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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="../commands/trtllm-build.html">trtllm-build</a></li>
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<p class="caption" role="heading"><span class="caption-text">Architecture</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/overview.html">TensorRT-LLM Architecture</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/core-concepts.html">Model Definition</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/core-concepts.html#multi-gpu-and-multi-node-support">Multi-GPU and Multi-Node Support</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/checkpoint.html">TensorRT-LLM Checkpoint</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/workflow.html">TensorRT-LLM Build Workflow</a></li>
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<p class="caption" role="heading"><span class="caption-text">Advanced</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/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="../advanced/gpt-runtime.html">C++ GPT Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/executor.html">Executor API</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/graph-rewriting.html">Graph Rewriting Module</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/inference-request.html">Inference Request</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/inference-request.html#responses">Responses</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/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="../advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/kv-cache-reuse.html">KV cache reuse</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/speculative-decoding.html">Speculative Sampling</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/disaggregated-service.html">Disaggregated-Service (experimental)</a></li>
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<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="../performance/perf-overview.html">Overview</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"><a class="reference internal" href="../performance/perf-best-practices.html">Best Practices</a></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 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/support-matrix.html">Support Matrix</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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</ul>
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<p class="caption" role="heading"><span class="caption-text">Blogs</span></p>
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<ul>
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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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<section id="installing-on-linux">
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<span id="linux"></span><h1>Installing on Linux<a class="headerlink" href="#installing-on-linux" title="Link to this heading"></a></h1>
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<ol class="arabic">
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<li><p>Install TensorRT-LLM (tested on Ubuntu 22.04).</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>apt-get<span class="w"> </span>-y<span class="w"> </span>install<span class="w"> </span>libopenmpi-dev<span class="w"> </span><span class="o">&&</span><span class="w"> </span>pip3<span class="w"> </span>install<span class="w"> </span>tensorrt_llm
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</pre></div>
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</div>
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</li>
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<li><p>Sanity check the installation by running the following in Python (tested on Python 3.10):</p>
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<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm</span><span class="w"> </span><span class="kn">import</span> <span class="n">LLM</span><span class="p">,</span> <span class="n">SamplingParams</span>
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<span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
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<span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
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<span class="s2">"Hello, my name is"</span><span class="p">,</span>
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<span class="s2">"The president of the United States is"</span><span class="p">,</span>
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<span class="s2">"The capital of France is"</span><span class="p">,</span>
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<span class="s2">"The future of AI is"</span><span class="p">,</span>
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<span class="p">]</span>
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<span class="n">sampling_params</span> <span class="o">=</span> <span class="n">SamplingParams</span><span class="p">(</span><span class="n">temperature</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">top_p</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
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<span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"TinyLlama/TinyLlama-1.1B-Chat-v1.0"</span><span class="p">)</span>
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<span class="n">outputs</span> <span class="o">=</span> <span class="n">llm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">prompts</span><span class="p">,</span> <span class="n">sampling_params</span><span class="p">)</span>
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<span class="c1"># Print the outputs.</span>
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<span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">:</span>
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<span class="n">prompt</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">prompt</span>
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<span class="n">generated_text</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span>
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<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Prompt: </span><span class="si">{</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">, Generated text: </span><span class="si">{</span><span class="n">generated_text</span><span class="si">!r}</span><span class="s2">"</span><span class="p">)</span>
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<span class="c1"># The entry point of the program need to be protected for spawning processes.</span>
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<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
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<span class="n">main</span><span class="p">()</span>
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</pre></div>
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</div>
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</li>
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</ol>
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<p><strong>Known limitations</strong></p>
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<p>There are some known limitations when you pip install pre-built TensorRT-LLM wheel package.</p>
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<ol class="arabic">
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<li><p>C++11 ABI</p>
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<p>The pre-built TensorRT-LLM wheel has linked against the public pytorch hosted on pypi, which turned off C++11 ABI.
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While the NVIDIA optimized pytorch inside NGC container nvcr.io/nvidia/pytorch:xx.xx-py3 turned on the C++11 ABI,
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see <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">NGC pytorch container page</a> .
|
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Thus we recommend users to build from source inside when using the NGC pytorch container. Build from source guideline can be found in
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<a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/installation/build-from-source-linux.html">Build from Source Code on Linux</a></p>
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</li>
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<li><p>MPI in the Slurm environment</p>
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<p>If you encounter an error while running TensorRT-LLM in a Slurm-managed cluster, you need to reconfigure the MPI installation to work with Slurm.
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The setup methods depends on your slurm configuration, pls check with your admin. This is not a TensorRT-LLM specific, rather a general mpi+slurm issue.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">The</span> <span class="n">application</span> <span class="n">appears</span> <span class="n">to</span> <span class="n">have</span> <span class="n">been</span> <span class="n">direct</span> <span class="n">launched</span> <span class="n">using</span> <span class="s2">"srun"</span><span class="p">,</span>
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<span class="n">but</span> <span class="n">OMPI</span> <span class="n">was</span> <span class="ow">not</span> <span class="n">built</span> <span class="k">with</span> <span class="n">SLURM</span> <span class="n">support</span><span class="o">.</span> <span class="n">This</span> <span class="n">usually</span> <span class="n">happens</span>
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<span class="n">when</span> <span class="n">OMPI</span> <span class="n">was</span> <span class="ow">not</span> <span class="n">configured</span> <span class="o">--</span><span class="k">with</span><span class="o">-</span><span class="n">slurm</span> <span class="ow">and</span> <span class="n">we</span> <span class="n">weren</span><span class="s1">'t able</span>
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<span class="n">to</span> <span class="n">discover</span> <span class="n">a</span> <span class="n">SLURM</span> <span class="n">installation</span> <span class="ow">in</span> <span class="n">the</span> <span class="n">usual</span> <span class="n">places</span><span class="o">.</span>
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</pre></div>
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</div>
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</li>
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<li><p>CUDA Toolkit</p>
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<p><code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">tensorrt-llm</span></code> won’t install CUDA toolkit in your system, and the CUDA Toolkit is not required if want to just deploy a TensorRT-LLM engine.
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TensorRT-LLM uses the <a class="reference external" href="https://nvidia.github.io/TensorRT-Model-Optimizer/">ModelOpt</a> to quantize a model, while the ModelOpt requires CUDA toolkit to jit compile certain kernels which is not included in the pytorch to do quantization effectively.
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Please install CUDA toolkit when you see the following message when running ModelOpt quantization.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">/</span><span class="n">usr</span><span class="o">/</span><span class="n">local</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="mf">.10</span><span class="o">/</span><span class="n">dist</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">modelopt</span><span class="o">/</span><span class="n">torch</span><span class="o">/</span><span class="n">utils</span><span class="o">/</span><span class="n">cpp_extension</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">65</span><span class="p">:</span>
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<span class="ne">UserWarning</span><span class="p">:</span> <span class="n">CUDA_HOME</span> <span class="n">environment</span> <span class="n">variable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="nb">set</span><span class="o">.</span> <span class="n">Please</span> <span class="nb">set</span> <span class="n">it</span> <span class="n">to</span> <span class="n">your</span> <span class="n">CUDA</span> <span class="n">install</span> <span class="n">root</span><span class="o">.</span>
|
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<span class="n">Unable</span> <span class="n">to</span> <span class="n">load</span> <span class="n">extension</span> <span class="n">modelopt_cuda_ext</span> <span class="ow">and</span> <span class="n">falling</span> <span class="n">back</span> <span class="n">to</span> <span class="n">CPU</span> <span class="n">version</span><span class="o">.</span>
|
||
</pre></div>
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<p>The installation of CUDA toolkit can be found in <a class="reference external" href="https://docs.nvidia.com/cuda/">CUDA Toolkit Documentation</a></p>
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