executor and genertion-only executor run on same node or different nodes. The <code class="docutils literal notranslate"><span class="pre">participantIds</span></code> and <code class="docutils literal notranslate"><span class="pre">deviceIds</span></code> used by each executor need to be explicitly set by the user, and the <code class="docutils literal notranslate"><span class="pre">participantIds</span></code> of each executor must not be intersecting.</p>
|
||
<p><em>Q. What’s the requirement for disaggregated-service in TRT-LLM?</em></p>
|
||
<p>A. TRT-LLM requires <code class="docutils literal notranslate"><span class="pre">UCX</span></code>-backend <code class="docutils literal notranslate"><span class="pre">CUDA-aware</span> <span class="pre">MPI</span></code> currently, TRT-LLM implements kvCache transfer with <a class="reference external" href="https://docs.open-mpi.org/en/v5.0.x/tuning-apps/networking/cuda.html#how-do-i-build-open-mpi-with-cuda-aware-support"><code class="docutils literal notranslate"><span class="pre">CUDA-aware</span> <span class="pre">MPI</span></code></a>, and will support more communication components for kvCache transfer in future version.</p>
|
||
</section>
|
||
<section id="debugging-faqs">
|
||
<h3>Debugging FAQs<a class="headerlink" href="#debugging-faqs" title="Link to this heading">#</a></h3>
|
||
<p><em>Q. How to handle error <code class="docutils literal notranslate"><span class="pre">Disaggregated</span> <span class="pre">serving</span> <span class="pre">is</span> <span class="pre">not</span> <span class="pre">enabled,</span> <span class="pre">please</span> <span class="pre">check</span> <span class="pre">the</span> <span class="pre">configuration?</span></code></em></p>
|
||
<p>A. please set env</p>
|
||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">TRTLLM_USE_MPI_KVCACHE</span><span class="o">=</span><span class="mi">1</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><em>Q. Why do some profiling tools show that TRT-LLM’s kvCache transfer does not utilize NVLink even on devices equipped with NVLink?</em></p>
|
||
<p>A. Ensure TRT-LLM is running with <code class="docutils literal notranslate"><span class="pre">UCX</span></code>-backend <code class="docutils literal notranslate"><span class="pre">CUDA-aware</span> <span class="pre">MPI</span></code> , and check version of <code class="docutils literal notranslate"><span class="pre">UCX</span></code> with <code class="docutils literal notranslate"><span class="pre">ucx_info</span> <span class="pre">-v</span></code>.
|
||
If version of UCX <=1.17, set env <code class="docutils literal notranslate"><span class="pre">UCX_RNDV_FRAG_MEM_TYPE=cuda</span></code> and <code class="docutils literal notranslate"><span class="pre">UCX_MEMTYPE_CACHE=n</span></code> to enable NVLink.
|
||
If version of UCX =1.18, set env <code class="docutils literal notranslate"><span class="pre">UCX_CUDA_COPY_ASYNC_MEM_TYPE=cuda</span></code>, <code class="docutils literal notranslate"><span class="pre">UCX_CUDA_COPY_DMABUF=no</span></code> and <code class="docutils literal notranslate"><span class="pre">UCX_MEMTYPE_CACHE=n</span></code>.</p>
|
||
</section>
|
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</section>
|
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