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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul> |
| <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> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </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> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</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> |
| <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> |
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| <h1>Source code for mxnet.executor</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=invalid-name, protected-access, too-many-locals, too-many-arguments</span> |
| <span class="sd">"""Symbolic Executor component of MXNet."""</span> |
| |
| <span class="kn">from</span> <span class="nn">array</span> <span class="kn">import</span> <span class="n">array</span> <span class="k">as</span> <span class="n">py_array</span> |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">copy</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">_LIB</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">mx_uint</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">SymbolHandle</span><span class="p">,</span> <span class="n">ExecutorHandle</span><span class="p">,</span> <span class="n">py_str</span><span class="p">,</span> <span class="n">mx_int</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">c_handle_array</span><span class="p">,</span> <span class="n">c_array_buf</span><span class="p">,</span> <span class="n">c_str_array</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">ndarray</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">NDArray</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">_ndarray_cls</span> |
| |
| <span class="c1"># those functions are not used here, we just import them to keep backward compatibility</span> |
| <span class="c1"># in case the end user calls them, as they originally lives here</span> |
| <span class="c1"># pylint: disable=unused-import</span> |
| <span class="kn">from</span> <span class="nn">.executor_manager</span> <span class="kn">import</span> <span class="n">_split_input_slice</span><span class="p">,</span> <span class="n">_check_arguments</span><span class="p">,</span> <span class="n">_load_data</span><span class="p">,</span> <span class="n">_load_label</span> |
| |
| <span class="k">def</span> <span class="nf">_monitor_callback_wrapper</span><span class="p">(</span><span class="n">callback</span><span class="p">):</span> |
| <span class="sd">"""A wrapper for the user-defined handle."""</span> |
| <span class="k">def</span> <span class="nf">callback_handle</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span> |
| <span class="sd">""" ctypes function """</span> |
| <span class="n">callback</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">callback_handle</span> |
| |
| <div class="viewcode-block" id="Executor"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor">[docs]</a><span class="k">class</span> <span class="nc">Executor</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""Executor is the object providing efficient symbolic graph execution and optimization.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # typical approach to create an executor is to bind symbol</span> |
| <span class="sd"> >>> a = mx.sym.Variable('a')</span> |
| <span class="sd"> >>> b = mx.sym.Variable('b')</span> |
| <span class="sd"> >>> c = 2 * a + b</span> |
| <span class="sd"> >>> texec = c.bind(mx.cpu(), {'a': mx.nd.array([1,2]), 'b':mx.nd.array([2,3])})</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">handle</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">grad_req</span><span class="p">,</span> <span class="n">group2ctx</span><span class="p">):</span> |
| <span class="sd">"""Constructor, used Symbol.bind and Symbol.simple_bind instead.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> handle: ExecutorHandle</span> |
| <span class="sd"> ExecutorHandle generated by calling `bind`.</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> Symbol.bind : to create executor.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">ExecutorHandle</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Handle type error"</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span> <span class="o">=</span> <span class="n">handle</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">arg_arrays</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">grad_arrays</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_outputs</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</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">symbol</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimized_symbol</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ctx</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">ctx</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</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">grad_req</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</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">group2ctx</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</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">MXExecutorFree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">))</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">_get_dict</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">ndarrays</span><span class="p">):</span> |
| <span class="sd">"""Get the dictionary given name and ndarray pairs."""</span> |
| <span class="n">nset</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">nm</span> <span class="ow">in</span> <span class="n">names</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">nm</span> <span class="ow">in</span> <span class="n">nset</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Duplicate names detected, </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">names</span><span class="p">))</span> |
| <span class="n">nset</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nm</span><span class="p">)</span> |
| <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">ndarrays</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">_get_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""List all the output NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> A list of ndarray bound to the heads of executor.</span> |
| <span class="sd"> """</span> |
| <span class="n">out_size</span> <span class="o">=</span> <span class="n">mx_uint</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">NDArrayHandle</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">MXExecutorOutputs</span><span class="p">(</span><span class="bp">self</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">out_size</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">num_output</span> <span class="o">=</span> <span class="n">out_size</span><span class="o">.</span><span class="n">value</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</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="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_output</span><span class="p">)]</span> |
| <span class="k">return</span> <span class="n">outputs</span> |
| |
| <div class="viewcode-block" id="Executor.forward"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Calculate the outputs specified by the bound symbol.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> is_train: bool, optional</span> |
| <span class="sd"> Whether this forward is for evaluation purpose. If True,</span> |
| <span class="sd"> a backward call is expected to follow.</span> |
| |
| <span class="sd"> **kwargs</span> |
| <span class="sd"> Additional specification of input arguments.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # doing forward by specifying data</span> |
| <span class="sd"> >>> texec.forward(is_train=True, data=mydata)</span> |
| <span class="sd"> >>> # doing forward by not specifying things, but copy to the executor before hand</span> |
| <span class="sd"> >>> mydata.copyto(texec.arg_dict['data'])</span> |
| <span class="sd"> >>> texec.forward(is_train=True)</span> |
| <span class="sd"> >>> # doing forward by specifying data and get outputs</span> |
| <span class="sd"> >>> outputs = texec.forward(is_train=True, data=mydata)</span> |
| <span class="sd"> >>> print(outputs[0].asnumpy())</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">arg_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</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">array</span><span class="p">,</span> <span class="p">(</span><span class="n">NDArray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'only accept keyword argument of NDArrays and numpy.ndarray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">arg_dict</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'Unknown argument </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Shape not match! Argument </span><span class="si">%s</span><span class="s1">, need: </span><span class="si">%s</span><span class="s1">, received: </span><span class="si">%s</span><span class="s1">'</span> |
| <span class="o">%</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span> |
| <span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">array</span> |
| |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorForward</span><span class="p">(</span> |
| <span class="bp">self</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">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">is_train</span><span class="p">))))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_outputs</span><span class="p">()</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span></div> |
| |
| <div class="viewcode-block" id="Executor.backward"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_grads</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="sd">"""Do backward pass to get the gradient of arguments.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> out_grads : NDArray or list of NDArray or dict of str to NDArray, optional</span> |
| <span class="sd"> Gradient on the outputs to be propagated back.</span> |
| <span class="sd"> This parameter is only needed when bind is called</span> |
| <span class="sd"> on outputs that are not a loss function.</span> |
| <span class="sd"> is_train : bool, default True</span> |
| <span class="sd"> Whether this backward is for training or inference. Note that in rare</span> |
| <span class="sd"> cases you want to call backward with is_train=False to get gradient</span> |
| <span class="sd"> during inference.</span> |
| |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # Example for binding on loss function symbol, which gives the loss value of the model.</span> |
| <span class="sd"> >>> # Equivalently it gives the head gradient for backward pass.</span> |
| <span class="sd"> >>> # In this example the built-in SoftmaxOutput is used as loss function.</span> |
| <span class="sd"> >>> # MakeLoss can be used to define customized loss function symbol.</span> |
| <span class="sd"> >>> net = mx.sym.Variable('data')</span> |
| <span class="sd"> >>> net = mx.sym.FullyConnected(net, name='fc', num_hidden=6)</span> |
| <span class="sd"> >>> net = mx.sym.Activation(net, name='relu', act_type="relu")</span> |
| <span class="sd"> >>> net = mx.sym.SoftmaxOutput(net, name='softmax')</span> |
| |
| <span class="sd"> >>> args = {'data': mx.nd.ones((1, 4)), 'fc_weight': mx.nd.ones((6, 4)),</span> |
| <span class="sd"> >>> 'fc_bias': mx.nd.array((1, 4, 4, 4, 5, 6)), 'softmax_label': mx.nd.ones((1))}</span> |
| <span class="sd"> >>> args_grad = {'fc_weight': mx.nd.zeros((6, 4)), 'fc_bias': mx.nd.zeros((6))}</span> |
| <span class="sd"> >>> texec = net.bind(ctx=mx.cpu(), args=args, args_grad=args_grad)</span> |
| <span class="sd"> >>> out = texec.forward(is_train=True)[0].copy()</span> |
| <span class="sd"> >>> print out.asnumpy()</span> |
| <span class="sd"> [[ 0.00378404 0.07600445 0.07600445 0.07600445 0.20660152 0.5616011 ]]</span> |
| <span class="sd"> >>> texec.backward()</span> |
| <span class="sd"> >>> print(texec.grad_arrays[1].asnumpy())</span> |
| <span class="sd"> [[ 0.00378404 0.00378404 0.00378404 0.00378404]</span> |
| <span class="sd"> [-0.92399555 -0.92399555 -0.92399555 -0.92399555]</span> |
| <span class="sd"> [ 0.07600445 0.07600445 0.07600445 0.07600445]</span> |
| <span class="sd"> [ 0.07600445 0.07600445 0.07600445 0.07600445]</span> |
| <span class="sd"> [ 0.20660152 0.20660152 0.20660152 0.20660152]</span> |
| <span class="sd"> [ 0.5616011 0.5616011 0.5616011 0.5616011 ]]</span> |
| <span class="sd"> >>></span> |
| <span class="sd"> >>> # Example for binding on non-loss function symbol.</span> |
| <span class="sd"> >>> # Here the binding symbol is neither built-in loss function</span> |
| <span class="sd"> >>> # nor customized loss created by MakeLoss.</span> |
| <span class="sd"> >>> # As a result the head gradient is not automatically provided.</span> |
| <span class="sd"> >>> a = mx.sym.Variable('a')</span> |
| <span class="sd"> >>> b = mx.sym.Variable('b')</span> |
| <span class="sd"> >>> # c is not a loss function symbol</span> |
| <span class="sd"> >>> c = 2 * a + b</span> |
| <span class="sd"> >>> args = {'a': mx.nd.array([1,2]), 'b':mx.nd.array([2,3])}</span> |
| <span class="sd"> >>> args_grad = {'a': mx.nd.zeros((2)), 'b': mx.nd.zeros((2))}</span> |
| <span class="sd"> >>> texec = c.bind(ctx=mx.cpu(), args=args, args_grad=args_grad)</span> |
| <span class="sd"> >>> out = texec.forward(is_train=True)[0].copy()</span> |
| <span class="sd"> >>> print(out.asnumpy())</span> |
| <span class="sd"> [ 4. 7.]</span> |
| <span class="sd"> >>> # out_grads is the head gradient in backward pass.</span> |
| <span class="sd"> >>> # Here we define 'c' as loss function.</span> |
| <span class="sd"> >>> # Then 'out' is passed as head gradient of backward pass.</span> |
| <span class="sd"> >>> texec.backward(out)</span> |
| <span class="sd"> >>> print(texec.grad_arrays[0].asnumpy())</span> |
| <span class="sd"> [ 8. 14.]</span> |
| <span class="sd"> >>> print(texec.grad_arrays[1].asnumpy())</span> |
| <span class="sd"> [ 4. 7.]</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">out_grads</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out_grads</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">out_grads</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out_grads</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">out_grads</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()]</span> |
| |
| <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">out_grads</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">obj</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"inputs must be NDArray"</span><span class="p">)</span> |
| <span class="n">handle_array</span> <span class="o">=</span> <span class="n">c_handle_array</span><span class="p">(</span><span class="n">out_grads</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">MXExecutorBackwardEx</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">out_grads</span><span class="p">)),</span> |
| <span class="n">handle_array</span><span class="p">,</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">is_train</span><span class="p">)))</span></div> |
| |
| <div class="viewcode-block" id="Executor.set_monitor_callback"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.set_monitor_callback">[docs]</a> <span class="k">def</span> <span class="nf">set_monitor_callback</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">,</span> <span class="n">monitor_all</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="sd">"""Install callback for monitor.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> callback : function</span> |
| <span class="sd"> Takes a string and an NDArrayHandle.</span> |
| <span class="sd"> monitor_all : bool, default False</span> |
| <span class="sd"> If true, monitor both input and output, otherwise monitor output only.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> def mon_callback(*args, **kwargs):</span> |
| <span class="sd"> >>> print("Do your stuff here.")</span> |
| <span class="sd"> >>></span> |
| <span class="sd"> >>> texe.set_monitor_callback(mon_callback)</span> |
| <span class="sd"> """</span> |
| <span class="n">cb_type</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">CFUNCTYPE</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span> <span class="o">=</span> <span class="n">cb_type</span><span class="p">(</span><span class="n">_monitor_callback_wrapper</span><span class="p">(</span><span class="n">callback</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="o">=</span> <span class="n">monitor_all</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorSetMonitorCallbackEX</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">,</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">monitor_all</span><span class="p">)))</span></div> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">arg_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get dictionary representation of argument arrrays.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> arg_dict : dict of str to NDArray</span> |
| <span class="sd"> The dictionary that maps the names of arguments to NDArrays.</span> |
| |
| <span class="sd"> Raises</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> ValueError : if there are duplicated names in the arguments.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_arrays</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">grad_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get dictionary representation of gradient arrays.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> grad_dict : dict of str to NDArray</span> |
| <span class="sd"> The dictionary that maps name of arguments to gradient arrays.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">aux_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get dictionary representation of auxiliary states arrays.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> aux_dict : dict of str to NDArray</span> |
| <span class="sd"> The dictionary that maps name of auxiliary states to NDArrays.</span> |
| |
| <span class="sd"> Raises</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> ValueError : if there are duplicated names in the auxiliary states.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">output_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get dictionary representation of output arrays.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> output_dict : dict of str to NDArray</span> |
| <span class="sd"> The dictionary that maps name of output names to NDArrays.</span> |
| |
| <span class="sd"> Raises</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> ValueError : if there are duplicated names in the outputs.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> |
| |
| <div class="viewcode-block" id="Executor.copy_params_from"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.copy_params_from">[docs]</a> <span class="k">def</span> <span class="nf">copy_params_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_extra_params</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="sd">"""Copy parameters from arg_params, aux_params into executor's internal array.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> arg_params : dict of str to NDArray</span> |
| <span class="sd"> Parameters, dict of name to NDArray of arguments.</span> |
| |
| <span class="sd"> aux_params : dict of str to NDArray, optional</span> |
| <span class="sd"> Parameters, dict of name to NDArray of auxiliary states.</span> |
| |
| <span class="sd"> allow_extra_params : boolean, optional</span> |
| <span class="sd"> Whether allow extra parameters that are not needed by symbol.</span> |
| <span class="sd"> If this is True, no error will be thrown when arg_params or aux_params</span> |
| <span class="sd"> contain extra parameters that is not needed by the executor.</span> |
| |
| <span class="sd"> Raises</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> ValueError</span> |
| <span class="sd"> If there is additional parameters in the dict but ``allow_extra_params=False``.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # set parameters with existing model checkpoint</span> |
| <span class="sd"> >>> model_prefix = 'mx_mlp'</span> |
| <span class="sd"> >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, 0)</span> |
| <span class="sd"> >>> texec.copy_params_from(arg_params, aux_params)</span> |
| <span class="sd"> """</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">:</span> |
| <span class="n">dst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">dst</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">([(</span><span class="s1">'bfloat16'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span><span class="p">)]):</span> |
| <span class="n">cast_array</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">amp_cast</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">cast_array</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="ow">not</span> <span class="n">allow_extra_params</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Find name </span><span class="se">\"</span><span class="si">%s</span><span class="se">\"</span><span class="s1"> that is not in the arguments'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">aux_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> |
| |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">aux_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">:</span> |
| <span class="n">dst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">dst</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">([(</span><span class="s1">'bfloat16'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span><span class="p">)]):</span> |
| <span class="n">cast_array</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">amp_cast</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">cast_array</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="ow">not</span> <span class="n">allow_extra_params</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Find name </span><span class="si">%s</span><span class="s1"> that is not in the auxiliary states'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Executor.reshape"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.reshape">[docs]</a> <span class="k">def</span> <span class="nf">reshape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">partial_shaping</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_up_sizing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Return a new executor with the same symbol and shared memory,</span> |
| <span class="sd"> but different input/output shapes.</span> |
| <span class="sd"> For runtime reshaping, variable length sequences, etc.</span> |
| <span class="sd"> The returned executor shares state with the current one,</span> |
| <span class="sd"> and cannot be used in parallel with it.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> partial_shaping : bool</span> |
| <span class="sd"> Whether to allow changing the shape of unspecified arguments.</span> |
| <span class="sd"> allow_up_sizing : bool</span> |
| <span class="sd"> Whether to allow allocating new ndarrays that's larger than the original.</span> |
| <span class="sd"> kwargs : dict of string to tuple of int</span> |
| <span class="sd"> New shape for arguments.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> exec : Executor</span> |
| <span class="sd"> A new executor that shares memory with self.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a = mx.sym.Variable('a')</span> |
| <span class="sd"> >>> b = mx.sym.Variable('b')</span> |
| <span class="sd"> >>> c = 2 * a + b</span> |
| <span class="sd"> >>> texec = c.bind(mx.cpu(), {'a': mx.nd.zeros((2, 1)), 'b': mx.nd.ones((2,1))})</span> |
| <span class="sd"> >>> new_shape = {'a': (4, 2), 'b': (4, 2)}</span> |
| <span class="sd"> >>> texec.reshape(allow_up_sizing=True, **new_shape)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable=too-many-branches</span> |
| <span class="n">provided_arg_shape_data</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># shape data</span> |
| <span class="c1"># argument shape index in sdata,</span> |
| <span class="c1"># e.g. [sdata[indptr[0]], sdata[indptr[1]]) is the shape of the first arg</span> |
| <span class="n">provided_arg_shape_idx</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">provided_arg_shape_names</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># provided argument names</span> |
| <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">provided_arg_shape_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> |
| <span class="n">provided_arg_shape_data</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> |
| <span class="n">provided_arg_shape_idx</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">provided_arg_shape_data</span><span class="p">))</span> |
| |
| <span class="n">ctx_map_keys</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">ctx_map_dev_types</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">ctx_map_dev_ids</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">ctx_map_keys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> |
| <span class="n">ctx_map_dev_types</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">device_typeid</span><span class="p">)</span> |
| <span class="n">ctx_map_dev_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">device_id</span><span class="p">)</span> |
| |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">ExecutorHandle</span><span class="p">()</span> |
| <span class="n">shared_handle</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle</span> |
| |
| <span class="n">num_in_args</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">()</span> |
| <span class="n">in_arg_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">NDArrayHandle</span><span class="p">)()</span> |
| <span class="n">arg_grad_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">NDArrayHandle</span><span class="p">)()</span> |
| <span class="n">num_aux_states</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">()</span> |
| <span class="n">aux_state_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">NDArrayHandle</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">MXExecutorReshapeEx</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">partial_shaping</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">allow_up_sizing</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">),</span> |
| <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ctx_map_keys</span><span class="p">)),</span> |
| <span class="n">c_str_array</span><span class="p">(</span><span class="n">ctx_map_keys</span><span class="p">),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span> |
| <span class="n">py_array</span><span class="p">(</span><span class="s1">'i'</span><span class="p">,</span> <span class="n">ctx_map_dev_types</span><span class="p">)),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span> |
| <span class="n">py_array</span><span class="p">(</span><span class="s1">'i'</span><span class="p">,</span> <span class="n">ctx_map_dev_ids</span><span class="p">)),</span> |
| <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">provided_arg_shape_names</span><span class="p">)),</span> |
| <span class="n">c_str_array</span><span class="p">(</span><span class="n">provided_arg_shape_names</span><span class="p">),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">mx_int</span><span class="p">,</span> |
| <span class="n">py_array</span><span class="p">(</span><span class="s1">'i'</span><span class="p">,</span> <span class="n">provided_arg_shape_data</span><span class="p">)),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">mx_uint</span><span class="p">,</span> |
| <span class="n">py_array</span><span class="p">(</span><span class="s1">'I'</span><span class="p">,</span> <span class="n">provided_arg_shape_idx</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_in_args</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">in_arg_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">arg_grad_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_aux_states</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">aux_state_handles</span><span class="p">),</span> |
| <span class="n">shared_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">handle</span><span class="p">)))</span> |
| |
| <span class="n">arg_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">in_arg_handles</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_in_args</span><span class="o">.</span><span class="n">value</span><span class="p">)]</span> |
| <span class="n">grad_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">arg_grad_handles</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> |
| <span class="k">if</span> <span class="n">arg_grad_handles</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="k">else</span> <span class="kc">None</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_in_args</span><span class="o">.</span><span class="n">value</span><span class="p">)]</span> |
| <span class="n">aux_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">aux_state_handles</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_aux_states</span><span class="o">.</span><span class="n">value</span><span class="p">)]</span> |
| |
| <span class="n">executor</span> <span class="o">=</span> <span class="n">Executor</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span><span class="p">)</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">arg_arrays</span> <span class="o">=</span> <span class="n">arg_arrays</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">grad_arrays</span> <span class="o">=</span> <span class="n">grad_arrays</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">aux_arrays</span> <span class="o">=</span> <span class="n">aux_arrays</span> |
| <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span> |
| <span class="c1"># rebind callback to the new executor if the callback is valid</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorSetMonitorCallbackEX</span><span class="p">(</span> |
| <span class="n">handle</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">,</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">executor</span></div> |
| |
| <div class="viewcode-block" id="Executor.debug_str"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.debug_str">[docs]</a> <span class="k">def</span> <span class="nf">debug_str</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get a debug string about internal execution plan.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> debug_str : string</span> |
| <span class="sd"> Debug string of the executor.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a = mx.sym.Variable('a')</span> |
| <span class="sd"> >>> b = mx.sym.sin(a)</span> |
| <span class="sd"> >>> c = 2 * a + b</span> |
| <span class="sd"> >>> texec = c.bind(mx.cpu(), {'a': mx.nd.array([1,2]), 'b':mx.nd.array([2,3])})</span> |
| <span class="sd"> >>> print(texec.debug_str())</span> |
| <span class="sd"> Symbol Outputs:</span> |
| <span class="sd"> output[0]=_plus0(0)</span> |
| <span class="sd"> Variable:a</span> |
| <span class="sd"> --------------------</span> |
| <span class="sd"> Op:_mul_scalar, Name=_mulscalar0</span> |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> arg[0]=a(0) version=0</span> |
| <span class="sd"> Attrs:</span> |
| <span class="sd"> scalar=2</span> |
| <span class="sd"> --------------------</span> |
| <span class="sd"> Op:sin, Name=sin0</span> |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> arg[0]=a(0) version=0</span> |
| <span class="sd"> --------------------</span> |
| <span class="sd"> Op:elemwise_add, Name=_plus0</span> |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> arg[0]=_mulscalar0(0)</span> |
| <span class="sd"> arg[1]=sin0(0)</span> |
| <span class="sd"> Total 0 MB allocated</span> |
| <span class="sd"> Total 11 TempSpace resource requested</span> |
| <span class="sd"> """</span> |
| <span class="n">debug_str</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</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">MXExecutorPrint</span><span class="p">(</span> |
| <span class="bp">self</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">debug_str</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">py_str</span><span class="p">(</span><span class="n">debug_str</span><span class="o">.</span><span class="n">value</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Executor.get_optimized_symbol"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.get_optimized_symbol">[docs]</a> <span class="k">def</span> <span class="nf">get_optimized_symbol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get an optimized version of the symbol from the executor.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> symbol : Symbol</span> |
| <span class="sd"> Optimized symbol from the executor.</span> |
| <span class="sd"> """</span> |
| <span class="kn">from</span> <span class="nn">.symbol</span> <span class="kn">import</span> <span class="n">Symbol</span> |
| <span class="n">sym_handle</span> <span class="o">=</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">MXExecutorGetOptimizedSymbol</span><span class="p">(</span><span class="bp">self</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">sym_handle</span><span class="p">)))</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">Symbol</span><span class="p">(</span><span class="n">sym_handle</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ret</span></div></div> |
| </pre></div> |
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