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update docs for 0.20.0rc2 Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
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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>
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<li class="toctree-l1"><a class="reference internal" href="../quick-start-guide.html">Quick Start Guide</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../key-features.html">Key Features</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../torch.html">PyTorch Backend</a></li>
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<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/grace-hopper.html">Installing on Grace Hopper</a></li>
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</ul>
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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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<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>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_customize.html">Generate text with customization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
|
||
</ul>
|
||
</details></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../examples/customization.html">LLM Common Customizations</a></li>
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<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>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_customize.html">Generate text with customization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_quantization.html">Generation with Quantization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference.html">Generate text</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
|
||
</ul>
|
||
</details></li>
|
||
<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/trtllm_serve_examples.html">Online Serving Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<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_chat_client_for_multimodal.html">Curl Chat Client For Multimodal</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
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<section id="kv-cache-manager">
|
||
<h1>KV Cache Manager<a class="headerlink" href="#kv-cache-manager" title="Link to this heading">#</a></h1>
|
||
<p>In Transformer-based models, the KV (Key-Value) Cache is a mechanism used to optimize decoding efficiency, particularly during autoregressive generation tasks.
|
||
Since KV Cache requires memory to store, it is also an important resource.
|
||
In TensorRT-LLM, KV Cache is managed by the <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>.</p>
|
||
<section id="kv-cache-manager-introduction">
|
||
<h2>KV Cache Manager Introduction<a class="headerlink" href="#kv-cache-manager-introduction" title="Link to this heading">#</a></h2>
|
||
<p><code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code> is a type of resource manager, inheriting from <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code>.
|
||
Therefore, it implements the interfaces declared by <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code>.</p>
|
||
<p>Note: As the project evolves, these interfaces may change.</p>
|
||
</section>
|
||
<section id="interfaces">
|
||
<h2>Interfaces<a class="headerlink" href="#interfaces" title="Link to this heading">#</a></h2>
|
||
<p>The interfaces from <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code> include:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>prepare_resources</strong>: Called at each step before model forward in <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code> for the current batch.
|
||
In <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>, this involves allocating KV Cache memory. This allocation varies depending on the request type.
|
||
For requests entering the context phase for the first time, KV Cache needs to be allocated for the entire context.
|
||
For requests already in the generation phase, KV Cache is allocated for the upcoming step.
|
||
If KV Cache is organized in blocks and free space is available within a block, actual allocation may not occur.</p></li>
|
||
<li><p><strong>update_resources</strong>: Called at the end of each step for the current batch to update allocated resources.
|
||
For KV Cache, updates may not be necessary, so this function currently performs no operations.
|
||
If KV Cache reuse is supported in Python, updates like KV Cache Radix Tree management occurs here.</p></li>
|
||
<li><p><strong>free_resources</strong>: Called when a request finishes to free the resources allocated for that request.
|
||
For KV Cache, if reuse is not enabled, the KV Cache memory used by the request should be recycled.
|
||
In the C++ binding implementation, this might involve calling the binding’s <code class="docutils literal notranslate"><span class="pre">remove_sequence</span></code> method to free the KV Cache memory related to that request.</p></li>
|
||
</ul>
|
||
<p>There are also two interfaces designed for <code class="docutils literal notranslate"><span class="pre">CapacityScheduler</span></code>:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>get_max_resource_count</strong>: Queries the maximum number of resources available. For <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>, this is usually the maximum number of KV Cache blocks.</p></li>
|
||
<li><p><strong>get_needed_resource_to_completion</strong>: Computes the resources needed for a single request to complete.
|
||
<code class="docutils literal notranslate"><span class="pre">CapacityScheduler</span></code> uses this to sum up the total resources needed and determine if new requests can be accommodated.</p></li>
|
||
</ul>
|
||
<p>In addition to the <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code> interfaces, <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code> has interfaces related to the <code class="docutils literal notranslate"><span class="pre">ModelEngine</span></code> in use.
|
||
For <code class="docutils literal notranslate"><span class="pre">PyTorchModelEngine</span></code>, common interfaces include:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>get_batch_cache_indices</strong>: Takes a list of <code class="docutils literal notranslate"><span class="pre">LlmRequest</span></code> and returns a <code class="docutils literal notranslate"><span class="pre">Dict[List[int]]</span></code>, indicating the block IDs for each request.</p></li>
|
||
<li><p><strong>get_buffers</strong>: Returns the buffer of the KV Cache pool for a given layer, used by the attention backend. The shape might be [<code class="docutils literal notranslate"><span class="pre">num_blocks</span></code>, 2, <code class="docutils literal notranslate"><span class="pre">num_tokens_per_block</span></code>, <code class="docutils literal notranslate"><span class="pre">num_kv_heads</span></code>, <code class="docutils literal notranslate"><span class="pre">head_dim</span></code>].</p></li>
|
||
<li><p><strong>get_num_free_blocks</strong>: Returns the number of free blocks available for allocation.</p></li>
|
||
</ul>
|
||
<p>There are also interfaces for warming up <code class="docutils literal notranslate"><span class="pre">PyTorchModelEngine</span></code>, especially when using CUDA graphs:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>add_padding_request</strong>: Adds a sequence of context length 1 to KV Cache as a warmup request.
|
||
This is optional if CUDA Graph is not used in your proof of concept.</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="customize-kv-cache-manager">
|
||
<h2>Customize KV Cache Manager<a class="headerlink" href="#customize-kv-cache-manager" title="Link to this heading">#</a></h2>
|
||
<p>To customize <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>, implement all the necessary interfaces.
|
||
Then, integrate it into the <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code>. For the PyTorch backend, the relevant code is in <a class="reference internal" href="#../../../tensorrt_llm/_torch/pyexecutor/backend_registries/pytorch_model_registry.py"><span class="xref myst">pytorch_model_registry.py</span></a>.
|
||
In the <code class="docutils literal notranslate"><span class="pre">create_pytorch_model_based_executor</span></code> function, the <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code> is instantiated as follows:</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span> <span class="n">kv_cache_manager</span> <span class="o">=</span> <span class="n">KVCacheManager</span><span class="p">(</span>
|
||
<span class="n">executor_config</span><span class="o">.</span><span class="n">kv_cache_config</span><span class="p">,</span>
|
||
<span class="n">tensorrt_llm</span><span class="o">.</span><span class="n">bindings</span><span class="o">.</span><span class="n">internal</span><span class="o">.</span><span class="n">batch_manager</span><span class="o">.</span><span class="n">CacheType</span><span class="o">.</span><span class="n">SELF</span><span class="p">,</span>
|
||
<span class="n">num_layers</span><span class="o">=</span><span class="n">model_engine</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_hidden_layers</span><span class="p">,</span>
|
||
<span class="n">num_kv_heads</span><span class="o">=</span><span class="n">model_engine</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_key_value_heads</span><span class="p">,</span>
|
||
<span class="n">head_dim</span><span class="o">=</span><span class="n">head_dim</span><span class="p">,</span>
|
||
<span class="n">tokens_per_block</span><span class="o">=</span><span class="n">tokens_per_block</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">max_batch_size</span><span class="o">=</span><span class="n">max_num_requests</span><span class="p">,</span>
|
||
<span class="n">mapping</span><span class="o">=</span><span class="n">mapping</span><span class="p">,</span>
|
||
<span class="n">dtype</span><span class="o">=</span><span class="n">kv_cache_dtype</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>For local testing or proof of concept, update these lines to use your implementation.
|
||
Then, test it to ensure the <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code> runs with your customized <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>.</p>
|
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</section>
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</section>
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