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 <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>
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 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
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 <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
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 <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>
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 <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>
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 <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>
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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>
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 <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>
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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>
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 <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>
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 <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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 <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>
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 <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>
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 <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>
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 <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>
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   <h1>Source code for mxnet.module.bucketing_module</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"># &quot;License&quot;); 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"># &quot;AS IS&quot; 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"># pylint: disable=too-many-instance-attributes, too-many-arguments, protected-access</span>
 <span class="c1"># pylint: disable=too-many-public-methods</span>
 <span class="sd">&quot;&quot;&quot;A `BucketingModule` implement the `BaseModule` API, and allows multiple</span>
 <span class="sd">symbols to be used depending on the `bucket_key` provided by each different</span>
 <span class="sd">mini-batch of data.</span>
 <span class="sd">&quot;&quot;&quot;</span>

 <span class="kn">import</span> <span class="nn">logging</span>
 <span class="kn">import</span> <span class="nn">warnings</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">..</span> <span class="kn">import</span> <span class="n">context</span> <span class="k">as</span> <span class="n">ctx</span>

 <span class="kn">from</span> <span class="nn">..initializer</span> <span class="kn">import</span> <span class="n">Uniform</span>
 <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">nd</span>
 <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">symbol</span> <span class="k">as</span> <span class="n">sym</span>

 <span class="kn">from</span> <span class="nn">.base_module</span> <span class="kn">import</span> <span class="n">BaseModule</span><span class="p">,</span> <span class="n">_check_input_names</span>
 <span class="kn">from</span> <span class="nn">.module</span> <span class="kn">import</span> <span class="n">Module</span>
 <span class="kn">from</span> <span class="nn">..model</span> <span class="kn">import</span> <span class="n">load_params</span>
 <span class="kn">from</span> <span class="nn">..name</span> <span class="kn">import</span> <span class="n">NameManager</span>

 <div class="viewcode-block" id="BucketingModule"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule">[docs]</a><span class="k">class</span> <span class="nc">BucketingModule</span><span class="p">(</span><span class="n">BaseModule</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;This module helps to deal efficiently with varying-length inputs.</span>

 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
 <span class="sd">    sym_gen : function</span>
 <span class="sd">        A function when called with a bucket key, returns a triple</span>
 <span class="sd">        ``(symbol, data_names, label_names)``.</span>
 <span class="sd">    default_bucket_key : str (or any python object)</span>
 <span class="sd">        The key for the default bucket.</span>
 <span class="sd">    logger : Logger</span>
 <span class="sd">    context : Context or list of Context</span>
 <span class="sd">        Defaults to ``mx.cpu()``</span>
 <span class="sd">    work_load_list : list of number</span>
 <span class="sd">        Defaults to ``None``, indicating uniform workload.</span>
 <span class="sd">    fixed_param_names: list of str</span>
 <span class="sd">        Defaults to ``None``, indicating no network parameters are fixed.</span>
 <span class="sd">    state_names : list of str</span>
 <span class="sd">        States are similar to data and label, but not provided by data iterator.</span>
 <span class="sd">        Instead they are initialized to 0 and can be set by set_states()</span>
 <span class="sd">    group2ctxs : dict of str to context or list of context,</span>
 <span class="sd">                 or list of dict of str to context</span>
 <span class="sd">        Default is `None`. Mapping the `ctx_group` attribute to the context assignment.</span>
 <span class="sd">    compression_params : dict</span>
 <span class="sd">        Specifies type of gradient compression and additional arguments depending</span>
 <span class="sd">        on the type of compression being used. For example, 2bit compression requires a threshold.</span>
 <span class="sd">        Arguments would then be {&#39;type&#39;:&#39;2bit&#39;, &#39;threshold&#39;:0.5}</span>
 <span class="sd">        See mxnet.KVStore.set_gradient_compression method for more details on gradient compression.</span>
 <span class="sd">    &quot;&quot;&quot;</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">sym_gen</span><span class="p">,</span> <span class="n">default_bucket_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">logger</span><span class="o">=</span><span class="n">logging</span><span class="p">,</span>
                  <span class="n">context</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">work_load_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">fixed_param_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">state_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">group2ctxs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">compression_params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">BucketingModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">)</span>

         <span class="k">assert</span> <span class="n">default_bucket_key</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span> <span class="o">=</span> <span class="n">default_bucket_key</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_sym_gen</span> <span class="o">=</span> <span class="n">sym_gen</span>

         <span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_sym_gen</span><span class="p">(</span><span class="n">default_bucket_key</span><span class="p">)</span>
         <span class="n">data_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">data_names</span><span class="p">)</span> <span class="k">if</span> <span class="n">data_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
         <span class="n">label_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">label_names</span><span class="p">)</span> <span class="k">if</span> <span class="n">label_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
         <span class="n">state_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">state_names</span><span class="p">)</span> <span class="k">if</span> <span class="n">state_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
         <span class="n">fixed_param_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">fixed_param_names</span><span class="p">)</span> <span class="k">if</span> <span class="n">fixed_param_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>

         <span class="n">_check_input_names</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
         <span class="n">_check_input_names</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">label_names</span><span class="p">,</span> <span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
         <span class="n">_check_input_names</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">state_names</span><span class="p">,</span> <span class="s2">&quot;state&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
         <span class="n">_check_input_names</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">fixed_param_names</span><span class="p">,</span> <span class="s2">&quot;fixed_param&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_compression_params</span> <span class="o">=</span> <span class="n">compression_params</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_fixed_param_names</span> <span class="o">=</span> <span class="n">fixed_param_names</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_state_names</span> <span class="o">=</span> <span class="n">state_names</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_context</span> <span class="o">=</span> <span class="n">context</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_work_load_list</span> <span class="o">=</span> <span class="n">work_load_list</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctxs</span> <span class="o">=</span> <span class="n">group2ctxs</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_bucket_key</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_monitor</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="kc">None</span>

     <span class="k">def</span> <span class="nf">_reset_bind</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Internal utility function to reset binding.&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_bucket_key</span> <span class="o">=</span> <span class="kc">None</span>

     <span class="k">def</span> <span class="nf">_call_sym_gen</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="k">with</span> <span class="n">NameManager</span><span class="p">():</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sym_gen</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">data_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;A list of names for data required by this module.&quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span><span class="p">:</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">data_names</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">_</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_sym_gen</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span><span class="p">)</span>
             <span class="k">return</span> <span class="n">data_names</span>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">output_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;A list of names for the outputs of this module.&quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span><span class="p">:</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">output_names</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">symbol</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_sym_gen</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span><span class="p">)</span>
             <span class="k">return</span> <span class="n">symbol</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()</span>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">data_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get data shapes.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        A list of `(name, shape)` pairs.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">data_shapes</span>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">label_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get label shapes.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        A list of `(name, shape)` pairs.</span>
 <span class="sd">            The return value could be ``None`` if the module does not need labels,</span>
 <span class="sd">            or if the module is not bound for training (in this case, label information</span>
 <span class="sd">            is not available).</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">label_shapes</span>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">output_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Gets output shapes.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        A list of `(name, shape)` pairs.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">output_shapes</span>

 <div class="viewcode-block" id="BucketingModule.get_params"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.get_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Gets current parameters.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        `(arg_params, aux_params)`</span>
 <span class="sd">            A pair of dictionaries each mapping parameter names to NDArray values.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span>
         <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="k">return</span> <span class="n">params</span></div>

 <div class="viewcode-block" id="BucketingModule.set_params"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.set_params">[docs]</a>    <span class="k">def</span> <span class="nf">set_params</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="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_init</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                    <span class="n">allow_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Assigns parameters and aux state values.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        arg_params : dict</span>
 <span class="sd">            Dictionary of name to value (`NDArray`) mapping.</span>
 <span class="sd">        aux_params : dict</span>
 <span class="sd">            Dictionary of name to value (`NDArray`) mapping.</span>
 <span class="sd">        allow_missing : bool</span>
 <span class="sd">            If true, params could contain missing values, and the initializer will be</span>
 <span class="sd">            called to fill those missing params.</span>
 <span class="sd">        force_init : bool</span>
 <span class="sd">            If true, will force re-initialize even if already initialized.</span>
 <span class="sd">        allow_extra : 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">        Examples</span>
 <span class="sd">        --------</span>
 <span class="sd">        &gt;&gt;&gt; # An example of setting module parameters.</span>
 <span class="sd">        &gt;&gt;&gt; sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, n_epoch_load)</span>
 <span class="sd">        &gt;&gt;&gt; mod.set_params(arg_params=arg_params, aux_params=aux_params)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">allow_missing</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="n">aux_params</span><span class="p">,</span>
                              <span class="n">allow_missing</span><span class="o">=</span><span class="n">allow_missing</span><span class="p">,</span> <span class="n">force_init</span><span class="o">=</span><span class="n">force_init</span><span class="p">)</span>
             <span class="k">return</span>

         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_init</span><span class="p">:</span>
             <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Parameters already initialized and force_init=False. &quot;</span>
                           <span class="s2">&quot;set_params call ignored.&quot;</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
             <span class="k">return</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="n">allow_missing</span><span class="p">,</span>
                                      <span class="n">force_init</span><span class="o">=</span><span class="n">force_init</span><span class="p">,</span> <span class="n">allow_extra</span><span class="o">=</span><span class="n">allow_extra</span><span class="p">)</span>

         <span class="c1"># because we didn&#39;t update self._arg_params, they are dirty now.</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span></div>

 <div class="viewcode-block" id="BucketingModule.init_params"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.init_params">[docs]</a>    <span class="k">def</span> <span class="nf">init_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initializer</span><span class="o">=</span><span class="n">Uniform</span><span class="p">(</span><span class="mf">0.01</span><span class="p">),</span> <span class="n">arg_params</span><span class="o">=</span><span class="kc">None</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_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_init</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Initializes parameters.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        initializer : Initializer</span>
 <span class="sd">        arg_params : dict</span>
 <span class="sd">            Defaults to ``None``. Existing parameters. This has higher priority</span>
 <span class="sd">            than `initializer`.</span>
 <span class="sd">        aux_params : dict</span>
 <span class="sd">            Defaults to ``None``. Existing auxiliary states. This has higher priority</span>
 <span class="sd">            than `initializer`.</span>
 <span class="sd">        allow_missing : bool</span>
 <span class="sd">            Allow missing values in `arg_params` and `aux_params` (if not ``None``).</span>
 <span class="sd">            In this case, missing values will be filled with `initializer`.</span>
 <span class="sd">        force_init : bool</span>
 <span class="sd">            Defaults to ``False``.</span>
 <span class="sd">        allow_extra : 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">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_init</span><span class="p">:</span>
             <span class="k">return</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span><span class="p">,</span> <span class="s1">&#39;call bind before initializing the parameters&#39;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">initializer</span><span class="o">=</span><span class="n">initializer</span><span class="p">,</span> <span class="n">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span>
                                       <span class="n">aux_params</span><span class="o">=</span><span class="n">aux_params</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="n">allow_missing</span><span class="p">,</span>
                                       <span class="n">force_init</span><span class="o">=</span><span class="n">force_init</span><span class="p">,</span> <span class="n">allow_extra</span><span class="o">=</span><span class="n">allow_extra</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span></div>

 <div class="viewcode-block" id="BucketingModule.get_states"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.get_states">[docs]</a>    <span class="k">def</span> <span class="nf">get_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">merge_multi_context</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Gets states from all devices.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        merge_multi_context : bool</span>
 <span class="sd">            Default is `True`. In the case when data-parallelism is used, the states</span>
 <span class="sd">            will be collected from multiple devices. A `True` value indicate that we</span>
 <span class="sd">            should merge the collected results so that they look like from a single</span>
 <span class="sd">            executor.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        list of NDArrays or list of list of NDArrays</span>
 <span class="sd">            If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it</span>
 <span class="sd">            is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output</span>
 <span class="sd">            elements are `NDArray`.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">get_states</span><span class="p">(</span><span class="n">merge_multi_context</span><span class="o">=</span><span class="n">merge_multi_context</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.set_states"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.set_states">[docs]</a>    <span class="k">def</span> <span class="nf">set_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Sets value for states. Only one of states &amp; values can be specified.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        states : list of list of NDArrays</span>
 <span class="sd">            Source states arrays formatted like ``[[state1_dev1, state1_dev2],</span>
 <span class="sd">            [state2_dev1, state2_dev2]]``.</span>
 <span class="sd">        value : number</span>
 <span class="sd">            A single scalar value for all state arrays.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">set_states</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.bind"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.bind">[docs]</a>    <span class="k">def</span> <span class="nf">bind</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">for_training</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
              <span class="n">inputs_need_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_rebind</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shared_module</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
              <span class="n">grad_req</span><span class="o">=</span><span class="s1">&#39;write&#39;</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Binding for a `BucketingModule` means setting up the buckets and binding the</span>
 <span class="sd">        executor for the default bucket key. Executors corresponding to other keys are</span>
 <span class="sd">        bound afterwards with `switch_bucket`.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        data_shapes : list of (str, tuple)</span>
 <span class="sd">            This should correspond to the symbol for the default bucket.</span>
 <span class="sd">        label_shapes : list of (str, tuple)</span>
 <span class="sd">            This should correspond to the symbol for the default bucket.</span>
 <span class="sd">        for_training : bool</span>
 <span class="sd">            Default is ``True``.</span>
 <span class="sd">        inputs_need_grad : bool</span>
 <span class="sd">            Default is ``False``.</span>
 <span class="sd">        force_rebind : bool</span>
 <span class="sd">            Default is ``False``.</span>
 <span class="sd">        shared_module : BucketingModule</span>
 <span class="sd">            Default is ``None``. This value is currently not used.</span>
 <span class="sd">        grad_req : str, list of str, dict of str to str</span>
 <span class="sd">            Requirement for gradient accumulation. Can be &#39;write&#39;, &#39;add&#39;, or &#39;null&#39;</span>
 <span class="sd">            (default to &#39;write&#39;).</span>
 <span class="sd">            Can be specified globally (str) or for each argument (list, dict).</span>
 <span class="sd">        bucket_key : str (or any python object)</span>
 <span class="sd">            bucket key for binding. by default use the default_bucket_key</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># in case we already initialized params, keep it</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</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="bp">self</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>

         <span class="c1"># force rebinding is typically used when one want to switch from</span>
         <span class="c1"># training to prediction phase.</span>
         <span class="k">if</span> <span class="n">force_rebind</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_reset_bind</span><span class="p">()</span>

         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s1">&#39;Already bound, ignoring bind()&#39;</span><span class="p">)</span>
             <span class="k">return</span>

         <span class="k">assert</span> <span class="n">shared_module</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;shared_module for BucketingModule is not supported&#39;</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">for_training</span> <span class="o">=</span> <span class="n">for_training</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">inputs_need_grad</span> <span class="o">=</span> <span class="n">inputs_need_grad</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="n">grad_req</span>

         <span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_sym_gen</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span><span class="p">)</span>
         <span class="n">module</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">:</span>
             <span class="n">module</span> <span class="o">=</span> <span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</span><span class="p">,</span> <span class="n">logger</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="p">,</span>
                             <span class="n">context</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">,</span> <span class="n">work_load_list</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_work_load_list</span><span class="p">,</span>
                             <span class="n">fixed_param_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_fixed_param_names</span><span class="p">,</span>
                             <span class="n">state_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_state_names</span><span class="p">,</span>
                             <span class="n">group2ctxs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_group2ctxs</span><span class="p">,</span>
                             <span class="n">compression_params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_compression_params</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">module</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span><span class="p">]</span>
         <span class="n">module</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="p">,</span> <span class="n">for_training</span><span class="p">,</span> <span class="n">inputs_need_grad</span><span class="p">,</span>
                     <span class="n">force_rebind</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shared_module</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</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">_curr_module</span> <span class="o">=</span> <span class="n">module</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_bucket_key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">module</span>

         <span class="c1"># copy back saved params, if already initialized</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.switch_bucket"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.switch_bucket">[docs]</a>    <span class="k">def</span> <span class="nf">switch_bucket</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bucket_key</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Switches to a different bucket. This will change ``self.curr_module``.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        bucket_key : str (or any python object)</span>
 <span class="sd">            The key of the target bucket.</span>
 <span class="sd">        data_shapes : list of (str, tuple)</span>
 <span class="sd">            Typically ``data_batch.provide_data``.</span>
 <span class="sd">        label_shapes : list of (str, tuple)</span>
 <span class="sd">            Typically ``data_batch.provide_label``.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span><span class="p">,</span> <span class="s1">&#39;call bind before switching bucket&#39;</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">bucket_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">:</span>
             <span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_sym_gen</span><span class="p">(</span><span class="n">bucket_key</span><span class="p">)</span>
             <span class="n">module</span> <span class="o">=</span> <span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</span><span class="p">,</span>
                             <span class="n">logger</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="p">,</span> <span class="n">context</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">,</span>
                             <span class="n">work_load_list</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_work_load_list</span><span class="p">,</span>
                             <span class="n">fixed_param_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_fixed_param_names</span><span class="p">,</span>
                             <span class="n">state_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_state_names</span><span class="p">,</span>
                             <span class="n">group2ctxs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_group2ctxs</span><span class="p">,</span>
                             <span class="n">compression_params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_compression_params</span><span class="p">)</span>
             <span class="n">module</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">for_training</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">inputs_need_grad</span><span class="p">,</span>
                         <span class="n">force_rebind</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shared_module</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span><span class="p">],</span>
                         <span class="n">grad_req</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span><span class="p">)</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_monitor</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">module</span><span class="o">.</span><span class="n">install_monitor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_monitor</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="n">bucket_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">module</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">module</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="n">bucket_key</span><span class="p">]</span>
             <span class="k">if</span> <span class="ow">not</span> <span class="n">module</span><span class="o">.</span><span class="n">binded</span><span class="p">:</span>
                 <span class="n">module</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">for_training</span><span class="p">,</span>
                             <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">inputs_need_grad</span><span class="p">,</span>
                             <span class="n">force_rebind</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shared_module</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_default_bucket_key</span><span class="p">],</span>
                             <span class="n">grad_req</span><span class="o">=</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">_curr_module</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="n">bucket_key</span><span class="p">]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_bucket_key</span> <span class="o">=</span> <span class="n">bucket_key</span></div>

 <div class="viewcode-block" id="BucketingModule.init_optimizer"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.init_optimizer">[docs]</a>    <span class="k">def</span> <span class="nf">init_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kvstore</span><span class="o">=</span><span class="s1">&#39;local&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;sgd&#39;</span><span class="p">,</span>
                        <span class="n">optimizer_params</span><span class="o">=</span><span class="p">((</span><span class="s1">&#39;learning_rate&#39;</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">),),</span>
                        <span class="n">force_init</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Installs and initializes optimizers.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        kvstore : str or KVStore</span>
 <span class="sd">            Defaults to `&#39;local&#39;`.</span>
 <span class="sd">        optimizer : str or Optimizer</span>
 <span class="sd">            Defaults to `&#39;sgd&#39;`</span>
 <span class="sd">        optimizer_params : dict</span>
 <span class="sd">            Defaults to `((&#39;learning_rate&#39;, 0.01),)`. The default value is not a dictionary,</span>
 <span class="sd">            just to avoid pylint warning of dangerous default values.</span>
 <span class="sd">        force_init : bool</span>
 <span class="sd">            Defaults to ``False``, indicating whether we should force re-initializing the</span>
 <span class="sd">            optimizer in the case an optimizer is already installed.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_init</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s1">&#39;optimizer already initialized, ignoring.&#39;</span><span class="p">)</span>
             <span class="k">return</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">init_optimizer</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">optimizer_params</span><span class="p">,</span>
                                          <span class="n">force_init</span><span class="o">=</span><span class="n">force_init</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">mod</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
             <span class="k">if</span> <span class="n">mod</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="p">:</span>
                 <span class="n">mod</span><span class="o">.</span><span class="n">borrow_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="p">)</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span> <span class="o">=</span> <span class="kc">True</span></div>

 <div class="viewcode-block" id="BucketingModule.prepare"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.prepare">[docs]</a>    <span class="k">def</span> <span class="nf">prepare</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_batch</span><span class="p">,</span> <span class="n">sparse_row_id_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&#39;&#39;&#39;Prepares the module for processing a data batch.</span>

 <span class="sd">        Usually involves switching bucket and reshaping.</span>
 <span class="sd">        For modules that contain `row_sparse` parameters in KVStore,</span>
 <span class="sd">        it prepares the `row_sparse` parameters based on the sparse_row_id_fn.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        data_batch : DataBatch</span>
 <span class="sd">            The current batch of data for forward computation.</span>

 <span class="sd">        sparse_row_id_fn : A callback function</span>
 <span class="sd">            The function  takes `data_batch` as an input and returns a dict of</span>
 <span class="sd">            str -&gt; NDArray. The resulting dict is used for pulling row_sparse</span>
 <span class="sd">            parameters from the kvstore, where the str key is the name of the param,</span>
 <span class="sd">            and the value is the row id of the param to pull.</span>
 <span class="sd">        &#39;&#39;&#39;</span>
         <span class="c1"># perform bind if haven&#39;t done so</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="n">bucket_key</span> <span class="o">=</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">bucket_key</span>
         <span class="n">original_bucket_key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_bucket_key</span>
         <span class="n">data_shapes</span> <span class="o">=</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_data</span>
         <span class="n">label_shapes</span> <span class="o">=</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_label</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">switch_bucket</span><span class="p">(</span><span class="n">bucket_key</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">data_batch</span><span class="p">,</span> <span class="n">sparse_row_id_fn</span><span class="o">=</span><span class="n">sparse_row_id_fn</span><span class="p">)</span>
         <span class="c1"># switch back</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">switch_bucket</span><span class="p">(</span><span class="n">original_bucket_key</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.forward"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.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">data_batch</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Forward computation.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        data_batch : DataBatch</span>
 <span class="sd">        is_train : bool</span>
 <span class="sd">            Defaults to ``None``, in which case `is_train` is take as ``self.for_training``.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">switch_bucket</span><span class="p">(</span><span class="n">data_batch</span><span class="o">.</span><span class="n">bucket_key</span><span class="p">,</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_data</span><span class="p">,</span>
                            <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_label</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">data_batch</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="n">is_train</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.backward"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.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="sd">&quot;&quot;&quot;Backward computation.&quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">out_grads</span><span class="o">=</span><span class="n">out_grads</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.update"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.update">[docs]</a>    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Updates parameters according to installed optimizer and the gradient computed</span>
 <span class="sd">        in the previous forward-backward cycle.</span>

 <span class="sd">        When KVStore is used to update parameters for multi-device or multi-machine training,</span>
 <span class="sd">        a copy of the parameters are stored in KVStore. Note that for `row_sparse` parameters,</span>
 <span class="sd">        this function does update the copy of parameters in KVStore, but doesn&#39;t broadcast the</span>
 <span class="sd">        updated parameters to all devices / machines. Please call `prepare` to broadcast</span>
 <span class="sd">        `row_sparse` parameters with the next batch of data.</span>

 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">update</span><span class="p">()</span></div>

 <div class="viewcode-block" id="BucketingModule.get_outputs"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.get_outputs">[docs]</a>    <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="n">merge_multi_context</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Gets outputs from a previous forward computation.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        merge_multi_context : bool</span>
 <span class="sd">            Defaults to ``True``. In the case when data-parallelism is used, the outputs</span>
 <span class="sd">            will be collected from multiple devices. A ``True`` value indicate that we</span>
 <span class="sd">            should merge the collected results so that they look like from a single</span>
 <span class="sd">            executor.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        list of numpy arrays or list of list of numpy arrays</span>
 <span class="sd">            If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it</span>
 <span class="sd">            is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output</span>
 <span class="sd">            elements are numpy arrays.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">(</span><span class="n">merge_multi_context</span><span class="o">=</span><span class="n">merge_multi_context</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.get_input_grads"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.get_input_grads">[docs]</a>    <span class="k">def</span> <span class="nf">get_input_grads</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">merge_multi_context</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Gets the gradients with respect to the inputs of the module.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        merge_multi_context : bool</span>
 <span class="sd">            Defaults to ``True``. In the case when data-parallelism is used, the outputs</span>
 <span class="sd">            will be collected from multiple devices. A ``True`` value indicate that we</span>
 <span class="sd">            should merge the collected results so that they look like from a single</span>
 <span class="sd">            executor.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        list of NDArrays or list of list of NDArrays</span>
 <span class="sd">            If `merge_multi_context` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it</span>
 <span class="sd">            is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output</span>
 <span class="sd">            elements are `NDArray`.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs_need_grad</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">get_input_grads</span><span class="p">(</span><span class="n">merge_multi_context</span><span class="o">=</span><span class="n">merge_multi_context</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.update_metric"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.update_metric">[docs]</a>    <span class="k">def</span> <span class="nf">update_metric</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eval_metric</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">pre_sliced</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Evaluates and accumulates evaluation metric on outputs of the last forward computation.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        eval_metric : EvalMetric</span>
 <span class="sd">        labels : list of NDArray</span>
 <span class="sd">            Typically ``data_batch.label``.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">update_metric</span><span class="p">(</span><span class="n">eval_metric</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">pre_sliced</span><span class="p">)</span></div>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">symbol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;The symbol of the current bucket being used.&quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">symbol</span>

 <div class="viewcode-block" id="BucketingModule.install_monitor"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.install_monitor">[docs]</a>    <span class="k">def</span> <span class="nf">install_monitor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mon</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Installs monitor on all executors &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_monitor</span> <span class="o">=</span> <span class="n">mon</span>
         <span class="k">for</span> <span class="n">mod</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
             <span class="n">mod</span><span class="o">.</span><span class="n">install_monitor</span><span class="p">(</span><span class="n">mon</span><span class="p">)</span></div>

 <div class="viewcode-block" id="BucketingModule.save_checkpoint"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.save_checkpoint">[docs]</a>    <span class="k">def</span> <span class="nf">save_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">remove_amp_cast</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Saves current progress to checkpoint for all buckets in BucketingModule</span>
 <span class="sd">        Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        prefix : str</span>
 <span class="sd">            The file prefix to checkpoint to.</span>
 <span class="sd">        epoch : int</span>
 <span class="sd">            The current epoch number.</span>
 <span class="sd">        &quot;&quot;&quot;</span>

         <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;Empty BucketingModule cannot be saved&quot;</span>
         <span class="n">param_name</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">-</span><span class="si">%04d</span><span class="s2">.params&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">save_params</span><span class="p">(</span><span class="n">param_name</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">bucket_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="p">:</span>
             <span class="n">symbol</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sym_gen</span><span class="p">(</span><span class="n">bucket_key</span><span class="p">)</span>
             <span class="n">symbol</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">-</span><span class="si">%s</span><span class="s2">-symbol.json&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">bucket_key</span><span class="p">),</span> <span class="n">remove_amp_cast</span><span class="o">=</span><span class="n">remove_amp_cast</span><span class="p">)</span>
         <span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">.buckets&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">),</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_buckets</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">))</span></div>

 <div class="viewcode-block" id="BucketingModule.load"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.load">[docs]</a>    <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">sym_gen</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default_bucket_key</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="sd">&quot;&quot;&quot;Creates a model from previously saved checkpoint.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        prefix : str</span>
 <span class="sd">            path prefix of saved model files. You should have</span>
 <span class="sd">            &quot;prefix-symbol.json&quot;, &quot;prefix-xxxx.params&quot;, and</span>
 <span class="sd">            optionally &quot;prefix-xxxx.states&quot;, where xxxx is the</span>
 <span class="sd">            epoch number.</span>
 <span class="sd">        epoch : int</span>
 <span class="sd">            epoch to load.</span>
 <span class="sd">        sym_gen : function</span>
 <span class="sd">            A function when called with a bucket key, returns a triple</span>
 <span class="sd">            ``(symbol, data_names, label_names)``.</span>
 <span class="sd">            provide sym_gen which was used when saving bucketing module.</span>
 <span class="sd">        logger : Logger</span>
 <span class="sd">            Default is `logging`.</span>
 <span class="sd">        context : Context or list of Context</span>
 <span class="sd">            Default is ``cpu()``.</span>
 <span class="sd">        work_load_list : list of number</span>
 <span class="sd">            Default ``None``, indicating uniform workload.</span>
 <span class="sd">        fixed_param_names: list of str</span>
 <span class="sd">            Default ``None``, indicating no network parameters are fixed.</span>
 <span class="sd">        state_names : list of str</span>
 <span class="sd">            States are similar to data and label, but not provided by data iterator.</span>
 <span class="sd">            Instead they are initialized to 0 and can be set by set_states()</span>
 <span class="sd">        group2ctxs : dict of str to context or list of context,</span>
 <span class="sd">                     or list of dict of str to context</span>
 <span class="sd">            Default is `None`. Mapping the `ctx_group` attribute to the context assignment.</span>
 <span class="sd">        compression_params : dict</span>
 <span class="sd">            Specifies type of gradient compression and additional arguments depending</span>
 <span class="sd">            on the type of compression being used. For example, 2bit compression requires a threshold.</span>
 <span class="sd">            Arguments would then be {&#39;type&#39;:&#39;2bit&#39;, &#39;threshold&#39;:0.5}</span>
 <span class="sd">            See mxnet.KVStore.set_gradient_compression method for more details on gradient compression.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="n">sym_gen</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
             <span class="s2">&quot;sym_gen is required for loading BucketingModule&quot;</span>
         <span class="k">assert</span> <span class="n">default_bucket_key</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
             <span class="s2">&quot;default_bucket_key is required for loading BucketingModule&quot;</span>
         <span class="n">buckets</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">.buckets&quot;</span> <span class="o">%</span> <span class="n">prefix</span><span class="p">)</span>
         <span class="n">buckets</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">buckets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">))</span>
         <span class="n">bucketing_mod</span> <span class="o">=</span> <span class="n">BucketingModule</span><span class="p">(</span><span class="n">sym_gen</span><span class="p">,</span> <span class="n">default_bucket_key</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">bucket_key</span> <span class="ow">in</span> <span class="n">buckets</span><span class="p">:</span>
             <span class="n">_</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</span> <span class="o">=</span> <span class="n">sym_gen</span><span class="p">(</span><span class="n">bucket_key</span><span class="p">)</span>
             <span class="n">symbol</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">-</span><span class="si">%s</span><span class="s2">-symbol.json&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">bucket_key</span><span class="p">))</span>
             <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="n">bucket_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</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="n">bucket_key</span> <span class="o">==</span> <span class="n">default_bucket_key</span><span class="p">:</span>
                 <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_curr_module</span> <span class="o">=</span> <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="n">bucket_key</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="n">load_params</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
         <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">_arg_params</span> <span class="o">=</span> <span class="n">arg_params</span>
         <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">_aux_params</span> <span class="o">=</span> <span class="n">aux_params</span>
         <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="k">return</span> <span class="n">bucketing_mod</span></div>

 <div class="viewcode-block" id="BucketingModule.load_dict"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.BucketingModule.load_dict">[docs]</a>    <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">load_dict</span><span class="p">(</span><span class="n">sym_dict</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sym_gen</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default_bucket_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">arg_params</span><span class="o">=</span><span class="kc">None</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Creates a model from a dict mapping bucket_key to symbols and shared arg_params</span>
 <span class="sd">        and aux_params.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        sym_dict : dict mapping bucket_key to symbol</span>
 <span class="sd">            Dict mapping bucket key to symbol</span>
 <span class="sd">        sym_gen : function</span>
 <span class="sd">            A function when called with a bucket key, returns a triple</span>
 <span class="sd">            ``(symbol, data_names, label_names)``.</span>
 <span class="sd">            provide sym_gen which was used when saving bucketing module.</span>
 <span class="sd">        default_bucket_key : str (or any python object)</span>
 <span class="sd">            The key for the default bucket.</span>
 <span class="sd">        arg_params : dict</span>
 <span class="sd">            Required for loading the BucketingModule.</span>
 <span class="sd">            Dict of name to parameter ndarrays.</span>
 <span class="sd">        aux_params : dict</span>
 <span class="sd">            Required for loading the BucketingModule.</span>
 <span class="sd">            Dict of name to auxiliary state ndarrays.</span>
 <span class="sd">        logger : Logger</span>
 <span class="sd">            Default is `logging`.</span>
 <span class="sd">        context : Context or list of Context</span>
 <span class="sd">            Default is ``cpu()``.</span>
 <span class="sd">        work_load_list : list of number</span>
 <span class="sd">            Default ``None``, indicating uniform workload.</span>
 <span class="sd">        fixed_param_names: list of str</span>
 <span class="sd">            Default ``None``, indicating no network parameters are fixed.</span>
 <span class="sd">        state_names : list of str</span>
 <span class="sd">            States are similar to data and label, but not provided by data iterator.</span>
 <span class="sd">            Instead they are initialized to 0 and can be set by set_states()</span>
 <span class="sd">        group2ctxs : dict of str to context or list of context,</span>
 <span class="sd">                     or list of dict of str to context</span>
 <span class="sd">            Default is `None`. Mapping the `ctx_group` attribute to the context assignment.</span>
 <span class="sd">        compression_params : dict</span>
 <span class="sd">            Specifies type of gradient compression and additional arguments depending</span>
 <span class="sd">            on the type of compression being used. For example, 2bit compression requires a threshold.</span>
 <span class="sd">            Arguments would then be {&#39;type&#39;:&#39;2bit&#39;, &#39;threshold&#39;:0.5}</span>
 <span class="sd">            See mxnet.KVStore.set_gradient_compression method for more details on gradient compression.</span>
 <span class="sd">        &quot;&quot;&quot;</span>

         <span class="k">assert</span> <span class="n">sym_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
             <span class="s2">&quot;sym_dict needs to be provided for BucketingModule.load_dict&quot;</span>
         <span class="k">assert</span> <span class="n">arg_params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
             <span class="s2">&quot;arg_params need to be provided for BucketingModule.load_dict&quot;</span>
         <span class="k">assert</span> <span class="n">aux_params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
             <span class="s2">&quot;aux_params need to be provided for BucketingModule.load_dict&quot;</span>
         <span class="k">assert</span> <span class="n">default_bucket_key</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
             <span class="s2">&quot;default_bucket_key needs to be provided for BucketingModule.load_dict&quot;</span>

         <span class="n">bucketing_mod</span> <span class="o">=</span> <span class="n">BucketingModule</span><span class="p">(</span><span class="n">sym_gen</span><span class="p">,</span> <span class="n">default_bucket_key</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">bucket_key</span><span class="p">,</span> <span class="n">loaded_sym</span> <span class="ow">in</span> <span class="n">sym_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
             <span class="n">_</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</span> <span class="o">=</span> <span class="n">sym_gen</span><span class="p">(</span><span class="n">default_bucket_key</span><span class="p">)</span>
             <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="n">bucket_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">Module</span><span class="p">(</span><span class="n">loaded_sym</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="n">label_names</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="n">bucket_key</span> <span class="o">==</span> <span class="n">default_bucket_key</span><span class="p">:</span>
                 <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_curr_module</span> <span class="o">=</span> <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_buckets</span><span class="p">[</span><span class="n">bucket_key</span><span class="p">]</span>
         <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">_arg_params</span> <span class="o">=</span> <span class="n">arg_params</span>
         <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">_aux_params</span> <span class="o">=</span> <span class="n">aux_params</span>
         <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">_curr_module</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="n">bucketing_mod</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="k">return</span> <span class="n">bucketing_mod</span></div></div>
 </pre></div>

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