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<h1>Source code for tensorrt_llm.models.llama.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">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</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">Union</span>
<span class="kn">from</span> <span class="nn">...layers</span> <span class="kn">import</span> <span class="n">MoeConfig</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">..convert_utils</span> <span class="kn">import</span> <span class="n">infer_dtype</span>
<span class="kn">from</span> <span class="nn">..modeling_utils</span> <span class="kn">import</span> <span class="n">PretrainedConfig</span><span class="p">,</span> <span class="n">QuantConfig</span>
<div class="viewcode-block" id="LLaMAConfig">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.LLaMAConfig">[docs]</a>
<span class="k">class</span> <span class="nc">LLaMAConfig</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">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">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">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">residual_mlp</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">disable_weight_only_quant_plugin</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">moe</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">MoeConfig</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">remove_duplicated_kv_heads</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="o">**</span><span class="n">kwargs</span><span class="p">):</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">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">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">residual_mlp</span> <span class="o">=</span> <span class="n">residual_mlp</span>
<span class="bp">self</span><span class="o">.</span><span class="n">disable_weight_only_quant_plugin</span> <span class="o">=</span> <span class="n">disable_weight_only_quant_plugin</span>
<span class="k">if</span> <span class="n">moe</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="c1"># Legacy MOE config fields</span>
<span class="n">moe</span> <span class="o">=</span> <span class="n">MoeConfig</span><span class="p">(</span>
<span class="n">num_experts</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;moe_num_experts&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
<span class="n">top_k</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;moe_top_k&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
<span class="n">normalization_mode</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;moe_normalization_mode&#39;</span><span class="p">,</span>
<span class="n">MoeConfig</span><span class="o">.</span><span class="n">ExpertScaleNormalizationMode</span><span class="o">.</span><span class="n">RENORMALIZE</span><span class="p">))</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">moe</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
<span class="n">moe</span> <span class="o">=</span> <span class="n">MoeConfig</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">moe</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">moe</span><span class="p">,</span> <span class="n">MoeConfig</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">moe</span> <span class="o">=</span> <span class="n">moe</span><span class="o">.</span><span class="n">validate</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">remove_duplicated_kv_heads</span> <span class="o">=</span> <span class="n">remove_duplicated_kv_heads</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc_after_embed</span> <span class="o">=</span> <span class="kc">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_input_layernorm_in_first_layer</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_last_layernorm</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layer_idx_offset</span> <span class="o">=</span> <span class="mi">0</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<div class="viewcode-block" id="LLaMAConfig.to_dict">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.LLaMAConfig.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="n">output</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="c1"># Serialize the fields added in LLaMAConfig</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;mlp_bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mlp_bias</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;attn_bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">attn_bias</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;rotary_base&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotary_base</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;rotary_scaling&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotary_scaling</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;residual_mlp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residual_mlp</span>
<span class="n">output</span><span class="p">[</span>
<span class="s1">&#39;disable_weight_only_quant_plugin&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">disable_weight_only_quant_plugin</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;fc_after_embed&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc_after_embed</span>
<span class="n">output</span><span class="p">[</span>
<span class="s1">&#39;use_input_layernorm_in_first_layer&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_input_layernorm_in_first_layer</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;use_last_layernorm&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_last_layernorm</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;layer_idx_offset&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layer_idx_offset</span>
<span class="n">output</span><span class="p">[</span><span class="s1">&#39;moe&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">moe</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
<span class="k">return</span> <span class="n">output</span></div>
<div class="viewcode-block" id="LLaMAConfig.from_hugging_face">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.LLaMAConfig.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="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="s1">&#39;transformers.PretrainedConfig&#39;</span><span class="p">],</span>
<span class="n">dtype</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span>
<span class="n">mapping</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Mapping</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">quant_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">QuantConfig</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="kn">import</span> <span class="nn">transformers</span>
<span class="n">trust_remote_code</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;trust_remote_code&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</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_dir</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">hf_config_or_dir</span><span class="p">)</span>
<span class="k">if</span> <span class="s2">&quot;vila&quot;</span> <span class="ow">in</span> <span class="n">hf_config_dir</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">hf_config_dir</span> <span class="o">+</span> <span class="s2">&quot;/../VILA&quot;</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">llava.model</span> <span class="kn">import</span> <span class="n">LlavaLlamaConfig</span> <span class="c1"># noqa</span>
<span class="kn">from</span> <span class="nn">llava.model</span> <span class="kn">import</span> <span class="n">LlavaLlamaModel</span>
<span class="n">transformers</span><span class="o">.</span><span class="n">AutoConfig</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">&quot;llava_llama&quot;</span><span class="p">,</span>
<span class="n">LlavaLlamaConfig</span><span class="p">)</span>
<span class="n">transformers</span><span class="o">.</span><span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">register</span><span class="p">(</span>
<span class="n">LlavaLlamaConfig</span><span class="p">,</span> <span class="n">LlavaLlamaModel</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">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
<span class="n">hf_config_dir</span><span class="p">,</span> <span class="n">trust_remote_code</span><span class="o">=</span><span class="n">trust_remote_code</span><span class="p">)</span>
<span class="k">if</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;llava&quot;</span><span class="p">:</span>
<span class="c1"># LLaVA = Vision model + Llama LLM</span>
<span class="c1"># We load a llava config and use its&#39; text config as llama config</span>
<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">LlavaConfig</span>
<span class="n">hf_config</span> <span class="o">=</span> <span class="n">LlavaConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
<span class="n">hf_config_dir</span><span class="p">)</span><span class="o">.</span><span class="n">text_config</span>
<span class="k">if</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;llava_next&quot;</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">LlavaNextConfig</span>
<span class="n">hf_config</span> <span class="o">=</span> <span class="n">LlavaNextConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
<span class="n">hf_config_dir</span><span class="p">)</span><span class="o">.</span><span class="n">text_config</span>
<span class="k">if</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;llava_llama&quot;</span><span class="p">:</span>
<span class="n">hf_config</span><span class="o">.</span><span class="n">llm_cfg</span><span class="p">[</span><span class="s2">&quot;architecture&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">llm_cfg</span><span class="p">[</span>
<span class="s2">&quot;architectures&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">hf_config</span><span class="o">.</span><span class="n">llm_cfg</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">llm_cfg</span><span class="p">[</span><span class="s2">&quot;torch_dtype&quot;</span><span class="p">]</span>
<span class="n">hf_config</span> <span class="o">=</span> <span class="n">PretrainedConfig</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">hf_config</span><span class="o">.</span><span class="n">llm_cfg</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="k">if</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;exaone&quot;</span><span class="p">:</span>
<span class="n">hidden_act</span> <span class="o">=</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">activation_function</span>
<span class="c1"># NOTE</span>
<span class="c1"># EXAONE also uses RMS norm but they represent as layer_norm_epsilon.</span>
<span class="n">norm_epsilon</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;layer_norm_epsilon&quot;</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hidden_act</span> <span class="o">=</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">hidden_act</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="n">head_dim</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;head_dim&quot;</span><span class="p">,</span>
<span class="n">hf_config</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">//</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">head_size</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;kv_channels&quot;</span><span class="p">,</span> <span class="n">head_dim</span><span class="p">)</span>
<span class="n">attn_bias</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="s1">&#39;bias&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">getattr</span><span class="p">(</span>
<span class="n">hf_config</span><span class="p">,</span> <span class="s1">&#39;attention_bias&#39;</span><span class="p">,</span> <span class="kc">False</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;rope_scaling&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">rotary_base</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;rope_theta&quot;</span><span class="p">,</span> <span class="mf">10000.0</span><span class="p">)</span>
<span class="n">residual_mlp</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;parallel_attn_mlp_res&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">disable_weight_only_quant_plugin</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;disable_weight_only_quant_plugin&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">remove_duplicated_kv_heads</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;remove_duplicated_kv_heads&#39;</span><span class="p">,</span>
<span class="kc">False</span><span class="p">)</span>
<span class="k">if</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;mixtral&quot;</span> <span class="ow">or</span> <span class="n">hf_config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;arctic&quot;</span><span class="p">:</span>
<span class="c1"># HF LLaMA-type models are implicitly using gated activation.</span>
<span class="c1"># With our MoE implementation, we must make it explicit</span>
<span class="n">hidden_act</span> <span class="o">=</span> <span class="s2">&quot;swiglu&quot;</span>
<span class="n">moe_normalization_mode</span> <span class="o">=</span> <span class="n">MoeConfig</span><span class="o">.</span><span class="n">ExpertScaleNormalizationMode</span><span class="o">.</span><span class="n">RENORMALIZE</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">moe_normalization_mode</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">moe_num_experts</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_local_experts&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">moe_top_k</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_experts_per_tok&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">moe_config</span> <span class="o">=</span> <span class="n">MoeConfig</span><span class="p">(</span><span class="n">num_experts</span><span class="o">=</span><span class="n">moe_num_experts</span><span class="p">,</span>
<span class="n">top_k</span><span class="o">=</span><span class="n">moe_top_k</span><span class="p">,</span>
<span class="n">normalization_mode</span><span class="o">=</span><span class="n">moe_normalization_mode</span><span class="p">)</span>
<span class="n">moe_config</span><span class="o">.</span><span class="n">validate</span><span class="p">()</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="n">infer_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">hf_config</span><span class="p">,</span> <span class="s1">&#39;torch_dtype&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
<span class="n">tie_word_embeddings</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="s1">&#39;tie_word_embeddings&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</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">num_hidden_layers</span><span class="o">=</span><span class="n">hf_config</span><span class="o">.</span><span class="n">num_hidden_layers</span><span class="p">,</span>
<span class="n">num_attention_heads</span><span class="o">=</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">hidden_size</span><span class="o">=</span><span class="n">hf_config</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span>
<span class="n">intermediate_size</span><span class="o">=</span><span class="n">hf_config</span><span class="o">.</span><span class="n">intermediate_size</span><span class="p">,</span>
<span class="n">num_key_value_heads</span><span class="o">=</span><span class="n">num_key_value_heads</span><span class="p">,</span>
<span class="n">head_size</span><span class="o">=</span><span class="n">head_size</span><span class="p">,</span>
<span class="n">vocab_size</span><span class="o">=</span><span class="n">hf_config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span>
<span class="n">position_embedding_type</span><span class="o">=</span><span class="s1">&#39;rope_gpt_neox&#39;</span><span class="p">,</span>
<span class="n">max_position_embeddings</span><span class="o">=</span><span class="n">hf_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">hidden_act</span><span class="p">,</span>
<span class="n">norm_epsilon</span><span class="o">=</span><span class="n">norm_epsilon</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">rotary_base</span><span class="o">=</span><span class="n">rotary_base</span><span class="p">,</span>
<span class="n">rotary_scaling</span><span class="o">=</span><span class="n">rotary_scaling</span><span class="p">,</span>
<span class="n">residual_mlp</span><span class="o">=</span><span class="n">residual_mlp</span><span class="p">,</span>
<span class="n">disable_weight_only_quant_plugin</span><span class="o">=</span><span class="n">disable_weight_only_quant_plugin</span><span class="p">,</span>
<span class="n">moe</span><span class="o">=</span><span class="n">moe_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="n">quantization</span><span class="o">=</span><span class="n">quant_config</span><span class="p">,</span>
<span class="n">remove_duplicated_kv_heads</span><span class="o">=</span><span class="n">remove_duplicated_kv_heads</span><span class="p">,</span>
<span class="n">tie_word_embeddings</span><span class="o">=</span><span class="n">tie_word_embeddings</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="LLaMAConfig.from_meta_ckpt">
<a class="viewcode-back" href="../../../../python-api/tensorrt_llm.models.html#tensorrt_llm.models.LLaMAConfig.from_meta_ckpt">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">from_meta_ckpt</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span>
<span class="n">meta_ckpt_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">dtype</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span>
<span class="n">mapping</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Mapping</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">quant_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">QuantConfig</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">meta_ckpt_dir</span><span class="p">,</span> <span class="s2">&quot;params.json&quot;</span><span class="p">))</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
<span class="n">meta_config</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
<span class="n">n_embd</span> <span class="o">=</span> <span class="n">meta_config</span><span class="p">[</span><span class="s2">&quot;dim&quot;</span><span class="p">]</span>
<span class="n">n_head</span> <span class="o">=</span> <span class="n">meta_config</span><span class="p">[</span><span class="s2">&quot;n_heads&quot;</span><span class="p">]</span>
<span class="n">n_kv_head</span> <span class="o">=</span> <span class="n">meta_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;n_kv_heads&quot;</span><span class="p">,</span> <span class="n">n_head</span><span class="p">)</span>
<span class="n">vocab_size</span> <span class="o">=</span> <span class="n">meta_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;vocab_size&quot;</span><span class="p">,</span> <span class="mi">32000</span><span class="p">)</span>
<span class="c1"># Reset vocab_size to 32000 for LLama v2 checkpoint.</span>
<span class="k">if</span> <span class="n">vocab_size</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="n">vocab_size</span> <span class="o">=</span> <span class="mi">32000</span>
<span class="k">if</span> <span class="s2">&quot;hidden_dim&quot;</span> <span class="ow">in</span> <span class="n">meta_config</span><span class="p">:</span>
<span class="n">inter_size</span> <span class="o">=</span> <span class="n">meta_config</span><span class="p">[</span><span class="s2">&quot;hidden_dim&quot;</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">multiple_of</span> <span class="o">=</span> <span class="n">meta_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;multiple_of&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">n_embd_</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">4</span> <span class="o">*</span> <span class="n">n_embd</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">ffn_dim_multiplier</span> <span class="o">=</span> <span class="n">meta_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;ffn_dim_multiplier&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">inter_size</span> <span class="o">=</span> <span class="n">multiple_of</span> <span class="o">*</span> <span class="p">(</span>
<span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">n_embd_</span> <span class="o">*</span> <span class="n">ffn_dim_multiplier</span><span class="p">)</span> <span class="o">+</span> <span class="n">multiple_of</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span>
<span class="n">multiple_of</span><span class="p">)</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="n">infer_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="s1">&#39;bfloat16&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">meta_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;use_scaled_rope&#39;</span><span class="p">):</span>
<span class="n">rotary_scaling</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;llama3&quot;</span><span class="p">}</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">rotary_scaling</span> <span class="o">=</span> <span class="n">meta_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;rope_scaling&quot;</span><span class="p">)</span>
<span class="c1"># meta checkpoint don&#39;t have vocab_size|hidden_act|rotary_base specified, use same default value as HF</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="s2">&quot;LlamaForCausalLM&quot;</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">num_hidden_layers</span><span class="o">=</span><span class="n">meta_config</span><span class="p">[</span><span class="s2">&quot;n_layers&quot;</span><span class="p">],</span>
<span class="n">num_attention_heads</span><span class="o">=</span><span class="n">n_head</span><span class="p">,</span>
<span class="n">hidden_size</span><span class="o">=</span><span class="n">n_embd</span><span class="p">,</span>
<span class="n">intermediate_size</span><span class="o">=</span><span class="n">inter_size</span><span class="p">,</span>
<span class="n">num_key_value_heads</span><span class="o">=</span><span class="n">n_kv_head</span><span class="p">,</span>
<span class="n">vocab_size</span><span class="o">=</span><span class="n">vocab_size</span><span class="p">,</span>
<span class="n">position_embedding_type</span><span class="o">=</span><span class="s1">&#39;rope_gpt_neox&#39;</span><span class="p">,</span>
<span class="n">max_position_embeddings</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span>
<span class="n">hidden_act</span><span class="o">=</span><span class="s1">&#39;silu&#39;</span><span class="p">,</span>
<span class="n">rotary_scaling</span><span class="o">=</span><span class="n">rotary_scaling</span><span class="p">,</span>
<span class="n">rotary_base</span><span class="o">=</span><span class="n">meta_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;rope_theta&#39;</span><span class="p">,</span> <span class="mi">10000</span><span class="p">),</span>
<span class="n">norm_epsilon</span><span class="o">=</span><span class="n">meta_config</span><span class="p">[</span><span class="s2">&quot;norm_eps&quot;</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">quantization</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></div>
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