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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
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| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <h1>Source code for mxnet.symbol.contrib</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. 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,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=wildcard-import, unused-wildcard-import,redefined-outer-name</span> |
| <span class="sd">"""Contrib Symbol API of MXNet."""</span> |
| <span class="kn">import</span> <span class="nn">math</span> |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">copy</span> |
| |
| <span class="kn">from</span> <span class="nn">.random</span> <span class="kn">import</span> <span class="n">uniform</span> |
| <span class="kn">from</span> <span class="nn">.symbol</span> <span class="kn">import</span> <span class="n">Symbol</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">.gen_contrib</span> <span class="kn">import</span> <span class="o">*</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="k">pass</span> |
| |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">symbol</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">_LIB</span><span class="p">,</span> <span class="n">check_call</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">SymbolHandle</span><span class="p">,</span> <span class="n">_as_list</span> |
| <span class="kn">from</span> <span class="nn">..attribute</span> <span class="kn">import</span> <span class="n">AttrScope</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"rand_zipfian"</span><span class="p">,</span> <span class="s2">"foreach"</span><span class="p">,</span> <span class="s2">"while_loop"</span><span class="p">,</span> <span class="s2">"cond"</span><span class="p">]</span> |
| |
| <div class="viewcode-block" id="rand_zipfian"><a class="viewcode-back" href="../../../api/symbol/contrib/index.html#mxnet.symbol.contrib.rand_zipfian">[docs]</a><span class="k">def</span> <span class="nf">rand_zipfian</span><span class="p">(</span><span class="n">true_classes</span><span class="p">,</span> <span class="n">num_sampled</span><span class="p">,</span> <span class="n">range_max</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from an approximately log-uniform or Zipfian distribution.</span> |
| |
| <span class="sd"> This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).</span> |
| <span class="sd"> The elements of sampled_candidates are drawn with replacement from the base distribution.</span> |
| |
| <span class="sd"> The base distribution for this operator is an approximately log-uniform or Zipfian distribution:</span> |
| |
| <span class="sd"> P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)</span> |
| |
| <span class="sd"> This sampler is useful when the true classes approximately follow such a distribution.</span> |
| <span class="sd"> For example, if the classes represent words in a lexicon sorted in decreasing order of \</span> |
| <span class="sd"> frequency. If your classes are not ordered by decreasing frequency, do not use this op.</span> |
| |
| <span class="sd"> Additionaly, it also returns the number of times each of the \</span> |
| <span class="sd"> true classes and the sampled classes is expected to occur.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> true_classes : Symbol</span> |
| <span class="sd"> The target classes in 1-D.</span> |
| <span class="sd"> num_sampled: int</span> |
| <span class="sd"> The number of classes to randomly sample.</span> |
| <span class="sd"> range_max: int</span> |
| <span class="sd"> The number of possible classes.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> samples: Symbol</span> |
| <span class="sd"> The sampled candidate classes in 1-D `int64` dtype.</span> |
| <span class="sd"> expected_count_true: Symbol</span> |
| <span class="sd"> The expected count for true classes in 1-D `float64` dtype.</span> |
| <span class="sd"> expected_count_sample: Symbol</span> |
| <span class="sd"> The expected count for sampled candidates in 1-D `float64` dtype.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> true_cls = mx.sym.Variable('true_cls')</span> |
| <span class="sd"> >>> samples, exp_count_true, exp_count_sample = mx.sym.contrib.rand_zipfian(true_cls, 4, 5)</span> |
| <span class="sd"> >>> samples.eval(true_cls=mx.nd.array([3]))[0].asnumpy()</span> |
| <span class="sd"> array([1, 3, 3, 3])</span> |
| <span class="sd"> >>> exp_count_true.eval(true_cls=mx.nd.array([3]))[0].asnumpy()</span> |
| <span class="sd"> array([0.12453879])</span> |
| <span class="sd"> >>> exp_count_sample.eval(true_cls=mx.nd.array([3]))[0].asnumpy()</span> |
| <span class="sd"> array([0.22629439, 0.12453879, 0.12453879, 0.12453879])</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">true_classes</span><span class="p">,</span> <span class="n">Symbol</span><span class="p">)),</span> <span class="s2">"unexpected type </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">true_classes</span><span class="p">)</span> |
| <span class="n">log_range</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">range_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">rand</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">log_range</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">num_sampled</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="c1"># make sure sampled_classes are in the range of [0, range_max)</span> |
| <span class="n">sampled_classes</span> <span class="o">=</span> <span class="p">(</span><span class="n">rand</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int64'</span><span class="p">)</span> <span class="o">%</span> <span class="n">range_max</span> |
| |
| <span class="n">true_classes</span> <span class="o">=</span> <span class="n">true_classes</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="n">expected_prob_true</span> <span class="o">=</span> <span class="p">((</span><span class="n">true_classes</span> <span class="o">+</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_classes</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">))</span><span class="o">.</span><span class="n">log</span><span class="p">()</span> <span class="o">/</span> <span class="n">log_range</span> |
| <span class="n">expected_count_true</span> <span class="o">=</span> <span class="n">expected_prob_true</span> <span class="o">*</span> <span class="n">num_sampled</span> |
| <span class="c1"># cast sampled classes to fp64 to avoid interget division</span> |
| <span class="n">sampled_cls_fp64</span> <span class="o">=</span> <span class="n">sampled_classes</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="n">expected_prob_sampled</span> <span class="o">=</span> <span class="p">((</span><span class="n">sampled_cls_fp64</span> <span class="o">+</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">sampled_cls_fp64</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">))</span><span class="o">.</span><span class="n">log</span><span class="p">()</span> <span class="o">/</span> <span class="n">log_range</span> |
| <span class="n">expected_count_sampled</span> <span class="o">=</span> <span class="n">expected_prob_sampled</span> <span class="o">*</span> <span class="n">num_sampled</span> |
| <span class="k">return</span> <span class="n">sampled_classes</span><span class="p">,</span> <span class="n">expected_count_true</span><span class="p">,</span> <span class="n">expected_count_sampled</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_flatten</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">inout_str</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">):</span> |
| <span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">())</span> |
| <span class="n">length</span> <span class="o">=</span> <span class="n">length</span> <span class="k">if</span> <span class="n">length</span> <span class="o">></span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">args</span><span class="p">],</span> <span class="nb">int</span><span class="p">(</span><span class="n">length</span><span class="p">)</span> |
| |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)),</span> \ |
| <span class="s2">"</span><span class="si">%s</span><span class="s2"> must be (nested) list of Symbol, "</span> \ |
| <span class="s2">"but got </span><span class="si">%s</span><span class="s2"> of type </span><span class="si">%s</span><span class="s2">"</span><span class="o">%</span><span class="p">(</span><span class="n">inout_str</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">args</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">)))</span> |
| <span class="n">flat</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">fmts</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span> |
| <span class="n">arg</span><span class="p">,</span> <span class="n">fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">inout_str</span><span class="p">)</span> |
| <span class="n">flat</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> |
| <span class="n">fmts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fmt</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">flat</span><span class="p">,</span> <span class="n">fmts</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_regroup</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">fmt</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fmt</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">fmt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> |
| <span class="k">return</span> <span class="n">args</span><span class="p">[:</span><span class="n">fmt</span><span class="p">],</span> <span class="n">args</span><span class="p">[</span><span class="n">fmt</span><span class="p">:]</span> |
| |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)),</span> \ |
| <span class="s2">"output must be (nested) list of Symbol, "</span> \ |
| <span class="s2">"but got </span><span class="si">%s</span><span class="s2"> of type </span><span class="si">%s</span><span class="s2">"</span><span class="o">%</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">args</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">)))</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">fmt</span><span class="p">:</span> |
| <span class="n">res</span><span class="p">,</span> <span class="n">args</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> |
| <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ret</span><span class="p">,</span> <span class="n">args</span> |
| |
| |
| <span class="c1"># We want to generate a unique name for input symbols to a control flow</span> |
| <span class="c1"># operator. The names are generated on purpose differently from the symbols</span> |
| <span class="c1"># cut from the graph.</span> |
| <span class="k">def</span> <span class="nf">_get_sym_uniq_name</span><span class="p">(</span><span class="n">sym</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s1">'</span><span class="si">{}</span><span class="s1">-</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">sym</span><span class="o">.</span><span class="n">attr</span><span class="p">(</span><span class="s1">'_value_index'</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">_get_graph_inputs</span><span class="p">(</span><span class="n">subg</span><span class="p">):</span> |
| <span class="n">num_handles</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">handles</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">SymbolHandle</span><span class="p">)()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSymbolGetInputSymbols</span><span class="p">(</span><span class="n">subg</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handles</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">num_handles</span><span class="p">)))</span> |
| |
| <span class="n">syms</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_handles</span><span class="o">.</span><span class="n">value</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="n">Symbol</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">handles</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">SymbolHandle</span><span class="p">))</span> |
| <span class="n">syms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">syms</span> |
| |
| <span class="k">def</span> <span class="nf">_cut_subgraph</span><span class="p">(</span><span class="n">subg</span><span class="p">):</span> |
| <span class="n">num_handles</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">handles</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">SymbolHandle</span><span class="p">)()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSymbolCutSubgraph</span><span class="p">(</span><span class="n">subg</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handles</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">num_handles</span><span class="p">)))</span> |
| |
| <span class="n">syms</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_handles</span><span class="o">.</span><span class="n">value</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="n">Symbol</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">handles</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">SymbolHandle</span><span class="p">))</span> |
| <span class="n">syms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">syms</span> |
| |
| <span class="k">def</span> <span class="nf">_get_unique_subgraph_name</span><span class="p">(</span><span class="n">subgraph_name</span><span class="p">):</span> |
| <span class="n">attrs</span> <span class="o">=</span> <span class="n">AttrScope</span><span class="o">.</span><span class="n">_current</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">_attr</span> |
| <span class="k">if</span> <span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"__subgraph_name__"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">""</span><span class="p">:</span> |
| <span class="n">subgraph_name</span> <span class="o">=</span> <span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">attrs</span><span class="p">[</span><span class="s2">"__subgraph_name__"</span><span class="p">],</span> <span class="s2">"$"</span><span class="p">,</span> <span class="n">subgraph_name</span><span class="p">])</span> |
| <span class="n">AttrScope</span><span class="o">.</span><span class="n">_subgraph_names</span><span class="p">[</span><span class="n">subgraph_name</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="n">subgraph_name</span> <span class="o">=</span> <span class="n">subgraph_name</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">AttrScope</span><span class="o">.</span><span class="n">_subgraph_names</span><span class="p">[</span><span class="n">subgraph_name</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">subgraph_name</span> |
| |
| <span class="c1"># This construct a subgraph for given output nodes.</span> |
| <span class="c1"># If an output node is one of the input nodes, we call identity to make sure</span> |
| <span class="c1"># that outputs nodes are different from input nodes.</span> |
| <span class="k">def</span> <span class="nf">_construct_subgraph</span><span class="p">(</span><span class="n">sym_out</span><span class="p">,</span> <span class="n">sym_states</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> |
| <span class="n">sym_out</span> <span class="o">=</span> <span class="n">_as_list</span><span class="p">(</span><span class="n">sym_out</span><span class="p">)</span> |
| <span class="n">sym_states</span> <span class="o">=</span> <span class="n">_as_list</span><span class="p">(</span><span class="n">sym_states</span><span class="p">)</span> |
| <span class="n">all_outputs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">all_outputs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">sym_out</span><span class="p">)</span> |
| <span class="n">all_outputs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">sym_states</span><span class="p">)</span> |
| <span class="n">g</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Group</span><span class="p">(</span><span class="n">all_outputs</span><span class="p">)</span> |
| |
| <span class="n">flat_out</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">all_input_names</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span> |
| <span class="n">output_names</span> <span class="o">=</span> <span class="p">{</span><span class="n">o</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">sym_out</span><span class="p">}</span> |
| <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">sym_out</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">o</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">all_input_names</span> <span class="ow">or</span> <span class="n">o</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"__subgraph_name__"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span> <span class="o">!=</span> <span class="n">name</span><span class="p">:</span> |
| <span class="n">flat_out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">symbol</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">o</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">flat_out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">o</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sym_states</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">all_input_names</span> <span class="ow">or</span> <span class="n">s</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">output_names</span> <span class="ow">or</span> \ |
| <span class="n">s</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"__subgraph_name__"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span> <span class="o">!=</span> <span class="n">name</span><span class="p">:</span> |
| <span class="n">flat_out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">symbol</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">s</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">flat_out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Group</span><span class="p">(</span><span class="n">flat_out</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_check_data</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">in_type</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span> |
| <span class="n">is_NDArray_or_list</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</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">i</span><span class="p">,</span> <span class="n">in_type</span><span class="p">):</span> |
| <span class="n">is_NDArray_or_list</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">break</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">is_NDArray_or_list</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">in_type</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">is_NDArray_or_list</span><span class="p">,</span> <span class="n">msg</span> |
| |
| <div class="viewcode-block" id="foreach"><a class="viewcode-back" href="../../../api/symbol/contrib/index.html#mxnet.symbol.contrib.foreach">[docs]</a><span class="k">def</span> <span class="nf">foreach</span><span class="p">(</span><span class="n">body</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">init_states</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"foreach"</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Run a for loop with user-defined computation over Symbols on dimension 0.</span> |
| |
| <span class="sd"> This operator simulates a for loop and body has the computation for an iteration</span> |
| <span class="sd"> of the for loop. It runs the computation in body on each slice from the input</span> |
| <span class="sd"> NDArrays.</span> |
| |
| <span class="sd"> body takes two arguments as input and outputs a tuple of two elements,</span> |
| <span class="sd"> as illustrated below:</span> |
| |
| <span class="sd"> out, states = body(data1, states)</span> |
| |
| <span class="sd"> data1 can be either a symbol or a list of symbols. If data is a symbol,</span> |
| <span class="sd"> data1 is a symbol. Otherwise, data1 is a list of symbols and has the same</span> |
| <span class="sd"> size as data. states is a list of symbols and have the same size as init_states.</span> |
| <span class="sd"> Similarly, out can be either a symbol or a list of symbols, which are concatenated</span> |
| <span class="sd"> as the first output of foreach; states from the last execution of body</span> |
| <span class="sd"> are the second output of foreach.</span> |
| |
| <span class="sd"> foreach can output only output data or states. If a user only wants states,</span> |
| <span class="sd"> the body function can return ([], states). Similarly, if a user only wants</span> |
| <span class="sd"> output data, the body function can return (out, []).</span> |
| |
| <span class="sd"> The computation done by this operator is equivalent to the pseudo code below</span> |
| <span class="sd"> when the input data is NDArray::</span> |
| |
| <span class="sd"> states = init_states</span> |
| <span class="sd"> outs = []</span> |
| <span class="sd"> for i in data.shape[0]:</span> |
| <span class="sd"> s = data[i]</span> |
| <span class="sd"> out, states = body(s, states)</span> |
| <span class="sd"> outs.append(out)</span> |
| <span class="sd"> outs = stack(*outs)</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> body : a Python function.</span> |
| <span class="sd"> Define computation in an iteration.</span> |
| <span class="sd"> data: a symbol or a list of symbols.</span> |
| <span class="sd"> The input data.</span> |
| <span class="sd"> init_states: a Symbol or nested lists of symbols.</span> |
| <span class="sd"> The initial values of the loop states.</span> |
| <span class="sd"> name: string.</span> |
| <span class="sd"> The name of the operator.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> outputs: a Symbol or nested lists of Symbols.</span> |
| <span class="sd"> The output data concatenated from the output of all iterations.</span> |
| <span class="sd"> states: a Symbol or nested lists of Symbols.</span> |
| <span class="sd"> The loop states in the last iteration.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> step = lambda data, states: (data + states[0], [states[0] * 2])</span> |
| <span class="sd"> >>> data = mx.sym.var('data')</span> |
| <span class="sd"> >>> states = [mx.sym.var('state')]</span> |
| <span class="sd"> >>> outs, states = mx.sym.contrib.foreach(step, data, states)</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">flatten_data</span><span class="p">,</span> <span class="n">data_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s2">"foreach input"</span><span class="p">)</span> |
| <span class="n">_check_data</span><span class="p">(</span><span class="n">flatten_data</span><span class="p">,</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">,</span> |
| <span class="s2">"data should be a symbol or a nested list of symbols"</span><span class="p">)</span> |
| <span class="n">init_flatten_states</span><span class="p">,</span> <span class="n">init_state_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">init_states</span><span class="p">,</span> <span class="s2">"foreach states"</span><span class="p">)</span> |
| <span class="n">_check_data</span><span class="p">(</span><span class="n">init_flatten_states</span><span class="p">,</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">,</span> |
| <span class="s2">"init_states should be a symbol or a nested list of symbols"</span><span class="p">)</span> |
| |
| <span class="c1"># If the input python function references to the symbols outside</span> |
| <span class="c1"># the python function, we need to prune the computation graph constructed from</span> |
| <span class="c1"># the function. One way of doing it is to mark the nodes in the computation graph</span> |
| <span class="c1"># with AttrScope and prune the nodes without the special attribute.</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="n">_get_unique_subgraph_name</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">AttrScope</span><span class="p">(</span><span class="n">__subgraph_name__</span><span class="o">=</span><span class="n">name</span><span class="p">):</span> |
| <span class="n">in_eles</span> <span class="o">=</span> <span class="p">[</span><span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">_get_sym_uniq_name</span><span class="p">(</span><span class="n">sym</span><span class="p">))</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">flatten_data</span><span class="p">]</span> |
| <span class="n">in_eles</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">in_eles</span><span class="p">,</span> <span class="n">data_fmt</span><span class="p">)</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">[</span><span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">_get_sym_uniq_name</span><span class="p">(</span><span class="n">s</span><span class="p">))</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">init_flatten_states</span><span class="p">]</span> |
| <span class="n">states</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">init_state_fmt</span><span class="p">))</span> |
| <span class="n">sym_out</span><span class="p">,</span> <span class="n">sym_states</span> <span class="o">=</span> <span class="n">body</span><span class="p">(</span><span class="n">in_eles</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span> |
| |
| <span class="n">sym_out</span><span class="p">,</span> <span class="n">out_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">sym_out</span><span class="p">,</span> <span class="s2">"foreach output"</span><span class="p">)</span> |
| <span class="n">sym_states</span><span class="p">,</span> <span class="n">state_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">sym_states</span><span class="p">,</span> <span class="s2">"foreach loop_vars"</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">init_state_fmt</span> <span class="o">==</span> <span class="n">state_fmt</span><span class="p">,</span> <span class="s2">"The input and output loop_vars have different format"</span> |
| <span class="n">_check_data</span><span class="p">(</span><span class="n">sym_out</span><span class="p">,</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">,</span> |
| <span class="s2">"the output should be an NDArray or a nested list of NDArrays"</span><span class="p">)</span> |
| <span class="n">_check_data</span><span class="p">(</span><span class="n">sym_states</span><span class="p">,</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">,</span> |
| <span class="s2">"the output states should be an NDArray or a nested list of NDArrays"</span><span class="p">)</span> |
| <span class="n">num_out_data</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sym_out</span><span class="p">)</span> |
| <span class="n">num_states</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sym_states</span><span class="p">)</span> |
| <span class="n">num_outputs</span> <span class="o">=</span> <span class="n">num_out_data</span> <span class="o">+</span> <span class="n">num_states</span> |
| <span class="n">g</span> <span class="o">=</span> <span class="n">_construct_subgraph</span><span class="p">(</span><span class="n">sym_out</span><span class="p">,</span> <span class="n">sym_states</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> |
| |
| <span class="n">input_syms</span> <span class="o">=</span> <span class="n">_get_graph_inputs</span><span class="p">(</span><span class="n">g</span><span class="p">)</span> |
| <span class="n">cut_syms</span> <span class="o">=</span> <span class="n">_cut_subgraph</span><span class="p">(</span><span class="n">g</span><span class="p">)</span> |
| <span class="n">input_syms</span> <span class="o">=</span> <span class="n">_get_graph_inputs</span><span class="p">(</span><span class="n">g</span><span class="p">)</span> |
| |
| <span class="c1"># Here we need to find out how the input symbols are ordered as well as</span> |
| <span class="c1"># where the loop states are located in the list of inputs.</span> |
| |
| <span class="c1"># This dict contains the symbols of the subgraph.</span> |
| <span class="n">input_syms</span> <span class="o">=</span> <span class="p">{</span><span class="n">sym</span><span class="o">.</span><span class="n">name</span><span class="p">:</span><span class="n">sym</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">input_syms</span><span class="p">}</span> |
| <span class="n">gin_names</span> <span class="o">=</span> <span class="n">input_syms</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> |
| <span class="c1"># This array contains the symbols for the inputs of foreach.</span> |
| <span class="c1"># They are ordered according to the inputs of the subgraph.</span> |
| <span class="n">state_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">_get_sym_uniq_name</span><span class="p">(</span><span class="n">sym</span><span class="p">)</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">init_flatten_states</span><span class="p">]</span> |
| <span class="n">data_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">_get_sym_uniq_name</span><span class="p">(</span><span class="n">sym</span><span class="p">)</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">flatten_data</span><span class="p">]</span> |
| <span class="n">cut_var_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">sym</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]:</span><span class="n">sym</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">cut_syms</span><span class="p">}</span> |
| <span class="n">cut_var_names</span> <span class="o">=</span> <span class="n">cut_var_map</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> |
| |
| <span class="n">subg_input_names</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">subg_input_names</span><span class="p">))</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">subg_input_names</span><span class="p">),</span> \ |
| <span class="s2">"The inputs of the subgraph don't have unique names: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">subg_input_names</span><span class="p">)</span> |
| <span class="c1"># ordered_ins contains input symbols in the following order:</span> |
| <span class="c1"># data_syms, state_syms, followed by cut_vars and vars in the closure.</span> |
| <span class="n">ordered_ins</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">flatten_data</span><span class="p">]</span> |
| <span class="c1"># this defines the location of data_syms in the list of subgraph inputs</span> |
| <span class="n">in_data_locs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">dname</span> <span class="ow">in</span> <span class="n">data_names</span><span class="p">:</span> |
| <span class="c1"># Some data may not be used.</span> |
| <span class="k">if</span> <span class="n">dname</span> <span class="ow">in</span> <span class="n">subg_input_names</span><span class="p">:</span> |
| <span class="n">in_data_locs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subg_input_names</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dname</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">"the data arrays have to be used in the loop body"</span><span class="p">)</span> |
| |
| <span class="n">ordered_ins</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">init_flatten_states</span><span class="p">)</span> |
| <span class="c1"># this defines the location of state_syms in the list of subgraph inputs.</span> |
| <span class="n">in_state_locs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">sname</span> <span class="ow">in</span> <span class="n">state_names</span><span class="p">:</span> |
| <span class="c1"># Some state may not be used.</span> |
| <span class="k">if</span> <span class="n">sname</span> <span class="ow">in</span> <span class="n">subg_input_names</span><span class="p">:</span> |
| <span class="n">in_state_locs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subg_input_names</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">sname</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">"the state arrays have to be used in the loop body"</span><span class="p">)</span> |
| |
| <span class="n">remain_locs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">in_name</span> <span class="ow">in</span> <span class="n">subg_input_names</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">in_name</span> <span class="ow">in</span> <span class="n">gin_names</span><span class="p">,</span> <span class="s2">"The input variable </span><span class="si">%s</span><span class="s2"> can't be found in graph inputs: </span><span class="si">%s</span><span class="s2">"</span> \ |
| <span class="o">%</span> <span class="p">(</span><span class="n">in_name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">gin_names</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">in_name</span> <span class="ow">in</span> <span class="n">cut_var_names</span><span class="p">:</span> |
| <span class="n">ordered_ins</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cut_var_map</span><span class="p">[</span><span class="n">in_name</span><span class="p">])</span> |
| <span class="n">remain_locs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subg_input_names</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">in_name</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="n">in_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">data_names</span> <span class="ow">and</span> <span class="n">in_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">state_names</span><span class="p">:</span> |
| <span class="c1"># The remaining inputs are the variable nodes created inside the UDF.</span> |
| <span class="c1"># The subgraph can't have nodes shared with the main graph. As such,</span> |
| <span class="c1"># we need to make a copy of these variable nodes.</span> |
| <span class="k">assert</span> <span class="n">in_name</span> <span class="ow">in</span> <span class="n">gin_names</span> |
| <span class="n">ordered_ins</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">input_syms</span><span class="p">[</span><span class="n">in_name</span><span class="p">]))</span> |
| <span class="n">remain_locs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subg_input_names</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">in_name</span><span class="p">))</span> |
| |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_foreach</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="o">*</span><span class="n">ordered_ins</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="n">num_outputs</span><span class="p">,</span> |
| <span class="n">num_out_data</span><span class="o">=</span><span class="n">num_out_data</span><span class="p">,</span> <span class="n">in_state_locs</span><span class="o">=</span><span class="n">in_state_locs</span><span class="p">,</span> |
| <span class="n">in_data_locs</span><span class="o">=</span><span class="n">in_data_locs</span><span class="p">,</span> <span class="n">remain_locs</span><span class="o">=</span><span class="n">remain_locs</span><span class="p">)</span> |
| <span class="n">outs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_outputs</span> <span class="o">-</span> <span class="n">num_states</span><span class="p">):</span> |
| <span class="n">outs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="n">outs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">outs</span><span class="p">,</span> <span class="n">out_fmt</span><span class="p">)</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_states</span><span class="p">):</span> |
| <span class="n">states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">num_outputs</span> <span class="o">-</span> <span class="n">num_states</span> <span class="o">+</span> <span class="n">i</span><span class="p">])</span> |
| <span class="n">states</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">state_fmt</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="p">(</span><span class="n">outs</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="while_loop"><a class="viewcode-back" href="../../../api/symbol/contrib/index.html#mxnet.symbol.contrib.while_loop">[docs]</a><span class="k">def</span> <span class="nf">while_loop</span><span class="p">(</span><span class="n">cond</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">loop_vars</span><span class="p">,</span> <span class="n">max_iterations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"while_loop"</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Run a while loop with user-defined computation and loop condition.</span> |
| |
| <span class="sd"> This operator simulates a while loop which iterately does customized computation</span> |
| <span class="sd"> as long as the condition is satisfied.</span> |
| |
| <span class="sd"> `loop_vars` is a Symbol or nested lists of Symbols on which the computation uses.</span> |
| |
| <span class="sd"> `cond` is a user-defined function, used as the loop condition.</span> |
| <span class="sd"> It consumes `loop_vars`, and produces a scalar MXNet symbol,</span> |
| <span class="sd"> indicating the termination of the loop.</span> |
| <span class="sd"> The loop ends when `cond` returns false (zero).</span> |
| <span class="sd"> The `cond` is variadic, and its signature should be</span> |
| <span class="sd"> `cond(*loop_vars) => Symbol`.</span> |
| |
| <span class="sd"> `func` is a user-defined function, used as the loop body.</span> |
| <span class="sd"> It also consumes `loop_vars`, and produces `step_output` and `new_loop_vars` at each step.</span> |
| <span class="sd"> In each step, `step_output` should contain the same number elements.</span> |
| <span class="sd"> Through all steps, the i-th element of `step_output` should have the same shape and dtype.</span> |
| <span class="sd"> Also, `new_loop_vars` should contain the same number of elements as `loop_vars`,</span> |
| <span class="sd"> and the corresponding element should have the same shape and dtype.</span> |
| <span class="sd"> The `func` is variadic, and its signature should be</span> |
| <span class="sd"> `func(*loop_vars) =></span> |
| <span class="sd"> (Symbol or nested List[Symbol] step_output, Symbol or nested List[Symbol] new_loop_vars)`.</span> |
| |
| <span class="sd"> `max_iterations` is a scalar that defines the maximum number of iterations allowed.</span> |
| |
| <span class="sd"> This function returns two lists.</span> |
| <span class="sd"> The first list has the length of `|step_output|`,</span> |
| <span class="sd"> in which the i-th element are all i-th elements of</span> |
| <span class="sd"> `step_output` from all steps, stacked along axis 0.</span> |
| <span class="sd"> The second list has the length of `|loop_vars|`,</span> |
| <span class="sd"> which represents final states of loop variables.</span> |
| |
| <span class="sd"> .. warning::</span> |
| |
| <span class="sd"> For now, the axis 0 of all Symbols in the first list are `max_iterations`,</span> |
| <span class="sd"> due to lack of dynamic shape inference.</span> |
| |
| <span class="sd"> .. warning::</span> |
| |
| <span class="sd"> Even if `cond` is never satisfied,</span> |
| <span class="sd"> while_loop returns a list of outputs with inferred dtype and shape.</span> |
| <span class="sd"> This is different from the Symbol version,</span> |
| <span class="sd"> where in this case `step_outputs` are assumed as an empty list.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> cond: a Python function.</span> |
| <span class="sd"> The loop condition.</span> |
| <span class="sd"> func: a Python function.</span> |
| <span class="sd"> The loop body.</span> |
| <span class="sd"> loop_vars: a Symbol or nested lists of Symbol.</span> |
| <span class="sd"> The initial values of the loop variables.</span> |
| <span class="sd"> max_iterations: a python int.</span> |
| <span class="sd"> Maximum number of iterations.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> outputs: a Symbol or nested lists of Symbols</span> |
| <span class="sd"> stacked output from each step</span> |
| <span class="sd"> states: a Symbol or nested lists of Symbols</span> |
| <span class="sd"> final state</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> cond = lambda i, s: i <= 5</span> |
| <span class="sd"> >>> func = lambda i, s: ([i + s], [i + 1, s + i])</span> |
| <span class="sd"> >>> loop_vars = (mx.sym.var('i'), mx.sym.var('s'))</span> |
| <span class="sd"> >>> outputs, states = mx.sym.contrib.while_loop(cond, func, loop_vars, max_iterations=10)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">_to_python_scalar</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">type_</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Converts "inputs", possibly typed mxnet NDArray, a numpy ndarray, other python types,</span> |
| <span class="sd"> to the given type</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="s2">"asscalar"</span><span class="p">):</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">type_</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> |
| <span class="k">except</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Cannot convert </span><span class="si">%s</span><span class="s2"> to python </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">type_</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">inputs</span> |
| |
| <span class="k">def</span> <span class="nf">_cond_wrapper</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">):</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">cond</span><span class="p">(</span><span class="o">*</span><span class="n">loop_vars</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">result</span><span class="p">,</span> <span class="n">Symbol</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Return of cond must be a Symbol"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">[],</span> <span class="p">[</span><span class="n">result</span><span class="p">],</span> <span class="p">[],</span> <span class="p">[]</span> |
| |
| <span class="k">def</span> <span class="nf">_func_wrapper</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""This wrapper unifies</span> |
| <span class="sd"> "func: loop_vars -> new_loop_vars"</span> |
| <span class="sd"> and "func: loop_vars -> (step_output, new_loop_vars)"</span> |
| <span class="sd"> into "func: loop_vars -> (list of step_outputs, tuple of new_loop_vars)</span> |
| <span class="sd"> """</span> |
| <span class="n">step_output</span><span class="p">,</span> <span class="n">new_loop_vars</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">loop_vars</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">step_output</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">step_output</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="n">new_loop_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">new_loop_vars</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">step_output</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">step_output</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">step_output</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">new_loop_vars</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">new_loop_vars</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">new_loop_vars</span><span class="p">)</span> |
| <span class="n">step_output</span><span class="p">,</span> <span class="n">out_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">step_output</span><span class="p">,</span> <span class="s2">"while output"</span><span class="p">)</span> |
| <span class="n">new_loop_vars</span><span class="p">,</span> <span class="n">var_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">new_loop_vars</span><span class="p">,</span> <span class="s2">"while loop_vars"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_loop_vars</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The number of loop_vars should be consistent during the loop"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">step_output</span><span class="p">,</span> <span class="n">new_loop_vars</span><span class="p">,</span> <span class="n">out_fmt</span><span class="p">,</span> <span class="n">var_fmt</span> |
| |
| <span class="k">def</span> <span class="nf">_create_subgraph</span><span class="p">(</span><span class="n">graph_vars</span><span class="p">,</span> <span class="n">graph_func</span><span class="p">,</span> <span class="n">subgraph_name</span><span class="p">):</span> |
| <span class="n">subgraph_name</span> <span class="o">=</span> <span class="n">_get_unique_subgraph_name</span><span class="p">(</span><span class="n">subgraph_name</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">AttrScope</span><span class="p">(</span><span class="n">__subgraph_name__</span><span class="o">=</span><span class="n">subgraph_name</span><span class="p">):</span> |
| <span class="c1"># create new variables with the same name,</span> |
| <span class="c1"># them feed them to the given func</span> |
| <span class="n">graph_vars</span><span class="p">,</span> <span class="n">var_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">graph_vars</span><span class="p">,</span> <span class="s2">"while loop_vars"</span><span class="p">)</span> |
| <span class="n">new_graph_vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">_get_sym_uniq_name</span><span class="p">(</span><span class="n">sym</span><span class="p">))</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">graph_vars</span><span class="p">]</span> |
| <span class="n">new_graph_vars</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">new_graph_vars</span><span class="p">,</span> <span class="n">var_fmt</span><span class="p">)</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">final_state</span><span class="p">,</span> <span class="n">out_fmt</span><span class="p">,</span> <span class="n">var_fmt</span> <span class="o">=</span> <span class="n">graph_func</span><span class="p">(</span><span class="n">new_graph_vars</span><span class="p">)</span> |
| <span class="c1"># first `num_out_data` elements belong to `outputs`</span> |
| <span class="c1"># other elements belong to `final_state`</span> |
| <span class="n">num_out_data</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> |
| <span class="n">num_outputs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">final_state</span><span class="p">)</span> |
| <span class="c1"># nnvm cut-graph does not allow inputs and outputs overlap</span> |
| <span class="c1"># so we calculate the name of inputs, and copy outputs once it overlaps with inputs</span> |
| <span class="c1"># group all outputs of graph_func</span> |
| <span class="n">all_input_names</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Group</span><span class="p">(</span><span class="n">outputs</span> <span class="o">+</span> <span class="n">final_state</span><span class="p">)</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span> |
| <span class="n">in_input</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">all_input_names</span> |
| <span class="n">in_graph</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"__subgraph_name__"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span> <span class="o">==</span> <span class="n">subgraph_name</span> |
| <span class="n">make_identity</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">symbol</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">in_input</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">in_graph</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> \ |
| <span class="k">else</span> <span class="n">x</span> |
| <span class="n">graph</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Group</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">make_identity</span><span class="p">,</span> <span class="n">outputs</span> <span class="o">+</span> <span class="n">final_state</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">graph</span><span class="p">,</span> <span class="n">num_out_data</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">,</span> <span class="n">out_fmt</span><span class="p">,</span> <span class="n">var_fmt</span> |
| |
| <span class="n">flatten_loop_vars</span><span class="p">,</span> <span class="n">init_loop_var_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">,</span> <span class="s2">"while loop_vars"</span><span class="p">)</span> |
| <span class="n">_check_data</span><span class="p">(</span><span class="n">flatten_loop_vars</span><span class="p">,</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">,</span> |
| <span class="s2">"loop_vars should be a symbol or a nested list of symbols"</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_union_inputs</span><span class="p">(</span><span class="o">*</span><span class="n">graphs</span><span class="p">):</span> |
| <span class="c1"># Given a list of graphs, each whose inputs are either from loop_vars or other variables.</span> |
| <span class="c1"># 1) calculate a list `inputs`, the union of their inputs.</span> |
| <span class="c1"># 2) for each graph, determine in which indices their inputs reside in `inputs`</span> |
| <span class="c1"># 3) for each variable in the input of `graph`, find which index it is</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># List[Symbol], result of 1)</span> |
| <span class="n">locs</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># List[Tuple(List[Int], List[Int])], a list of tuples,</span> |
| <span class="c1"># where tuples are results of 2) and 3)</span> |
| <span class="n">input_id_to_loc</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># Dict[int, int], given id(sym), input_id_to_loc maps it</span> |
| <span class="c1"># to a `loc`, where inputs[loc] = sym</span> |
| <span class="k">for</span> <span class="n">graph</span> <span class="ow">in</span> <span class="n">graphs</span><span class="p">:</span> |
| <span class="c1"># some loop_vars are inputs to `graph`, some are not</span> |
| <span class="n">name_to_loop_vars</span> <span class="o">=</span> <span class="p">{</span><span class="n">_get_sym_uniq_name</span><span class="p">(</span><span class="n">sym</span><span class="p">):</span> <span class="n">sym</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">flatten_loop_vars</span><span class="p">}</span> |
| <span class="c1"># other inputs to `graph` created by cut_graph</span> |
| <span class="n">name_to_cut_g_syms</span> <span class="o">=</span> <span class="p">{</span><span class="n">sym</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]:</span> <span class="n">sym</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">_cut_subgraph</span><span class="p">(</span><span class="n">graph</span><span class="p">)}</span> |
| <span class="c1"># input_syms: all inputs to the `graph`</span> |
| <span class="n">name_to_input_syms</span> <span class="o">=</span> <span class="p">{</span><span class="n">sym</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">sym</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">_get_graph_inputs</span><span class="p">(</span><span class="n">graph</span><span class="p">)}</span> |
| <span class="c1"># also we collect the mapping from var's name to var's loc in loop_vars</span> |
| <span class="n">name_to_var_locs</span> <span class="o">=</span> <span class="p">{</span><span class="n">_get_sym_uniq_name</span><span class="p">(</span><span class="n">sym</span><span class="p">):</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sym</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">flatten_loop_vars</span><span class="p">)}</span> |
| <span class="c1"># collect arguments for each subgraph</span> |
| <span class="n">input_locs</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># results from the second step</span> |
| <span class="n">var_locs</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">flatten_loop_vars</span><span class="p">)</span> <span class="c1"># results from the third step</span> |
| <span class="n">subg_input_names</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">subg_input_names</span><span class="p">))</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">subg_input_names</span><span class="p">),</span> \ |
| <span class="s2">"The inputs of the subgraph don't have unique names: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">subg_input_names</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">subg_input_names</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">name_to_input_syms</span> <span class="c1"># it should obviously hold</span> |
| <span class="c1"># name -> sym</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">name_to_loop_vars</span><span class="p">:</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">name_to_loop_vars</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">name_to_cut_g_syms</span><span class="p">:</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">name_to_cut_g_syms</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">name_to_input_syms</span><span class="p">[</span><span class="n">name</span><span class="p">])</span> |
| <span class="c1"># do 2), and 1) is implicitly done</span> |
| <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">sym</span><span class="p">)</span> <span class="ow">in</span> <span class="n">input_id_to_loc</span><span class="p">:</span> |
| <span class="n">loc</span> <span class="o">=</span> <span class="n">input_id_to_loc</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="n">sym</span><span class="p">)]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">loc</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_id_to_loc</span><span class="p">)</span> |
| <span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sym</span><span class="p">)</span> |
| <span class="n">input_id_to_loc</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="n">sym</span><span class="p">)]</span> <span class="o">=</span> <span class="n">loc</span> |
| <span class="n">input_locs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loc</span><span class="p">)</span> |
| <span class="c1"># do 3)</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">name_to_var_locs</span><span class="p">:</span> |
| <span class="n">var_locs</span><span class="p">[</span><span class="n">name_to_var_locs</span><span class="p">[</span><span class="n">name</span><span class="p">]]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_locs</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="n">locs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">input_locs</span><span class="p">,</span> <span class="n">var_locs</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">locs</span> |
| <span class="k">if</span> <span class="n">max_iterations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"max_iterations should be specified"</span><span class="p">)</span> |
| <span class="n">max_iterations</span> <span class="o">=</span> <span class="n">_to_python_scalar</span><span class="p">(</span><span class="n">max_iterations</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="s2">"max_iteration"</span><span class="p">)</span> |
| <span class="c1"># It should be work as fine if loop_vars are empty I guess,</span> |
| <span class="c1"># but it is semantically unnecessary to include this case.</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"loop_vars should contain at least one element"</span><span class="p">)</span> |
| <span class="c1"># create graph for `cond'</span> |
| <span class="n">cond_g</span><span class="p">,</span> <span class="n">num_out_data</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> \ |
| <span class="n">_create_subgraph</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">,</span> <span class="n">_cond_wrapper</span><span class="p">,</span> <span class="n">name</span> <span class="o">+</span> <span class="s2">"_cond"</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">num_out_data</span> <span class="o">==</span> <span class="mi">0</span> |
| <span class="k">assert</span> <span class="n">num_outputs</span> <span class="o">==</span> <span class="mi">1</span> |
| <span class="c1"># create graph for `func`</span> |
| <span class="n">func_g</span><span class="p">,</span> <span class="n">num_out_data</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">,</span> <span class="n">out_fmt</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> \ |
| <span class="n">_create_subgraph</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">,</span> <span class="n">_func_wrapper</span><span class="p">,</span> <span class="n">name</span> <span class="o">+</span> <span class="s2">"_func"</span><span class="p">)</span> |
| <span class="c1"># find symbols used in either cond_g or func_g</span> |
| <span class="n">input_syms</span><span class="p">,</span> <span class="p">((</span><span class="n">cond_input_locs</span><span class="p">,</span> <span class="n">_</span><span class="p">),</span> <span class="p">(</span><span class="n">func_input_locs</span><span class="p">,</span> <span class="n">func_var_locs</span><span class="p">))</span> <span class="o">=</span> \ |
| <span class="n">_union_inputs</span><span class="p">(</span><span class="n">cond_g</span><span class="p">,</span> <span class="n">func_g</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i_th</span><span class="p">,</span> <span class="n">loc</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">func_var_locs</span><span class="p">,</span> <span class="mi">1</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">loc</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The </span><span class="si">%d</span><span class="s2">-th loop_var doesn't involve into the computation"</span> <span class="o">%</span> <span class="n">i_th</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_while_loop</span><span class="p">(</span> |
| <span class="n">cond_g</span><span class="p">,</span> |
| <span class="n">func_g</span><span class="p">,</span> |
| <span class="o">*</span><span class="n">input_syms</span><span class="p">,</span> |
| <span class="n">max_iterations</span><span class="o">=</span><span class="n">max_iterations</span><span class="p">,</span> |
| <span class="n">cond_input_locs</span><span class="o">=</span><span class="n">cond_input_locs</span><span class="p">,</span> |
| <span class="n">func_input_locs</span><span class="o">=</span><span class="n">func_input_locs</span><span class="p">,</span> |
| <span class="n">func_var_locs</span><span class="o">=</span><span class="n">func_var_locs</span><span class="p">,</span> |
| <span class="n">num_out_data</span><span class="o">=</span><span class="n">num_out_data</span><span class="p">,</span> |
| <span class="n">num_outputs</span><span class="o">=</span><span class="n">num_outputs</span> |
| <span class="p">)</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">result</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_out_data</span><span class="p">)]</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">out_fmt</span><span class="p">)</span> |
| <span class="n">final_loop_vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">result</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_out_data</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">)]</span> |
| <span class="n">final_loop_vars</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">final_loop_vars</span><span class="p">,</span> <span class="n">init_loop_var_fmt</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">final_loop_vars</span></div> |
| |
| <div class="viewcode-block" id="cond"><a class="viewcode-back" href="../../../api/symbol/contrib/index.html#mxnet.symbol.contrib.cond">[docs]</a><span class="k">def</span> <span class="nf">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">then_func</span><span class="p">,</span> <span class="n">else_func</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"cond"</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Run an if-then-else using user-defined condition and computation</span> |
| |
| <span class="sd"> This operator simulates a if-like branch which chooses to do one of</span> |
| <span class="sd"> the two customized computations according to the specified condition.</span> |
| |
| <span class="sd"> `pred` is a scalar MXNet Symbol,</span> |
| <span class="sd"> indicating which branch of computation should be used.</span> |
| |
| <span class="sd"> `then_func` is a user-defined function, used as computation of the then branch.</span> |
| <span class="sd"> It produces `outputs`, which is a list of Symbols.</span> |
| <span class="sd"> The signature of `then_func` should be</span> |
| <span class="sd"> `then_func() => nested List[Symbol]`.</span> |
| |
| <span class="sd"> `else_func` is a user-defined function, used as computation of the else branch.</span> |
| <span class="sd"> It produces `outputs`, which is a list of Symbols.</span> |
| <span class="sd"> The signature of `else_func` should be</span> |
| <span class="sd"> `else_func() => nested List[Symbol]`.</span> |
| |
| <span class="sd"> The `outputs` produces by `then_func` and `else_func` should have the same number</span> |
| <span class="sd"> of elements, all of which should be in the same shape, of the same dtype and stype.</span> |
| |
| <span class="sd"> This function returns a list of symbols, representing the computation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pred: a MXNet Symbol representing a scalar.</span> |
| <span class="sd"> The branch condition.</span> |
| <span class="sd"> then_func: a Python function.</span> |
| <span class="sd"> The computation to be executed if `pred` is true.</span> |
| <span class="sd"> else_func: a Python function.</span> |
| <span class="sd"> The computation to be executed if `pred` is false.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> outputs: a Symbol or nested lists of Symbols, representing the result of computation.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a, b = mx.sym.var('a'), mx.sym.var('b')</span> |
| <span class="sd"> >>> pred = a * b < 5</span> |
| <span class="sd"> >>> then_func = lambda: (a + 5) * (b + 5)</span> |
| <span class="sd"> >>> else_func = lambda: (a - 5) * (b - 5)</span> |
| <span class="sd"> >>> outputs = mx.sym.contrib.cond(pred, then_func, else_func)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="nf">_create_subgraph</span><span class="p">(</span><span class="n">graph_vars</span><span class="p">,</span> <span class="n">graph_func</span><span class="p">,</span> <span class="n">subgraph_name</span><span class="p">):</span> |
| <span class="n">subgraph_name</span> <span class="o">=</span> <span class="n">_get_unique_subgraph_name</span><span class="p">(</span><span class="n">subgraph_name</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">AttrScope</span><span class="p">(</span><span class="n">__subgraph_name__</span><span class="o">=</span><span class="n">subgraph_name</span><span class="p">):</span> |
| <span class="c1"># create new variables with the same name,</span> |
| <span class="c1"># them feed them to the given func</span> |
| <span class="n">new_graph_vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">graph_vars</span><span class="p">]</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">graph_func</span><span class="p">(</span><span class="o">*</span><span class="n">new_graph_vars</span><span class="p">)</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">out_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="s2">"cond outputs"</span><span class="p">)</span> |
| <span class="n">num_outputs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> |
| <span class="c1"># nnvm cut-graph does not allow inputs and outputs overlap</span> |
| <span class="c1"># so we calculate the name of inputs, and copy outputs once it overlaps with inputs</span> |
| <span class="c1"># group all outputs of graph_func</span> |
| <span class="n">all_input_names</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Group</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span> |
| <span class="n">in_input</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">all_input_names</span> |
| <span class="n">in_graph</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"__subgraph_name__"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span> <span class="o">==</span> <span class="n">subgraph_name</span> |
| <span class="n">make_identity</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">symbol</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">in_input</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">in_graph</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> \ |
| <span class="k">else</span> <span class="n">x</span> |
| <span class="n">graph</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Group</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">make_identity</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">graph</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">,</span> <span class="n">out_fmt</span> |
| |
| <span class="k">def</span> <span class="nf">_union_inputs</span><span class="p">(</span><span class="o">*</span><span class="n">graphs</span><span class="p">):</span> |
| <span class="c1"># Given a list of graphs, each whose inputs are either from input_vars or other variables.</span> |
| <span class="c1"># 1) calculate a list `inputs`, the union of their inputs.</span> |
| <span class="c1"># 2) for each graph, determine in which indices their inputs reside in `inputs`</span> |
| <span class="c1"># 3) for each variable in the input of `graph`, find which index it is</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># List[Symbol], result of 1)</span> |
| <span class="n">locs</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># List[Tuple(List[Int], List[Int])], a list of tuples,</span> |
| <span class="c1"># where tuples are results of 2) and 3)</span> |
| <span class="n">input_id_to_loc</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># Dict[int, int], given id(sym), input_id_to_loc maps it</span> |
| <span class="c1"># to a `loc`, where inputs[loc] = sym</span> |
| <span class="k">for</span> <span class="n">graph</span> <span class="ow">in</span> <span class="n">graphs</span><span class="p">:</span> |
| <span class="c1"># some input_vars are inputs to `graph`, some are not</span> |
| <span class="n">name_to_input_vars</span> <span class="o">=</span> <span class="p">{</span><span class="n">sym</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">sym</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">}</span> |
| <span class="c1"># other inputs to `graph` created by cut_graph</span> |
| <span class="n">name_to_cut_g_syms</span> <span class="o">=</span> <span class="p">{</span><span class="n">sym</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]:</span> <span class="n">sym</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">_cut_subgraph</span><span class="p">(</span><span class="n">graph</span><span class="p">)}</span> |
| <span class="c1"># input_syms: all inputs to the `graph`</span> |
| <span class="n">name_to_input_syms</span> <span class="o">=</span> <span class="p">{</span><span class="n">sym</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">sym</span> <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">_get_graph_inputs</span><span class="p">(</span><span class="n">graph</span><span class="p">)}</span> |
| <span class="c1"># collect arguments for each subgraph</span> |
| <span class="n">input_locs</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># results from the second step</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">graph</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">():</span> |
| <span class="k">assert</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">name_to_input_syms</span> <span class="c1"># it should obviously hold</span> |
| <span class="c1"># name -> sym</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">name_to_input_vars</span><span class="p">:</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">name_to_input_vars</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">name_to_cut_g_syms</span><span class="p">:</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">name_to_cut_g_syms</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">name_to_input_syms</span><span class="p">[</span><span class="n">name</span><span class="p">])</span> |
| <span class="c1"># do 2), and 1) is implicitly done</span> |
| <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">sym</span><span class="p">)</span> <span class="ow">in</span> <span class="n">input_id_to_loc</span><span class="p">:</span> |
| <span class="n">loc</span> <span class="o">=</span> <span class="n">input_id_to_loc</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="n">sym</span><span class="p">)]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">loc</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_id_to_loc</span><span class="p">)</span> |
| <span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sym</span><span class="p">)</span> |
| <span class="n">input_id_to_loc</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="n">sym</span><span class="p">)]</span> <span class="o">=</span> <span class="n">loc</span> |
| <span class="n">input_locs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loc</span><span class="p">)</span> |
| <span class="n">locs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">input_locs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">locs</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="c1"># create graph for `cond_func'</span> |
| <span class="n">cond_g</span><span class="p">,</span> <span class="n">cond_num_outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_create_subgraph</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="k">lambda</span><span class="p">:</span> <span class="n">pred</span><span class="p">,</span> <span class="n">name</span> <span class="o">+</span> <span class="s2">"_pred"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">cond_num_outputs</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"pred should always be a single output"</span><span class="p">)</span> |
| <span class="c1"># create graph for `then`</span> |
| <span class="n">then_g</span><span class="p">,</span> <span class="n">then_num_outputs</span><span class="p">,</span> <span class="n">then_fmt</span> <span class="o">=</span> <span class="n">_create_subgraph</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">then_func</span><span class="p">,</span> <span class="n">name</span> <span class="o">+</span> <span class="s2">"_then"</span><span class="p">)</span> |
| <span class="c1"># create graph for `else`</span> |
| <span class="n">else_g</span><span class="p">,</span> <span class="n">else_num_outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_create_subgraph</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">else_func</span><span class="p">,</span> <span class="n">name</span> <span class="o">+</span> <span class="s2">"_else"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">then_num_outputs</span> <span class="o">!=</span> <span class="n">else_num_outputs</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Number of outputs differs between then-branch and else-branch"</span><span class="p">)</span> |
| <span class="c1"># find symbols used in either cond_g or func_g</span> |
| <span class="n">input_syms</span><span class="p">,</span> <span class="p">(</span><span class="n">cond_input_locs</span><span class="p">,</span> <span class="n">then_input_locs</span><span class="p">,</span> <span class="n">else_input_locs</span><span class="p">)</span> <span class="o">=</span> \ |
| <span class="n">_union_inputs</span><span class="p">(</span><span class="n">cond_g</span><span class="p">,</span> <span class="n">then_g</span><span class="p">,</span> <span class="n">else_g</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_cond</span><span class="p">(</span> |
| <span class="c1"># [cond, then_g, else_g, *input_syms]</span> |
| <span class="n">cond_g</span><span class="p">,</span> |
| <span class="n">then_g</span><span class="p">,</span> |
| <span class="n">else_g</span><span class="p">,</span> |
| <span class="o">*</span><span class="n">input_syms</span><span class="p">,</span> |
| <span class="n">cond_input_locs</span><span class="o">=</span><span class="n">cond_input_locs</span><span class="p">,</span> |
| <span class="n">then_input_locs</span><span class="o">=</span><span class="n">then_input_locs</span><span class="p">,</span> |
| <span class="n">else_input_locs</span><span class="o">=</span><span class="n">else_input_locs</span><span class="p">,</span> |
| <span class="n">num_outputs</span><span class="o">=</span><span class="n">then_num_outputs</span> |
| <span class="p">)</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">result</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">then_num_outputs</span><span class="p">)]</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">then_fmt</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">outputs</span></div> |
| |
| <span class="k">def</span> <span class="nf">adamw_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> |
| <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</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">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">Symbol</span><span class="p">):</span> |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,),</span> <span class="n">val</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">symbol</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_adamw_update</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="o">=</span><span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">,</span> |
| <span class="n">rescale_grad</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> |
| <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="n">beta2</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">mp_adamw_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weight32</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> |
| <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">name</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">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">Symbol</span><span class="p">):</span> |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,),</span> <span class="n">val</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">symbol</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_mp_adamw_update</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="o">=</span><span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">,</span> |
| <span class="n">weight32</span><span class="o">=</span><span class="n">weight32</span><span class="p">,</span> |
| <span class="n">rescale_grad</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> |
| <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="n">beta2</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
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