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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 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>
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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>
<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>
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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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<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/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</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-l2"><a class="reference internal" href="../../../../performance/performance-tuning-guide/useful-build-time-flags.html">Useful Build-Time Flags</a></li>
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<h1>Source code for tensorrt_llm.models.eagle.model</h1><div class="highlight"><pre>
<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION &amp; AFFILIATES. All rights reserved.</span>
<span class="c1"># SPDX-License-Identifier: Apache-2.0</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">collections</span><span class="w"> </span><span class="kn">import</span> <span class="n">OrderedDict</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">Optional</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tensorrt</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">trt</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">tensorrt_llm.mapping</span><span class="w"> </span><span class="kn">import</span> <span class="n">Mapping</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.models.generation_mixin</span><span class="w"> </span><span class="kn">import</span> <span class="n">GenerationMixin</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.models.llama.model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LLaMAForCausalLM</span><span class="p">,</span> <span class="n">LLaMAModel</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.models.model_weights_loader</span><span class="w"> </span><span class="kn">import</span> <span class="n">ModelWeightsLoader</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..._common</span><span class="w"> </span><span class="kn">import</span> <span class="n">default_net</span><span class="p">,</span> <span class="n">default_trtnet</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">pad_vocab_size</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">KVCacheType</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">...functional</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">_create_tensor</span><span class="p">,</span> <span class="n">cast</span><span class="p">,</span> <span class="n">concat</span><span class="p">,</span>
<span class="n">gather_last_token_logits</span><span class="p">,</span> <span class="n">index_select</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">...layers</span><span class="w"> </span><span class="kn">import</span> <span class="n">AttentionParams</span><span class="p">,</span> <span class="n">ColumnLinear</span><span class="p">,</span> <span class="n">SpecDecodingParams</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">...module</span><span class="w"> </span><span class="kn">import</span> <span class="n">Module</span><span class="p">,</span> <span class="n">ModuleList</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">...plugin</span><span class="w"> </span><span class="kn">import</span> <span class="n">TRT_LLM_PLUGIN_NAMESPACE</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">..modeling_utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">QuantConfig</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.config</span><span class="w"> </span><span class="kn">import</span> <span class="n">EagleConfig</span>
<span class="k">class</span><span class="w"> </span><span class="nc">TreeParams</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">paths</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">paths</span> <span class="o">=</span> <span class="n">paths</span> <span class="c1"># on GPU</span>
<span class="k">def</span><span class="w"> </span><span class="nf">eagle_sample_and_accept_draft_plugin</span><span class="p">(</span><span class="n">lm_logits</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">draft_tokens</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">draft_lens</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">eagle_temperature</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">rand_data_validation</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">posterior_alpha</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">posterior_threshold</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">tree_params</span><span class="p">:</span> <span class="n">TreeParams</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">greedy_sampling</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">use_dynamic_tree</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> Takes input logits and samples golden token + predictions from draft tokens.</span>
<span class="sd"> Runs acceptance algorithm to accept draft tokens.</span>
<span class="sd"> When greedy_sampling is True, all decoding is done using Top1 and token equality is used</span>
<span class="sd"> for acceptance. Otherwise, typical acceptance and multinomial samplings are used.</span>
<span class="sd"> Visit tests/model/eagle/test_sample_accept_draft_tokens.py for input/output examples.</span>
<span class="sd"> Parameters:</span>
<span class="sd"> lm_logits : Tensor</span>
<span class="sd"> [num_tokens, vocab_size]</span>
<span class="sd"> Logits produced by the base model.</span>
<span class="sd"> draft_tokens : Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> Input draft tokens. Only the first draft_lens[bi] tokens are relevant for bi&#39;th row.</span>
<span class="sd"> draft_lens : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Lengths of the draft_tokens. 0 for context request. Actual draft length for generation requests.</span>
<span class="sd"> eagle_temperature : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Temperature of the decoding.</span>
<span class="sd"> rand_data_validation : Tensor</span>
<span class="sd"> [batch_size, max_decoding_tokens]</span>
<span class="sd"> Random data for multinomial sampling.</span>
<span class="sd"> posterior_alpha : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Delta in typical acceptance in https://arxiv.org/pdf/2401.10774.</span>
<span class="sd"> posterior_threshold : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Minimum probability threshold.</span>
<span class="sd"> Epsilon in typical acceptance in https://arxiv.org/pdf/2401.10774.</span>
<span class="sd"> tree_params : TreeParams</span>
<span class="sd"> Tree params of the input draft tokens.</span>
<span class="sd"> greedy_sampling : Tensor</span>
<span class="sd"> Whether to do greedy or non-greedy sampling.</span>
<span class="sd"> use_dynamic_tree: Tensor</span>
<span class="sd"> Whether to use dynamic tree (i.e., Eagle-2)</span>
<span class="sd"> Return:</span>
<span class="sd"> accepted_tokens : Tensor</span>
<span class="sd"> [batch_size, max_path_len]</span>
<span class="sd"> Accepted token ids. Only the first num_accepted_tokens[bi] tokens are relevant for bi&#39;th row,</span>
<span class="sd"> num_accepted_tokens : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Number of accepted tokens per request. Each entry is &gt;= 1.</span>
<span class="sd"> accepted_paths : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Indices of the accepted path per request of the input paths in tree_params.paths.</span>
<span class="sd"> next_draft_tokens : Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> Empty tensor used to allocate space for the next draft tokens.</span>
<span class="sd"> next_draft_lens : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Empty tensor used to allocate space for lens of the next draft tokens.</span>
<span class="sd"> next_draft_paths : Tensor</span>
<span class="sd"> [batch_size, max_decoding_len, max_path_len]</span>
<span class="sd"> For EAGLE-1 just a copy of input path.</span>
<span class="sd"> hidden_size_batch_level_starts : Tensor</span>
<span class="sd"> [max_draft_path_len * batch_size + 1]</span>
<span class="sd"> Empty tensor used to allocate space for eagle_prepare_drafter_inputs_plugin.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="n">plg_creator</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">get_plugin_registry</span><span class="p">()</span><span class="o">.</span><span class="n">get_plugin_creator</span><span class="p">(</span>
<span class="s1">&#39;EagleSampleAndAcceptDraftTokens&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="n">TRT_LLM_PLUGIN_NAMESPACE</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">plg_creator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">pf_type</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginField</span><span class="p">(</span><span class="s2">&quot;type_id&quot;</span><span class="p">,</span>
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">lm_logits</span><span class="o">.</span><span class="n">dtype</span><span class="p">)],</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
<span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldType</span><span class="o">.</span><span class="n">INT32</span><span class="p">)</span>
<span class="n">pfc</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldCollection</span><span class="p">([</span><span class="n">pf_type</span><span class="p">])</span>
<span class="n">plugin</span> <span class="o">=</span> <span class="n">plg_creator</span><span class="o">.</span><span class="n">create_plugin</span><span class="p">(</span><span class="s2">&quot;eagle_sample_and_accept_draft_plugin&quot;</span><span class="p">,</span>
<span class="n">pfc</span><span class="p">)</span>
<span class="n">plug_inputs</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">lm_logits</span><span class="p">,</span> <span class="n">draft_tokens</span><span class="p">,</span> <span class="n">draft_lens</span><span class="p">,</span> <span class="n">eagle_temperature</span><span class="p">,</span>
<span class="n">rand_data_validation</span><span class="p">,</span> <span class="n">posterior_alpha</span><span class="p">,</span> <span class="n">posterior_threshold</span><span class="p">,</span>
<span class="n">tree_params</span><span class="o">.</span><span class="n">paths</span><span class="p">,</span> <span class="n">greedy_sampling</span><span class="p">,</span> <span class="n">use_dynamic_tree</span>
<span class="p">]</span>
<span class="n">plug_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">trt_tensor</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">plug_inputs</span><span class="p">]</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">default_trtnet</span><span class="p">()</span><span class="o">.</span><span class="n">add_plugin_v2</span><span class="p">(</span><span class="n">plug_inputs</span><span class="p">,</span> <span class="n">plugin</span><span class="p">)</span>
<span class="n">accepted_tokens</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">num_accepted_tokens</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">accepted_paths</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">next_draft_tokens</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">next_draft_lens</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">next_draft_paths</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">hidden_size_batch_level_starts</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">6</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">([</span>
<span class="n">accepted_tokens</span><span class="p">,</span> <span class="n">num_accepted_tokens</span><span class="p">,</span> <span class="n">accepted_paths</span><span class="p">,</span> <span class="n">next_draft_tokens</span><span class="p">,</span>
<span class="n">next_draft_lens</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span> <span class="n">hidden_size_batch_level_starts</span>
<span class="p">])</span>
<span class="k">def</span><span class="w"> </span><span class="nf">eagle_draft_decoder_plugin</span><span class="p">(</span>
<span class="n">layer_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_eagle_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">top_k_sampling</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
<span class="n">logits</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">num_last_token_indices</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">input_paths</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">use_dynamic_tree</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">dynamic_tree_max_topK</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">input_draft_token_ids</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">input_draft_lens</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">input_prev_scores</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">input_current_expand_indices</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">input_all_layers_scores</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">input_all_layers_draft_token_ids</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">input_all_layers_draft_token_ids_predecessor</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> Parameters:</span>
<span class="sd"> layer_idx : int</span>
<span class="sd"> The index of the EagleNet.</span>
<span class="sd"> num_eagle_layers: int</span>
<span class="sd"> The total number of eagle layers.</span>
<span class="sd"> top_k_sampling: bool</span>
<span class="sd"> Whether to use top K sampling. Otherwise, use multinomial sampling.</span>
<span class="sd"> logits : Tensor</span>
<span class="sd"> [num_logits, vocab_size]</span>
<span class="sd"> Input logits.</span>
<span class="sd"> num_last_token_indices : Tensor</span>
<span class="sd"> [1]</span>
<span class="sd"> Number of valid logits in logits.</span>
<span class="sd"> input_paths: Tensor</span>
<span class="sd"> [batch_size, max_decoding_tokens, max_path_len]</span>
<span class="sd"> Input paths</span>
<span class="sd"> use_dynamic_tree: Tensor</span>
<span class="sd"> [1]</span>
<span class="sd"> Whether use dynamic tree (i.e., Eagle-2)</span>
<span class="sd"> dynamic_tree_max_topK: Tensor</span>
<span class="sd"> [1]</span>
<span class="sd"> Number of draft tokens expand in Eagle-2.</span>
<span class="sd"> input_draft_token_ids: Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> Draft tokens generated by previous EagleNets.</span>
<span class="sd"> input_draft_lens: Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Number of draft tokens for each request.</span>
<span class="sd"> input_prev_scores: Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> Last layer&#39;s scores</span>
<span class="sd"> input_current_expand_indices: Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> The indices of the nodes that expand in this layer.</span>
<span class="sd"> The index is related to the final output tree, which has max_decoding_draft_tokens draft tokens.</span>
<span class="sd"> input_all_layers_scores: Tensor</span>
<span class="sd"> [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]</span>
<span class="sd"> For Eagle-2, record scores from all EagleNets</span>
<span class="sd"> input_all_layers_draft_token_ids: Tensor</span>
<span class="sd"> [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]</span>
<span class="sd"> For Eagle-2, record all draft tokens from all EagleNets</span>
<span class="sd"> input_all_layers_draft_token_ids_predecessor: Tensor</span>
<span class="sd"> [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]</span>
<span class="sd"> For Eagle-2, record all draft tokens&#39; predecessor</span>
<span class="sd"> Return:</span>
<span class="sd"> output_draft_token_ids: Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> Draft tokens generated by this EagleNets, also include the previous draft tokens.</span>
<span class="sd"> output_draft_draft_lens: Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Number of draft tokens for each request.</span>
<span class="sd"> output_paths: Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens, max_path_len]</span>
<span class="sd"> The latest path.</span>
<span class="sd"> output_current_scores: Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> This layer&#39;s scores, which will be used in next layer.</span>
<span class="sd"> output_next_expand_indices:</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> The indices of the nodes that expand in next layer.</span>
<span class="sd"> The index is related to the final output tree, which has max_decoding_draft_tokens draft tokens.</span>
<span class="sd"> output_all_layers_scores:</span>
<span class="sd"> [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]</span>
<span class="sd"> For Eagle-2, record scores from all EagleNets</span>
<span class="sd"> output_all_layers_draft_token_ids: Tensor</span>
<span class="sd"> [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]</span>
<span class="sd"> For Eagle-2, record all draft tokens from all EagleNets</span>
<span class="sd"> output_all_layers_draft_token_ids_predecessor: Tensor</span>
<span class="sd"> [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]</span>
<span class="sd"> For Eagle-2, record all draft tokens&#39; predecessor</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="n">plg_creator</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">get_plugin_registry</span><span class="p">()</span><span class="o">.</span><span class="n">get_plugin_creator</span><span class="p">(</span>
<span class="s1">&#39;EagleDecodeDraftTokens&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="n">TRT_LLM_PLUGIN_NAMESPACE</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">plg_creator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">pf_type</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginField</span><span class="p">(</span><span class="s2">&quot;type_id&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">logits</span><span class="o">.</span><span class="n">dtype</span><span class="p">)],</span>
<span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
<span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldType</span><span class="o">.</span><span class="n">INT32</span><span class="p">)</span>
<span class="n">layer_idx_t</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginField</span><span class="p">(</span><span class="s2">&quot;layer_idx&quot;</span><span class="p">,</span>
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">layer_idx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
<span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldType</span><span class="o">.</span><span class="n">INT32</span><span class="p">)</span>
<span class="n">num_eagle_layers_t</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginField</span><span class="p">(</span>
<span class="s2">&quot;num_eagle_layers&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">num_eagle_layers</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
<span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldType</span><span class="o">.</span><span class="n">INT32</span><span class="p">)</span>
<span class="n">top_k_sampling_t</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">top_k_sampling</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">top_k_sampling_t</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginField</span><span class="p">(</span>
<span class="s2">&quot;top_k_sampling&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">top_k_sampling_t</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
<span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldType</span><span class="o">.</span><span class="n">INT32</span><span class="p">)</span>
<span class="n">pfc</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldCollection</span><span class="p">(</span>
<span class="p">[</span><span class="n">pf_type</span><span class="p">,</span> <span class="n">layer_idx_t</span><span class="p">,</span> <span class="n">num_eagle_layers_t</span><span class="p">,</span> <span class="n">top_k_sampling_t</span><span class="p">])</span>
<span class="n">plugin</span> <span class="o">=</span> <span class="n">plg_creator</span><span class="o">.</span><span class="n">create_plugin</span><span class="p">(</span><span class="s2">&quot;eagle_draft_decoder_plugin&quot;</span><span class="p">,</span> <span class="n">pfc</span><span class="p">)</span>
<span class="n">plug_inputs</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">logits</span><span class="p">,</span> <span class="n">input_paths</span><span class="p">,</span> <span class="n">num_last_token_indices</span><span class="p">,</span> <span class="n">use_dynamic_tree</span><span class="p">,</span>
<span class="n">dynamic_tree_max_topK</span><span class="p">,</span> <span class="n">input_draft_token_ids</span><span class="p">,</span> <span class="n">input_draft_lens</span><span class="p">,</span>
<span class="n">input_prev_scores</span><span class="p">,</span> <span class="n">input_current_expand_indices</span><span class="p">,</span>
<span class="n">input_all_layers_scores</span><span class="p">,</span> <span class="n">input_all_layers_draft_token_ids</span><span class="p">,</span>
<span class="n">input_all_layers_draft_token_ids_predecessor</span>
<span class="p">]</span>
<span class="n">plug_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">trt_tensor</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">plug_inputs</span><span class="p">]</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">default_trtnet</span><span class="p">()</span><span class="o">.</span><span class="n">add_plugin_v2</span><span class="p">(</span><span class="n">plug_inputs</span><span class="p">,</span> <span class="n">plugin</span><span class="p">)</span>
<span class="n">output_draft_token_ids</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">output_draft_lens</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">output_paths</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">output_current_scores</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">output_next_expand_indices</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">output_all_layers_scores</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">output_all_layers_draft_token_ids</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">6</span><span class="p">),</span>
<span class="n">layer</span><span class="p">)</span>
<span class="n">output_all_layers_draft_token_ids_predecessor</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span>
<span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">7</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">([</span>
<span class="n">output_draft_token_ids</span><span class="p">,</span> <span class="n">output_draft_lens</span><span class="p">,</span> <span class="n">output_paths</span><span class="p">,</span>
<span class="n">output_current_scores</span><span class="p">,</span> <span class="n">output_next_expand_indices</span><span class="p">,</span>
<span class="n">output_all_layers_scores</span><span class="p">,</span> <span class="n">output_all_layers_draft_token_ids</span><span class="p">,</span>
<span class="n">output_all_layers_draft_token_ids_predecessor</span>
<span class="p">])</span>
<span class="k">def</span><span class="w"> </span><span class="nf">eagle_prepare_drafter_inputs_plugin</span><span class="p">(</span>
<span class="n">layer_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">max_non_leaves_per_layer</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">attention_params</span><span class="p">:</span> <span class="n">AttentionParams</span><span class="p">,</span> <span class="n">input_ids</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">chunked_context_next_tokens</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">accepted_token_ids</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">accepted_lens</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">accepted_path_ids</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">next_draft_tokens</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">next_draft_paths</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">prev_draft_lens</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">prev_draft_paths</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">hidden_size_batch_level_starts</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">input_gen_tokens</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
<span class="n">input_spec_decoding_generation_lengths</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> Prepares inputs for the EagleNet inference.</span>
<span class="sd"> Visit tests/model/eagle/test_prepare_drafter_inputs.py for input/output examples.</span>
<span class="sd"> Parameters:</span>
<span class="sd"> layer_idx : int</span>
<span class="sd"> Index of the EagleNet. 0 means context phase EagleNet or EagleNet0,</span>
<span class="sd"> &gt; 0 means EagleNetX or generation phase of EagleNet</span>
<span class="sd"> num_layers : int</span>
<span class="sd"> Number of Eagle layers.</span>
<span class="sd"> max_non_leaves_per_layer : int</span>
<span class="sd"> Number of nodes that can be non leaf in the tree at each level of the tree.</span>
<span class="sd"> attention_params : AttentionParams</span>
<span class="sd"> input_ids : Tensor</span>
<span class="sd"> [num_tokens]</span>
<span class="sd"> Tokens ids, inputs to the base model.</span>
<span class="sd"> chunked_context_next_tokens : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> The first token of the next chunk in chunked context.</span>
<span class="sd"> -1 if current chunk is the last chunk or requests is in the gen phase.</span>
<span class="sd"> accepted_token_ids : Tensor</span>
<span class="sd"> [batch_size, max_path_len]</span>
<span class="sd"> Accepted tokens ids.</span>
<span class="sd"> accepted_lens : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Number of accepted tokens.</span>
<span class="sd"> accepted_path_ids : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Idx of the accepted path in prev_draft_paths.</span>
<span class="sd"> next_draft_tokens : Tensor</span>
<span class="sd"> [batch_size, max_decoding_draft_tokens]</span>
<span class="sd"> Tokens ids of the draft tokens being drafted by EagleNet</span>
<span class="sd"> next_draft_lens : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Number of drafted tokens in next_draft_tokens</span>
<span class="sd"> next_draft_paths : Tensor</span>
<span class="sd"> [batch_size, max_decoding_tokens, max_path_len]</span>
<span class="sd"> Paths of the draft tokens for the next iteration. In EAGLE-1 is the same as prev_draft_paths</span>
<span class="sd"> prev_draft_lens : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Number of draft tokens, inputs to the base model.</span>
<span class="sd"> 0 for ctx requests and actual draft len for gen requests.</span>
<span class="sd"> prev_draft_paths : Tensor</span>
<span class="sd"> [batch_size, max_decoding_tokens, max_path_len]</span>
<span class="sd"> Paths of the draft tokens inputs to the base model.</span>
<span class="sd"> hidden_size_batch_level_starts : Tensor</span>
<span class="sd"> [max_draft_path_len * batch_size + 1]</span>
<span class="sd"> Exclusive sum of the starts of the segments of the hidden states in the concatenated array.</span>
<span class="sd"> Hidden states shape is (flattened and w/o padding)</span>
<span class="sd"> [max_draft_path_len, batch_size, num_output_tokens_i_j], where num_output_tokens_i_j</span>
<span class="sd"> depends on the path of request j at level i.</span>
<span class="sd"> input_gen_tokens : Tensor</span>
<span class="sd"> [num_gen_tokens]</span>
<span class="sd"> Only needed to infer number of generation tokens from its shape. The content is irrelevant</span>
<span class="sd"> input_spec_decoding_generation_lengths : Tensor</span>
<span class="sd"> [num_gen_requests]</span>
<span class="sd"> Number of tokens for the base model. Only used to infer num_gen_requests from its shape, the content is irrelevant.</span>
<span class="sd"> Return:</span>
<span class="sd"> sequence_length : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Sequence length of the next EagleNet iteration.</span>
<span class="sd"> For EagleNet0 equals to the (prompt_len + num_generated_tokens + accepted_lens).</span>
<span class="sd"> For EagleNetX (X &gt; 0) (seq_len_eagle_net_0 + spec_decoding_generation_lengths).</span>
<span class="sd"> context_length : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Context length of the next EagleNet iteration.</span>
<span class="sd"> For EagleNet0 it is either the actual context length of the request (for ctx requests)</span>
<span class="sd"> or the number of accepted tokens in this iteration. EagleNet0&#39;s attn does chunked context attn.</span>
<span class="sd"> For EagleNetX (X &gt; 0), context length equals to the sequence length of the EagleNet0.</span>
<span class="sd"> spec_decoding_generation_lengths : Tensor</span>
<span class="sd"> [batch_size]</span>
<span class="sd"> Only relevant for EagleNetX (X &gt; 0).</span>
<span class="sd"> Number of draft tokens.</span>
<span class="sd"> spec_decoding_position_offsets : Tensor</span>
<span class="sd"> [batch_size, max_decoding_tokens]</span>
<span class="sd"> Only relevant for EagleNetX (X &gt; 0).</span>
<span class="sd"> Position offsets of the selected tokens from output_ids.</span>
<span class="sd"> spec_decoding_packed_mask : Tensor</span>
<span class="sd"> [batch_size, max_decoding_tokens, ceil(max_decoding_tokens / 32)]</span>
<span class="sd"> Only relevant for EagleNetX (X &gt; 0).</span>
<span class="sd"> uint32_t packed masks.</span>
<span class="sd"> output_ids : Tensor</span>
<span class="sd"> [batch_size * max_non_leaves_per_layer * layer_idx] for layer_idx &gt; 0</span>
<span class="sd"> [num_tokens - num_gen_tokens + num_gen_requests * (num_layers + 1)] for layer_idx == 0</span>
<span class="sd"> Token ids selected for the EagleNet iteration.</span>
<span class="sd"> Tensor&#39;s actual size is larger than num_output_tokens.</span>
<span class="sd"> position_ids : Tensor</span>
<span class="sd"> [batch_size] for layer_idx &gt; 0</span>
<span class="sd"> [num_tokens - num_gen_tokens + num_gen_requests * (num_layers + 1)] for layer_idx == 0</span>
<span class="sd"> Position ids of the tokens selected for the EagleNet iteration.</span>
<span class="sd"> Tensor&#39;s actual size is larger than num_output_tokens.</span>
<span class="sd"> hidden_states_indices : Tensor</span>
<span class="sd"> [batch_size * max_non_leaves_per_layer * layer_idx] for layer_idx &gt; 0</span>
<span class="sd"> [num_tokens - num_gen_tokens + num_gen_requests * (num_layers + 1)] for layer_idx == 0</span>
<span class="sd"> Indices of the hidden states to be selected from aggregated hidden states for the next iteration.</span>
<span class="sd"> Tensor&#39;s actual size is larger than num_output_tokens.</span>
<span class="sd"> last_token_indices : Tensor</span>
<span class="sd"> [batch_size * max_non_leaves_per_layer]</span>
<span class="sd"> Indices of the hidden states to be converted to logits after the next EagleNet iteration.</span>
<span class="sd"> Tensor&#39;s actual size is larger than num_output_tokens.</span>
<span class="sd"> num_last_token_indices : Tensor</span>
<span class="sd"> []</span>
<span class="sd"> Number of logits selected after the next EagleNet iteration.</span>
<span class="sd"> Tensors containing size of the outputs of V3 plugins. 0-D tensor.</span>
<span class="sd"> out_hidden_size_batch_level_starts : Tensor</span>
<span class="sd"> [max_draft_path_len * batch_size + 1]</span>
<span class="sd"> Same as hidden_size_batch_level_starts, but with updated path lens for the next level.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="n">plg_creator</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">get_plugin_registry</span><span class="p">()</span><span class="o">.</span><span class="n">get_creator</span><span class="p">(</span>
<span class="s1">&#39;EaglePrepareDrafterInputs&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="n">TRT_LLM_PLUGIN_NAMESPACE</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">plg_creator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">layer_idx</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginField</span><span class="p">(</span><span class="s2">&quot;layer_idx&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">layer_idx</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
<span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldType</span><span class="o">.</span><span class="n">INT32</span><span class="p">)</span>
<span class="n">num_layers</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginField</span><span class="p">(</span><span class="s2">&quot;num_layers&quot;</span><span class="p">,</span>
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">num_layers</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
<span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldType</span><span class="o">.</span><span class="n">INT32</span><span class="p">)</span>
<span class="n">max_non_leaves_per_layer</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginField</span><span class="p">(</span>
<span class="s2">&quot;max_non_leaves_per_layer&quot;</span><span class="p">,</span>
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">max_non_leaves_per_layer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
<span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldType</span><span class="o">.</span><span class="n">INT32</span><span class="p">)</span>
<span class="n">pfc</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">PluginFieldCollection</span><span class="p">(</span>
<span class="p">[</span><span class="n">layer_idx</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">max_non_leaves_per_layer</span><span class="p">])</span>
<span class="n">plugin</span> <span class="o">=</span> <span class="n">plg_creator</span><span class="o">.</span><span class="n">create_plugin</span><span class="p">(</span><span class="s2">&quot;eagle_prepare_drafter_inputs_plugin&quot;</span><span class="p">,</span>
<span class="n">pfc</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">TensorRTPhase</span><span class="o">.</span><span class="n">BUILD</span><span class="p">)</span>
<span class="n">plug_inputs</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">sequence_length</span><span class="p">,</span> <span class="n">attention_params</span><span class="o">.</span><span class="n">context_lengths</span><span class="p">,</span>
<span class="n">input_ids</span><span class="p">,</span> <span class="n">chunked_context_next_tokens</span><span class="p">,</span> <span class="n">accepted_token_ids</span><span class="p">,</span>
<span class="n">accepted_lens</span><span class="p">,</span> <span class="n">accepted_path_ids</span><span class="p">,</span> <span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span>
<span class="n">next_draft_paths</span><span class="p">,</span> <span class="n">prev_draft_lens</span><span class="p">,</span> <span class="n">prev_draft_paths</span><span class="p">,</span>
<span class="n">hidden_size_batch_level_starts</span><span class="p">,</span> <span class="n">input_gen_tokens</span><span class="p">,</span>
<span class="n">input_spec_decoding_generation_lengths</span>
<span class="p">]</span>
<span class="n">plug_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">trt_tensor</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">plug_inputs</span><span class="p">]</span>
<span class="n">shape_inputs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">default_trtnet</span><span class="p">()</span><span class="o">.</span><span class="n">add_plugin_v3</span><span class="p">(</span><span class="n">plug_inputs</span><span class="p">,</span> <span class="n">shape_inputs</span><span class="p">,</span> <span class="n">plugin</span><span class="p">)</span>
<span class="n">sequence_length</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">context_length</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">spec_decoding_generation_lengths</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span>
<span class="n">layer</span><span class="p">)</span>
<span class="n">spec_decoding_position_offsets</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">spec_decoding_packed_mask</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">output_ids</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">position_ids</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">6</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">hidden_states_indices</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">7</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">last_token_indices</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">8</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">num_last_token_indices</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">9</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>
<span class="n">out_hidden_size_batch_level_starts</span> <span class="o">=</span> <span class="n">_create_tensor</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
<span class="n">layer</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">([</span>
<span class="n">sequence_length</span><span class="p">,</span> <span class="n">context_length</span><span class="p">,</span> <span class="n">spec_decoding_generation_lengths</span><span class="p">,</span>
<span class="n">spec_decoding_position_offsets</span><span class="p">,</span> <span class="n">spec_decoding_packed_mask</span><span class="p">,</span> <span class="n">output_ids</span><span class="p">,</span>
<span class="n">position_ids</span><span class="p">,</span> <span class="n">hidden_states_indices</span><span class="p">,</span> <span class="n">last_token_indices</span><span class="p">,</span>
<span class="n">num_last_token_indices</span><span class="p">,</span> <span class="n">out_hidden_size_batch_level_starts</span>
<span class="p">])</span>
<span class="k">class</span><span class="w"> </span><span class="nc">EagleNet</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">logits_dtype</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drafter</span> <span class="o">=</span> <span class="n">LLaMAModel</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="o">.</span><span class="n">logits_dtype</span> <span class="o">=</span> <span class="n">logits_dtype</span>
<span class="n">vocab_size_padded</span> <span class="o">=</span> <span class="n">pad_vocab_size</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span>
<span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">tp_size</span><span class="p">)</span>
<span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">is_last_pp_rank</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span> <span class="o">=</span> <span class="n">ColumnLinear</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span>
<span class="n">vocab_size_padded</span><span class="p">,</span>
<span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
<span class="n">tp_group</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">tp_group</span><span class="p">,</span>
<span class="n">tp_size</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">tp_size</span><span class="p">,</span>
<span class="n">gather_output</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="bp">self</span><span class="o">.</span><span class="n">lm_head</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">input_ids</span><span class="p">,</span>
<span class="n">position_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">hidden_states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">last_token_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">spec_decoding_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">kv_cache_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">attention_params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">hidden_states</span><span class="p">,</span> <span class="n">cache</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">drafter</span><span class="p">(</span>
<span class="n">input_ids</span><span class="p">,</span>
<span class="n">position_ids</span><span class="o">=</span><span class="n">position_ids</span><span class="p">,</span>
<span class="n">use_cache</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">spec_decoding_params</span><span class="o">=</span><span class="n">spec_decoding_params</span><span class="p">,</span>
<span class="n">kv_cache_params</span><span class="o">=</span><span class="n">kv_cache_params</span><span class="p">,</span>
<span class="n">attention_params</span><span class="o">=</span><span class="n">attention_params</span><span class="p">,</span>
<span class="n">hidden_states_for_embed</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">is_last_pp_rank</span><span class="p">():</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="n">gather_last_token_logits</span><span class="p">(</span>
<span class="n">hidden_states</span><span class="p">,</span> <span class="n">last_token_indices</span><span class="p">,</span>
<span class="n">default_net</span><span class="p">()</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">remove_input_padding</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">),</span>
<span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">logits_dtype</span><span class="p">),</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="n">cache</span>
<span class="k">return</span> <span class="kc">None</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="n">cache</span>
<div class="viewcode-block" id="EagleForCausalLM">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.EagleForCausalLM">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">EagleForCausalLM</span><span class="p">(</span><span class="n">LLaMAForCausalLM</span><span class="p">):</span>
<span class="n">config_class</span> <span class="o">=</span> <span class="n">EagleConfig</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">EagleConfig</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">num_eagle_layers</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_non_leaves_per_layer</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">max_non_leaves_per_layer</span>
<span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">hidden_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span>
<span class="n">vocab_size_padded</span> <span class="o">=</span> <span class="n">pad_vocab_size</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span>
<span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">tp_size</span><span class="p">)</span>
<span class="n">eagle_net_config</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">eagle_net_config</span>
<span class="n">eagle_net_config</span><span class="o">.</span><span class="n">mapping</span> <span class="o">=</span> <span class="n">Mapping</span><span class="p">(</span><span class="n">world_size</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span>
<span class="n">rank</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">rank</span><span class="p">,</span>
<span class="n">cp_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">tp_size</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span>
<span class="n">pp_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">eagle_net_config</span><span class="o">.</span><span class="n">fc_after_embed</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">eagle_net_config</span><span class="o">.</span><span class="n">use_input_layernorm_in_first_layer</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">eagle_net_config</span><span class="o">.</span><span class="n">use_last_layernorm</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">eagle_net_config</span><span class="o">.</span><span class="n">layer_idx_offset</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">num_hidden_layers</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">is_last_pp_rank</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">eagle_nets</span> <span class="o">=</span> <span class="n">ModuleList</span><span class="p">([</span>
<span class="n">EagleNet</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">eagle_net_config</span><span class="p">,</span>
<span class="n">logits_dtype</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">logits_dtype</span><span class="p">)</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span><span class="p">)</span>
<span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">max_draft_len</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_prepare_drafter_inputs</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">layer_idx</span><span class="p">,</span> <span class="n">input_ids</span><span class="p">,</span> <span class="n">chunked_context_next_tokens</span><span class="p">,</span>
<span class="n">accepted_token_ids</span><span class="p">,</span> <span class="n">accepted_lens</span><span class="p">,</span> <span class="n">accepted_path_ids</span><span class="p">,</span>
<span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span>
<span class="n">prev_draft_lens</span><span class="p">,</span> <span class="n">prev_draft_paths</span><span class="p">,</span> <span class="n">input_attention_params</span><span class="p">,</span>
<span class="n">input_kv_cache_params</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span>
<span class="n">host_ctx_eagle_net_request_types</span><span class="p">,</span>
<span class="n">host_ctx_eagle_net_context_lengths</span><span class="p">,</span>
<span class="n">host_ctx_eagle_net_past_key_value_lengths</span><span class="p">,</span>
<span class="n">host_gen_eagle_net_request_types</span><span class="p">,</span>
<span class="n">host_gen_eagle_net_context_lengths</span><span class="p">,</span>
<span class="n">host_gen_eagle_net_past_key_value_lengths</span><span class="p">,</span>
<span class="n">hidden_size_batch_level_starts</span><span class="p">,</span> <span class="n">input_gen_tokens</span><span class="p">,</span>
<span class="n">input_spec_decoding_generation_lengths</span><span class="p">,</span> <span class="n">spec_decoding_use</span><span class="p">):</span>
<span class="n">drafter_inputs</span> <span class="o">=</span> <span class="n">eagle_prepare_drafter_inputs_plugin</span><span class="p">(</span>
<span class="n">layer_idx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_non_leaves_per_layer</span><span class="p">,</span>
<span class="n">input_attention_params</span><span class="p">,</span> <span class="n">input_ids</span><span class="p">,</span> <span class="n">chunked_context_next_tokens</span><span class="p">,</span>
<span class="n">accepted_token_ids</span><span class="p">,</span> <span class="n">accepted_lens</span><span class="p">,</span> <span class="n">accepted_path_ids</span><span class="p">,</span>
<span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span>
<span class="n">prev_draft_lens</span><span class="p">,</span> <span class="n">prev_draft_paths</span><span class="p">,</span> <span class="n">hidden_size_batch_level_starts</span><span class="p">,</span>
<span class="n">input_gen_tokens</span><span class="p">,</span> <span class="n">input_spec_decoding_generation_lengths</span><span class="p">)</span>
<span class="n">sequence_length</span><span class="p">,</span> <span class="n">context_lengths</span><span class="p">,</span> \
<span class="n">spec_decoding_generation_lengths</span><span class="p">,</span> <span class="n">spec_decoding_position_offsets</span><span class="p">,</span> \
<span class="n">spec_decoding_packed_mask</span><span class="p">,</span> <span class="n">output_ids</span><span class="p">,</span> <span class="n">position_ids</span><span class="p">,</span> <span class="n">hidden_states_indices</span><span class="p">,</span> \
<span class="n">last_token_indices</span><span class="p">,</span> <span class="n">num_last_token_indices</span><span class="p">,</span> <span class="n">out_hidden_size_batch_level_starts</span> \
<span class="o">=</span> <span class="n">drafter_inputs</span>
<span class="n">attention_params</span> <span class="o">=</span> <span class="n">input_attention_params</span>
<span class="n">kv_cache_params</span> <span class="o">=</span> <span class="n">input_kv_cache_params</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">sequence_length</span> <span class="o">=</span> <span class="n">sequence_length</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">context_lengths</span> <span class="o">=</span> <span class="n">context_lengths</span>
<span class="k">if</span> <span class="n">layer_idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">host_request_types</span> <span class="o">=</span> <span class="n">host_ctx_eagle_net_request_types</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">host_context_lengths</span> <span class="o">=</span> <span class="n">host_ctx_eagle_net_context_lengths</span>
<span class="n">kv_cache_params</span><span class="o">.</span><span class="n">host_past_key_value_lengths</span> <span class="o">=</span> <span class="n">host_ctx_eagle_net_past_key_value_lengths</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">host_request_types</span> <span class="o">=</span> <span class="n">host_gen_eagle_net_request_types</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">host_context_lengths</span> <span class="o">=</span> <span class="n">host_gen_eagle_net_context_lengths</span>
<span class="n">kv_cache_params</span><span class="o">.</span><span class="n">host_past_key_value_lengths</span> <span class="o">=</span> <span class="n">host_gen_eagle_net_past_key_value_lengths</span>
<span class="n">spec_decoding_params</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">layer_idx</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">spec_decoding_params</span> <span class="o">=</span> <span class="n">SpecDecodingParams</span><span class="p">(</span>
<span class="kc">True</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span><span class="p">,</span> <span class="n">spec_decoding_generation_lengths</span><span class="p">,</span>
<span class="n">spec_decoding_position_offsets</span><span class="p">,</span> <span class="n">spec_decoding_packed_mask</span><span class="p">,</span>
<span class="n">spec_decoding_use</span><span class="p">)</span>
<span class="c1"># Get hidden states for accepted ids</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slice_hidden_states</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">,</span>
<span class="n">hidden_states_indices</span><span class="p">)</span>
<span class="n">eagle_net_inputs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">output_ids</span>
<span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;position_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">position_ids</span>
<span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;last_token_indices&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">last_token_indices</span>
<span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;attention_params&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">attention_params</span>
<span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;kv_cache_params&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kv_cache_params</span>
<span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;spec_decoding_params&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">spec_decoding_params</span>
<span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;hidden_states&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hidden_states</span>
<span class="k">return</span> <span class="n">eagle_net_inputs</span><span class="p">,</span> <span class="n">out_hidden_size_batch_level_starts</span><span class="p">,</span> <span class="n">num_last_token_indices</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_slice_hidden_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="n">index_select</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">indices</span><span class="p">)</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="n">hidden_states</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">concat</span><span class="p">(</span>
<span class="p">[</span><span class="n">shape</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]),</span>
<span class="n">zero_is_placeholder</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">return</span> <span class="n">hidden_states</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_eagle_fwd_helper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lm_logits</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> EAGLE inference can be viewed as</span>
<span class="sd"> TRT_Engine(Target -&gt; Draft0 -&gt; Draft1 -&gt; .. -&gt; DraftK-1) -&gt; Runtime -&gt; TRT_Engine(..) -&gt; ..</span>
<span class="sd"> Target is Base model and Draft is EagleNet.</span>
<span class="sd"> Each EagleNet call can be viewed as call to Draft LLM in TensorRT-LLM.</span>
<span class="sd"> We have to</span>
<span class="sd"> 1. prepare input tensors before the EagleNet call (like in the the runtime),</span>
<span class="sd"> 2. call EagleNet,</span>
<span class="sd"> 3. decode draft tokens after the EagleNet.</span>
<span class="sd"> The only difference with normal execution of the Draft model is that in EAGLE,</span>
<span class="sd"> all these 3 things happen inside of the TensorRT engine execution.</span>
<span class="sd"> We do 1 and 3 inside of the plugins.</span>
<span class="sd"> For 1. We call eagle_prepare_drafter_inputs_plugin and for 3. eagle_draft_decoder_plugin.</span>
<span class="sd"> The first call to the EagleNet (Draft0 == EagleNet0) is the context phase.</span>
<span class="sd"> For context request we populate the KV cache of the EagleNet.</span>
<span class="sd"> For generation request that have accepted tokens we emulate KV cache reuse by doing chunked attention,</span>
<span class="sd"> where chunk is the newly accepted tokens -- all previous tokens are already in the KV cache.</span>
<span class="sd"> The following calls to the EagleNet (EagleNetX (X &gt; 0)) are generation phase.</span>
<span class="sd"> For each EagleNetX we select tokens based on the current path which are going to be used for the generation.</span>
<span class="sd"> Let&#39;s consider an example: prompt ABCD. EAGLE-1, i.e tree is fixed for the iteration.</span>
<span class="sd"> Tree:</span>
<span class="sd"> ┌───┐</span>
<span class="sd"> │ 0 │</span>
<span class="sd"> └─┬─┘</span>
<span class="sd"> ┌─────┴─────┐</span>
<span class="sd"> ┌─┴─┐ ┌─┴─┐ ┌─┴─┐</span>
<span class="sd"> │ 1 │ │ 2 │ │ 3 │</span>
<span class="sd"> └─┬─┘ └─┬─┘ └───┘</span>
<span class="sd"> ┌─┴─┐ ┌─┴─┐</span>
<span class="sd"> │ 4 │ │ 5 │</span>
<span class="sd"> └─┬─┘ └─┬─┘</span>
<span class="sd"> ┌─┴─┐ ┌─┴─┐</span>
<span class="sd"> │ 6 │ │ 7 │</span>
<span class="sd"> └───┘ └───┘</span>
<span class="sd"> First iteration of the TRT engine. Request is context request:</span>
<span class="sd"> 1. Base model is called for [ABCD] tokens produces token E.</span>
<span class="sd"> 2. Draft0 is called for tokens [BCDE] and produces</span>
<span class="sd"> three possibilities F, G and H for positions 1, 2 and 3 respectively.</span>
<span class="sd"> 3. Since H (position 3) is a leaf, it is not chosen as the input to Draft1 inference.</span>
<span class="sd"> 4. Draft1 is called for tokens [FG] with appropriate mask of:</span>
<span class="sd"> |F|G</span>
<span class="sd"> F|1|0</span>
<span class="sd"> G|0|1</span>
<span class="sd"> It produces tokens I and J for positions 4 and 5.</span>
<span class="sd"> 6. Draft2 is called for inputs [FGIJ] with mask of</span>
<span class="sd"> |F|G|I|J</span>
<span class="sd"> F|1|0|0|0</span>
<span class="sd"> G|0|1|0|0</span>
<span class="sd"> I|1|0|1|0</span>
<span class="sd"> J|0|1|0|1</span>
<span class="sd"> Note that we could&#39;ve stored FG in KV cache and provide only IJ tokens here</span>
<span class="sd"> with mask for past KV cache, but it is not supported in TensorRT-LLM attention at the moment.</span>
<span class="sd"> Draft2 produces tokens K and L at positions 6 and 7.</span>
<span class="sd"> 7. Resulting outputs are:</span>
<span class="sd"> 7.1 accepted_ids [E]</span>
<span class="sd"> 7.2 next_draft_tokens [FGHIJKL]</span>
<span class="sd"> Second iteration of the TRT engine. Request is the generation request.</span>
<span class="sd"> 1. Base model is called for [EFGHIJKL]. Let&#39;s assume that it accepts [FIKM], i.e. the left-most path in the tree.</span>
<span class="sd"> 2. Draft0 is called as context phase for [FIKM] -- to append to kv cache of the existing [BCDE].</span>
<span class="sd"> It produces tokens N, O and P for positions 1, 2 and 3.</span>
<span class="sd"> 3. Draft1 is called as generation phase for [NO] tokens.</span>
<span class="sd"> etc.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="n">input_tree_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;tree_params&quot;</span><span class="p">]</span>
<span class="n">draft_tokens</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;draft_tokens&#39;</span><span class="p">]</span>
<span class="n">draft_lens</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;draft_lens&#39;</span><span class="p">]</span>
<span class="n">eagle_temperature</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;eagle_temperature&#39;</span><span class="p">]</span>
<span class="n">rand_data_validation</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;rand_data_validation&#39;</span><span class="p">]</span>
<span class="n">posterior_alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;posterior_alpha&#39;</span><span class="p">]</span>
<span class="n">posterior_threshold</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;posterior_threshold&#39;</span><span class="p">]</span>
<span class="n">input_ids</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span>
<span class="n">chunked_context_next_tokens</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;chunked_context_next_tokens&#39;</span><span class="p">]</span>
<span class="n">host_ctx_eagle_net_request_types</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span>
<span class="s1">&#39;host_ctx_eagle_net_request_types&#39;</span><span class="p">]</span>
<span class="n">host_ctx_eagle_net_context_lengths</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span>
<span class="s1">&#39;host_ctx_eagle_net_context_lengths&#39;</span><span class="p">]</span>
<span class="n">host_ctx_eagle_net_past_key_value_lengths</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span>
<span class="s1">&#39;host_ctx_eagle_net_past_key_value_lengths&#39;</span><span class="p">]</span>
<span class="n">host_gen_eagle_net_request_types</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span>
<span class="s1">&#39;host_gen_eagle_net_request_types&#39;</span><span class="p">]</span>
<span class="n">host_gen_eagle_net_context_lengths</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span>
<span class="s1">&#39;host_gen_eagle_net_context_lengths&#39;</span><span class="p">]</span>
<span class="n">host_gen_eagle_net_past_key_value_lengths</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span>
<span class="s1">&#39;host_gen_eagle_net_past_key_value_lengths&#39;</span><span class="p">]</span>
<span class="n">input_gen_tokens</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;input_gen_tokens&quot;</span><span class="p">]</span>
<span class="n">greedy_sampling</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;greedy_sampling&quot;</span><span class="p">]</span>
<span class="c1"># Eagle-2</span>
<span class="n">use_dynamic_tree</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;use_dynamic_tree&#39;</span><span class="p">]</span>
<span class="n">dynamic_tree_max_topK</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;dynamic_tree_max_topK&#39;</span><span class="p">]</span>
<span class="n">prev_scores</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;prev_scores&#39;</span><span class="p">]</span>
<span class="n">current_expand_indices</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;current_expand_indices&#39;</span><span class="p">]</span>
<span class="n">all_layers_scores</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;all_layers_scores&#39;</span><span class="p">]</span>
<span class="n">all_layers_draft_token_ids</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;all_layers_draft_token_ids&#39;</span><span class="p">]</span>
<span class="n">all_layers_draft_token_ids_predecessor</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span>
<span class="s1">&#39;all_layers_draft_token_ids_predecessor&#39;</span><span class="p">]</span>
<span class="c1"># Sample target tokens and accept them</span>
<span class="c1"># next_draft_tokens, next_draft_lens, hidden_size_batch_level_starts are outputted here just to</span>
<span class="c1"># reserve the tensor with max size, which eagle_draft_decoder_plugin and</span>
<span class="c1"># eagle_prepare_drafter_inputs_plugin are going to directly write to</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">eagle_sample_and_accept_draft_plugin</span><span class="p">(</span>
<span class="n">lm_logits</span><span class="p">,</span> <span class="n">draft_tokens</span><span class="p">,</span> <span class="n">draft_lens</span><span class="p">,</span> <span class="n">eagle_temperature</span><span class="p">,</span>
<span class="n">rand_data_validation</span><span class="p">,</span> <span class="n">posterior_alpha</span><span class="p">,</span> <span class="n">posterior_threshold</span><span class="p">,</span>
<span class="n">input_tree_params</span><span class="p">,</span> <span class="n">greedy_sampling</span><span class="p">,</span> <span class="n">use_dynamic_tree</span><span class="p">)</span>
<span class="n">accepted_tokens</span><span class="p">,</span> <span class="n">num_accepted_tokens</span><span class="p">,</span> <span class="n">accepted_paths</span><span class="p">,</span> <span class="n">next_draft_tokens</span><span class="p">,</span> \
<span class="n">next_draft_lens</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span> <span class="n">hidden_size_batch_level_starts</span> <span class="o">=</span> <span class="n">output</span>
<span class="n">attention_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;attention_params&quot;</span><span class="p">]</span>
<span class="n">kv_cache_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;kv_cache_params&quot;</span><span class="p">]</span>
<span class="n">spec_decoding_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;spec_decoding_params&quot;</span><span class="p">]</span>
<span class="n">input_hidden_states</span> <span class="o">=</span> <span class="n">hidden_states</span>
<span class="c1"># Run EAGLE nets</span>
<span class="k">for</span> <span class="n">li</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span><span class="p">):</span>
<span class="c1"># Prepare EAGLE Net inputs.</span>
<span class="n">eagle_net_inputs</span><span class="p">,</span> <span class="n">hidden_size_batch_level_starts</span><span class="p">,</span> <span class="n">num_last_token_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_drafter_inputs</span><span class="p">(</span>
<span class="n">layer_idx</span><span class="o">=</span><span class="n">li</span><span class="p">,</span>
<span class="n">input_ids</span><span class="o">=</span><span class="n">input_ids</span><span class="p">,</span>
<span class="n">chunked_context_next_tokens</span><span class="o">=</span><span class="n">chunked_context_next_tokens</span><span class="p">,</span>
<span class="n">accepted_token_ids</span><span class="o">=</span><span class="n">accepted_tokens</span><span class="p">,</span>
<span class="n">accepted_lens</span><span class="o">=</span><span class="n">num_accepted_tokens</span><span class="p">,</span>
<span class="n">accepted_path_ids</span><span class="o">=</span><span class="n">accepted_paths</span><span class="p">,</span>
<span class="n">next_draft_tokens</span><span class="o">=</span><span class="n">next_draft_tokens</span><span class="p">,</span>
<span class="n">next_draft_lens</span><span class="o">=</span><span class="n">next_draft_lens</span><span class="p">,</span>
<span class="n">next_draft_paths</span><span class="o">=</span><span class="n">next_draft_paths</span><span class="p">,</span>
<span class="n">prev_draft_lens</span><span class="o">=</span><span class="n">draft_lens</span><span class="p">,</span>
<span class="n">prev_draft_paths</span><span class="o">=</span><span class="n">input_tree_params</span><span class="o">.</span><span class="n">paths</span><span class="p">,</span>
<span class="n">input_attention_params</span><span class="o">=</span><span class="n">attention_params</span><span class="p">,</span>
<span class="n">input_kv_cache_params</span><span class="o">=</span><span class="n">kv_cache_params</span><span class="p">,</span>
<span class="n">hidden_states</span><span class="o">=</span><span class="n">input_hidden_states</span><span class="p">,</span>
<span class="n">host_ctx_eagle_net_request_types</span><span class="o">=</span>
<span class="n">host_ctx_eagle_net_request_types</span><span class="p">,</span>
<span class="n">host_ctx_eagle_net_context_lengths</span><span class="o">=</span>
<span class="n">host_ctx_eagle_net_context_lengths</span><span class="p">,</span>
<span class="n">host_ctx_eagle_net_past_key_value_lengths</span><span class="o">=</span>
<span class="n">host_ctx_eagle_net_past_key_value_lengths</span><span class="p">,</span>
<span class="n">host_gen_eagle_net_request_types</span><span class="o">=</span>
<span class="n">host_gen_eagle_net_request_types</span><span class="p">,</span>
<span class="n">host_gen_eagle_net_context_lengths</span><span class="o">=</span>
<span class="n">host_gen_eagle_net_context_lengths</span><span class="p">,</span>
<span class="n">host_gen_eagle_net_past_key_value_lengths</span><span class="o">=</span>
<span class="n">host_gen_eagle_net_past_key_value_lengths</span><span class="p">,</span>
<span class="n">hidden_size_batch_level_starts</span><span class="o">=</span><span class="n">hidden_size_batch_level_starts</span><span class="p">,</span>
<span class="n">input_gen_tokens</span><span class="o">=</span><span class="n">input_gen_tokens</span><span class="p">,</span>
<span class="n">input_spec_decoding_generation_lengths</span><span class="o">=</span><span class="n">spec_decoding_params</span><span class="o">.</span>
<span class="n">spec_decoding_generation_lengths</span><span class="p">,</span>
<span class="n">spec_decoding_use</span><span class="o">=</span><span class="n">spec_decoding_params</span><span class="o">.</span><span class="n">spec_decoding_use</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">single_eagle_net_iter</span><span class="p">(</span><span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span>
<span class="n">next_draft_paths</span><span class="p">,</span> <span class="n">prev_scores</span><span class="p">,</span>
<span class="n">current_expand_indices</span><span class="p">,</span> <span class="n">all_layers_scores</span><span class="p">,</span>
<span class="n">all_layers_draft_token_ids</span><span class="p">,</span>
<span class="n">all_layers_draft_token_ids_predecessor</span><span class="p">):</span>
<span class="c1"># Run EAGLE Net</span>
<span class="c1"># NOTE: handle base net kv cache and eagle net kv cache are in the same tensor.</span>
<span class="c1"># EagleNet&#39;s kv cache is located starting at numBaseNetHiddenLayers in the kv tensor.</span>
<span class="n">logits</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">eagle_nets</span><span class="p">[</span><span class="n">li</span><span class="p">](</span>
<span class="o">**</span><span class="n">eagle_net_inputs</span><span class="p">)</span>
<span class="c1"># FIXME We need to take top_k_sampling as an input</span>
<span class="n">top_k_sampling</span> <span class="o">=</span> <span class="kc">True</span>
<span class="c1"># Decode draft tokens</span>
<span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span> <span class="n">prev_scores</span><span class="p">,</span> <span class="n">current_expand_indices</span><span class="p">,</span> <span class="n">all_layers_scores</span><span class="p">,</span> <span class="n">all_layers_draft_token_ids</span><span class="p">,</span> <span class="n">all_layers_draft_token_ids_predecessor</span> <span class="o">=</span> <span class="n">eagle_draft_decoder_plugin</span><span class="p">(</span>
<span class="n">li</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span><span class="p">,</span> <span class="n">top_k_sampling</span><span class="p">,</span> <span class="n">logits</span><span class="p">,</span>
<span class="n">num_last_token_indices</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span> <span class="n">use_dynamic_tree</span><span class="p">,</span>
<span class="n">dynamic_tree_max_topK</span><span class="p">,</span> <span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span>
<span class="n">prev_scores</span><span class="p">,</span> <span class="n">current_expand_indices</span><span class="p">,</span> <span class="n">all_layers_scores</span><span class="p">,</span>
<span class="n">all_layers_draft_token_ids</span><span class="p">,</span>
<span class="n">all_layers_draft_token_ids_predecessor</span><span class="p">)</span>
<span class="k">return</span> <span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span> <span class="n">prev_scores</span><span class="p">,</span> <span class="n">current_expand_indices</span><span class="p">,</span> <span class="n">all_layers_scores</span><span class="p">,</span> <span class="n">all_layers_draft_token_ids</span><span class="p">,</span> <span class="n">all_layers_draft_token_ids_predecessor</span>
<span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span> <span class="n">prev_scores</span><span class="p">,</span> \
<span class="n">current_expand_indices</span><span class="p">,</span> <span class="n">all_layers_scores</span><span class="p">,</span> <span class="n">all_layers_draft_token_ids</span><span class="p">,</span> <span class="n">all_layers_draft_token_ids_predecessor</span> \
<span class="o">=</span> <span class="n">single_eagle_net_iter</span><span class="p">(</span><span class="n">next_draft_tokens</span><span class="p">,</span> <span class="n">next_draft_lens</span><span class="p">,</span> <span class="n">next_draft_paths</span><span class="p">,</span> \
<span class="n">prev_scores</span><span class="p">,</span> <span class="n">current_expand_indices</span><span class="p">,</span> <span class="n">all_layers_scores</span><span class="p">,</span> \
<span class="n">all_layers_draft_token_ids</span><span class="p">,</span> <span class="n">all_layers_draft_token_ids_predecessor</span><span class="p">)</span>
<span class="c1"># Update params</span>
<span class="k">if</span> <span class="n">li</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">eagle_net_0_sequence_length</span> <span class="o">=</span> <span class="n">eagle_net_inputs</span><span class="p">[</span>
<span class="s2">&quot;attention_params&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">sequence_length</span>
<span class="n">input_hidden_states</span> <span class="o">=</span> <span class="n">hidden_states</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">input_hidden_states</span> <span class="o">=</span> <span class="n">concat</span><span class="p">(</span>
<span class="p">[</span><span class="n">input_hidden_states</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">])</span>
<span class="n">kv_cache_params</span> <span class="o">=</span> <span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;kv_cache_params&quot;</span><span class="p">]</span>
<span class="n">attention_params</span> <span class="o">=</span> <span class="n">eagle_net_inputs</span><span class="p">[</span><span class="s2">&quot;attention_params&quot;</span><span class="p">]</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">context_lengths</span> <span class="o">=</span> <span class="n">eagle_net_0_sequence_length</span>
<span class="n">attention_params</span><span class="o">.</span><span class="n">sequence_length</span> <span class="o">=</span> <span class="n">eagle_net_0_sequence_length</span>
<span class="c1"># Mark tensors as output</span>
<span class="n">accepted_tokens</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;accepted_tokens&#39;</span><span class="p">)</span>
<span class="n">num_accepted_tokens</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;num_accepted_tokens&#39;</span><span class="p">)</span>
<span class="n">accepted_paths</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;accepted_paths&#39;</span><span class="p">)</span>
<span class="n">next_draft_tokens</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;next_draft_tokens&#39;</span><span class="p">)</span>
<span class="n">next_draft_lens</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;next_draft_lens&#39;</span><span class="p">)</span>
<span class="n">next_draft_paths</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;next_draft_paths&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">next_draft_tokens</span>
<div class="viewcode-block" id="EagleForCausalLM.forward">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.EagleForCausalLM.forward">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">extra_args</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">&quot;draft_tokens&quot;</span><span class="p">,</span> <span class="s2">&quot;draft_lens&quot;</span><span class="p">,</span> <span class="s2">&quot;eagle_temperature&quot;</span><span class="p">,</span>
<span class="s2">&quot;rand_data_validation&quot;</span><span class="p">,</span> <span class="s2">&quot;tree_params&quot;</span><span class="p">,</span>
<span class="s2">&quot;host_ctx_eagle_net_request_types&quot;</span><span class="p">,</span>
<span class="s2">&quot;host_ctx_eagle_net_context_lengths&quot;</span><span class="p">,</span>
<span class="s2">&quot;host_ctx_eagle_net_past_key_value_lengths&quot;</span><span class="p">,</span>
<span class="s2">&quot;host_gen_eagle_net_request_types&quot;</span><span class="p">,</span>
<span class="s2">&quot;host_gen_eagle_net_context_lengths&quot;</span><span class="p">,</span>
<span class="s2">&quot;host_gen_eagle_net_past_key_value_lengths&quot;</span><span class="p">,</span> <span class="s2">&quot;input_gen_tokens&quot;</span><span class="p">,</span>
<span class="s2">&quot;chunked_context_next_tokens&quot;</span><span class="p">,</span> <span class="s2">&quot;posterior_alpha&quot;</span><span class="p">,</span>
<span class="s2">&quot;posterior_threshold&quot;</span><span class="p">,</span> <span class="s2">&quot;greedy_sampling&quot;</span><span class="p">,</span> <span class="s2">&quot;use_dynamic_tree&quot;</span><span class="p">,</span>
<span class="s2">&quot;dynamic_tree_max_topK&quot;</span><span class="p">,</span> <span class="s2">&quot;prev_scores&quot;</span><span class="p">,</span> <span class="s2">&quot;current_expand_indices&quot;</span><span class="p">,</span>
<span class="s2">&quot;all_layers_scores&quot;</span><span class="p">,</span> <span class="s2">&quot;all_layers_draft_token_ids&quot;</span><span class="p">,</span>
<span class="s2">&quot;all_layers_draft_token_ids_predecessor&quot;</span>
<span class="p">]</span>
<span class="n">base_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">extra_args</span><span class="p">}</span>
<span class="c1"># Base model forward</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">base_kwargs</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">is_last_pp_rank</span><span class="p">():</span>
<span class="n">extra_args</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;hidden_states&quot;</span><span class="p">]</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">extra_args</span><span class="p">}</span>
<span class="k">assert</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;use_cache&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">default_net</span><span class="p">(</span>
<span class="p">)</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">paged_kv_cache</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">is_last_pp_rank</span><span class="p">():</span>
<span class="n">lm_logits</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="n">all_hidden_states</span> <span class="o">=</span> <span class="n">hidden_states</span>
<span class="n">lm_logits</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">lm_logits</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">logits_dtype</span><span class="p">)</span>
<span class="c1"># Call eagle logic to accept prev draft tokens and predict next draft tokens</span>
<span class="n">next_draft_tokens</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eagle_fwd_helper</span><span class="p">(</span><span class="n">lm_logits</span><span class="p">,</span>
<span class="n">all_hidden_states</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hidden_states</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;hidden_states_output&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapping</span><span class="o">.</span><span class="n">is_last_pp_rank</span><span class="p">():</span>
<span class="k">return</span> <span class="n">next_draft_tokens</span>
<span class="k">return</span> <span class="n">hidden_states</span></div>
<div class="viewcode-block" id="EagleForCausalLM.prepare_inputs">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.EagleForCausalLM.prepare_inputs">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">prepare_inputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Inputs needed:</span>
<span class="sd"> device_request_types: [bs]</span>
<span class="sd"> draft_tokens: [bs, max_draft_len]</span>
<span class="sd"> draft_lens: [bs]</span>
<span class="sd"> spec_decoding_generation_lengths: [bs]</span>
<span class="sd"> spec_decoding_position_offsets: [bs, max_gen_tokens]</span>
<span class="sd"> spec_decoding_packed_mask: [bs, max_draft_len, packed_length] **</span>
<span class="sd"> eagle_temperature: [bs]</span>
<span class="sd"> rand_data_validation: [bs, max_draft_tokens]</span>
<span class="sd"> ** The mask is tricky since the boolean mask will need to be</span>
<span class="sd"> packed in runtime. So, the last dim will be:</span>
<span class="sd"> packed_length = ceil((max_draft_tokens+1)/32)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">default_range</span> <span class="o">=</span> <span class="n">GenerationMixin</span><span class="o">.</span><span class="n">default_range</span>
<span class="n">remove_input_padding</span> <span class="o">=</span> <span class="n">default_net</span><span class="p">()</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">remove_input_padding</span>
<span class="n">use_gpt_attention_plugin</span> <span class="o">=</span> <span class="n">default_net</span><span class="p">(</span>
<span class="p">)</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">gpt_attention_plugin</span>
<span class="n">use_gemm_plugin</span> <span class="o">=</span> <span class="n">default_net</span><span class="p">()</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">gemm_plugin</span>
<span class="n">paged_kv_cache</span> <span class="o">=</span> <span class="n">default_net</span><span class="p">()</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">paged_kv_cache</span>
<span class="n">multiple_profiles</span> <span class="o">=</span> <span class="n">default_net</span><span class="p">()</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">multiple_profiles</span>
<span class="n">max_batch_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;max_batch_size&#39;</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">max_batch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">gt_range</span> <span class="o">=</span> <span class="n">default_range</span><span class="p">(</span><span class="n">max_batch_size</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">min_range</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">opt_offset</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;speculative_decoding_draft_tokens_external&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;max_draft_len&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;spec_decoding_is_generation_length_variable&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">kwargs</span><span class="p">[</span>
<span class="s1">&#39;num_hidden_layers&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_hidden_layers</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">eagle_net_config</span><span class="o">.</span><span class="n">num_hidden_layers</span>
<span class="c1"># Call base class prepare inputs</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">prepare_inputs</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;spec_decoding_params&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">kv_cache_type</span> <span class="o">=</span> <span class="n">KVCacheType</span><span class="o">.</span><span class="n">PAGED</span> <span class="k">if</span> <span class="n">paged_kv_cache</span> <span class="k">else</span> <span class="n">KVCacheType</span><span class="o">.</span><span class="n">CONTINUOUS</span>
<span class="n">enable_ctx_gen_opt_profiles</span> <span class="o">=</span> <span class="n">GenerationMixin</span><span class="o">.</span><span class="n">has_ctx_gen_opt_profiles</span><span class="p">(</span>
<span class="n">use_gpt_attention_plugin</span><span class="o">=</span><span class="n">use_gpt_attention_plugin</span><span class="p">,</span>
<span class="n">use_gemm_plugin</span><span class="o">=</span><span class="n">use_gemm_plugin</span><span class="p">,</span>
<span class="n">remove_input_padding</span><span class="o">=</span><span class="n">remove_input_padding</span><span class="p">,</span>
<span class="n">kv_cache_type</span><span class="o">=</span><span class="n">kv_cache_type</span><span class="p">)</span>
<span class="n">num_profiles</span><span class="p">,</span> <span class="n">ranges</span> <span class="o">=</span> <span class="n">GenerationMixin</span><span class="o">.</span><span class="n">get_profiles_ranges</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_beam_width</span><span class="o">=</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;max_beam_width&#39;</span><span class="p">],</span>
<span class="n">max_input_len</span><span class="o">=</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;max_input_len&#39;</span><span class="p">],</span>
<span class="n">max_num_tokens</span><span class="o">=</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;max_num_tokens&#39;</span><span class="p">],</span>
<span class="n">max_draft_len</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span><span class="p">,</span>
<span class="n">opt_batch_size</span><span class="o">=</span><span class="kc">None</span>
<span class="k">if</span> <span class="s1">&#39;opt_batch_size&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;opt_batch_size&#39;</span><span class="p">],</span>
<span class="n">opt_num_tokens</span><span class="o">=</span><span class="kc">None</span>
<span class="k">if</span> <span class="s1">&#39;opt_num_tokens&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;opt_num_tokens&#39;</span><span class="p">],</span>
<span class="n">enable_ctx_gen_opt_profiles</span><span class="o">=</span><span class="n">enable_ctx_gen_opt_profiles</span><span class="p">,</span>
<span class="n">multiple_profiles</span><span class="o">=</span><span class="n">multiple_profiles</span><span class="p">,</span>
<span class="n">kv_cache_type</span><span class="o">=</span><span class="n">kv_cache_type</span><span class="p">)</span>
<span class="n">bb_range</span> <span class="o">=</span> <span class="n">ranges</span><span class="p">[</span><span class="s1">&#39;bb_range&#39;</span><span class="p">]</span>
<span class="n">draft_len_range</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bb_range</span><span class="p">))]</span>
<span class="n">decoding_len_range</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bb_range</span><span class="p">))]</span>
<span class="n">path_len_range</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bb_range</span><span class="p">))]</span>
<span class="n">gen_tokens_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">gt_range</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bb_range</span><span class="p">))]</span>
<span class="n">num_eagle_layers_range</span> <span class="o">=</span> <span class="p">[</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bb_range</span><span class="p">))</span>
<span class="p">]</span>
<span class="n">draft_len_square_range</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span><span class="p">)</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bb_range</span><span class="p">))]</span>
<span class="n">draft_tokens</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;draft_tokens&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;draft_len&#39;</span><span class="p">,</span> <span class="n">draft_len_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">draft_lens</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;draft_lens&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">eagle_temperature</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;eagle_temperature&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">rand_data_validation</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;rand_data_validation&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;decoding_len&#39;</span><span class="p">,</span> <span class="n">decoding_len_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">posterior_alpha</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;posterior_alpha&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">posterior_threshold</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;posterior_threshold&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">draft_paths</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;draft_paths&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;decoding_len&#39;</span><span class="p">,</span> <span class="n">decoding_len_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;path_len&#39;</span><span class="p">,</span> <span class="n">path_len_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">host_ctx_eagle_net_request_types</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;host_ctx_eagle_net_request_types&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">host_ctx_eagle_net_context_lengths</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;host_ctx_eagle_net_context_lengths&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">host_ctx_eagle_net_past_key_value_lengths</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;host_ctx_eagle_net_past_key_value_lengths&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">host_gen_eagle_net_request_types</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;host_gen_eagle_net_request_types&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">host_gen_eagle_net_context_lengths</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;host_gen_eagle_net_context_lengths&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">host_gen_eagle_net_past_key_value_lengths</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;host_gen_eagle_net_past_key_value_lengths&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">input_gen_tokens</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;input_gen_tokens&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;gen_tokens&#39;</span><span class="p">,</span> <span class="n">gen_tokens_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">chunked_context_next_tokens</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;chunked_context_next_tokens&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">greedy_sampling</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;greedy_sampling&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">use_dynamic_tree</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;use_dynamic_tree&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">dynamic_tree_max_topK</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;dynamic_tree_max_topK&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">prev_scores</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;prev_scores&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;draft_len&#39;</span><span class="p">,</span> <span class="n">draft_len_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">current_expand_indices</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;current_expand_indices&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span><span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;draft_len&#39;</span><span class="p">,</span> <span class="n">draft_len_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">all_layers_scores</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;all_layers_scores&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span>
<span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span>
<span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;num_eagle_layers&#39;</span><span class="p">,</span>
<span class="n">num_eagle_layers_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;draft_len_square&#39;</span><span class="p">,</span>
<span class="n">draft_len_square_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">all_layers_draft_token_ids</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;all_layers_draft_token_ids&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span>
<span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span>
<span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;num_eagle_layers&#39;</span><span class="p">,</span> <span class="n">num_eagle_layers_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;draft_len_square&#39;</span><span class="p">,</span> <span class="n">draft_len_square_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">all_layers_draft_token_ids_predecessor</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;all_layers_draft_token_ids_predecessor&#39;</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
<span class="n">shape</span><span class="o">=</span><span class="p">[</span>
<span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_eagle_layers</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_draft_len</span>
<span class="p">],</span>
<span class="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;num_eagle_layers&#39;</span><span class="p">,</span> <span class="n">num_eagle_layers_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;draft_len_square&#39;</span><span class="p">,</span> <span class="n">draft_len_square_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">tree_params</span> <span class="o">=</span> <span class="n">TreeParams</span><span class="p">(</span><span class="n">paths</span><span class="o">=</span><span class="n">draft_paths</span><span class="p">)</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;draft_tokens&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">draft_tokens</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;draft_lens&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">draft_lens</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;eagle_temperature&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">eagle_temperature</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;posterior_alpha&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">posterior_alpha</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;posterior_threshold&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">posterior_threshold</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;rand_data_validation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">rand_data_validation</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;tree_params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tree_params</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;host_ctx_eagle_net_request_types&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">host_ctx_eagle_net_request_types</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;host_ctx_eagle_net_context_lengths&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">host_ctx_eagle_net_context_lengths</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;host_ctx_eagle_net_past_key_value_lengths&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">host_ctx_eagle_net_past_key_value_lengths</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;host_gen_eagle_net_request_types&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">host_gen_eagle_net_request_types</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;host_gen_eagle_net_context_lengths&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">host_gen_eagle_net_context_lengths</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;host_gen_eagle_net_past_key_value_lengths&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">host_gen_eagle_net_past_key_value_lengths</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;input_gen_tokens&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_gen_tokens</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;chunked_context_next_tokens&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">chunked_context_next_tokens</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;greedy_sampling&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">greedy_sampling</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;use_dynamic_tree&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">use_dynamic_tree</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;dynamic_tree_max_topK&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dynamic_tree_max_topK</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;prev_scores&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">prev_scores</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;current_expand_indices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">current_expand_indices</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;all_layers_scores&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_layers_scores</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;all_layers_draft_token_ids&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_layers_draft_token_ids</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;all_layers_draft_token_ids_predecessor&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_layers_draft_token_ids_predecessor</span>
<span class="k">return</span> <span class="n">inputs</span></div>
<div class="viewcode-block" id="EagleForCausalLM.from_hugging_face">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.EagleForCausalLM.from_hugging_face">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_hugging_face</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">hf_model_or_dir</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="s1">&#39;transformers.PreTrainedModel&#39;</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="s1">&#39;auto&#39;</span><span class="p">,</span>
<span class="n">mapping</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Mapping</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">quant_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">QuantConfig</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="k">assert</span> <span class="n">hf_model_or_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">speculative_model_dir</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;speculative_model&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">tllm_config</span> <span class="o">=</span> <span class="n">EagleConfig</span><span class="o">.</span><span class="n">from_hugging_face</span><span class="p">(</span><span class="n">hf_model_or_dir</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">mapping</span><span class="o">=</span><span class="n">mapping</span><span class="p">,</span>
<span class="n">quant_config</span><span class="o">=</span><span class="n">quant_config</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="c1"># for rank in range(mapping.world_size):</span>
<span class="n">tllm_config</span><span class="o">.</span><span class="n">mapping</span> <span class="o">=</span> <span class="n">Mapping</span><span class="p">(</span><span class="n">world_size</span><span class="o">=</span><span class="n">mapping</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span>
<span class="n">rank</span><span class="o">=</span><span class="n">mapping</span><span class="o">.</span><span class="n">rank</span><span class="p">,</span>
<span class="n">cp_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">tp_size</span><span class="o">=</span><span class="n">mapping</span><span class="o">.</span><span class="n">tp_size</span><span class="p">,</span>
<span class="n">pp_size</span><span class="o">=</span><span class="n">mapping</span><span class="o">.</span><span class="n">pp_size</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">EagleForCausalLM</span><span class="p">(</span><span class="n">tllm_config</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">check_and_update</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="s1">&#39;tllm_to_externel_key_dict&#39;</span><span class="p">):</span>
<span class="n">module</span><span class="o">.</span><span class="n">tllm_to_externel_key_dict</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">module</span><span class="o">.</span><span class="n">tllm_to_externel_key_dict</span> <span class="o">=</span> <span class="nb">dict</span>
<span class="k">def</span><span class="w"> </span><span class="nf">copy</span><span class="p">(</span><span class="n">tensors</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="k">if</span> <span class="kc">None</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">:</span>
<span class="k">return</span> <span class="n">tensors</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="p">[</span><span class="n">tensor</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">tensors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">tensors</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">tensors</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="n">shared_weight_prefixs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">tllm_weights</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">customized_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;drafter&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">}</span>
<span class="k">if</span> <span class="n">speculative_model_dir</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="c1"># Single checkpoint for ModelOpt</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">eagle_net</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">eagle_nets</span><span class="p">):</span>
<span class="n">check_and_update</span><span class="p">(</span><span class="n">eagle_net</span><span class="o">.</span><span class="n">drafter</span><span class="o">.</span><span class="n">fc</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;fc&quot;</span><span class="p">:</span> <span class="s2">&quot;fc&quot;</span><span class="p">})</span>
<span class="n">check_and_update</span><span class="p">(</span><span class="n">eagle_net</span><span class="o">.</span><span class="n">drafter</span><span class="o">.</span><span class="n">vocab_embedding</span><span class="p">,</span>
<span class="p">{</span><span class="sa">f</span><span class="s2">&quot;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">:</span> <span class="s2">&quot;model&quot;</span><span class="p">})</span>
<span class="n">check_and_update</span><span class="p">(</span><span class="n">eagle_net</span><span class="o">.</span><span class="n">lm_head</span><span class="p">,</span> <span class="p">{</span><span class="sa">f</span><span class="s2">&quot;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">})</span>
<span class="n">shared_weight_prefixs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">customized_dict</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;eagle_module&#39;</span>
<span class="n">loader</span> <span class="o">=</span> <span class="n">ModelWeightsLoader</span><span class="p">(</span><span class="n">speculative_model_dir</span><span class="p">,</span> <span class="n">customized_dict</span><span class="p">)</span>
<span class="n">loader</span><span class="o">.</span><span class="n">update_key_mapping</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="k">for</span> <span class="n">tllm_key</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()):</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">([</span>
<span class="n">tllm_key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="n">prefix</span><span class="p">)</span>
<span class="k">for</span> <span class="n">prefix</span> <span class="ow">in</span> <span class="n">shared_weight_prefixs</span>
<span class="p">]):</span>
<span class="n">tllm_weights</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">tllm_key</span><span class="p">,</span> <span class="n">preprocess</span><span class="o">=</span><span class="n">copy</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tllm_weights</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">tllm_key</span><span class="p">))</span>
<span class="n">loader</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tllm_weights</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Double checkpoint for HF</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">eagle_net</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">eagle_nets</span><span class="p">):</span>
<span class="n">check_and_update</span><span class="p">(</span><span class="n">eagle_net</span><span class="o">.</span><span class="n">drafter</span><span class="o">.</span><span class="n">fc</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;fc&quot;</span><span class="p">:</span> <span class="s2">&quot;fc&quot;</span><span class="p">})</span>
<span class="n">check_and_update</span><span class="p">(</span><span class="n">eagle_net</span><span class="o">.</span><span class="n">drafter</span><span class="o">.</span><span class="n">vocab_embedding</span><span class="p">,</span>
<span class="p">{</span><span class="sa">f</span><span class="s2">&quot;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">})</span>
<span class="n">check_and_update</span><span class="p">(</span><span class="n">eagle_net</span><span class="o">.</span><span class="n">lm_head</span><span class="p">,</span> <span class="p">{</span><span class="sa">f</span><span class="s2">&quot;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">})</span>
<span class="n">shared_weight_prefixs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">customized_dict</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
<span class="c1"># Load base model</span>
<span class="n">base_loader</span> <span class="o">=</span> <span class="n">ModelWeightsLoader</span><span class="p">(</span><span class="n">hf_model_or_dir</span><span class="p">)</span>
<span class="n">base_loader</span><span class="o">.</span><span class="n">update_key_mapping</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="k">for</span> <span class="n">tllm_key</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()):</span>
<span class="n">tllm_weights</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">base_loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;transformer.&quot;</span> <span class="o">+</span> <span class="n">tllm_key</span><span class="p">))</span>
<span class="n">tllm_weights</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">base_loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;lm_head.weight&quot;</span><span class="p">))</span>
<span class="c1"># for idx in range(args.num_eagle_layers):</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
<span class="n">tllm_weights</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
<span class="n">base_loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;eagle_nets.</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">.lm_head.weight&quot;</span><span class="p">,</span>
<span class="n">preprocess</span><span class="o">=</span><span class="n">copy</span><span class="p">))</span>
<span class="c1"># Load eagle model</span>
<span class="n">eagle_loader</span> <span class="o">=</span> <span class="n">ModelWeightsLoader</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">speculative_model_dir</span><span class="p">),</span>
<span class="n">customized_dict</span><span class="p">)</span>
<span class="n">eagle_loader</span><span class="o">.</span><span class="n">update_key_mapping</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="k">for</span> <span class="n">tllm_key</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">eagle_nets</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">tllm_key</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">&quot;lm_head.weight&quot;</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">([</span>
<span class="n">tllm_key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="n">prefix</span><span class="p">)</span>
<span class="k">for</span> <span class="n">prefix</span> <span class="ow">in</span> <span class="n">shared_weight_prefixs</span>
<span class="p">]):</span>
<span class="n">tllm_weights</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
<span class="n">eagle_loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;eagle_nets.&quot;</span> <span class="o">+</span> <span class="n">tllm_key</span><span class="p">,</span>
<span class="n">preprocess</span><span class="o">=</span><span class="n">copy</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tllm_weights</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
<span class="n">eagle_loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;eagle_nets.&quot;</span> <span class="o">+</span> <span class="n">tllm_key</span><span class="p">))</span>
<span class="n">base_loader</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tllm_weights</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span></div>
</div>
</pre></div>
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<p>This page is generated by TensorRT-LLM commit <a href="https://github.com/NVIDIA/TensorRT-LLM/tree/9c012d5">9c012d5</a>.</p>
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