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<h1>Source code for tensorrt_llm.models.bert.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="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">OrderedDict</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">transformers</span>
<span class="kn">from</span> <span class="nn">tensorrt_llm.models.modeling_utils</span> <span class="kn">import</span> <span class="n">PretrainedModel</span>
<span class="kn">from</span> <span class="nn">..._common</span> <span class="kn">import</span> <span class="n">default_net</span>
<span class="kn">from</span> <span class="nn">...functional</span> <span class="kn">import</span> <span class="p">(</span><span class="n">ACT2FN</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">concat</span><span class="p">,</span> <span class="n">constant</span><span class="p">,</span> <span class="n">cumsum</span><span class="p">,</span> <span class="n">expand</span><span class="p">,</span>
<span class="n">index_select</span><span class="p">,</span> <span class="n">select</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="nb">slice</span><span class="p">,</span> <span class="n">unsqueeze</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">...layers</span> <span class="kn">import</span> <span class="n">MLP</span><span class="p">,</span> <span class="n">BertAttention</span><span class="p">,</span> <span class="n">Embedding</span><span class="p">,</span> <span class="n">LayerNorm</span><span class="p">,</span> <span class="n">Linear</span>
<span class="kn">from</span> <span class="nn">...mapping</span> <span class="kn">import</span> <span class="n">Mapping</span>
<span class="kn">from</span> <span class="nn">...module</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="nn">..modeling_utils</span> <span class="kn">import</span> <span class="n">QuantConfig</span>
<span class="kn">from</span> <span class="nn">.config</span> <span class="kn">import</span> <span class="n">BERTConfig</span>
<span class="kn">from</span> <span class="nn">.convert</span> <span class="kn">import</span> <span class="p">(</span><span class="n">load_hf_bert_base</span><span class="p">,</span> <span class="n">load_hf_bert_cls</span><span class="p">,</span> <span class="n">load_hf_bert_qa</span><span class="p">,</span>
<span class="n">load_weights_from_hf_model</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">BertEmbedding</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">vocab_size</span><span class="p">,</span>
<span class="n">hidden_size</span><span class="p">,</span>
<span class="n">max_position_embeddings</span><span class="p">,</span>
<span class="n">type_vocab_size</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="kc">None</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">vocab_embedding</span> <span class="o">=</span> <span class="n">Embedding</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">position_embedding</span> <span class="o">=</span> <span class="n">Embedding</span><span class="p">(</span><span class="n">max_position_embeddings</span><span class="p">,</span>
<span class="n">hidden_size</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">token_embedding</span> <span class="o">=</span> <span class="n">Embedding</span><span class="p">(</span><span class="n">type_vocab_size</span><span class="p">,</span>
<span class="n">hidden_size</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_position_embeddings</span> <span class="o">=</span> <span class="n">max_position_embeddings</span>
<span class="bp">self</span><span class="o">.</span><span class="n">embedding_ln</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">normalized_shape</span><span class="o">=</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">def</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="p">,</span> <span class="n">token_type_ids</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocab_embedding</span><span class="p">(</span><span class="n">input_ids</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">position_embedding</span><span class="p">(</span><span class="n">position_ids</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">token_embedding</span><span class="p">(</span><span class="n">token_type_ids</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">embedding_ln</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">BertEncoderLayer</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">hidden_size</span><span class="p">,</span>
<span class="n">num_attention_heads</span><span class="p">,</span>
<span class="n">max_position_embeddings</span><span class="p">,</span>
<span class="n">hidden_act</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span>
<span class="n">tp_group</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">tp_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="kc">None</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">input_layernorm</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">normalized_shape</span><span class="o">=</span><span class="n">hidden_size</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">attention</span> <span class="o">=</span> <span class="n">BertAttention</span><span class="p">(</span>
<span class="n">hidden_size</span><span class="o">=</span><span class="n">hidden_size</span><span class="p">,</span>
<span class="n">num_attention_heads</span><span class="o">=</span><span class="n">num_attention_heads</span><span class="p">,</span>
<span class="n">max_position_embeddings</span><span class="o">=</span><span class="n">max_position_embeddings</span><span class="p">,</span>
<span class="n">tp_group</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">tp_size</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mlp</span> <span class="o">=</span> <span class="n">MLP</span><span class="p">(</span><span class="n">hidden_size</span><span class="o">=</span><span class="n">hidden_size</span><span class="p">,</span>
<span class="n">ffn_hidden_size</span><span class="o">=</span><span class="n">hidden_size</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span>
<span class="n">hidden_act</span><span class="o">=</span><span class="n">hidden_act</span><span class="p">,</span>
<span class="n">tp_group</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">tp_size</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">post_layernorm</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">normalized_shape</span><span class="o">=</span><span class="n">hidden_size</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</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">attention_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">input_lengths</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">max_input_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">residual</span> <span class="o">=</span> <span class="n">hidden_states</span>
<span class="n">attention_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">attention</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">,</span>
<span class="n">attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">,</span>
<span class="n">input_lengths</span><span class="o">=</span><span class="n">input_lengths</span><span class="p">,</span>
<span class="n">max_input_length</span><span class="o">=</span><span class="n">max_input_length</span><span class="p">)</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="n">residual</span> <span class="o">+</span> <span class="n">attention_output</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_layernorm</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
<span class="n">residual</span> <span class="o">=</span> <span class="n">hidden_states</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mlp</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="n">residual</span> <span class="o">+</span> <span class="n">hidden_states</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">post_layernorm</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
<span class="k">return</span> <span class="n">hidden_states</span>
<span class="k">class</span> <span class="nc">BertBase</span><span class="p">(</span><span class="n">PretrainedModel</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> Base class that provides from_huggingface() and prepare_inputs() methods</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="n">config_class</span> <span class="o">=</span> <span class="n">BERTConfig</span>
<span class="k">def</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">BERTConfig</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="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">load_hf_bert</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">model_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">load_model_on_cpu</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
<span class="n">dtype</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Use as the abstractmethod, load corresponding HF model.</span>
<span class="sd"> Subclass must implement this method!</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">!=</span> <span class="s2">&quot;BertBase&quot;</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;Never call from BertBase class!&quot;</span>
<span class="k">if</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;BertModel&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">load_hf_bert_base</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="n">load_model_on_cpu</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;BertForQuestionAnswering&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">load_hf_bert_qa</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="n">load_model_on_cpu</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;BertForSequenceClassification&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">load_hf_bert_cls</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="n">load_model_on_cpu</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="kc">False</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;Unknown class </span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">!&quot;</span>
<span class="nd">@classmethod</span>
<span class="k">def</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;float16&#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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create a BertModel object from give parameters</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">transformers</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">use_preloading</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">hf_model_or_dir</span><span class="p">,</span>
<span class="n">transformers</span><span class="o">.</span><span class="n">PreTrainedModel</span><span class="p">)</span>
<span class="k">if</span> <span class="n">use_preloading</span><span class="p">:</span>
<span class="n">hf_model</span> <span class="o">=</span> <span class="n">hf_model_or_dir</span>
<span class="n">hf_config_or_dir</span> <span class="o">=</span> <span class="n">hf_model</span><span class="o">.</span><span class="n">config</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hf_model_dir</span> <span class="o">=</span> <span class="n">hf_model_or_dir</span>
<span class="n">hf_config_or_dir</span> <span class="o">=</span> <span class="n">hf_model_or_dir</span>
<span class="n">load_model_on_cpu</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;load_model_on_cpu&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">tllm_config</span> <span class="o">=</span> <span class="n">BERTConfig</span><span class="o">.</span><span class="n">from_hugging_face</span><span class="p">(</span>
<span class="n">hf_config_or_dir</span><span class="o">=</span><span class="n">hf_config_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">#NOTE: override architecture info</span>
<span class="n">RobertaCls_mapping</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">&quot;BertModel&quot;</span><span class="p">:</span> <span class="s2">&quot;RobertaModel&quot;</span><span class="p">,</span>
<span class="s2">&quot;BertForQuestionAnswering&quot;</span><span class="p">:</span> <span class="s2">&quot;RobertaForQuestionAnswering&quot;</span><span class="p">,</span>
<span class="s2">&quot;BertForSequenceClassification&quot;</span><span class="p">:</span> <span class="s2">&quot;RobertaForSequenceClassification&quot;</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">if</span> <span class="n">tllm_config</span><span class="o">.</span><span class="n">is_roberta</span><span class="p">:</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">tllm_config</span><span class="p">,</span> <span class="s1">&#39;architecture&#39;</span><span class="p">,</span>
<span class="n">RobertaCls_mapping</span><span class="p">[</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">tllm_config</span><span class="p">,</span> <span class="s1">&#39;architecture&#39;</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">torch_dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s1">&#39;float16&#39;</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">use_preloading</span><span class="p">:</span>
<span class="n">hf_model</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">load_hf_bert</span><span class="p">(</span><span class="n">model_dir</span><span class="o">=</span><span class="n">hf_model_dir</span><span class="p">,</span>
<span class="n">load_model_on_cpu</span><span class="o">=</span><span class="n">load_model_on_cpu</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">torch_dtype</span><span class="p">)</span>
<span class="n">weights</span> <span class="o">=</span> <span class="n">load_weights_from_hf_model</span><span class="p">(</span><span class="n">hf_model</span><span class="o">=</span><span class="n">hf_model</span><span class="p">,</span>
<span class="n">config</span><span class="o">=</span><span class="n">tllm_config</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">tllm_config</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
<span class="c1"># Override the PretrainedModel&#39;s meothd, can unify in the future.</span>
<span class="k">def</span> <span class="nf">prepare_inputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_batch_size</span><span class="p">,</span> <span class="n">max_input_len</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</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="c1"># opt_shape is set to half of max batch_size and seq_len by default</span>
<span class="c1"># tune this according to real data distribution</span>
<span class="n">bs_range</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="n">max_batch_size</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">max_batch_size</span><span class="p">]</span>
<span class="n">inlen_range</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="n">max_input_len</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">max_input_len</span><span class="p">]</span>
<span class="n">num_tokens_range</span> <span class="o">=</span> <span class="p">[</span>
<span class="mi">1</span><span class="p">,</span>
<span class="p">(</span><span class="n">max_input_len</span> <span class="o">*</span> <span class="n">max_batch_size</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
<span class="n">max_input_len</span> <span class="o">*</span> <span class="n">max_batch_size</span><span class="p">,</span>
<span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">remove_input_padding</span><span class="p">:</span>
<span class="n">input_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;input_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="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="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">bs_range</span><span class="p">]),</span>
<span class="p">(</span><span class="s1">&#39;input_len&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">inlen_range</span><span class="p">])]),</span>
<span class="p">)</span>
<span class="c1"># also called segment_ids</span>
<span class="n">token_type_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;token_type_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="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="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">bs_range</span><span class="p">]),</span>
<span class="p">(</span><span class="s1">&#39;input_len&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">inlen_range</span><span class="p">])]),</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">input_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="s2">&quot;input_ids&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="s2">&quot;num_tokens&quot;</span><span class="p">,</span> <span class="p">[</span><span class="n">num_tokens_range</span><span class="p">])]),</span>
<span class="p">)</span>
<span class="n">token_type_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;token_type_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="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([(</span><span class="s1">&#39;num_tokens&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">num_tokens_range</span><span class="p">])]),</span>
<span class="p">)</span>
<span class="n">position_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;position_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="n">dim_range</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">([(</span><span class="s1">&#39;num_tokens&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">num_tokens_range</span><span class="p">])]),</span>
<span class="p">)</span>
<span class="n">max_input_length</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;max_input_length&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="s2">&quot;max_input_length&quot;</span><span class="p">,</span> <span class="p">[</span><span class="n">inlen_range</span><span class="p">])]),</span>
<span class="p">)</span>
<span class="n">input_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;input_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="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">bs_range</span><span class="p">])</span>
<span class="p">]))</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">&#39;input_ids&#39;</span><span class="p">:</span> <span class="n">input_ids</span><span class="p">,</span>
<span class="s1">&#39;input_lengths&#39;</span><span class="p">:</span> <span class="n">input_lengths</span><span class="p">,</span>
<span class="s1">&#39;token_type_ids&#39;</span><span class="p">:</span> <span class="n">token_type_ids</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">if</span> <span class="n">remove_input_padding</span><span class="p">:</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;position_ids&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">position_ids</span>
<span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;max_input_length&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_input_length</span>
<span class="k">return</span> <span class="n">inputs</span>
<div class="viewcode-block" id="BertModel">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.BertModel">[docs]</a>
<span class="k">class</span> <span class="nc">BertModel</span><span class="p">(</span><span class="n">BertBase</span><span class="p">):</span>
<span class="k">def</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">BERTConfig</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">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_position_embeddings</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">max_position_embeddings</span>
<span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">pad_token_id</span>
<span class="bp">self</span><span class="o">.</span><span class="n">is_roberta</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">is_roberta</span>
<span class="bp">self</span><span class="o">.</span><span class="n">embedding</span> <span class="o">=</span> <span class="n">BertEmbedding</span><span class="p">(</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="p">,</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="p">,</span>
<span class="n">max_position_embeddings</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">max_position_embeddings</span><span class="p">,</span>
<span class="n">type_vocab_size</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">type_vocab_size</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="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">ModuleList</span><span class="p">([</span>
<span class="n">BertEncoderLayer</span><span class="p">(</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="p">,</span>
<span class="n">num_attention_heads</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">num_attention_heads</span><span class="p">,</span>
<span class="n">max_position_embeddings</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">max_position_embeddings</span><span class="p">,</span>
<span class="n">hidden_act</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">hidden_act</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">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="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">num_hidden_layers</span><span class="p">)</span>
<span class="p">])</span>
<div class="viewcode-block" id="BertModel.forward">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.BertModel.forward">[docs]</a>
<span class="k">def</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="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">input_lengths</span><span class="o">=</span><span class="kc">None</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">token_type_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">max_input_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="c1"># remove_input_padding requires these fields as explicit input</span>
<span class="n">mask</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="ow">not</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="n">seq_len_2d</span> <span class="o">=</span> <span class="n">concat</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="p">(</span><span class="n">input_ids</span><span class="p">,</span> <span class="mi">1</span><span class="p">)])</span>
<span class="c1"># create position ids</span>
<span class="n">position_ids_buffer</span> <span class="o">=</span> <span class="n">constant</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_position_embeddings</span><span class="p">)</span><span class="o">.</span><span class="n">astype</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="mi">0</span><span class="p">))</span>
<span class="n">tmp_position_ids</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">position_ids_buffer</span><span class="p">,</span>
<span class="n">starts</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="n">sizes</span><span class="o">=</span><span class="n">seq_len_2d</span><span class="p">)</span>
<span class="n">tmp_position_ids</span> <span class="o">=</span> <span class="n">expand</span><span class="p">(</span><span class="n">tmp_position_ids</span><span class="p">,</span> <span class="n">shape</span><span class="p">(</span><span class="n">input_ids</span><span class="p">))</span> <span class="c1">#BxL</span>
<span class="n">tmp_input_lengths</span> <span class="o">=</span> <span class="n">unsqueeze</span><span class="p">(</span><span class="n">input_lengths</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1">#Bx1</span>
<span class="n">tmp_input_lengths</span> <span class="o">=</span> <span class="n">expand</span><span class="p">(</span><span class="n">tmp_input_lengths</span><span class="p">,</span>
<span class="n">shape</span><span class="p">(</span><span class="n">input_ids</span><span class="p">))</span> <span class="c1">#BxL</span>
<span class="n">mask</span> <span class="o">=</span> <span class="n">tmp_position_ids</span> <span class="o">&lt;</span> <span class="n">tmp_input_lengths</span> <span class="c1"># BxL</span>
<span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">position_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_roberta</span><span class="p">:</span>
<span class="c1"># see create_position_ids_from_input_ids() in https://github.com/huggingface/transformers/blob/main/src/transformers/models/roberta/modeling_roberta.py</span>
<span class="n">position_ids</span> <span class="o">=</span> <span class="p">(</span><span class="n">tmp_position_ids</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">mask</span>
<span class="n">position_ids</span> <span class="o">=</span> <span class="n">position_ids</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">position_ids</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">position_ids_buffer</span><span class="p">,</span>
<span class="n">starts</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="n">sizes</span><span class="o">=</span><span class="n">seq_len_2d</span><span class="p">)</span>
<span class="n">position_ids</span> <span class="o">=</span> <span class="n">expand</span><span class="p">(</span><span class="n">position_ids</span><span class="p">,</span> <span class="n">shape</span><span class="p">(</span><span class="n">input_ids</span><span class="p">))</span>
<span class="c1"># create token_type_ids</span>
<span class="k">if</span> <span class="n">token_type_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">token_type_ids_buffer</span> <span class="o">=</span> <span class="n">constant</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_position_embeddings</span><span class="p">)</span><span class="o">.</span><span class="n">astype</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="mi">0</span><span class="p">))</span>
<span class="n">token_type_ids</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">token_type_ids_buffer</span><span class="p">,</span>
<span class="n">starts</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="n">sizes</span><span class="o">=</span><span class="n">seq_len_2d</span><span class="p">)</span>
<span class="n">token_type_ids</span> <span class="o">=</span> <span class="n">expand</span><span class="p">(</span><span class="n">token_type_ids</span><span class="p">,</span> <span class="n">shape</span><span class="p">(</span><span class="n">input_ids</span><span class="p">))</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span><span class="n">input_ids</span><span class="p">,</span> <span class="n">position_ids</span><span class="p">,</span> <span class="n">token_type_ids</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">register_network_output</span><span class="p">(</span><span class="s1">&#39;embedding_output&#39;</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">):</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="n">hidden_states</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">,</span>
<span class="n">input_lengths</span><span class="o">=</span><span class="n">input_lengths</span><span class="p">,</span>
<span class="n">attention_mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span>
<span class="n">max_input_length</span><span class="o">=</span><span class="n">max_input_length</span><span class="p">)</span>
<span class="c1"># keep the last layer output name as hidden_states</span>
<span class="k">if</span> <span class="p">((</span><span class="n">idx</span> <span class="o">==</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">num_hidden_layers</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span> <span class="ow">and</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">architecture</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BertModel&quot;</span><span class="p">,</span> <span class="s2">&quot;RobertaModel&quot;</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&#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">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">register_network_output</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;layer_</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">_output&quot;</span><span class="p">,</span>
<span class="n">hidden_states</span><span class="p">)</span>
<span class="k">return</span> <span class="n">hidden_states</span></div>
</div>
<span class="n">RobertaModel</span> <span class="o">=</span> <span class="n">BertModel</span>
<div class="viewcode-block" id="BertForQuestionAnswering">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.BertForQuestionAnswering">[docs]</a>
<span class="k">class</span> <span class="nc">BertForQuestionAnswering</span><span class="p">(</span><span class="n">BertBase</span><span class="p">):</span>
<span class="k">def</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">BERTConfig</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">bert</span> <span class="o">=</span> <span class="n">BertModel</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_labels</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">num_labels</span>
<span class="bp">self</span><span class="o">.</span><span class="n">qa_outputs</span> <span class="o">=</span> <span class="n">Linear</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">config</span><span class="o">.</span><span class="n">num_labels</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>
<div class="viewcode-block" id="BertForQuestionAnswering.forward">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.BertForQuestionAnswering.forward">[docs]</a>
<span class="k">def</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="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">input_lengths</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">token_type_ids</span><span class="o">=</span><span class="kc">None</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">max_input_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</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="k">if</span> <span class="n">remove_input_padding</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">token_type_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> \
<span class="n">position_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> \
<span class="n">max_input_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
<span class="s2">&quot;token_type_ids, position_ids, max_input_length is required &quot;</span> \
<span class="s2">&quot;in remove_input_padding mode&quot;</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">forward</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">input_lengths</span><span class="o">=</span><span class="n">input_lengths</span><span class="p">,</span>
<span class="n">token_type_ids</span><span class="o">=</span><span class="n">token_type_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">hidden_states</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">,</span>
<span class="n">max_input_length</span><span class="o">=</span><span class="n">max_input_length</span><span class="p">)</span>
<span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">qa_outputs</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
<span class="n">logits</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;logits&#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">logits_dtype</span><span class="p">)</span>
<span class="k">return</span> <span class="n">logits</span></div>
</div>
<span class="n">RobertaForQuestionAnswering</span> <span class="o">=</span> <span class="n">BertForQuestionAnswering</span>
<span class="k">class</span> <span class="nc">BertPooler</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">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">dense</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">ACT2FN</span><span class="p">[</span><span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">forward</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">input_lengths</span><span class="p">,</span> <span class="n">remove_input_padding</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">remove_input_padding</span><span class="p">:</span>
<span class="c1"># We &quot;pool&quot; the model by simply taking the hidden state corresponding</span>
<span class="c1"># to the first token.</span>
<span class="n">first_token_tensor</span> <span class="o">=</span> <span class="n">select</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="mi">0</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># when remove_input_padding is enabled, the shape of hidden_states is [num_tokens, hidden_size]</span>
<span class="c1"># We can take the first token of each sequence according to input_lengths,</span>
<span class="c1"># and then do pooling similar to padding mode.</span>
<span class="c1"># For example, if input_lengths is [8, 5, 6], then the indices of first tokens</span>
<span class="c1"># should be [0, 8, 13]</span>
<span class="n">first_token_indices</span> <span class="o">=</span> <span class="n">cumsum</span><span class="p">(</span>
<span class="n">concat</span><span class="p">([</span>
<span class="mi">0</span><span class="p">,</span>
<span class="nb">slice</span><span class="p">(</span><span class="n">input_lengths</span><span class="p">,</span>
<span class="n">starts</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">sizes</span><span class="o">=</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">input_lengths</span><span class="p">)</span> <span class="o">-</span>
<span class="n">constant</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="mi">1</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="p">]),</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">first_token_tensor</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">first_token_indices</span><span class="p">)</span>
<span class="n">pooled_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">first_token_tensor</span><span class="p">)</span>
<span class="n">pooled_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">pooled_output</span><span class="p">)</span>
<span class="k">return</span> <span class="n">pooled_output</span>
<span class="k">class</span> <span class="nc">RobertaClassificationHead</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Head for sentence-level classification tasks.&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">num_labels</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">dense</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">out_proj</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</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">input_lengths</span><span class="p">,</span> <span class="n">remove_input_padding</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">remove_input_padding</span><span class="p">:</span>
<span class="c1"># We &quot;pool&quot; the model by simply taking the hidden state corresponding</span>
<span class="c1"># to the first token.</span>
<span class="n">first_token_tensor</span> <span class="o">=</span> <span class="n">select</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="mi">0</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># when remove_input_padding is enabled, the shape of hidden_states is [num_tokens, hidden_size]</span>
<span class="c1"># We can take the first token of each sequence according to input_lengths,</span>
<span class="c1"># and then do pooling similar to padding mode.</span>
<span class="c1"># For example, if input_lengths is [8, 5, 6], then the indices of first tokens</span>
<span class="c1"># should be [0, 8, 13]</span>
<span class="n">first_token_indices</span> <span class="o">=</span> <span class="n">cumsum</span><span class="p">(</span>
<span class="n">concat</span><span class="p">([</span>
<span class="mi">0</span><span class="p">,</span>
<span class="nb">slice</span><span class="p">(</span><span class="n">input_lengths</span><span class="p">,</span>
<span class="n">starts</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">sizes</span><span class="o">=</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">input_lengths</span><span class="p">)</span> <span class="o">-</span>
<span class="n">constant</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="mi">1</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="p">]),</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">first_token_tensor</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">first_token_indices</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">first_token_tensor</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">ACT2FN</span><span class="p">[</span><span class="s1">&#39;tanh&#39;</span><span class="p">](</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">out_proj</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<div class="viewcode-block" id="BertForSequenceClassification">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.BertForSequenceClassification">[docs]</a>
<span class="k">class</span> <span class="nc">BertForSequenceClassification</span><span class="p">(</span><span class="n">BertBase</span><span class="p">):</span>
<span class="k">def</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">BERTConfig</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">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="o">.</span><span class="n">is_roberta</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">is_roberta</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bert</span> <span class="o">=</span> <span class="n">BertModel</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_labels</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">num_labels</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">config</span><span class="o">.</span><span class="n">is_roberta</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pooler</span> <span class="o">=</span> <span class="n">BertPooler</span><span class="p">(</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="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="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">Linear</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">config</span><span class="o">.</span><span class="n">num_labels</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="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">RobertaClassificationHead</span><span class="p">(</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="p">,</span>
<span class="n">num_labels</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">num_labels</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>
<div class="viewcode-block" id="BertForSequenceClassification.forward">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.BertForSequenceClassification.forward">[docs]</a>
<span class="k">def</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">input_lengths</span><span class="p">,</span>
<span class="n">token_type_ids</span><span class="o">=</span><span class="kc">None</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">max_input_length</span><span class="o">=</span><span class="kc">None</span><span class="p">):</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="c1"># required as explicit input in remove_input_padding mode</span>
<span class="c1"># see examples/bert/run_remove_input_padding.py for how to create them from input_ids and input_lengths</span>
<span class="k">if</span> <span class="n">remove_input_padding</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">token_type_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> \
<span class="n">position_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> \
<span class="n">max_input_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
<span class="s2">&quot;token_type_ids, position_ids, max_input_length is required &quot;</span> \
<span class="s2">&quot;in remove_input_padding mode&quot;</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">forward</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">input_lengths</span><span class="o">=</span><span class="n">input_lengths</span><span class="p">,</span>
<span class="n">token_type_ids</span><span class="o">=</span><span class="n">token_type_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">hidden_states</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">,</span>
<span class="n">max_input_length</span><span class="o">=</span><span class="n">max_input_length</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_roberta</span><span class="p">:</span>
<span class="n">pooled_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pooler</span><span class="p">(</span>
<span class="n">hidden_states</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">,</span>
<span class="n">input_lengths</span><span class="o">=</span><span class="n">input_lengths</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">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">pooled_output</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">hidden_states</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">,</span>
<span class="n">input_lengths</span><span class="o">=</span><span class="n">input_lengths</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">logits</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="s1">&#39;logits&#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">logits_dtype</span><span class="p">)</span>
<span class="k">return</span> <span class="n">logits</span></div>
</div>
<span class="n">RobertaForSequenceClassification</span> <span class="o">=</span> <span class="n">BertForSequenceClassification</span>
</pre></div>
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