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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in C++</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling a Transformer using torch.compile and TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
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<h1>Source code for torch_tensorrt.ptq</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">>=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">11</span><span class="p">):</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Self</span>
<span class="k">else</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">Self</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
<span class="kn">from</span> <span class="nn">torch_tensorrt.logging</span> <span class="kn">import</span> <span class="n">Level</span><span class="p">,</span> <span class="n">log</span>
<div class="viewcode-block" id="CalibrationAlgo"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo">[docs]</a><span class="k">class</span> <span class="nc">CalibrationAlgo</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="n">ENTROPY_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span>
<span class="n">ENTROPY_CALIBRATION_2</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span>
<span class="n">LEGACY_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span>
<span class="n">MINMAX_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span></div>
<div class="viewcode-block" id="get_cache_mode_batch"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_cache_mode_batch">[docs]</a><span class="k">def</span> <span class="nf">get_cache_mode_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span></div>
<div class="viewcode-block" id="get_batch_size"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_batch_size">[docs]</a><span class="k">def</span> <span class="nf">get_batch_size</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">1</span></div>
<div class="viewcode-block" id="get_batch"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_batch">[docs]</a><span class="k">def</span> <span class="nf">get_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span> <span class="n">_</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_batch_idx</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">></span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">):</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="n">batch</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset_iterator</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">current_batch_idx</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
<span class="n">inputs_gpu</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">:</span>
<span class="n">inputs_gpu</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">example</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">inputs_gpu</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
<span class="k">return</span> <span class="n">inputs_gpu</span></div>
<div class="viewcode-block" id="read_calibration_cache"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.read_calibration_cache">[docs]</a><span class="k">def</span> <span class="nf">read_calibration_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bytes</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cache</span><span class="p">:</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">b</span><span class="p">:</span> <span class="nb">bytes</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="k">return</span> <span class="n">b</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="sa">b</span><span class="s2">""</span></div>
<div class="viewcode-block" id="write_calibration_cache"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.write_calibration_cache">[docs]</a><span class="k">def</span> <span class="nf">write_calibration_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span> <span class="n">cache</span><span class="p">:</span> <span class="nb">bytes</span><span class="p">)</span> <span class="o">-></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">cache_file</span><span class="p">:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">cache</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span></div>
<span class="c1"># deepcopy (which involves pickling) is performed on the compile_spec internally during compilation.</span>
<span class="c1"># We register this __reduce__ function for pickler to identity the calibrator object returned by DataLoaderCalibrator during deepcopy.</span>
<span class="c1"># This should be the object's local name relative to the module https://docs.python.org/3/library/pickle.html#object.__reduce__</span>
<span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
<div class="viewcode-block" id="DataLoaderCalibrator"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator">[docs]</a><span class="k">class</span> <span class="nc">DataLoaderCalibrator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces</span>
<span class="sd"> data which is passed during calibration.</span>
<span class="sd"> Args:</span>
<span class="sd"> dataloader: an instance of pytorch dataloader which iterates through a given dataset.</span>
<span class="sd"> algo_type: choice of calibration algorithm.</span>
<span class="sd"> cache_file: path to cache file.</span>
<span class="sd"> use_cache: flag which enables usage of pre-existing cache.</span>
<span class="sd"> device: device on which calibration data is copied to.</span>
<span class="sd"> """</span>
<div class="viewcode-block" id="DataLoaderCalibrator.__init__"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator.__init__">[docs]</a> <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="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
<span class="k">pass</span></div>
<span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
<span class="n">dataloader</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">algo_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"algo_type"</span><span class="p">,</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">)</span>
<span class="n">cache_file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"cache_file"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">use_cache</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"use_cache"</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">device</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"device"</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda:0"</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataloader</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">):</span>
<span class="n">log</span><span class="p">(</span>
<span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span>
<span class="s2">"Dataloader : </span><span class="si">{}</span><span class="s2"> is not a valid instance of torch.utils.data.DataLoader"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">dataloader</span>
<span class="p">),</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">cache_file</span><span class="p">:</span>
<span class="k">if</span> <span class="n">use_cache</span><span class="p">:</span>
<span class="n">log</span><span class="p">(</span>
<span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span>
<span class="s2">"Using existing cache_file </span><span class="si">{}</span><span class="s2"> for calibration"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cache_file</span><span class="p">),</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span> <span class="s2">"Overwriting existing calibration cache file."</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">use_cache</span><span class="p">:</span>
<span class="n">log</span><span class="p">(</span>
<span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span>
<span class="s2">"Input cache file is None but use_cache is set to True in INT8 mode."</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Define attributes and member functions for the calibrator class</span>
<span class="n">attribute_mapping</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"data_loader"</span><span class="p">:</span> <span class="n">dataloader</span><span class="p">,</span>
<span class="s2">"current_batch_idx"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">"batch_size"</span><span class="p">:</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
<span class="s2">"dataset_iterator"</span><span class="p">:</span> <span class="nb">iter</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span>
<span class="s2">"cache_file"</span><span class="p">:</span> <span class="n">cache_file</span><span class="p">,</span>
<span class="s2">"device"</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
<span class="s2">"use_cache"</span><span class="p">:</span> <span class="n">use_cache</span><span class="p">,</span>
<span class="s2">"get_batch_size"</span><span class="p">:</span> <span class="n">get_batch_size</span><span class="p">,</span>
<span class="s2">"get_batch"</span><span class="p">:</span> <span class="n">get_cache_mode_batch</span> <span class="k">if</span> <span class="n">use_cache</span> <span class="k">else</span> <span class="n">get_batch</span><span class="p">,</span>
<span class="s2">"read_calibration_cache"</span><span class="p">:</span> <span class="n">read_calibration_cache</span><span class="p">,</span>
<span class="s2">"write_calibration_cache"</span><span class="p">:</span> <span class="n">write_calibration_cache</span><span class="p">,</span>
<span class="s2">"__reduce__"</span><span class="p">:</span> <span class="n">__reduce__</span><span class="p">,</span> <span class="c1"># used when you deepcopy the DataLoaderCalibrator object</span>
<span class="p">}</span>
<span class="c1"># Using type metaclass to construct calibrator class based on algorithm type</span>
<span class="k">if</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span><span class="p">:</span>
<span class="n">calib_ec</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="s2">"Int8EntropyCalibrator"</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
<span class="p">)()</span>
<span class="k">return</span> <span class="n">calib_ec</span>
<span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">:</span>
<span class="n">calib_ec2</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="s2">"Int8EntropyCalibrator2"</span><span class="p">,</span>
<span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator2</span><span class="p">,),</span>
<span class="n">attribute_mapping</span><span class="p">,</span>
<span class="p">)()</span>
<span class="k">return</span> <span class="n">calib_ec2</span>
<span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span><span class="p">:</span>
<span class="n">calib_lc</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="s2">"Int8LegacyCalibrator"</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8LegacyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
<span class="p">)()</span>
<span class="k">return</span> <span class="n">calib_lc</span>
<span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span><span class="p">:</span>
<span class="n">calib_mmc</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="s2">"Int8MinMaxCalibrator"</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
<span class="p">)()</span>
<span class="k">return</span> <span class="n">calib_mmc</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">"Invalid calibration algorithm type. Please select among ENTROPY_CALIBRATION, ENTROPY_CALIBRATION, LEGACY_CALIBRATION or MINMAX_CALIBRATION"</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="CacheCalibrator"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator">[docs]</a><span class="k">class</span> <span class="nc">CacheCalibrator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.</span>
<span class="sd"> Args:</span>
<span class="sd"> cache_file: path to cache file.</span>
<span class="sd"> algo_type: choice of calibration algorithm.</span>
<span class="sd"> """</span>
<div class="viewcode-block" id="CacheCalibrator.__init__"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator.__init__">[docs]</a> <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="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
<span class="k">pass</span></div>
<span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
<span class="n">cache_file</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">algo_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"algo_type"</span><span class="p">,</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">cache_file</span><span class="p">):</span>
<span class="n">log</span><span class="p">(</span>
<span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span>
<span class="s2">"Using existing cache_file </span><span class="si">{}</span><span class="s2"> for calibration"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cache_file</span><span class="p">),</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span> <span class="s2">"Invalid calibration cache file."</span><span class="p">)</span>
<span class="c1"># Define attributes and member functions for the calibrator class</span>
<span class="n">attribute_mapping</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"use_cache"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="s2">"cache_file"</span><span class="p">:</span> <span class="n">cache_file</span><span class="p">,</span>
<span class="s2">"get_batch_size"</span><span class="p">:</span> <span class="n">get_batch_size</span><span class="p">,</span>
<span class="s2">"get_batch"</span><span class="p">:</span> <span class="n">get_cache_mode_batch</span><span class="p">,</span>
<span class="s2">"read_calibration_cache"</span><span class="p">:</span> <span class="n">read_calibration_cache</span><span class="p">,</span>
<span class="s2">"write_calibration_cache"</span><span class="p">:</span> <span class="n">write_calibration_cache</span><span class="p">,</span>
<span class="p">}</span>
<span class="c1"># Using type metaclass to construct calibrator class based on algorithm type</span>
<span class="k">if</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span><span class="p">:</span>
<span class="n">calib_ec</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="s2">"DataLoaderCalibrator"</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
<span class="p">)()</span>
<span class="k">return</span> <span class="n">calib_ec</span>
<span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">:</span>
<span class="n">calib_ec2</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="s2">"DataLoaderCalibrator"</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
<span class="p">)()</span>
<span class="k">return</span> <span class="n">calib_ec2</span>
<span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span><span class="p">:</span>
<span class="n">calib_lc</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="s2">"DataLoaderCalibrator"</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8LegacyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
<span class="p">)()</span>
<span class="k">return</span> <span class="n">calib_lc</span>
<span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span><span class="p">:</span>
<span class="n">calib_mmc</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="s2">"DataLoaderCalibrator"</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
<span class="p">)()</span>
<span class="k">return</span> <span class="n">calib_mmc</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">"Invalid calibration algorithm type. Please select among ENTROPY_CALIBRATION, ENTROPY_CALIBRATION, LEGACY_CALIBRATION or MINMAX_CALIBRATION"</span>
<span class="p">)</span></div>
</pre></div>
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