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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>
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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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<li class="toctree-l1"><a class="reference internal" href="../../../llm-api/index.html">API Introduction</a></li>
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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_medusa_decoding.html">Generate Text Using Medusa Decoding</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_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
<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_distributed.html">Distributed LLM Generation</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_eagle2_decoding.html">Generate Text Using Eagle2 Decoding</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_lookahead_decoding.html">Generate Text Using Lookahead Decoding</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_async_streaming.html">Generate Text in Streaming</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_inference.html">Generate text</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_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>
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<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_medusa_decoding.html">Generate Text Using Medusa Decoding</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_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
<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_distributed.html">Distributed LLM Generation</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_eagle2_decoding.html">Generate Text Using Eagle2 Decoding</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_lookahead_decoding.html">Generate Text Using Lookahead Decoding</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_async_streaming.html">Generate Text in Streaming</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_inference.html">Generate text</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_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>
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</ul>
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<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>
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<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>
<li class="toctree-l2"><a class="reference internal" href="../../../examples/genai_perf_client_for_multimodal.html">Genai Perf Client For Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../../examples/openai_chat_client.html">OpenAI Chat Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../../examples/openai_chat_client_for_multimodal.html">OpenAI Chat Client</a></li>
<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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<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/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>
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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"><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/checkpoint.html">TensorRT-LLM Checkpoint</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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<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/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-management.html">KV Cache Management: Pools, Blocks, and Events</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>
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<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>
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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>
<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>
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<h1>Source code for tensorrt_llm.llmapi.llm</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">atexit</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">json</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tempfile</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">weakref</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">transformers</span><span class="w"> </span><span class="kn">import</span> <span class="n">PreTrainedTokenizerBase</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.builder</span><span class="w"> </span><span class="kn">import</span> <span class="n">BuildConfig</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.inputs.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">TextPrompt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.inputs.registry</span><span class="w"> </span><span class="kn">import</span> <span class="n">DefaultInputProcessor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..</span><span class="w"> </span><span class="kn">import</span> <span class="n">bindings</span> <span class="k">as</span> <span class="n">tllm</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.._utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">nvtx_range_debug</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..bindings</span><span class="w"> </span><span class="kn">import</span> <span class="n">executor</span> <span class="k">as</span> <span class="n">tllm</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..builder</span><span class="w"> </span><span class="kn">import</span> <span class="n">EngineConfig</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..disaggregated_params</span><span class="w"> </span><span class="kn">import</span> <span class="n">DisaggregatedParams</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..executor</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">DetokenizedGenerationResultBase</span><span class="p">,</span> <span class="n">GenerationExecutor</span><span class="p">,</span>
<span class="n">GenerationResult</span><span class="p">,</span> <span class="n">IterationResult</span><span class="p">,</span> <span class="n">LoRARequest</span><span class="p">,</span>
<span class="n">PostprocWorkerConfig</span><span class="p">,</span> <span class="n">PromptAdapterRequest</span><span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..executor.postproc_worker</span><span class="w"> </span><span class="kn">import</span> <span class="n">PostprocParams</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..executor.utils</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">create_mpi_comm_session</span><span class="p">,</span>
<span class="n">get_spawn_proxy_process_env</span><span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..inputs</span><span class="w"> </span><span class="kn">import</span> <span class="n">PromptInputs</span><span class="p">,</span> <span class="n">create_input_processor</span><span class="p">,</span> <span class="n">prompt_inputs</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..logger</span><span class="w"> </span><span class="kn">import</span> <span class="n">logger</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..sampling_params</span><span class="w"> </span><span class="kn">import</span> <span class="n">SamplingParams</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.llm_args</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">LLMARGS_EXPLICIT_DOCSTRING</span><span class="p">,</span> <span class="n">PybindMirror</span><span class="p">,</span> <span class="n">TorchLlmArgs</span><span class="p">,</span>
<span class="n">TrtLlmArgs</span><span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.llm_utils</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">CachedModelLoader</span><span class="p">,</span> <span class="n">KvCacheRetentionConfig</span><span class="p">,</span>
<span class="n">LlmBuildStats</span><span class="p">,</span> <span class="n">ModelLoader</span><span class="p">,</span> <span class="n">_ModelRuntimeContext</span><span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.mpi_session</span><span class="w"> </span><span class="kn">import</span> <span class="n">MpiPoolSession</span><span class="p">,</span> <span class="n">external_mpi_comm_available</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.tokenizer</span><span class="w"> </span><span class="kn">import</span> <span class="n">TokenizerBase</span><span class="p">,</span> <span class="n">_xgrammar_tokenizer_info</span>
<span class="c1"># TODO[chunweiy]: move the following symbols back to utils scope, and remove the following import</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.utils</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">append_docstring</span><span class="p">,</span> <span class="n">exception_handler</span><span class="p">,</span> <span class="n">get_device_count</span><span class="p">,</span>
<span class="n">print_colored_debug</span><span class="p">)</span>
<div class="viewcode-block" id="RequestOutput">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.RequestOutput">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">RequestOutput</span><span class="p">(</span><span class="n">DetokenizedGenerationResultBase</span><span class="p">,</span> <span class="n">GenerationResult</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;The output data of a completion request to the LLM.</span>
<span class="sd"> Attributes:</span>
<span class="sd"> request_id (int): The unique ID of the request.</span>
<span class="sd"> prompt (str, optional): The prompt string of the request.</span>
<span class="sd"> prompt_token_ids (List[int]): The token ids of the prompt.</span>
<span class="sd"> outputs (List[CompletionOutput]): The output sequences of the request.</span>
<span class="sd"> context_logits (torch.Tensor, optional): The logits on the prompt token ids.</span>
<span class="sd"> finished (bool): Whether the whole request is finished.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<div class="viewcode-block" id="RequestOutput.__init__">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.RequestOutput.__init__">[docs]</a>
<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="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> is designed to be instantiated using </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">._from_generation_result by GenerationExecutor. &quot;</span>
<span class="sa">f</span><span class="s2">&quot;Users are not expected to create </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> directly.&quot;</span>
<span class="p">)</span></div>
<span class="nd">@classmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_from_generation_result</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">generation_result</span><span class="p">:</span> <span class="n">GenerationResult</span><span class="p">,</span>
<span class="n">prompt</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">tokenizer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TokenizerBase</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="s1">&#39;RequestOutput&#39;</span><span class="p">:</span>
<span class="n">inst</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
<span class="n">inst</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">generation_result</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
<span class="n">inst</span><span class="o">.</span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">tokenizer</span>
<span class="n">inst</span><span class="o">.</span><span class="n">_streaming</span> <span class="o">=</span> <span class="n">generation_result</span><span class="o">.</span><span class="n">_streaming</span>
<span class="n">inst</span><span class="o">.</span><span class="n">_prompt</span> <span class="o">=</span> <span class="n">prompt</span>
<span class="k">return</span> <span class="n">inst</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">prompt</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prompt</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_repr_fields</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span>
<span class="s2">&quot;request_id&quot;</span><span class="p">,</span> <span class="s2">&quot;prompt&quot;</span><span class="p">,</span> <span class="s2">&quot;prompt_token_ids&quot;</span><span class="p">,</span> <span class="s2">&quot;outputs&quot;</span><span class="p">,</span> <span class="s2">&quot;finished&quot;</span>
<span class="p">]</span></div>
<span class="n">LLM_DOCSTRING</span> <span class="o">=</span> <span class="n">LLMARGS_EXPLICIT_DOCSTRING</span> <span class="o">+</span> <span class="s2">&quot;&quot;&quot;</span>
<span class="s2"> kwargs (Any): Advanced arguments passed to `LlmArgs`.</span>
<span class="s2"> Attributes:</span>
<span class="s2"> tokenizer (tensorrt_llm.llmapi.tokenizer.TokenizerBase, optional): The tokenizer loaded by LLM instance, if any.</span>
<span class="s2"> workspace (pathlib.Path): The directory to store intermediate files.</span>
<span class="s2">&quot;&quot;&quot;</span>
<div class="viewcode-block" id="LLM">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM">[docs]</a>
<span class="nd">@append_docstring</span><span class="p">(</span><span class="n">LLM_DOCSTRING</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">LLM</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;LLM class is the main class for running a LLM model.</span>
<span class="sd"> Parameters:</span>
<span class="sd">&quot;&quot;&quot;</span>
<div class="viewcode-block" id="LLM.__init__">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.__init__">[docs]</a>
<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">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Path</span><span class="p">],</span>
<span class="n">tokenizer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Path</span><span class="p">,</span> <span class="n">TokenizerBase</span><span class="p">,</span>
<span class="n">PreTrainedTokenizerBase</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">tokenizer_mode</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="s1">&#39;slow&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span>
<span class="n">skip_tokenizer_init</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">trust_remote_code</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">tensor_parallel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">dtype</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;auto&quot;</span><span class="p">,</span>
<span class="n">revision</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">tokenizer_revision</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_executor_cls</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;executor_cls&quot;</span><span class="p">,</span> <span class="n">GenerationExecutor</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">llm_args_cls</span> <span class="o">=</span> <span class="n">TorchLlmArgs</span> <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
<span class="s1">&#39;backend&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> <span class="s1">&#39;pytorch&#39;</span> <span class="k">else</span> <span class="n">TrtLlmArgs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span> <span class="o">=</span> <span class="n">llm_args_cls</span><span class="o">.</span><span class="n">from_kwargs</span><span class="p">(</span>
<span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
<span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
<span class="n">tokenizer_mode</span><span class="o">=</span><span class="n">tokenizer_mode</span><span class="p">,</span>
<span class="n">skip_tokenizer_init</span><span class="o">=</span><span class="n">skip_tokenizer_init</span><span class="p">,</span>
<span class="n">trust_remote_code</span><span class="o">=</span><span class="n">trust_remote_code</span><span class="p">,</span>
<span class="n">tensor_parallel_size</span><span class="o">=</span><span class="n">tensor_parallel_size</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
<span class="n">revision</span><span class="o">=</span><span class="n">revision</span><span class="p">,</span>
<span class="n">tokenizer_revision</span><span class="o">=</span><span class="n">tokenizer_revision</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;Failed to parse the arguments for the LLM constructor: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">raise</span> <span class="n">e</span>
<span class="n">print_colored_debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;LLM.args.mpi_session: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">mpi_session</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span>
<span class="s2">&quot;yellow&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">mpi_session</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">is_multi_gpu</span><span class="p">:</span>
<span class="k">if</span> <span class="n">get_device_count</span><span class="p">(</span>
<span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">world_size_per_node</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;Only </span><span class="si">{</span><span class="n">get_device_count</span><span class="p">()</span><span class="si">}</span><span class="s2"> GPUs are available, but </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">world_size</span><span class="si">}</span><span class="s2"> are required.&quot;</span>
<span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
<span class="sa">f</span><span class="s1">&#39;start MpiSession with </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">world_size</span><span class="si">}</span><span class="s1"> workers&#39;</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span><span class="p">:</span>
<span class="n">mpi_process_pre_spawned</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">get_spawn_proxy_process_env</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">mpi_process_pre_spawned</span><span class="p">:</span>
<span class="n">print_colored_debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;LLM create MpiPoolSession</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span>
<span class="s2">&quot;yellow&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span> <span class="o">=</span> <span class="n">MpiPoolSession</span><span class="p">(</span>
<span class="n">n_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">world_size</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">print_colored_debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;LLM create MpiCommSession</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span>
<span class="s2">&quot;yellow&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span> <span class="o">=</span> <span class="n">create_mpi_comm_session</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">world_size</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="c1"># Due to the Executor can only accept a engine path, we need to save the engine to a directory</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Path</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_executor</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">GenerationExecutor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_on_trt_backend</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_workspace</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span><span class="p">(</span>
<span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;-llm-workspace&quot;</span><span class="p">,</span> <span class="nb">dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">workspace</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_hf_model_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Path</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">runtime_context</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_ModelRuntimeContext</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">llm_build_stats</span> <span class="o">=</span> <span class="n">LlmBuildStats</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_build_model</span><span class="p">()</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="k">raise</span> <span class="n">e</span>
<span class="n">exception_handler</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;shutdown&#39;</span><span class="p">)</span>
<span class="n">atexit</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">LLM</span><span class="o">.</span><span class="n">_shutdown_wrapper</span><span class="p">,</span> <span class="n">weakref</span><span class="o">.</span><span class="n">ref</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span></div>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">workspace</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Path</span><span class="p">:</span>
<span class="k">return</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_workspace</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_on_trt_backend</span> <span class="k">else</span> <span class="kc">None</span>
<div class="viewcode-block" id="LLM.generate">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.generate">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">generate</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">inputs</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">PromptInputs</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">PromptInputs</span><span class="p">]],</span>
<span class="n">sampling_params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">SamplingParams</span><span class="p">,</span>
<span class="n">List</span><span class="p">[</span><span class="n">SamplingParams</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">use_tqdm</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
<span class="n">lora_request</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">LoRARequest</span><span class="p">,</span>
<span class="n">Sequence</span><span class="p">[</span><span class="n">LoRARequest</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">prompt_adapter_request</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span>
<span class="n">PromptAdapterRequest</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">PromptAdapterRequest</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">kv_cache_retention_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span>
<span class="n">KvCacheRetentionConfig</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">KvCacheRetentionConfig</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">disaggregated_params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span>
<span class="n">DisaggregatedParams</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">DisaggregatedParams</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">RequestOutput</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">RequestOutput</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Generate output for the given prompts in the synchronous mode.</span>
<span class="sd"> Synchronous generation accepts either single prompt or batched prompts.</span>
<span class="sd"> Args:</span>
<span class="sd"> inputs (tensorrt_llm.inputs.data.PromptInputs, Sequence[tensorrt_llm.inputs.data.PromptInputs]): The prompt text or token ids.</span>
<span class="sd"> It can be single prompt or batched prompts.</span>
<span class="sd"> sampling_params (tensorrt_llm.sampling_params.SamplingParams, List[tensorrt_llm.sampling_params.SamplingParams], optional): The sampling params for the generation. Defaults to None.</span>
<span class="sd"> A default one will be used if not provided.</span>
<span class="sd"> use_tqdm (bool): Whether to use tqdm to display the progress bar. Defaults to True.</span>
<span class="sd"> lora_request (tensorrt_llm.executor.request.LoRARequest, Sequence[tensorrt_llm.executor.request.LoRARequest], optional):</span>
<span class="sd"> LoRA request to use for generation, if any. Defaults to None.</span>
<span class="sd"> prompt_adapter_request (tensorrt_llm.executor.request.PromptAdapterRequest, Sequence[tensorrt_llm.executor.request.PromptAdapterRequest], optional):</span>
<span class="sd"> Prompt Adapter request to use for generation, if any. Defaults to None.</span>
<span class="sd"> kv_cache_retention_config (tensorrt_llm.bindings.executor.KvCacheRetentionConfig, Sequence[tensorrt_llm.bindings.executor.KvCacheRetentionConfig], optional):</span>
<span class="sd"> Configuration for the request&#39;s retention in the KV Cache. Defaults to None.</span>
<span class="sd"> disaggregated_params (tensorrt_llm.disaggregated_params.DisaggregatedParams, Sequence[tensorrt_llm.disaggregated_params.DisaggregatedParams], optional):</span>
<span class="sd"> Disaggregated parameters. Defaults to None.</span>
<span class="sd"> Returns:</span>
<span class="sd"> Union[tensorrt_llm.llmapi.RequestOutput, List[tensorrt_llm.llmapi.RequestOutput]]: The output data of the completion request to the LLM.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">unbatched</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">unbatched</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">int</span><span class="p">):</span>
<span class="n">unbatched</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="n">unbatched</span><span class="p">:</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">]</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">prompt_inputs</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_item_at</span><span class="p">(</span><span class="n">maybe_batched</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]],</span> <span class="n">pos</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">maybe_batched</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="k">return</span> <span class="n">maybe_batched</span><span class="p">[</span><span class="n">pos</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">maybe_batched</span>
<span class="n">futures</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">request_inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">inputs</span><span class="p">):</span>
<span class="n">future</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_async</span><span class="p">(</span>
<span class="n">request_inputs</span><span class="p">,</span>
<span class="n">sampling_params</span><span class="o">=</span><span class="n">_item_at</span><span class="p">(</span><span class="n">sampling_params</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span>
<span class="n">lora_request</span><span class="o">=</span><span class="n">_item_at</span><span class="p">(</span><span class="n">lora_request</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span>
<span class="n">prompt_adapter_request</span><span class="o">=</span><span class="n">_item_at</span><span class="p">(</span><span class="n">prompt_adapter_request</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span>
<span class="n">kv_cache_retention_config</span><span class="o">=</span><span class="n">_item_at</span><span class="p">(</span><span class="n">kv_cache_retention_config</span><span class="p">,</span>
<span class="n">i</span><span class="p">),</span>
<span class="n">disaggregated_params</span><span class="o">=</span><span class="n">_item_at</span><span class="p">(</span><span class="n">disaggregated_params</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span>
<span class="n">streaming</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">futures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">future</span><span class="p">)</span>
<span class="k">for</span> <span class="n">future</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">futures</span><span class="p">,</span>
<span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Processed requests&quot;</span><span class="p">,</span>
<span class="n">dynamic_ncols</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="n">use_tqdm</span><span class="p">):</span>
<span class="n">future</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
<span class="k">if</span> <span class="n">unbatched</span><span class="p">:</span>
<span class="n">futures</span> <span class="o">=</span> <span class="n">futures</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="n">futures</span></div>
<div class="viewcode-block" id="LLM.generate_async">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.generate_async">[docs]</a>
<span class="nd">@nvtx_range_debug</span><span class="p">(</span><span class="s2">&quot;LLM.generate_async&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="s2">&quot;LLM&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">generate_async</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">inputs</span><span class="p">:</span> <span class="n">PromptInputs</span><span class="p">,</span>
<span class="n">sampling_params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">SamplingParams</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">lora_request</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">LoRARequest</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">prompt_adapter_request</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">PromptAdapterRequest</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">streaming</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">kv_cache_retention_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">KvCacheRetentionConfig</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">disaggregated_params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DisaggregatedParams</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">_postproc_params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">PostprocParams</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RequestOutput</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Generate output for the given prompt in the asynchronous mode.</span>
<span class="sd"> Asynchronous generation accepts single prompt only.</span>
<span class="sd"> Args:</span>
<span class="sd"> inputs (tensorrt_llm.inputs.data.PromptInputs): The prompt text or token ids; it must be single prompt.</span>
<span class="sd"> sampling_params (tensorrt_llm.sampling_params.SamplingParams, optional): The sampling params for the generation. Defaults to None.</span>
<span class="sd"> A default one will be used if not provided.</span>
<span class="sd"> lora_request (tensorrt_llm.executor.request.LoRARequest, optional): LoRA request to use for generation, if any. Defaults to None.</span>
<span class="sd"> prompt_adapter_request (tensorrt_llm.executor.request.PromptAdapterRequest, optional): Prompt Adapter request to use for generation, if any. Defaults to None.</span>
<span class="sd"> streaming (bool): Whether to use the streaming mode for the generation. Defaults to False.</span>
<span class="sd"> kv_cache_retention_config (tensorrt_llm.bindings.executor.KvCacheRetentionConfig, optional): Configuration for the request&#39;s retention in the KV Cache. Defaults to None.</span>
<span class="sd"> disaggregated_params (tensorrt_llm.disaggregated_params.DisaggregatedParams, optional): Disaggregated parameters. Defaults to None.</span>
<span class="sd"> Returns:</span>
<span class="sd"> tensorrt_llm.llmapi.RequestOutput: The output data of the completion request to the LLM.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">sampling_params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_sampling_params</span><span class="p">(</span><span class="n">sampling_params</span><span class="p">)</span>
<span class="c1"># With pytorch backend, py_executor has logic to handle max_tokens of 1,</span>
<span class="c1"># so set to 1 to avoid allocating unnecessary KV cache blocks for single request</span>
<span class="c1"># TODO: Also support for trt backend</span>
<span class="k">if</span> <span class="p">(</span><span class="n">disaggregated_params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="ow">and</span> <span class="n">disaggregated_params</span><span class="o">.</span><span class="n">request_type</span> <span class="o">==</span> <span class="s2">&quot;context_only&quot;</span>
<span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_on_trt_backend</span><span class="p">):</span>
<span class="n">sampling_params</span><span class="o">.</span><span class="n">max_tokens</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">prompt_inputs</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;prompt&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
<span class="s2">&quot;prompt_token_ids&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
<span class="s2">&quot;multi_modal_data&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_processor</span><span class="p">,</span> <span class="n">DefaultInputProcessor</span><span class="p">):</span>
<span class="c1"># VLMs need to process/tokenize the prompt in their own way</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;prompt_token_ids&#39;</span><span class="p">])</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">TextPrompt</span><span class="p">(</span>
<span class="n">prompt</span><span class="o">=</span><span class="n">prompt</span><span class="p">,</span>
<span class="n">multi_modal_data</span><span class="o">=</span><span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;multi_modal_data&quot;</span><span class="p">),</span>
<span class="n">mm_processor_kwargs</span><span class="o">=</span><span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;mm_processor_kwargs&quot;</span><span class="p">))</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">add_special_tokens</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
<span class="s2">&quot;Setting add_special_tokens to False because prompt_token_ids were provided to generate. VLMs will re-encode the prompt.&quot;</span>
<span class="p">)</span>
<span class="n">sampling_params</span><span class="o">.</span><span class="n">add_special_tokens</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">query_token_ids</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">multimodal_embedding</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">mrope_config</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="s2">&quot;prompt_token_ids&quot;</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
<span class="n">prompt_token_ids</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;prompt_token_ids&#39;</span><span class="p">]</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">query_token_ids</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;query_token_ids&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">elif</span> <span class="s2">&quot;prompt&quot;</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
<span class="k">with</span> <span class="n">nvtx_range_debug</span><span class="p">(</span><span class="s2">&quot;input_processor&quot;</span><span class="p">):</span>
<span class="n">prompt_token_ids</span><span class="p">,</span> <span class="n">extra_processed_inputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_processor</span><span class="p">(</span>
<span class="n">inputs</span><span class="p">,</span> <span class="n">sampling_params</span><span class="p">)</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;prompt&#39;</span><span class="p">]</span>
<span class="k">if</span> <span class="n">extra_processed_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">query_token_ids</span> <span class="o">=</span> <span class="n">extra_processed_inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;query_token_ids&#39;</span><span class="p">)</span>
<span class="n">multimodal_embedding</span> <span class="o">=</span> <span class="n">extra_processed_inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
<span class="s1">&#39;mm_embedding&#39;</span><span class="p">)</span>
<span class="n">mrope_config</span> <span class="o">=</span> <span class="n">extra_processed_inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mrope_config&#39;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;The inputs must be type str or list of int, but got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_arguments</span><span class="p">(</span>
<span class="nb">len</span><span class="p">(</span><span class="n">prompt_token_ids</span><span class="p">),</span>
<span class="nb">len</span><span class="p">(</span><span class="n">query_token_ids</span><span class="p">)</span> <span class="k">if</span> <span class="n">query_token_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span>
<span class="n">sampling_params</span><span class="p">)</span>
<span class="k">if</span> <span class="n">_postproc_params</span><span class="p">:</span>
<span class="n">_postproc_params</span><span class="o">.</span><span class="n">postproc_args</span><span class="o">.</span><span class="n">num_prompt_tokens</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span>
<span class="n">prompt_token_ids</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_executor</span><span class="o">.</span><span class="n">generate_async</span><span class="p">(</span>
<span class="n">prompt_token_ids</span><span class="p">,</span>
<span class="n">query_token_ids</span><span class="o">=</span><span class="n">query_token_ids</span><span class="p">,</span>
<span class="n">sampling_params</span><span class="o">=</span><span class="n">sampling_params</span><span class="p">,</span>
<span class="n">lora_request</span><span class="o">=</span><span class="n">lora_request</span><span class="p">,</span>
<span class="n">prompt_adapter_request</span><span class="o">=</span><span class="n">prompt_adapter_request</span><span class="p">,</span>
<span class="n">streaming</span><span class="o">=</span><span class="n">streaming</span><span class="p">,</span>
<span class="n">multimodal_embedding</span><span class="o">=</span><span class="n">multimodal_embedding</span><span class="p">,</span>
<span class="n">mrope_config</span><span class="o">=</span><span class="n">mrope_config</span><span class="p">,</span>
<span class="n">kv_cache_retention_config</span><span class="o">=</span><span class="n">kv_cache_retention_config</span><span class="p">,</span>
<span class="n">disaggregated_params</span><span class="o">=</span><span class="n">disaggregated_params</span><span class="p">,</span>
<span class="n">postproc_params</span><span class="o">=</span><span class="n">_postproc_params</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">RequestOutput</span><span class="o">.</span><span class="n">_from_generation_result</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">prompt</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">)</span></div>
<div class="viewcode-block" id="LLM.get_stats">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.get_stats">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">timeout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">dict</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;Get iteration statistics from the runtime.</span>
<span class="sd"> To collect statistics, call this function after prompts have been submitted with LLM().generate().</span>
<span class="sd"> Args:</span>
<span class="sd"> timeout (float, optional): Max wait time in seconds when retrieving stats from queue. Defaults to 2.</span>
<span class="sd"> Returns:</span>
<span class="sd"> List[dict]: A list of runtime stats as dict.</span>
<span class="sd"> e.g., [&#39;{&quot;cpuMemUsage&quot;: ..., &quot;iter&quot;: 0, ...}&#39;, &#39;{&quot;cpuMemUsage&quot;: ..., &quot;iter&quot;: 1, ...}&#39;]</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_executor</span><span class="o">.</span><span class="n">get_stats</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">)</span></div>
<div class="viewcode-block" id="LLM.get_stats_async">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.get_stats_async">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_stats_async</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">timeout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">IterationResult</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;Get iteration statistics from the runtime.</span>
<span class="sd"> To collect statistics, you can call this function in an async coroutine or the /metrics endpoint (if you&#39;re using trtllm-serve)</span>
<span class="sd"> after prompts have been submitted.</span>
<span class="sd"> Args:</span>
<span class="sd"> timeout (float, optional): Max wait time in seconds when retrieving stats from queue. Defaults to 2.</span>
<span class="sd"> Returns:</span>
<span class="sd"> tensorrt_llm.executor.result.IterationResult: An async iterable object containing runtime stats.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_executor</span><span class="o">.</span><span class="n">aget_stats</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">)</span></div>
<div class="viewcode-block" id="LLM.get_kv_cache_events">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.get_kv_cache_events">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_kv_cache_events</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">timeout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">dict</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;Get iteration KV events from the runtime.</span>
<span class="sd"> KV events are used to track changes and operations within the KV Cache. Types of events:</span>
<span class="sd"> - KVCacheCreatedData: Indicates the creation of cache blocks.</span>
<span class="sd"> - KVCacheStoredData: Represents a sequence of stored blocks.</span>
<span class="sd"> - KVCacheRemovedData: Contains the hashes of blocks that are being removed from the cache.</span>
<span class="sd"> - KVCacheUpdatedData: Captures updates to existing cache blocks.</span>
<span class="sd"> To enable KV events:</span>
<span class="sd"> - set `event_buffer_max_size` to a positive integer in the `KvCacheConfig`.</span>
<span class="sd"> - set `enable_block_reuse` to True in the `KvCacheConfig`.</span>
<span class="sd"> Args:</span>
<span class="sd"> timeout (float, optional): Max wait time in seconds when retrieving events from queue. Defaults to 2.</span>
<span class="sd"> Returns:</span>
<span class="sd"> List[dict]: A list of runtime events as dict.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_executor</span><span class="o">.</span><span class="n">get_kv_events</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">)</span></div>
<div class="viewcode-block" id="LLM.get_kv_cache_events_async">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.get_kv_cache_events_async">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_kv_cache_events_async</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">timeout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">IterationResult</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;Get iteration KV events from the runtime.</span>
<span class="sd"> KV events are used to track changes and operations within the KV Cache. Types of events:</span>
<span class="sd"> - KVCacheCreatedData: Indicates the creation of cache blocks.</span>
<span class="sd"> - KVCacheStoredData: Represents a sequence of stored blocks.</span>
<span class="sd"> - KVCacheRemovedData: Contains the hashes of blocks that are being removed from the cache.</span>
<span class="sd"> - KVCacheUpdatedData: Captures updates to existing cache blocks.</span>
<span class="sd"> To enable KV events:</span>
<span class="sd"> - set `event_buffer_max_size` to a positive integer in the `KvCacheConfig`.</span>
<span class="sd"> - set `enable_block_reuse` to True in the `KvCacheConfig`.</span>
<span class="sd"> Args:</span>
<span class="sd"> timeout (float, optional): Max wait time in seconds when retrieving events from queue. . Defaults to 2.</span>
<span class="sd"> Returns:</span>
<span class="sd"> tensorrt_llm.executor.result.IterationResult: An async iterable object containing runtime events.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_executor</span><span class="o">.</span><span class="n">aget_kv_events</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">)</span></div>
<span class="k">def</span><span class="w"> </span><span class="nf">_prepare_sampling_params</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">sampling_params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">SamplingParams</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">SamplingParams</span><span class="p">:</span>
<span class="k">if</span> <span class="n">sampling_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">&quot;tokenizer is required to initialize a default sampling_params, or you can explicitly specify a sampling_params&quot;</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">SamplingParams</span><span class="p">(</span><span class="n">end_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span><span class="p">,</span>
<span class="n">pad_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sampling_params</span><span class="p">,</span> <span class="n">SamplingParams</span><span class="p">):</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">end_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">&quot;tokenizer is required to reset end_id if it is None, or you can explicitly specify the end_id for sampling_params&quot;</span>
<span class="p">)</span>
<span class="n">sampling_params</span><span class="o">.</span><span class="n">_setup</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">)</span>
<span class="c1"># auto enabled context and/or generation logits flags, as they are required by logprob computation for TRT backend.</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">backend</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;pytorch&quot;</span><span class="p">,</span> <span class="s2">&quot;autodeploy&quot;</span><span class="p">]:</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">prompt_logprobs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">return_context_logits</span><span class="p">:</span>
<span class="n">sampling_params</span><span class="o">.</span><span class="n">return_context_logits</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">sampling_params</span><span class="o">.</span><span class="n">_context_logits_auto_enabled</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">logprobs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">return_generation_logits</span><span class="p">:</span>
<span class="n">sampling_params</span><span class="o">.</span><span class="n">return_generation_logits</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">sampling_params</span><span class="o">.</span><span class="n">_generation_logits_auto_enabled</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">return</span> <span class="n">sampling_params</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;The sampling_params must be type SamplingParams or None, but got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">sampling_params</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_arguments</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prompt_len</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">query_len</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">sampling_params</span><span class="p">:</span> <span class="n">SamplingParams</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;pytorch&quot;</span><span class="p">:</span>
<span class="c1"># TODO: remove these checks after PyTorch backend</span>
<span class="c1"># fully support TopK prompt and generation logprobs.</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">prompt_logprobs</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;`prompt_logprobs` in sampling_params is not supported in the PyTorch backend yet. Received `prompt_logprobs=</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">prompt_logprobs</span><span class="si">}</span><span class="s2">`. Please unset this field.&quot;</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">logprobs</span> <span class="ow">and</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">logprobs</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;PyTorch backend currently only supports `logprobs=1`. Received `logprobs=</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">logprobs</span><span class="si">}</span><span class="s2">` (Top</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">logprobs</span><span class="si">}</span><span class="s2"> logprobs). Please set `logprobs=1` in `sampling_params` instead.&quot;</span>
<span class="p">)</span>
<span class="k">return</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;autodeploy&quot;</span><span class="p">:</span>
<span class="k">return</span>
<span class="n">build_config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">build_config</span>
<span class="n">built_enging_cfg_file</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">model</span><span class="p">)</span> <span class="o">/</span> <span class="s1">&#39;config.json&#39;</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">built_enging_cfg_file</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">built_enging_cfg</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="n">max_seq_len</span> <span class="o">=</span> <span class="n">built_enging_cfg</span><span class="p">[</span><span class="s1">&#39;build_config&#39;</span><span class="p">][</span>
<span class="s1">&#39;max_seq_len&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="s1">&#39;build_config&#39;</span> <span class="ow">in</span> <span class="n">built_enging_cfg</span> <span class="k">else</span> <span class="n">build_config</span><span class="o">.</span><span class="n">max_seq_len</span>
<span class="c1"># TODO: Remove this check and left the request verification to cpp runtime</span>
<span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">enable_chunked_prefill</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span>
<span class="n">prompt_len</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">cp_size</span> <span class="o">+</span> <span class="n">query_len</span> <span class="o">+</span>
<span class="p">(</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">max_tokens</span> <span class="ow">or</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">max_seq_len</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;The sum of prompt length (</span><span class="si">{</span><span class="n">prompt_len</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">cp_size</span><span class="si">}</span><span class="s2">) and query length (</span><span class="si">{</span><span class="n">query_len</span><span class="si">}</span><span class="s2">) max_tokens (</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">max_tokens</span><span class="si">}</span><span class="s2">) should not exceed &quot;</span>
<span class="sa">f</span><span class="s2">&quot;max_seq_len (</span><span class="si">{</span><span class="n">build_config</span><span class="o">.</span><span class="n">max_seq_len</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">use_beam_search</span> <span class="ow">and</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">best_of</span> <span class="o">&gt;</span> <span class="n">build_config</span><span class="o">.</span><span class="n">max_beam_width</span><span class="p">:</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">n</span> <span class="o">==</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">best_of</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;sampling_params.n (</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">n</span><span class="si">}</span><span class="s2">) cannot exceed max_beam_width (</span><span class="si">{</span><span class="n">build_config</span><span class="o">.</span><span class="n">max_beam_width</span><span class="si">}</span><span class="s2">) when use_beam_search is True&quot;</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;sampling_params.best_of (</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">best_of</span><span class="si">}</span><span class="s2">) cannot exceed max_beam_width (</span><span class="si">{</span><span class="n">build_config</span><span class="o">.</span><span class="n">max_beam_width</span><span class="si">}</span><span class="s2">) when use_beam_search is True&quot;</span>
<span class="p">)</span>
<span class="n">max_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_batch_size</span>
<span class="k">if</span> <span class="n">max_batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">max_batch_size</span> <span class="o">=</span> <span class="n">build_config</span><span class="o">.</span><span class="n">max_batch_size</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">use_beam_search</span> <span class="ow">and</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">best_of</span> <span class="o">&gt;</span> <span class="n">max_batch_size</span><span class="p">:</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">n</span> <span class="o">==</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">best_of</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;sampling_params.n (</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">n</span><span class="si">}</span><span class="s2">) cannot exceed max_batch_size (</span><span class="si">{</span><span class="n">max_batch_size</span><span class="si">}</span><span class="s2">) when use_beam_search is False&quot;</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;sampling_params.best_of (</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">best_of</span><span class="si">}</span><span class="s2">) cannot exceed max_batch_size (</span><span class="si">{</span><span class="n">max_batch_size</span><span class="si">}</span><span class="s2">) when use_beam_search is False&quot;</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">prompt_logprobs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">build_config</span><span class="o">.</span><span class="n">gather_context_logits</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;`sampling_params&#39;s prompt_logprobs=</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">prompt_logprobs</span><span class="si">}</span><span class="s2">` requires `gather_context_logits=True` &quot;</span>
<span class="sa">f</span><span class="s2">&quot;in the `BuildConfig` when constructing the LLM. &quot;</span>
<span class="sa">f</span><span class="s2">&quot;Example: LLM(..., build_config=BuildConfig(gather_context_logits=True)).&quot;</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">sampling_params</span><span class="o">.</span><span class="n">logprobs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gather_generation_logits</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;`sampling_params.logprobs=</span><span class="si">{</span><span class="n">sampling_params</span><span class="o">.</span><span class="n">logprobs</span><span class="si">}</span><span class="s2">` requires `gather_generation_logits=True` &quot;</span>
<span class="sa">f</span><span class="s2">&quot;to be passed explicitly to the `LLM()` constructor.&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_build_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">model_loader</span> <span class="o">=</span> <span class="n">CachedModelLoader</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span>
<span class="n">mpi_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span><span class="p">,</span>
<span class="n">workspace</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workspace</span><span class="p">,</span>
<span class="n">llm_build_stats</span><span class="o">=</span><span class="n">weakref</span><span class="o">.</span><span class="n">proxy</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">llm_build_stats</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hf_model_dir</span> <span class="o">=</span> <span class="n">model_loader</span><span class="p">()</span>
<span class="c1"># update the model_dir to a local dir for the runtime, such as tokenizer loading.</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span>
<span class="c1"># Tokenizer loading should be after calling model_loader(), since model_loader() may download the model from HF hub.</span>
<span class="c1"># It should also be before bindings ExecutorConfig, which may depend on tokenizer info.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_tokenizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_try_load_tokenizer</span><span class="p">()</span>
<span class="c1"># Multimodal special handling:</span>
<span class="c1"># 1. Default load_tokenizer may fail because MM has different tokenizer configuration. Hence we initialize it inside input processor</span>
<span class="c1"># 2. May need to modify model weights for MM (e.g., resize vocab embedding). We must do such operation via input processor&#39;s __init__</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_processor</span> <span class="o">=</span> <span class="n">create_input_processor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_hf_model_dir</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_processor</span><span class="o">.</span><span class="n">tokenizer</span>
<span class="n">max_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_batch_size</span>
<span class="n">max_num_tokens</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_num_tokens</span>
<span class="n">max_seq_len</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_seq_len</span>
<span class="n">build_config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">build_config</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_on_trt_backend</span> <span class="k">else</span> <span class="n">BuildConfig</span><span class="p">(</span>
<span class="p">)</span>
<span class="n">max_batch_size</span> <span class="o">=</span> <span class="n">max_batch_size</span> <span class="ow">or</span> <span class="n">build_config</span><span class="o">.</span><span class="n">max_batch_size</span>
<span class="n">max_num_tokens</span> <span class="o">=</span> <span class="n">max_num_tokens</span> <span class="ow">or</span> <span class="n">build_config</span><span class="o">.</span><span class="n">max_num_tokens</span>
<span class="n">max_seq_len</span> <span class="o">=</span> <span class="n">max_seq_len</span> <span class="ow">or</span> <span class="n">build_config</span><span class="o">.</span><span class="n">max_seq_len</span>
<span class="n">executor_config</span> <span class="o">=</span> <span class="n">tllm</span><span class="o">.</span><span class="n">ExecutorConfig</span><span class="p">(</span>
<span class="n">max_beam_width</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_beam_width</span><span class="p">,</span>
<span class="n">scheduler_config</span><span class="o">=</span><span class="n">PybindMirror</span><span class="o">.</span><span class="n">maybe_to_pybind</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">scheduler_config</span><span class="p">),</span>
<span class="n">batching_type</span><span class="o">=</span><span class="n">PybindMirror</span><span class="o">.</span><span class="n">maybe_to_pybind</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">batching_type</span><span class="p">)</span>
<span class="ow">or</span> <span class="n">tllm</span><span class="o">.</span><span class="n">BatchingType</span><span class="o">.</span><span class="n">INFLIGHT</span><span class="p">,</span>
<span class="n">max_batch_size</span><span class="o">=</span><span class="n">max_batch_size</span><span class="p">,</span>
<span class="n">max_num_tokens</span><span class="o">=</span><span class="n">max_num_tokens</span><span class="p">,</span>
<span class="n">gather_generation_logits</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gather_generation_logits</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">backend</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="c1"># also set executor_config.max_seq_len in TRT workflow, to deduce default max_tokens</span>
<span class="k">if</span> <span class="n">max_seq_len</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">max_seq_len</span> <span class="o">=</span> <span class="n">max_seq_len</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">engine_config</span> <span class="o">=</span> <span class="n">EngineConfig</span><span class="o">.</span><span class="n">from_json_file</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span> <span class="o">/</span>
<span class="s2">&quot;config.json&quot;</span><span class="p">)</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">max_seq_len</span> <span class="o">=</span> <span class="n">engine_config</span><span class="o">.</span><span class="n">build_config</span><span class="o">.</span><span class="n">max_seq_len</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">kv_cache_config</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">kv_cache_config</span> <span class="o">=</span> <span class="n">PybindMirror</span><span class="o">.</span><span class="n">maybe_to_pybind</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">kv_cache_config</span><span class="p">)</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;FORCE_DETERMINISTIC&quot;</span><span class="p">,</span> <span class="s2">&quot;0&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;1&quot;</span><span class="p">:</span>
<span class="c1"># Disable KV cache reuse for deterministic mode</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">kv_cache_config</span><span class="o">.</span><span class="n">enable_block_reuse</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">kv_cache_config</span><span class="o">.</span><span class="n">enable_partial_reuse</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">peft_cache_config</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">peft_cache_config</span> <span class="o">=</span> <span class="n">PybindMirror</span><span class="o">.</span><span class="n">maybe_to_pybind</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">peft_cache_config</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_on_trt_backend</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">build_config</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">lora_plugin</span><span class="p">:</span>
<span class="n">engine_config</span> <span class="o">=</span> <span class="n">EngineConfig</span><span class="o">.</span><span class="n">from_json_file</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span> <span class="o">/</span>
<span class="s2">&quot;config.json&quot;</span><span class="p">)</span>
<span class="n">lora_config</span> <span class="o">=</span> <span class="n">engine_config</span><span class="o">.</span><span class="n">build_config</span><span class="o">.</span><span class="n">lora_config</span>
<span class="n">max_lora_rank</span> <span class="o">=</span> <span class="n">lora_config</span><span class="o">.</span><span class="n">max_lora_rank</span>
<span class="n">num_lora_modules</span> <span class="o">=</span> <span class="n">engine_config</span><span class="o">.</span><span class="n">pretrained_config</span><span class="o">.</span><span class="n">num_hidden_layers</span> <span class="o">*</span> \
<span class="nb">len</span><span class="p">(</span><span class="n">lora_config</span><span class="o">.</span><span class="n">lora_target_modules</span> <span class="o">+</span> <span class="n">lora_config</span><span class="o">.</span><span class="n">missing_qkv_modules</span><span class="p">)</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">peft_cache_config</span> <span class="o">=</span> <span class="n">tllm</span><span class="o">.</span><span class="n">PeftCacheConfig</span><span class="p">(</span>
<span class="n">num_device_module_layer</span><span class="o">=</span><span class="n">max_lora_rank</span> <span class="o">*</span> <span class="n">num_lora_modules</span> <span class="o">*</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_loras</span><span class="p">,</span>
<span class="n">num_host_module_layer</span><span class="o">=</span><span class="n">max_lora_rank</span> <span class="o">*</span> <span class="n">num_lora_modules</span> <span class="o">*</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_cpu_loras</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">decoding_config</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">decoding_config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">decoding_config</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">guided_decoding_backend</span> <span class="o">==</span> <span class="s1">&#39;xgrammar&#39;</span><span class="p">:</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">guided_decoding_config</span> <span class="o">=</span> <span class="n">tllm</span><span class="o">.</span><span class="n">GuidedDecodingConfig</span><span class="p">(</span>
<span class="n">backend</span><span class="o">=</span><span class="n">tllm</span><span class="o">.</span><span class="n">GuidedDecodingConfig</span><span class="o">.</span><span class="n">GuidedDecodingBackend</span><span class="o">.</span>
<span class="n">XGRAMMAR</span><span class="p">,</span>
<span class="o">**</span><span class="n">_xgrammar_tokenizer_info</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">))</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">guided_decoding_backend</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;Unrecognized guided decoding backend </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">guided_decoding_backend</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="p">)</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">normalize_log_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">normalize_log_probs</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">enable_chunked_context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">enable_chunked_prefill</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">max_beam_width</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_beam_width</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">build_config</span><span class="o">.</span><span class="n">max_beam_width</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_on_trt_backend</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">extended_runtime_perf_knob_config</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">extended_runtime_perf_knob_config</span> <span class="o">=</span> <span class="n">PybindMirror</span><span class="o">.</span><span class="n">maybe_to_pybind</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">extended_runtime_perf_knob_config</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">cache_transceiver_config</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">cache_transceiver_config</span> <span class="o">=</span> <span class="n">PybindMirror</span><span class="o">.</span><span class="n">maybe_to_pybind</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">cache_transceiver_config</span><span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm._torch.pyexecutor.config</span><span class="w"> </span><span class="kn">import</span> <span class="n">update_executor_config</span>
<span class="n">update_executor_config</span><span class="p">(</span>
<span class="n">executor_config</span><span class="p">,</span>
<span class="n">backend</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">backend</span><span class="p">,</span>
<span class="n">pytorch_backend_config</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">get_pytorch_backend_config</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;pytorch&quot;</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">mapping</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">to_mapping</span><span class="p">(),</span>
<span class="n">build_config</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">build_config</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_on_trt_backend</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">speculative_config</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">speculative_config</span><span class="p">,</span>
<span class="n">hf_model_dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_hf_model_dir</span><span class="p">,</span>
<span class="n">trt_engine_dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span><span class="p">,</span>
<span class="n">max_input_len</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_input_len</span><span class="p">,</span>
<span class="n">max_seq_len</span><span class="o">=</span><span class="n">max_seq_len</span><span class="p">)</span>
<span class="n">executor_config</span><span class="o">.</span><span class="n">llm_parallel_config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span>
<span class="n">return_logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gather_generation_logits</span> <span class="ow">or</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_on_trt_backend</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">build_config</span>
<span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">build_config</span><span class="o">.</span><span class="n">gather_context_logits</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_executor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_executor_cls</span><span class="o">.</span><span class="n">create</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span><span class="p">,</span>
<span class="n">executor_config</span><span class="o">=</span><span class="n">executor_config</span><span class="p">,</span>
<span class="n">batched_logits_processor</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">batched_logits_processor</span><span class="p">,</span>
<span class="n">model_world_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span>
<span class="n">mpi_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span><span class="p">,</span>
<span class="n">reuse_mpi_comm</span><span class="o">=</span><span class="n">external_mpi_comm_available</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_config</span><span class="o">.</span><span class="n">world_size</span><span class="p">),</span>
<span class="n">return_logits</span><span class="o">=</span><span class="n">return_logits</span><span class="p">,</span>
<span class="n">postproc_worker_config</span><span class="o">=</span><span class="n">PostprocWorkerConfig</span><span class="p">(</span>
<span class="n">num_postprocess_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">num_postprocess_workers</span><span class="p">,</span>
<span class="n">postprocess_tokenizer_dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">postprocess_tokenizer_dir</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">is_llm_executor</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">lora_config</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">lora_config</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_on_trt_backend</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">TrtLlmArgs</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_try_load_tokenizer</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TokenizerBase</span><span class="p">]:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">skip_tokenizer_init</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">tokenizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">TokenizerBase</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">tokenizer</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">runtime_context</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">runtime_context</span><span class="o">.</span><span class="n">tokenizer</span>
<span class="c1"># TODO smor- need to refine what is the desired behavior if lora is enabled</span>
<span class="c1"># in terms of the tokenizer initialization process</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="s2">&quot;backend&quot;</span>
<span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;pytorch&quot;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">lora_config</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">num_lora_dirs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">lora_config</span><span class="o">.</span><span class="n">lora_dir</span><span class="p">)</span>
<span class="k">if</span> <span class="n">num_lora_dirs</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">tokenizer_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">lora_config</span><span class="o">.</span><span class="n">lora_dir</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">ModelLoader</span><span class="o">.</span><span class="n">load_hf_tokenizer</span><span class="p">(</span>
<span class="n">tokenizer_path</span><span class="p">,</span>
<span class="n">trust_remote_code</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">trust_remote_code</span><span class="p">,</span>
<span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">tokenizer_mode</span> <span class="o">!=</span> <span class="s1">&#39;slow&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">tokenizer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tokenizer_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">model</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">tokenizer</span>
<span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
<span class="n">tokenizer_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">model</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tokenizer_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">model</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tokenizer_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">model</span>
<span class="k">return</span> <span class="n">ModelLoader</span><span class="o">.</span><span class="n">load_hf_tokenizer</span><span class="p">(</span>
<span class="n">tokenizer_path</span><span class="p">,</span>
<span class="n">trust_remote_code</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">trust_remote_code</span><span class="p">,</span>
<span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">tokenizer_mode</span> <span class="o">!=</span> <span class="s1">&#39;slow&#39;</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">tokenizer</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TokenizerBase</span><span class="p">]:</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;input_processor&quot;</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_processor</span><span class="p">,</span> <span class="s2">&quot;tokenizer&quot;</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_processor</span><span class="o">.</span><span class="n">tokenizer</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tokenizer</span>
<span class="nd">@tokenizer</span><span class="o">.</span><span class="n">setter</span>
<span class="k">def</span><span class="w"> </span><span class="nf">tokenizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">:</span> <span class="n">TokenizerBase</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_tokenizer</span> <span class="o">=</span> <span class="n">tokenizer</span>
<div class="viewcode-block" id="LLM.save">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.save">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">engine_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Save the built engine to the given path.</span>
<span class="sd"> Args:</span>
<span class="sd"> engine_dir (str): The path to save the engine.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Save model to </span><span class="si">{</span><span class="n">engine_dir</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;The engine is not built yet.&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span><span class="o">.</span><span class="n">absolute</span><span class="p">()</span> <span class="o">==</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">engine_dir</span><span class="p">):</span>
<span class="k">return</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span><span class="o">.</span><span class="n">is_comm_session</span><span class="p">():</span>
<span class="n">shutil</span><span class="o">.</span><span class="n">copytree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span><span class="p">,</span> <span class="n">engine_dir</span><span class="p">,</span> <span class="n">dirs_exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># NFS is fragile, so we copy files one by one</span>
<span class="n">target_engine_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">engine_dir</span><span class="p">)</span>
<span class="n">target_engine_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># copy files one by one</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine_dir</span><span class="o">.</span><span class="n">iterdir</span><span class="p">():</span>
<span class="n">print_colored_debug</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;Copying </span><span class="si">{</span><span class="n">file</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">target_engine_dir</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">file</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">target_engine_dir</span> <span class="o">/</span> <span class="n">file</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
<div class="viewcode-block" id="LLM.shutdown">
<a class="viewcode-back" href="../../../llm-api/reference.html#tensorrt_llm.llmapi.LLM.shutdown">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">shutdown</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_executor&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_executor</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_executor</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_executor</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;mpi_session&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mpi_session</span> <span class="o">=</span> <span class="kc">None</span></div>
<span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_shutdown_wrapper</span><span class="p">(</span><span class="n">self_ref</span><span class="p">):</span>
<span class="c1"># Retrieve the instance if it still exists</span>
<span class="n">instance</span> <span class="o">=</span> <span class="n">self_ref</span><span class="p">()</span>
<span class="k">if</span> <span class="n">instance</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">instance</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">traceback</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">del</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">traceback</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="k">return</span> <span class="kc">False</span> <span class="c1"># propagate exceptions</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;LLM object can not be pickled.&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span></div>
</pre></div>
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