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5e2cf02f46
TensorRT-LLMs/architecture/add-model.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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<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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<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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<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>
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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.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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<li class="toctree-l1"><a class="reference internal" href="../_cpp_gen/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>
<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-serve.html">trtllm-serve</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Architecture</span></p>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">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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<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>
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<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>
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<section id="adding-a-model">
<span id="add-model"></span><h1>Adding a Model<a class="headerlink" href="#adding-a-model" title="Link to this heading">#</a></h1>
<p>This document describes how to add a typical decoder-only model in TensorRT-LLM.</p>
<section id="step-1-write-modeling-part">
<h2>Step 1. Write Modeling Part<a class="headerlink" href="#step-1-write-modeling-part" title="Link to this heading">#</a></h2>
<p>TensorRT-LLM provides different levels of APIs:</p>
<ul class="simple">
<li><p>Low-level functions, for example, <code class="docutils literal notranslate"><span class="pre">concat</span></code>, <code class="docutils literal notranslate"><span class="pre">add</span></code>, and <code class="docutils literal notranslate"><span class="pre">sum</span></code>.</p></li>
<li><p>Basic layers, such as, <code class="docutils literal notranslate"><span class="pre">Linear</span></code> and <code class="docutils literal notranslate"><span class="pre">LayerNorm</span></code>.</p></li>
<li><p>High-level layers, such as, <code class="docutils literal notranslate"><span class="pre">MLP</span></code> and <code class="docutils literal notranslate"><span class="pre">Attention</span></code>.</p></li>
<li><p>Base class for typical decoder-only models, such as, <code class="docutils literal notranslate"><span class="pre">DecoderModelForCausalLM</span></code>.</p></li>
</ul>
<ol class="arabic simple">
<li><p>Create a model directory in <code class="docutils literal notranslate"><span class="pre">tensorrt_llm/models</span></code>, for example <code class="docutils literal notranslate"><span class="pre">my_model</span></code>.</p></li>
<li><p>Write a <code class="docutils literal notranslate"><span class="pre">model.py</span></code> with TensorRT-LLM’s APIs</p></li>
</ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">MyDecoderLayer</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">PretrainedConfig</span><span class="p">,</span> <span class="n">layer_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layer_idx</span> <span class="o">=</span> <span class="n">layer_idx</span>
<span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_layernorm</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">attention</span> <span class="o">=</span> <span class="n">Attention</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">post_layernorm</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mlp</span> <span class="o">=</span> <span class="n">MLP</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="o">...</span><span class="p">):</span>
<span class="c1"># decoder layer forward</span>
<span class="k">return</span> <span class="n">hidden_states</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MyModel</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">PretrainedConfig</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vocab_embedding</span> <span class="o">=</span> <span class="n">Embedding</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">DecoderLayerList</span><span class="p">(</span><span class="n">MyDecoderLayer</span><span class="p">,</span> <span class="n">config</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ln_f</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_ids</span><span class="p">,</span> <span class="o">...</span><span class="p">):</span>
<span class="c1"># model forward</span>
<span class="k">return</span> <span class="n">hidden_states</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MyModelForCausalLM</span><span class="p">(</span><span class="n">DecoderModelForCausalLM</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">PretrainedConfig</span><span class="p">):</span>
<span class="n">transformer</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="n">lm_head</span> <span class="o">=</span> <span class="n">ColumnLinear</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">transformer</span><span class="p">,</span> <span class="n">lm_head</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="step-2-implement-weight-conversion">
<h2>Step 2. Implement Weight Conversion<a class="headerlink" href="#step-2-implement-weight-conversion" title="Link to this heading">#</a></h2>
<p>The weights from source framework need to be converted and bound to the new added TensorRT-LLM model. Here is an example of converting HuggingFace weights:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">MyModelForCausalLM</span><span class="p">(</span><span class="n">DecoderModelForCausalLM</span><span class="p">):</span>
<span class="nd">@classmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_hugging_face</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">hf_model_dir</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float16&#39;</span><span class="p">,</span>
<span class="n">mapping</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Mapping</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">MyModelForCausalLM</span>
<span class="c1"># create a TensorRT-LLM MyModelForCausalLM model object</span>
<span class="c1"># convert HuggingFace checkpoint to TensorRT-LLM expected weights dict</span>
<span class="c1"># load the weights to MyModelForCausalLM object</span>
</pre></div>
</div>
<p>It’s optional to develop a <code class="docutils literal notranslate"><span class="pre">convert_checkpoint.py</span></code> script in the <code class="docutils literal notranslate"><span class="pre">examples/my_model/</span></code> directory for the convenience of offline weights conversion.</p>
</section>
<section id="step-3-register-new-model">
<h2>Step 3. Register New Model<a class="headerlink" href="#step-3-register-new-model" title="Link to this heading">#</a></h2>
<p>Please register the new model class <code class="docutils literal notranslate"><span class="pre">MyModelForCausalLM</span></code> in <code class="docutils literal notranslate"><span class="pre">tensorrt_llm/models/__init__.py</span></code>.</p>
</section>
<section id="step-4-verify-new-model">
<h2>Step 4. Verify New Model<a class="headerlink" href="#step-4-verify-new-model" title="Link to this heading">#</a></h2>
<p>At last, let’s verify the new model. The typical commands are as following:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>examples/my_model/
python<span class="w"> </span>convert_checkpoint.py<span class="w"> </span>--model_dir<span class="w"> </span>hf_model_dir<span class="w"> </span>--output_dir<span class="w"> </span>tllm_ckpt_dir
trtllm-build<span class="w"> </span>--checkpoint_dir<span class="w"> </span>tllm_ckpt_dir<span class="w"> </span>--output_dir<span class="w"> </span>tllm_engine_dir
<span class="c1"># try the model with a single prompt</span>
python<span class="w"> </span>../run.py<span class="w"> </span>--engine_dir<span class="w"> </span>tllm_engine_dir<span class="w"> </span>--tokenizer_dir<span class="w"> </span>hf_model_dir<span class="w"> </span>--input_text<span class="w"> </span><span class="s2">&quot;Born in north-east France, Soyer trained as a&quot;</span>
<span class="c1"># run summarization task</span>
python<span class="w"> </span>../summarize.py<span class="w"> </span>--engine_dir<span class="w"> </span>tllm_engine_dir<span class="w"> </span>--hf_model_dir<span class="w"> </span>hf_model_dir<span class="w"> </span>--test_trt_llm
</pre></div>
</div>
</section>
<section id="reference">
<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">#</a></h2>
<p>It’s recommended to read the workflow[./workflow.md] and checkpoint[./checkpoint.md] documents for more details.</p>
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