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<li class="toctree-l1"><a class="reference internal" href="../blogs/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/H200launch.html">H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/Falcon180B-H200.html">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/quantization-in-TRT-LLM.html">Speed up inference with SOTA quantization techniques in TRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
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<section id="kv-cache-manager">
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<h1>KV Cache Manager<a class="headerlink" href="#kv-cache-manager" title="Link to this heading"></a></h1>
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<p>In Transformer-based models, the KV (Key-Value) Cache is a mechanism used to optimize decoding efficiency, particularly during autoregressive generation tasks.
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Since KV Cache requires memory to store, it is also an important resource.
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In TensorRT-LLM, KV Cache is managed by the <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>.</p>
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<section id="kv-cache-manager-introduction">
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<h2>KV Cache Manager Introduction<a class="headerlink" href="#kv-cache-manager-introduction" title="Link to this heading"></a></h2>
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<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>.
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Therefore, it implements the interfaces declared by <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code>.</p>
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<p>Note: As the project evolves, these interfaces may change.</p>
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</section>
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<section id="interfaces">
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<h2>Interfaces<a class="headerlink" href="#interfaces" title="Link to this heading"></a></h2>
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<p>The interfaces from <code class="docutils literal notranslate"><span class="pre">BaseResourceManager</span></code> include:</p>
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<ul class="simple">
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<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.
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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.
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For requests entering the context phase for the first time, KV Cache needs to be allocated for the entire context.
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For requests already in the generation phase, KV Cache is allocated for the upcoming step.
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If KV Cache is organized in blocks and free space is available within a block, actual allocation may not occur.</p></li>
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<li><p><strong>update_resources</strong>: Called at the end of each step for the current batch to update allocated resources.
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For KV Cache, updates may not be necessary, so this function currently performs no operations.
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If KV Cache reuse is supported in Python, updates like KV Cache Radix Tree management occurs here.</p></li>
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<li><p><strong>free_resources</strong>: Called when a request finishes to free the resources allocated for that request.
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For KV Cache, if reuse is not enabled, the KV Cache memory used by the request should be recycled.
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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>
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</ul>
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<p>There are also two interfaces designed for <code class="docutils literal notranslate"><span class="pre">CapacityScheduler</span></code>:</p>
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<ul class="simple">
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<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>
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<li><p><strong>get_needed_resource_to_completion</strong>: Computes the resources needed for a single request to complete.
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<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>
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</ul>
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<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.
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For <code class="docutils literal notranslate"><span class="pre">PyTorchModelEngine</span></code>, common interfaces include:</p>
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<ul class="simple">
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<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>
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<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>
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<li><p><strong>get_num_free_blocks</strong>: Returns the number of free blocks available for allocation.</p></li>
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</ul>
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<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>
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<ul class="simple">
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<li><p><strong>add_padding_request</strong>: Adds a sequence of context length 1 to KV Cache as a warmup request.
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This is optional if CUDA Graph is not used in your proof of concept.</p></li>
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</ul>
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</section>
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<section id="customize-kv-cache-manager">
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<h2>Customize KV Cache Manager<a class="headerlink" href="#customize-kv-cache-manager" title="Link to this heading"></a></h2>
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<p>To customize <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>, implement all the necessary interfaces.
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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 download internal" download="" href="../_downloads/7bf66a65dff8703efe2c82828509f35d/pytorch_model_registry.py"><span class="xref download myst">pytorch_model_registry.py</span></a>.
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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>
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<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>
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<span class="n">executor_config</span><span class="o">.</span><span class="n">kv_cache_config</span><span class="p">,</span>
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<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>
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<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>
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<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_attention_heads</span><span class="p">,</span>
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<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>
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<span class="n">head_dim</span><span class="p">,</span>
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<span class="n">tokens_per_block</span><span class="p">,</span>
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<span class="n">max_seq_len</span><span class="p">,</span>
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<span class="n">max_num_requests</span><span class="p">,</span>
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<span class="n">mapping</span><span class="p">,</span>
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<span class="n">dtype</span><span class="o">=</span><span class="n">kv_cache_dtype</span><span class="p">,</span>
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<span class="p">)</span>
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</pre></div>
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</div>
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<p>For local testing or proof of concept, update these lines to use your implementation.
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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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