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<li class="toctree-l1"><a class="reference internal" href="architecture.html">TensorRT-LLM Architecture</a></li>
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<li class="toctree-l1"><a class="reference internal" href="performance.html">Performance of TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="2023-05-19-how-to-debug.html">How to debug</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"><a class="reference internal" href="perf_best_practices.html">Best Practices for Tuning the Performance of TensorRT-LLM</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Performance Analysis of TensorRT-LLM</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="#feature-descriptions">Feature Descriptions</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#inference-time-command-line-options">Inference Time Command Line Options</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#inference-time-environment-variables">Inference Time Environment Variables</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#coordinating-with-nvidia-nsight-systems-launch">Coordinating with NVIDIA Nsight Systems Launch</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#examples">Examples</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#profiling-a-single-ifb-iteration-executing-on-a-single-rank-of-a-multi-gpu-model">Profiling a single IFB iteration executing on a single rank of a multi-GPU model</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="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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<section id="performance-analysis-of-tensorrt-llm">
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<h1>Performance Analysis of TensorRT-LLM<a class="headerlink" href="#performance-analysis-of-tensorrt-llm" title="Link to this heading"></a></h1>
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<p>NVIDIA Nsight Systems reports at the application level are highly informative. Metric sampling capabilities have increased over generations and provide a clean middle-ground between timing analysis and kernel-level deep dives with NVIDIA Nsight Compute.</p>
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<p>Given the potential long runtimes of Large Languages Models (LLMs) and the diversity of workloads a model may experience during a single inference pass or binary execution, we have added features to TensorRT-LLM to get the most out of Nsight Systems capabilities. This document outlines those features as well as provides examples of how to best utilize them to understand your application.</p>
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<section id="feature-descriptions">
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<h2>Feature Descriptions<a class="headerlink" href="#feature-descriptions" title="Link to this heading"></a></h2>
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<p>The main functionality here:</p>
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<ul class="simple">
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<li><p>Relies on toggling the CUDA profiler runtime API on and off.</p></li>
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<li><p>Provides a means to understand which regions a user may want to focus on.</p></li>
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</ul>
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<p>Toggling the CUDA profiler runtime API on and off:</p>
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<ul class="simple">
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<li><p>Allows users to know specifically what the profiled region corresponds to.</p></li>
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<li><p>Results in smaller files to post-process (for metric extraction or similar).</p></li>
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</ul>
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</section>
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<section id="usage">
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<h2>Usage<a class="headerlink" href="#usage" title="Link to this heading"></a></h2>
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<section id="inference-time-command-line-options">
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<h3>Inference Time Command Line Options<a class="headerlink" href="#inference-time-command-line-options" title="Link to this heading"></a></h3>
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<ul class="simple">
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<li><p><code class="docutils literal notranslate"><span class="pre">--log_iteration_data</span></code>, for use with gptManagerBenchmark. The runtime decides the specifics of each decoder iteration launch. This option prints to stdout metadata on each decoder iteration:</p></li>
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</ul>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">TensorRT</span><span class="o">-</span><span class="n">LLM</span><span class="p">][</span><span class="n">INFO</span><span class="p">]</span> <span class="p">{</span><span class="s2">"Active Request Count"</span><span class="p">:</span><span class="mi">249</span><span class="p">,</span><span class="s2">"Context Requests"</span><span class="p">:</span><span class="mi">8</span><span class="p">,</span><span class="s2">"Free KV cache blocks"</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s2">"Generation Requests"</span><span class="p">:</span><span class="mi">231</span><span class="p">,</span><span class="s2">"Iteration Counter"</span><span class="p">:</span><span class="mi">90</span><span class="p">,</span><span class="s2">"Max KV cache blocks"</span><span class="p">:</span><span class="mi">2448</span><span class="p">,</span><span class="s2">"Max Request Count"</span><span class="p">:</span><span class="mi">256</span><span class="p">,</span><span class="s2">"MicroBatch ID"</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s2">"Runtime CPU Memory Usage"</span><span class="p">:</span><span class="mi">28784</span><span class="p">,</span><span class="s2">"Runtime GPU Memory Usage"</span><span class="p">:</span><span class="mi">540173600</span><span class="p">,</span><span class="s2">"Runtime Pinned Memory Usage"</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s2">"Scheduled Requests"</span><span class="p">:</span><span class="mi">239</span><span class="p">,</span><span class="s2">"Timestamp"</span><span class="p">:</span><span class="s2">"12-13-2023 14:55:14"</span><span class="p">,</span><span class="s2">"Tokens per KV cache block"</span><span class="p">:</span><span class="mi">128</span><span class="p">,</span><span class="s2">"Total Context Tokens"</span><span class="p">:</span><span class="mi">6904</span><span class="p">,</span><span class="s2">"Used KV cache blocks"</span><span class="p">:</span><span class="mi">2448</span><span class="p">}</span>
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</pre></div>
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</div>
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</section>
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<section id="inference-time-environment-variables">
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<h3>Inference Time Environment Variables<a class="headerlink" href="#inference-time-environment-variables" title="Link to this heading"></a></h3>
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<ul class="simple">
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<li><p><code class="docutils literal notranslate"><span class="pre">TLLM_GPTM_PROFILE_START_STOP</span></code>, a csv of iterations to trigger start/stop for gptManagerBenchmark (corresponds to “Iteration Counter” in output above</p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">TLLM_GPTS_PROFILE_START_STOP</span></code>, a csv of static batching iteration indexes to trigger start/stop for gptSessionBenchmark</p></li>
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</ul>
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</section>
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</section>
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<section id="coordinating-with-nvidia-nsight-systems-launch">
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<h2>Coordinating with NVIDIA Nsight Systems Launch<a class="headerlink" href="#coordinating-with-nvidia-nsight-systems-launch" title="Link to this heading"></a></h2>
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<p>Consult the Nsight Systems User Guide for full overview of options.</p>
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<p>Say we want to profile the context phase and the first output token computation of a model with gptSessionBenchmark.</p>
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<p>To profile just those iterations, in addition to setting <code class="docutils literal notranslate"><span class="pre">TLLM_GPTS_PROFILE_START_STOP="0,1"</span></code>:</p>
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<ul class="simple">
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<li><p>We need to tell Nsight Systems to look for explicit API triggers to profile (<code class="docutils literal notranslate"><span class="pre">-c</span> <span class="pre">cudaProfilerApi</span></code>)</p></li>
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<li><p>We need to tell Nsight Systems to keep profiling after seeing a profile stop API call (<code class="docutils literal notranslate"><span class="pre">--capture-range-end="repeat[]"</span></code>)</p></li>
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</ul>
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</section>
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<section id="examples">
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<h2>Examples<a class="headerlink" href="#examples" title="Link to this heading"></a></h2>
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<p>Consult the Nsight Systems User Guide for full overview of MPI-related options.</p>
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</section>
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<section id="profiling-a-single-ifb-iteration-executing-on-a-single-rank-of-a-multi-gpu-model">
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<h2>Profiling a single IFB iteration executing on a single rank of a multi-GPU model<a class="headerlink" href="#profiling-a-single-ifb-iteration-executing-on-a-single-rank-of-a-multi-gpu-model" title="Link to this heading"></a></h2>
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<p>Say we have run once using <code class="docutils literal notranslate"><span class="pre">--log_iteration_data</span></code> and want to analyze iterations 0, 63 and 127 based on the metadata output. We also want to capture metrics at an increased resolution. To do this we create a bash file as describe in the Nsight Systems User Guide:</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="ch">#!/bin/bash</span>
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<span class="c1"># Use $PMI_RANK for MPICH and $SLURM_PROCID with srun.</span>
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<span class="k">if</span><span class="w"> </span><span class="o">[</span><span class="w"> </span><span class="nv">$OMPI_COMM_WORLD_LOCAL_RANK</span><span class="w"> </span>-eq<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="o">]</span><span class="p">;</span><span class="w"> </span><span class="k">then</span>
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<span class="w"> </span>nsys<span class="w"> </span>profile<span class="w"> </span>-e<span class="w"> </span><span class="s2">"NSYS_MPI_STORE_TEAMS_PER_RANK=1"</span><span class="w"> </span>-t<span class="w"> </span>cuda,nvtx<span class="w"> </span>--gpu-metrics-device<span class="o">=</span><span class="si">${</span><span class="nv">OMPI_COMM_WORLD_LOCAL_RANK</span><span class="si">}</span><span class="w"> </span>-c<span class="w"> </span>cudaProfilerApi<span class="w"> </span>--capture-range-end<span class="o">=</span><span class="s2">"repeat[]"</span><span class="w"> </span>--gpu-metrics-frequency<span class="o">=</span><span class="m">100000</span><span class="w"> </span><span class="s2">"</span><span class="nv">$@</span><span class="s2">"</span>
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<span class="k">else</span>
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<span class="w"> </span><span class="s2">"</span><span class="nv">$@</span><span class="s2">"</span>
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<span class="k">fi</span>
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</pre></div>
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</div>
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<p>We name this file <code class="docutils literal notranslate"><span class="pre">profile_rank_0.bash</span></code> and then launch our application specifying the iterations to capture:</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mpirun<span class="w"> </span>-n<span class="w"> </span><span class="m">2</span><span class="w"> </span>env<span class="w"> </span><span class="nv">TLLM_GPTM_PROFILE_START_STOP</span><span class="o">=</span><span class="s2">"0,63,127"</span><span class="w"> </span>./profile_rank_0.bash<span class="w"> </span>./benchmarks/gptManagerBenchmark<span class="w"> </span><benchmark/model<span class="w"> </span>options>
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</pre></div>
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