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<h1>Source code for tensorrt_llm.models.gemma.config</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">TYPE_CHECKING</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">Literal</span>
<span class="kn">from</span> <span class="nn">tensorrt_llm._utils</span> <span class="kn">import</span> <span class="n">torch_dtype_to_str</span>
<span class="kn">from</span> <span class="nn">tensorrt_llm.functional</span> <span class="kn">import</span> <span class="n">PositionEmbeddingType</span>
<span class="kn">from</span> <span class="nn">tensorrt_llm.logger</span> <span class="kn">import</span> <span class="n">logger</span>
<span class="kn">from</span> <span class="nn">tensorrt_llm.mapping</span> <span class="kn">import</span> <span class="n">Mapping</span>
<span class="kn">from</span> <span class="nn">tensorrt_llm.models.modeling_utils</span> <span class="kn">import</span> <span class="p">(</span><span class="n">Gemma2ConfigGroup</span><span class="p">,</span>
<span class="n">PretrainedConfig</span><span class="p">,</span> <span class="n">QuantConfig</span><span class="p">)</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">PathLike</span>
<span class="kn">import</span> <span class="nn">transformers</span>
<span class="n">HfConfigOrDir</span> <span class="o">=</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">PathLike</span><span class="p">,</span> <span class="n">transformers</span><span class="o">.</span><span class="n">PretrainedConfig</span><span class="p">]</span>
<span class="n">GEMMA_ARCHITECTURE</span> <span class="o">=</span> <span class="s2">&quot;GemmaForCausalLM&quot;</span>
<span class="n">GEMMA2_ARCHITECTURE</span> <span class="o">=</span> <span class="s2">&quot;Gemma2ForCausalLM&quot;</span>
<div class="viewcode-block" id="GemmaConfig">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.GemmaConfig">[docs]</a>
<span class="k">class</span> <span class="nc">GemmaConfig</span><span class="p">(</span><span class="n">PretrainedConfig</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="o">*</span><span class="p">,</span>
<span class="n">architecture</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">rotary_base</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">10000.0</span><span class="p">,</span>
<span class="n">rotary_scaling</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">attn_bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">mlp_bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">share_embedding_table</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="kc">True</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
<span class="n">position_embedding_type</span><span class="p">:</span> <span class="n">PositionEmbeddingType</span> <span class="o">=</span> <span class="n">PositionEmbeddingType</span><span class="o">.</span>
<span class="n">rope_gpt_neox</span><span class="p">,</span>
<span class="n">query_pre_attn_scalar</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">final_logit_softcapping</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">attn_logit_softcapping</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</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">Union</span><span class="p">[</span><span class="n">Mapping</span><span class="p">,</span> <span class="nb">dict</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="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">share_embedding_table</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> We always pass `True` - the passed value is `False` by default, and ignored either way.</span>
<span class="sd"> We can&#39;t just raise an exception here, because this will force the user to explicitly pass `LLM(share_embedding_table=False)`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Using `share_embedding_table=True` for Gemma&quot;</span><span class="p">)</span>
<span class="n">use_parallel_embedding</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">mapping</span><span class="p">:</span>
<span class="n">use_parallel_embedding</span> <span class="o">=</span> <span class="n">mapping</span><span class="o">.</span><span class="n">tp_size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
<span class="n">mapping</span><span class="p">,</span> <span class="n">Mapping</span><span class="p">)</span> <span class="k">else</span> <span class="n">mapping</span><span class="p">[</span><span class="s2">&quot;tp_size&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">use_parallel_embedding</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="s2">&quot;use_parallel_embedding&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> We always pass `bool(mapping.tp_size &gt; 1)` - the passed value is `False` by default, and ignored either way.</span>
<span class="sd"> We can&#39;t just raise an exception here, because this will force the user to explicitly pass `LLM(use_parallel_embedding=True)`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;Using `use_parallel_embedding=</span><span class="si">{</span><span class="n">use_parallel_embedding</span><span class="si">}</span><span class="s2">` for Gemma&quot;</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">architecture</span><span class="o">=</span><span class="n">architecture</span><span class="p">,</span>
<span class="n">share_embedding_table</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">use_parallel_embedding</span><span class="o">=</span><span class="n">use_parallel_embedding</span><span class="p">,</span>
<span class="n">rotary_base</span><span class="o">=</span><span class="n">rotary_base</span><span class="p">,</span>
<span class="n">attn_bias</span><span class="o">=</span><span class="n">attn_bias</span><span class="p">,</span>
<span class="n">mlp_bias</span><span class="o">=</span><span class="n">mlp_bias</span><span class="p">,</span>
<span class="n">position_embedding_type</span><span class="o">=</span><span class="n">position_embedding_type</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="o">**</span><span class="n">kwargs</span><span class="p">,</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rotary_base</span> <span class="o">=</span> <span class="n">rotary_base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rotary_scaling</span> <span class="o">=</span> <span class="n">rotary_scaling</span>
<span class="bp">self</span><span class="o">.</span><span class="n">attn_bias</span> <span class="o">=</span> <span class="n">attn_bias</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mlp_bias</span> <span class="o">=</span> <span class="n">mlp_bias</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inter_layernorms</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_gemma_2</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inter_layernorms</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">assert</span> <span class="n">query_pre_attn_scalar</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;Gemma2 models must configure `query_pre_attn_scalar`&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">query_pre_attn_scalar</span> <span class="o">=</span> <span class="n">query_pre_attn_scalar</span>
<span class="bp">self</span><span class="o">.</span><span class="n">final_logit_softcapping</span> <span class="o">=</span> <span class="n">final_logit_softcapping</span>
<span class="bp">self</span><span class="o">.</span><span class="n">attn_logit_softcapping</span> <span class="o">=</span> <span class="n">attn_logit_softcapping</span>
<span class="n">GEMMA_ADDED_FIELDS</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">&quot;rotary_base&quot;</span><span class="p">,</span> <span class="s2">&quot;rotary_scaling&quot;</span><span class="p">,</span> <span class="s2">&quot;attn_bias&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_bias&quot;</span><span class="p">,</span>
<span class="s2">&quot;inter_layernorms&quot;</span>
<span class="p">}</span>
<span class="n">GEMMA2_ADDED_FIELDS</span> <span class="o">=</span> <span class="n">Gemma2ConfigGroup</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
<span class="n">VERBATIM</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">&quot;num_hidden_layers&quot;</span><span class="p">,</span> <span class="s2">&quot;num_attention_heads&quot;</span><span class="p">,</span> <span class="s2">&quot;hidden_size&quot;</span><span class="p">,</span>
<span class="s2">&quot;intermediate_size&quot;</span><span class="p">,</span> <span class="s2">&quot;vocab_size&quot;</span><span class="p">,</span> <span class="s2">&quot;max_position_embeddings&quot;</span><span class="p">,</span>
<span class="s2">&quot;hidden_act&quot;</span><span class="p">,</span> <span class="s2">&quot;use_parallel_embedding&quot;</span>
<span class="p">}</span> <span class="o">|</span> <span class="n">GEMMA2_ADDED_FIELDS</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">is_gemma_2</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">architecture</span> <span class="o">==</span> <span class="n">GEMMA2_ARCHITECTURE</span>
<div class="viewcode-block" id="GemmaConfig.gemma2_config">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.GemmaConfig.gemma2_config">[docs]</a>
<span class="k">def</span> <span class="nf">gemma2_config</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_gemma_2</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_config_group</span><span class="p">(</span><span class="n">Gemma2ConfigGroup</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">None</span></div>
<div class="viewcode-block" id="GemmaConfig.to_dict">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.GemmaConfig.to_dict">[docs]</a>
<span class="k">def</span> <span class="nf">to_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Serialize the fields added in GemmaConfig&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="p">{</span>
<span class="o">**</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span>
<span class="o">**</span><span class="p">{</span><span class="n">f</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">GEMMA_ADDED_FIELDS</span><span class="p">},</span>
<span class="o">**</span><span class="p">({</span><span class="n">f</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">GEMMA2_ADDED_FIELDS</span><span class="p">}</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_gemma_2</span> <span class="k">else</span> <span class="p">{})</span>
<span class="p">}</span></div>
<div class="viewcode-block" id="GemmaConfig.from_hugging_face">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.GemmaConfig.from_hugging_face">[docs]</a>
<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_config_or_dir</span><span class="p">:</span> <span class="s2">&quot;HfConfigOrDir&quot;</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="s2">&quot;auto&quot;</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="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="o">**</span><span class="n">kwargs</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;GemmaConfig&quot;</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">transformers</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">hf_config_or_dir</span><span class="p">,</span> <span class="n">transformers</span><span class="o">.</span><span class="n">PretrainedConfig</span><span class="p">):</span>
<span class="n">hf_config</span> <span class="o">=</span> <span class="n">hf_config_or_dir</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hf_config</span> <span class="o">=</span> <span class="n">transformers</span><span class="o">.</span><span class="n">GemmaConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
<span class="n">hf_config_or_dir</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">hf_config</span><span class="p">,</span> <span class="s2">&quot;torch_dtype&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="s2">&quot;float16&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</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="n">dtype</span> <span class="o">=</span> <span class="n">torch_dtype_to_str</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">&quot;float32&quot;</span><span class="p">:</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="s2">&quot;float16&quot;</span>
<span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
<span class="n">architecture</span><span class="o">=</span><span class="n">hf_config</span><span class="o">.</span><span class="n">architectures</span><span class="p">[</span><span class="mi">0</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">head_size</span><span class="o">=</span><span class="n">hf_config</span><span class="o">.</span><span class="n">head_dim</span><span class="p">,</span>
<span class="n">norm_epsilon</span><span class="o">=</span><span class="n">hf_config</span><span class="o">.</span><span class="n">rms_norm_eps</span><span class="p">,</span>
<span class="n">num_key_value_heads</span><span class="o">=</span><span class="nb">getattr</span><span class="p">(</span><span class="n">hf_config</span><span class="p">,</span> <span class="s2">&quot;num_key_value_heads&quot;</span><span class="p">,</span>
<span class="n">hf_config</span><span class="o">.</span><span class="n">num_attention_heads</span><span class="p">),</span>
<span class="n">rotary_scaling</span><span class="o">=</span><span class="nb">getattr</span><span class="p">(</span><span class="n">hf_config</span><span class="p">,</span> <span class="s2">&quot;rotary_scaling&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">quantization</span><span class="o">=</span><span class="n">quant_config</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="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">hf_config</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</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">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">VERBATIM</span>
<span class="p">},</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
<span class="p">)</span></div>
</div>
</pre></div>
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