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5e2cf02f46
TensorRT-LLMs/torch/arch_overview.html
Shi Xiaowei 5e2cf02f46
Update gh-pages (#4284) 
update docs for 0.20.0rc2

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
2025-05-14 11:12:52 +08:00

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<div class="bd-toc-item navbar-nav"><p aria-level="2" class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../overview.html">Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../quick-start-guide.html">Quick Start Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="../key-features.html">Key Features</a></li>
<li class="toctree-l1"><a class="reference internal" href="../torch.html">PyTorch Backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../release-notes.html">Release Notes</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Installation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../installation/linux.html">Installing on Linux</a></li>
<li class="toctree-l1"><a class="reference internal" href="../installation/build-from-source-linux.html">Building from Source Code on Linux</a></li>
<li class="toctree-l1"><a class="reference internal" href="../installation/grace-hopper.html">Installing on Grace Hopper</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api/index.html">API Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="../llm-api/reference.html">API Reference</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Examples</span></p>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../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>
<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>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../examples/customization.html">LLM Common Customizations</a></li>
<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>
<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>
</details></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_chat_client.html">Curl Chat Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_chat_client_for_multimodal.html">Curl Chat Client For Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_completion_client.html">Curl Completion Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client.html">Genai Perf Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client.html">OpenAI Completion Client</a></li>
</ul>
</details></li>
</ul>
<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.layers.html">Layers</a></li>
<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.functional.html">Functionals</a></li>
<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>
<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.runtime.html">Runtime</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">C++ API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_gen/executor.html">Executor</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_gen/runtime.html">Runtime</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Command-Line Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-build.html">trtllm-build</a></li>
<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-serve.html">trtllm-serve</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Architecture</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../architecture/overview.html">TensorRT-LLM Architecture</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/core-concepts.html">Model Definition</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/checkpoint.html">TensorRT-LLM Checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/workflow.html">TensorRT-LLM Build Workflow</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/add-model.html">Adding a Model</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-runtime.html">C++ GPT Runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/executor.html">Executor API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/graph-rewriting.html">Graph Rewriting Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/lora.html">Run gpt-2b + LoRA using Executor / cpp runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/kv-cache-reuse.html">KV cache reuse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/speculative-decoding.html">Speculative Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/disaggregated-service.html">Disaggregated-Service (experimental)</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Performance</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../performance/perf-overview.html">Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../performance/perf-benchmarking.html">Benchmarking</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/benchmarking-default-performance.html">Benchmarking Default Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-build-time-flags.html">Useful Build-Time Flags</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/deciding-model-sharding-strategy.html">Deciding Model Sharding Strategy</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/fp8-quantization.html">FP8 Quantization</a></li>
<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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<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>
<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>
<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>
<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>
<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="architecture-ovewiew">
<h1>Architecture Ovewiew<a class="headerlink" href="#architecture-ovewiew" title="Link to this heading">#</a></h1>
<p>TensorRT-LLM is a toolkit designed to create optimized solutions for Large Language Model (LLM) inference.
Besides TensorRT, PyTorch can also serve as the backend for TensorRT-LLM. This document provides an overview of the PyTorch Backend architecture.</p>
<section id="top-level-api">
<h2>Top Level API<a class="headerlink" href="#top-level-api" title="Link to this heading">#</a></h2>
<p>The interface for PyTorch backend is <code class="docutils literal notranslate"><span class="pre">tensorrt._torch.LLM</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm._torch</span><span class="w"> </span><span class="kn">import</span> <span class="n">LLM</span>
<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">=&lt;</span><span class="n">path_to_llama_from_hf</span><span class="o">&gt;</span><span class="p">)</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">LLM</span></code> also manages the tokenization and detokenization processes of the input.</p>
</section>
<section id="pyexecutor">
<h2>PyExecutor<a class="headerlink" href="#pyexecutor" title="Link to this heading">#</a></h2>
<p>Similar to the TensorRT backend, which uses <a class="reference internal" href="../advanced/executor.html"><span class="std std-doc">Executor API</span></a>, the PyTorch backend employs a <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code> class.
This class has a similar interface to Executor, allowing it to be integrated into LLM as an alternative backend.
Key components of the <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code> include:</p>
<ul class="simple">
<li><p>Model Engine: Holds the language model and efficiently supports single-step model forward.</p></li>
<li><p>Decoder: Generates output tokens based on Model Engine outputs. Currently, only greedy search is supported.</p></li>
<li><p>Scheduler: Decides whether to allocate resources (like KV Cache) for a request and whether to run forward for each request at the current step.</p></li>
</ul>
<p>The single-step flow of PyExecutor involves:</p>
<ul class="simple">
<li><p>Fetching new requests from the request queue, if any.</p></li>
<li><p>Scheduling some requests.</p></li>
<li><p>Running model forward for scheduled requests.</p></li>
<li><p>Running the decoder using the model forward outputs for the scheduled requests.</p></li>
<li><p>Adding output tokens for each request and handling finished requests.</p></li>
</ul>
</section>
<section id="model-engine">
<h2>Model Engine<a class="headerlink" href="#model-engine" title="Link to this heading">#</a></h2>
<p>The core component of <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code> is the <code class="docutils literal notranslate"><span class="pre">ModelEngine</span></code>, responsible for executing the model’s forward pass efficiently on the GPU.
The key method of <code class="docutils literal notranslate"><span class="pre">ModelEngine</span></code> is <code class="docutils literal notranslate"><span class="pre">forward</span></code>, which handles the forward pass computation.
For the PyTorch backend, the derived class is <code class="docutils literal notranslate"><span class="pre">PyTorchModelEngine</span></code>, declared in <a class="reference internal" href="#../../../tensorrt_llm/_torch/pyexecutor/pytorch_model_engine.py"><span class="xref myst">pytorch_model_engine.py</span></a>.</p>
</section>
<section id="decoder">
<h2>Decoder<a class="headerlink" href="#decoder" title="Link to this heading">#</a></h2>
<p>The Decoder generates output tokens based on Model Engine outputs and supports greedy search decoding.</p>
</section>
<section id="scheduler">
<h2>Scheduler<a class="headerlink" href="#scheduler" title="Link to this heading">#</a></h2>
<p>The scheduler operates in two steps:</p>
<ol class="arabic simple">
<li><p>CapacityScheduler: Determines if there are enough resources to accommodate a request.</p></li>
<li><p>MicroBatchScheduler: Selects some requests for the model to run forward.</p></li>
</ol>
<p>Both CapacityScheduler and MicroBatchScheduler currently use C++ bindings.
However, since the interfaces are implemented in Python, customization is possible.
The document <a class="reference internal" href="scheduler.html"><span class="std std-doc">scheduler.md</span></a> explains how to implement customized scheduling logic.</p>
</section>
<section id="resourcemanager">
<h2>ResourceManager<a class="headerlink" href="#resourcemanager" title="Link to this heading">#</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">ResourceManager</span></code> helps allocate and manage these resources that may be needed to run inference for a single request.
It is a container of objects inherited from <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code>, each managing a specific type of resource.
There are three important interfaces for <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code>:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">prepare_resources</span></code>: Called at each step before model forward in PyExecutor for the current batch.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">update_resources</span></code>: Called at each step finish for the current batch.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">free_resources</span></code>: Called at each request finish.</p></li>
</ul>
<p>One crucial resource is the KV Cache for transformer models. The <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code> for KV Cache is <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>.</p>
<section id="kvcachemanager">
<h3>KVCacheManager<a class="headerlink" href="#kvcachemanager" title="Link to this heading">#</a></h3>
<p>Currently, the KVCacheManager uses C++ binding. However, customization in Python is possible, as its interface is implemented in Python.
The document <a class="reference internal" href="kv_cache_manager.html"><span class="std std-doc">kv_cache_manager.md</span></a> details how to implement a customized KVCacheManager.</p>
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