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TensorRT-LLMs/_modules/tensorrt_llm/models/redrafter/model.html
Kaiyu Xie 2a8d25db7c Update GitHub pages in root to v1.0.0rc1
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<li class="toctree-l1"><a class="reference internal" href="../../../../overview.html">Overview</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API</span></p>
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<li class="toctree-l1 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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<li class="toctree-l2"><a class="reference internal" href="../../../../examples/curl_chat_client.html">Curl Chat Client</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Model Definition API</span></p>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Architecture</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/lora.html">Run gpt-2b + LoRA using Executor / cpp runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
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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/benchmarking-default-performance.html">Benchmarking Default Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../../../performance/performance-tuning-guide/useful-build-time-flags.html">Useful Build-Time Flags</a></li>
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<h1>Source code for tensorrt_llm.models.redrafter.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">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">tensorrt_llm._common</span><span class="w"> </span><span class="kn">import</span> <span class="n">default_net</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.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">tensorrt_llm.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">cast</span><span class="p">,</span> <span class="n">categorical_sample</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.models</span><span class="w"> </span><span class="kn">import</span> <span class="n">LLaMAForCausalLM</span><span class="p">,</span> <span class="n">QWenForCausalLM</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">..._utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">pad_vocab_size</span><span class="p">,</span> <span class="n">str_dtype_to_trt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.drafter</span><span class="w"> </span><span class="kn">import</span> <span class="n">Drafter</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.redrafter_helper</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">_beam_search_candidates</span><span class="p">,</span> <span class="n">_beams2tree</span><span class="p">,</span>
<span class="n">_process_logits_and_hidden_states</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ReDrafterMixin</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="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">dtype</span> <span class="o">=</span> <span class="n">str_dtype_to_trt</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</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="bp">self</span><span class="o">.</span><span class="n">drafter</span> <span class="o">=</span> <span class="n">Drafter</span><span class="o">.</span><span class="n">from_config</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">vocab_size_padded</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_beams</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">redrafter_num_beams</span>
<span class="bp">self</span><span class="o">.</span><span class="n">beam_candidate_length</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">redrafter_draft_len_per_beam</span>
<span class="bp">self</span><span class="o">.</span><span class="n">beam_length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_candidate_length</span> <span class="o">+</span> <span class="mi">1</span> <span class="c1"># including true token</span>
<span class="bp">self</span><span class="o">.</span><span class="n">greedy_search</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">redrafter_greedy_search</span>
<span class="bp">self</span><span class="o">.</span><span class="n">is_rnn</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">redrafter_is_rnn</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">drafter</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2"> != </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">drafter</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_fwd_helper</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">lm_logits</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">drafter</span><span class="p">,</span>
<span class="n">kwargs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> Must enable remove_input_padding:</span>
<span class="sd"> hidden_states [total_tokens, H]</span>
<span class="sd"> lm_logits [total_tokens, V]</span>
<span class="sd"> 1. process_logits: context vs gen</span>
<span class="sd"> a. Context: just return the last hidden states, and logits/probs</span>
<span class="sd"> b. Gen:</span>
<span class="sd"> i. verify: use lm_logits, draft_probs, draft_indices, draft_tokens</span>
<span class="sd"> ii. select hidden state and update probs</span>
<span class="sd"> 3. Sample token based on probs</span>
<span class="sd"> 4. Generate candidates using hidden_states, sampled token</span>
<span class="sd"> 5. Using beams, generate validation buffers, mark them as output</span>
<span class="sd"> 6. Mark all the outputs</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="n">num_beams</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_beams</span>
<span class="n">beam_length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_length</span>
<span class="c1"># Get the inputs needed</span>
<span class="n">rand_data_sample</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;rand_data_sample&#39;</span><span class="p">]</span>
<span class="n">position_ids_base</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;position_ids_base&#39;</span><span class="p">]</span>
<span class="c1"># Step 1: Process logits and hidden states</span>
<span class="c1"># process the base model output (verify for gen-phase)</span>
<span class="n">probs</span><span class="p">,</span> <span class="n">draft_input</span><span class="p">,</span> <span class="n">num_accepted_tokens</span><span class="p">,</span> \
<span class="n">accepted_beam_index</span> <span class="o">=</span> <span class="n">_process_logits_and_hidden_states</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="n">kwargs</span><span class="p">)</span>
<span class="c1"># NOTE: num_accepted_tokens doesn&#39;t include true token so add 1 here</span>
<span class="n">num_accepted_tokens</span> <span class="o">=</span> <span class="n">num_accepted_tokens</span> <span class="o">+</span> <span class="mi">1</span>
<span class="c1"># At this point:</span>
<span class="c1"># probs : [bs, V]</span>
<span class="c1"># hidden_states : [bs, H]</span>
<span class="c1"># Step 2: Sample token</span>
<span class="n">next_token</span> <span class="o">=</span> <span class="n">categorical_sample</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">rand_data_sample</span><span class="p">)</span>
<span class="c1"># Step 3: beam search</span>
<span class="n">new_draft_tokens</span><span class="p">,</span> <span class="n">new_draft_logits</span> <span class="o">=</span> <span class="n">_beam_search_candidates</span><span class="p">(</span>
<span class="n">draft_input</span><span class="p">,</span> <span class="n">next_token</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">drafter</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_beams</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">beam_length</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_rnn</span><span class="p">)</span>
<span class="c1"># Step 4: tree processing</span>
<span class="n">active_tokens_flattened</span><span class="p">,</span> <span class="n">new_draft_token_indices</span><span class="p">,</span> <span class="n">new_mask</span><span class="p">,</span> \
<span class="n">new_position_offsets</span><span class="p">,</span> <span class="n">packed_position_ids</span><span class="p">,</span> <span class="n">next_num_gen_tokens</span><span class="p">,</span> <span class="n">max_gen_token</span><span class="p">,</span> \
<span class="n">total_gen_token</span> <span class="o">=</span> <span class="n">_beams2tree</span><span class="p">(</span><span class="n">new_draft_tokens</span><span class="p">,</span> <span class="n">num_beams</span><span class="p">,</span> <span class="n">beam_length</span><span class="p">,</span>
<span class="n">position_ids_base</span> <span class="o">+</span> <span class="n">num_accepted_tokens</span><span class="p">)</span>
<span class="c1"># Step 5: mark all the tensors we need</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_beam_index</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;accepted_beam_index&#39;</span><span class="p">)</span>
<span class="n">max_gen_token</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;max_gen_token&#39;</span><span class="p">)</span>
<span class="n">total_gen_token</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;total_gen_token&#39;</span><span class="p">)</span>
<span class="n">next_num_gen_tokens</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;next_spec_decoding_generation_lengths&#39;</span><span class="p">)</span>
<span class="n">active_tokens_flattened</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;next_flat_tokens&#39;</span><span class="p">)</span>
<span class="n">new_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">new_draft_logits</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;next_draft_probs&#39;</span><span class="p">)</span>
<span class="n">new_draft_token_indices</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;next_draft_indices&#39;</span><span class="p">)</span>
<span class="n">new_mask</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;spec_decoding_mask&#39;</span><span class="p">)</span>
<span class="n">new_position_offsets</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;next_spec_decoding_position_offsets&#39;</span><span class="p">)</span>
<span class="n">packed_position_ids</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;packed_position_ids&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">next_token</span><span class="p">,</span> <span class="n">probs</span><span class="p">,</span> <span class="n">draft_input</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="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"> 0. run base model, get logits, hidden_states</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">extra_args</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">&#39;draft_tokens&#39;</span><span class="p">,</span>
<span class="s1">&#39;draft_indices&#39;</span><span class="p">,</span>
<span class="s1">&#39;draft_probs&#39;</span><span class="p">,</span>
<span class="s1">&#39;device_request_types&#39;</span><span class="p">,</span>
<span class="s1">&#39;redrafter_inverted_temperature&#39;</span><span class="p">,</span>
<span class="s1">&#39;rand_data_validation&#39;</span><span class="p">,</span>
<span class="s1">&#39;rand_data_sample&#39;</span><span class="p">,</span>
<span class="s1">&#39;position_ids_base&#39;</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">use_cache</span> <span class="o">=</span> <span class="kc">True</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="k">if</span> <span class="n">use_cache</span> <span class="ow">and</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="ow">is</span> <span class="kc">False</span><span class="p">:</span>
<span class="n">lm_logits</span><span class="p">,</span> <span class="n">presents</span><span class="p">,</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">else</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">_</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="c1"># lm_logits could be in fp32</span>
<span class="n">lm_logits_cast</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">dtype</span><span class="p">)</span> <span class="c1"># no-op if same type</span>
<span class="bp">self</span><span class="o">.</span><span class="n">register_network_output</span><span class="p">(</span><span class="s2">&quot;hidden_states&quot;</span><span class="p">,</span>
<span class="n">hidden_states</span><span class="p">)</span> <span class="c1"># debugging</span>
<span class="n">new_draft_tokens</span><span class="p">,</span> <span class="n">new_draft_logits</span><span class="p">,</span> <span class="n">probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fwd_helper</span><span class="p">(</span>
<span class="n">hidden_states</span><span class="p">,</span>
<span class="n">lm_logits_cast</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">vocab_embedding</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drafter</span><span class="p">,</span>
<span class="n">kwargs</span><span class="o">=</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">new_draft_tokens</span><span class="p">,</span> <span class="n">new_draft_logits</span><span class="p">,</span> <span class="n">probs</span>
<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"> Assuming, max_gen_tokens = 1 + nb*(bl - 1), counting true token</span>
<span class="sd"> device_request_types: [bs]</span>
<span class="sd"> draft_tokens: [bs, nb, bl]</span>
<span class="sd"> draft_indices: [bs, nb, bl]</span>
<span class="sd"> draft_probs: [bs, nb, bl-1, V]</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_gen_tokens, packed_length] **</span>
<span class="sd"> redrafter_inverted_temperature: [bs]</span>
<span class="sd"> rand_data_sample: [bs]</span>
<span class="sd"> rand_data_validation: [bs, nb, bl-1]</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_gen_tokens/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">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">bb_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="p">)</span>
<span class="n">bb0_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="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">num_beam_tokens</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_beams</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_length</span>
<span class="n">max_draft_tokens</span> <span class="o">=</span> <span class="n">num_beam_tokens</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_beams</span> <span class="c1"># ignore the true token</span>
<span class="n">max_gen_token_len</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">max_draft_tokens</span> <span class="c1"># for the true token</span>
<span class="n">max_gen_token_len_range</span> <span class="o">=</span> <span class="n">default_range</span><span class="p">(</span><span class="n">max_gen_token_len</span><span class="p">)</span>
<span class="n">bb_max_gen_token_len_range</span> <span class="o">=</span> <span class="n">default_range</span><span class="p">(</span><span class="n">max_gen_token_len</span> <span class="o">*</span>
<span class="n">max_batch_size</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">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="n">max_draft_tokens</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">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">enable_two_optimization_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">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="p">)</span>
<span class="k">if</span> <span class="n">enable_two_optimization_profiles</span><span class="p">:</span>
<span class="n">bb_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">bb_range</span><span class="p">,</span> <span class="n">bb_range</span><span class="p">]</span>
<span class="n">bb0_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">bb0_range</span><span class="p">,</span> <span class="n">bb0_range</span><span class="p">]</span>
<span class="n">max_gen_token_len_range</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">max_gen_token_len_range</span><span class="p">,</span> <span class="n">max_gen_token_len_range</span>
<span class="p">]</span>
<span class="n">bb_max_gen_token_len_range</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">bb_max_gen_token_len_range</span><span class="p">,</span> <span class="n">bb_max_gen_token_len_range</span>
<span class="p">]</span>
<span class="n">num_beams_range</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">num_beams</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_beams</span><span class="p">]</span>
<span class="n">beam_length_range</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">beam_length</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_length</span><span class="p">]</span>
<span class="n">candidate_length_range</span> <span class="o">=</span> <span class="p">[</span>
<span class="bp">self</span><span class="o">.</span><span class="n">beam_candidate_length</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_candidate_length</span>
<span class="p">]</span>
<span class="n">vocab_size_range</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">bb_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">bb_range</span><span class="p">]</span>
<span class="n">bb0_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">bb0_range</span><span class="p">]</span>
<span class="n">max_gen_token_len_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">max_gen_token_len_range</span><span class="p">]</span>
<span class="n">bb_max_gen_token_len_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">bb_max_gen_token_len_range</span><span class="p">]</span>
<span class="n">num_beams_range</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">num_beams</span><span class="p">]</span>
<span class="n">beam_length_range</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">beam_length</span><span class="p">]</span>
<span class="n">candidate_length_range</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">beam_candidate_length</span><span class="p">]</span>
<span class="n">vocab_size_range</span> <span class="o">=</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">device_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;device_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">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">num_beams</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_length</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_wt0&#39;</span><span class="p">,</span> <span class="n">bb0_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;num_beams&#39;</span><span class="p">,</span> <span class="n">num_beams_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;beam_length&#39;</span><span class="p">,</span> <span class="n">beam_length_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">draft_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;draft_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">num_beams</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_length</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_wt0&#39;</span><span class="p">,</span> <span class="n">bb0_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;num_beams&#39;</span><span class="p">,</span> <span class="n">num_beams_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;beam_length&#39;</span><span class="p">,</span> <span class="n">beam_length_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">draft_probs</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_probs&#39;</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">dtype</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_beams</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_length</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">vocab_size</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_wt0&#39;</span><span class="p">,</span> <span class="n">bb0_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;num_beams&#39;</span><span class="p">,</span> <span class="n">num_beams_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;candidate_length&#39;</span><span class="p">,</span> <span class="n">candidate_length_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;vocab_size&#39;</span><span class="p">,</span> <span class="n">vocab_size_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">redrafter_inverted_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;redrafter_inverted_temperature&#39;</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">dtype</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="bp">self</span><span class="o">.</span><span class="n">dtype</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_beams</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">beam_length</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_wt0&#39;</span><span class="p">,</span> <span class="n">bb0_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;num_beams&#39;</span><span class="p">,</span> <span class="n">num_beams_range</span><span class="p">),</span>
<span class="p">(</span><span class="s1">&#39;candidate_length&#39;</span><span class="p">,</span> <span class="n">candidate_length_range</span><span class="p">),</span>
<span class="p">]))</span>
<span class="n">rand_data_sample</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_sample&#39;</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">dtype</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">position_ids_base</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="s2">&quot;position_ids_base&quot;</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="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="p">)</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;device_request_types&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">device_request_types</span> <span class="c1"># needed by process_logits</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_indices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">draft_indices</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;draft_probs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">draft_probs</span>
<span class="n">inputs</span><span class="p">[</span>
<span class="s1">&#39;redrafter_inverted_temperature&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">redrafter_inverted_temperature</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;rand_data_sample&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">rand_data_sample</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;position_ids_base&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">position_ids_base</span>
<span class="k">return</span> <span class="n">inputs</span>
<div class="viewcode-block" id="ReDrafterForQWenLM">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.ReDrafterForQWenLM">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">ReDrafterForQWenLM</span><span class="p">(</span><span class="n">ReDrafterMixin</span><span class="p">,</span> <span class="n">QWenForCausalLM</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;ReDrafter implementation for QWen models.</span>
<span class="sd"> Combines:</span>
<span class="sd"> - Base QWen model functionality from QWenForCausalLM</span>
<span class="sd"> - Drafting/speculative decoding logic from ReDrafterMixin</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
<div class="viewcode-block" id="ReDrafterForLLaMALM">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.ReDrafterForLLaMALM">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">ReDrafterForLLaMALM</span><span class="p">(</span><span class="n">ReDrafterMixin</span><span class="p">,</span> <span class="n">LLaMAForCausalLM</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;ReDrafter implementation for LLaMA models.</span>
<span class="sd"> Combines:</span>
<span class="sd"> - Base LLaMA model functionality from LLaMAForCausalLM</span>
<span class="sd"> - Drafting/speculative decoding logic from ReDrafterMixin</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
</pre></div>
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