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<h1>Source code for mxnet.executor_manager</h1><div class="highlight"><pre>
<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
<span class="c1"># distributed with this work for additional information</span>
<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
<span class="c1"># "License"); you may not use this file except in compliance</span>
<span class="c1"># with the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
<span class="c1"># software distributed under the License is distributed on an</span>
<span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
<span class="c1"># KIND, either express or implied. See the License for the</span>
<span class="c1"># specific language governing permissions and limitations</span>
<span class="c1"># under the License.</span>
<span class="c1"># coding: utf-8</span>
<span class="c1"># pylint: disable=invalid-name, protected-access, too-many-locals, too-many-arguments, too-many-statements</span>
<span class="sd">"""Executor manager."""</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">mx_real_t</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">.context</span> <span class="kn">import</span> <span class="n">cpu</span>
<span class="kn">from</span> <span class="nn">.io</span> <span class="kn">import</span> <span class="n">DataDesc</span>
<span class="k">def</span> <span class="nf">_split_input_slice</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">work_load_list</span><span class="p">):</span>
<span class="sd">"""Get input slice from the input shape.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> batch_size : int</span>
<span class="sd"> The number of samples in a mini-batch.</span>
<span class="sd"> work_load_list : list of float or int, optional</span>
<span class="sd"> The list of work load for different devices,</span>
<span class="sd"> in the same order as `ctx`.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> slices : list of slice</span>
<span class="sd"> The split slices to get a specific slice.</span>
<span class="sd"> Raises</span>
<span class="sd"> ------</span>
<span class="sd"> ValueError</span>
<span class="sd"> In case of too many splits, leading to some empty slices.</span>
<span class="sd"> """</span>
<span class="n">total_work_load</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">work_load_list</span><span class="p">)</span>
<span class="n">batch_num_list</span> <span class="o">=</span> <span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">work_load</span> <span class="o">*</span> <span class="n">batch_size</span> <span class="o">/</span> <span class="n">total_work_load</span><span class="p">)</span>
<span class="k">for</span> <span class="n">work_load</span> <span class="ow">in</span> <span class="n">work_load_list</span><span class="p">]</span>
<span class="n">batch_num_sum</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">batch_num_list</span><span class="p">)</span>
<span class="k">if</span> <span class="n">batch_num_sum</span> <span class="o"><</span> <span class="n">batch_size</span><span class="p">:</span>
<span class="n">batch_num_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">batch_size</span> <span class="o">-</span> <span class="n">batch_num_sum</span>
<span class="n">slices</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">end</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">batch_num</span> <span class="ow">in</span> <span class="n">batch_num_list</span><span class="p">:</span>
<span class="n">begin</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">min</span><span class="p">((</span><span class="n">end</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)))</span>
<span class="n">end</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">min</span><span class="p">((</span><span class="n">begin</span> <span class="o">+</span> <span class="n">batch_num</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)))</span>
<span class="k">if</span> <span class="n">begin</span> <span class="o">>=</span> <span class="n">end</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Too many slices. Some splits are empty.'</span><span class="p">)</span>
<span class="n">slices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="p">))</span>
<span class="k">return</span> <span class="n">slices</span>
<span class="k">def</span> <span class="nf">_check_arguments</span><span class="p">(</span><span class="n">symbol</span><span class="p">):</span>
<span class="sd">"""Check the argument names of symbol.</span>
<span class="sd"> This function checks the duplication of arguments in Symbol.</span>
<span class="sd"> The check is done for feedforward net for now.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> symbol : Symbol</span>
<span class="sd"> The network configuration.</span>
<span class="sd"> """</span>
<span class="n">arg_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="n">arg_names</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_names</span><span class="p">:</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_set</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">((</span><span class="s1">'Find duplicated argument name </span><span class="se">\"</span><span class="si">%s</span><span class="se">\"</span><span class="s1">, '</span> <span class="o">+</span>
<span class="s1">'please make the weight name non-duplicated(using name arguments), '</span> <span class="o">+</span>
<span class="s1">'arguments are </span><span class="si">%s</span><span class="s1">'</span><span class="p">)</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">arg_names</span><span class="p">)))</span>
<span class="n">arg_set</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="n">aux_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="n">aux_names</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">aux_names</span><span class="p">:</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">aux_set</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="p">(</span><span class="s1">'Find duplicated auxiliary param name </span><span class="se">\"</span><span class="si">%s</span><span class="se">\"</span><span class="s1">, '</span> <span class="o">+</span>
<span class="s1">'please make the weight name non-duplicated(using name arguments), '</span> <span class="o">+</span>
<span class="s1">'arguments are </span><span class="si">%s</span><span class="s1">, auxiliary params are </span><span class="si">%s</span><span class="s1">'</span>
<span class="p">)</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">arg_names</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">aux_names</span><span class="p">)))</span>
<span class="n">aux_set</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_load_general</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="sd">"""Load a list of arrays into a list of arrays specified by slices."""</span>
<span class="k">for</span> <span class="n">d_src</span><span class="p">,</span> <span class="n">d_targets</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">d_targets</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
<span class="n">d_src</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">d_targets</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">d_targets</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stop</span> <span class="o">==</span> <span class="n">d_src</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> \
<span class="s2">"Batch size miss match. Expected </span><span class="si">%d</span><span class="s2">, got </span><span class="si">%d</span><span class="s2">"</span><span class="o">%</span><span class="p">(</span> \
<span class="n">d_targets</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stop</span><span class="p">,</span> <span class="n">d_src</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">for</span> <span class="n">slice_idx</span><span class="p">,</span> <span class="n">d_dst</span> <span class="ow">in</span> <span class="n">d_targets</span><span class="p">:</span>
<span class="n">d_src</span><span class="p">[</span><span class="n">slice_idx</span><span class="p">]</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">d_dst</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_load_data</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="sd">"""Load data into sliced arrays."""</span>
<span class="n">_load_general</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_load_label</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="sd">"""Load label into sliced arrays."""</span>
<span class="n">_load_general</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
<span class="c1"># pylint: disable=too-many-branches</span>
<span class="k">def</span> <span class="nf">_bind_exec</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">input_shapes</span><span class="p">,</span> <span class="n">param_names</span><span class="p">,</span> <span class="n">need_grad</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
<span class="n">base_exec</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">shared_data_arrays</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">input_types</span><span class="o">=</span><span class="bp">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="sd">"""bind executor for bucketing, potentially sharing data with an existing executor."""</span>
<span class="n">arg_shape</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">aux_shape</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">infer_shape</span><span class="p">(</span><span class="o">**</span><span class="n">input_shapes</span><span class="p">)</span>
<span class="k">assert</span><span class="p">(</span><span class="n">arg_shape</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">)</span>
<span class="k">if</span> <span class="n">input_types</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">input_types</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">mx_real_t</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">input_shapes</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>
<span class="n">arg_types</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">aux_types</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">infer_type</span><span class="p">(</span><span class="o">**</span><span class="n">input_types</span><span class="p">)</span>
<span class="k">assert</span><span class="p">(</span><span class="n">arg_types</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">)</span>
<span class="n">arg_arrays</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">grad_arrays</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">if</span> <span class="n">need_grad</span> <span class="o">!=</span> <span class="bp">False</span> <span class="k">else</span> <span class="bp">None</span>
<span class="n">arg_names</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span>
<span class="k">if</span> <span class="n">need_grad</span> <span class="ow">is</span> <span class="bp">False</span><span class="p">:</span>
<span class="n">need_grad</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="k">elif</span> <span class="n">need_grad</span> <span class="ow">is</span> <span class="bp">True</span><span class="p">:</span>
<span class="n">need_grad</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">arg_names</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">input_shapes</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">need_grad</span><span class="p">,</span> <span class="nb">set</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">"need_grad must be boolean or set."</span><span class="p">)</span>
<span class="n">grad_req</span> <span class="o">=</span> <span class="p">{</span><span class="n">name</span><span class="p">:(</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">need_grad</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_names</span><span class="p">}</span>
<span class="c1"># create or borrow arguments and gradients</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">arg_names</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">param_names</span><span class="p">:</span>
<span class="c1"># data or label</span>
<span class="k">if</span> <span class="n">shared_data_arrays</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span> <span class="ow">and</span> \
<span class="n">name</span> <span class="ow">in</span> <span class="n">shared_data_arrays</span><span class="p">:</span>
<span class="n">arg_arr</span> <span class="o">=</span> <span class="n">shared_data_arrays</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">arg_arr</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">>=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">arg_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]):</span>
<span class="c1"># good, we can share this memory</span>
<span class="k">assert</span><span class="p">(</span><span class="n">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">arg_arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">arg_arr</span> <span class="o">=</span> <span class="n">arg_arr</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">arg_shape</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">((</span><span class="s1">'bucketing: data "</span><span class="si">%s</span><span class="s1">" has a shape </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">arg_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">+</span>
<span class="p">(</span><span class="s1">', which is larger than already allocated '</span><span class="p">)</span> <span class="o">+</span>
<span class="p">(</span><span class="s1">'shape </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">arg_arr</span><span class="o">.</span><span class="n">shape</span><span class="p">,))</span> <span class="o">+</span>
<span class="p">(</span><span class="s1">'. Need to re-allocate. Consider putting '</span><span class="p">)</span> <span class="o">+</span>
<span class="p">(</span><span class="s1">'default_bucket_key to be the bucket taking the largest '</span><span class="p">)</span> <span class="o">+</span>
<span class="p">(</span><span class="s1">'input for better memory sharing.'</span><span class="p">))</span>
<span class="n">arg_arr</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">arg_shape</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="c1"># replace existing shared array because the new one is bigger</span>
<span class="n">shared_data_arrays</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">arg_arr</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">arg_arr</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">arg_shape</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">if</span> <span class="n">shared_data_arrays</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">shared_data_arrays</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">arg_arr</span>
<span class="n">arg_arrays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arg_arr</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># model parameter</span>
<span class="k">if</span> <span class="n">base_exec</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">arg_arr</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">arg_shape</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">need_grad</span><span class="p">:</span>
<span class="n">grad_arr</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">arg_shape</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">grad_arrays</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">grad_arr</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">arg_arr</span> <span class="o">=</span> <span class="n">base_exec</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">arg_arr</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">arg_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">arg_arr</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">need_grad</span><span class="p">:</span>
<span class="n">grad_arrays</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">base_exec</span><span class="o">.</span><span class="n">grad_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="n">arg_arrays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arg_arr</span><span class="p">)</span>
<span class="c1"># create or borrow aux variables</span>
<span class="k">if</span> <span class="n">base_exec</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">aux_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">aux_shape</span><span class="p">,</span> <span class="n">aux_types</span><span class="p">)]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">base_exec</span><span class="o">.</span><span class="n">aux_arrays</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">aux_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span>
<span class="k">assert</span> <span class="n">aux_types</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span>
<span class="n">aux_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">base_exec</span><span class="o">.</span><span class="n">aux_arrays</span><span class="p">]</span>
<span class="n">executor</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="n">arg_arrays</span><span class="p">,</span> <span class="n">args_grad</span><span class="o">=</span><span class="n">grad_arrays</span><span class="p">,</span>
<span class="n">aux_states</span><span class="o">=</span><span class="n">aux_arrays</span><span class="p">,</span>
<span class="n">grad_req</span><span class="o">=</span><span class="n">grad_req</span><span class="p">,</span> <span class="n">shared_exec</span><span class="o">=</span><span class="n">base_exec</span><span class="p">)</span>
<span class="k">return</span> <span class="n">executor</span>
<div class="viewcode-block" id="DataParallelExecutorGroup"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorGroup">[docs]</a><span class="k">class</span> <span class="nc">DataParallelExecutorGroup</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""A group of executors living on different devices, for data parallelization.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> sym: Symbol</span>
<span class="sd"> The network configuration.</span>
<span class="sd"> arg_names: list of str</span>
<span class="sd"> Equals `sym.list_arguments()`</span>
<span class="sd"> param_names: list of str</span>
<span class="sd"> List of names of all trainable parameters.</span>
<span class="sd"> ctx: list of Context</span>
<span class="sd"> List of devices for training (data parallelization).</span>
<span class="sd"> slices: list of int</span>
<span class="sd"> Describes how the data parallelization splits data into different devices.</span>
<span class="sd"> train_data: DataIter (or DataBatch)</span>
<span class="sd"> The dataset for training. It could be any object with `provide_data` and</span>
<span class="sd"> `provide_label` properties. Loading of actual data is not necessarily needed</span>
<span class="sd"> at this stage.</span>
<span class="sd"> shared_grop: DataParallelExecutorGroup</span>
<span class="sd"> An existing executor group, if to share parameters with it.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sym</span><span class="p">,</span> <span class="n">arg_names</span><span class="p">,</span> <span class="n">param_names</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">slices</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">shared_group</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="c1"># make sure the architecture is valid</span>
<span class="n">_check_arguments</span><span class="p">(</span><span class="n">sym</span><span class="p">)</span>
<span class="k">if</span> <span class="n">shared_group</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shared_data_arrays</span> <span class="o">=</span> <span class="p">[{}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">ctx</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shared_data_arrays</span> <span class="o">=</span> <span class="n">shared_group</span><span class="o">.</span><span class="n">shared_data_arrays</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_data</span><span class="o">.</span><span class="n">provide_data</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">label_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_data</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">aux_names</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">arg_names</span><span class="p">))</span> <span class="k">if</span> <span class="n">arg_names</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">in</span> <span class="n">param_names</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg_names</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_idx</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ctxi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ctx</span><span class="p">):</span>
<span class="n">data_shapes</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">data_types</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_data</span><span class="o">.</span><span class="n">provide_data</span> <span class="o">+</span> <span class="n">train_data</span><span class="o">.</span><span class="n">provide_label</span><span class="p">:</span>
<span class="n">data_shapes</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">slices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">stop</span> <span class="o">-</span> <span class="n">slices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">start</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">:]))</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">DataDesc</span><span class="p">):</span>
<span class="n">data_types</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data_types</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="n">mx_real_t</span>
<span class="n">shared_exec</span> <span class="o">=</span> <span class="bp">None</span> <span class="k">if</span> <span class="n">shared_group</span> <span class="ow">is</span> <span class="bp">None</span> <span class="k">else</span> <span class="n">shared_group</span><span class="o">.</span><span class="n">train_execs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">train_exec</span> <span class="o">=</span> <span class="n">_bind_exec</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">ctxi</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_names</span><span class="p">,</span>
<span class="n">need_grad</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">base_exec</span><span class="o">=</span><span class="n">shared_exec</span><span class="p">,</span>
<span class="n">shared_data_arrays</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shared_data_arrays</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
<span class="n">input_types</span><span class="o">=</span><span class="n">data_types</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_exec</span><span class="p">)</span>
<span class="c1"># data structure</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data_arrays</span> <span class="o">=</span> <span class="p">[[(</span><span class="n">slices</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">e</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_names</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">label_arrays</span> <span class="o">=</span> <span class="p">[[(</span><span class="n">slices</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">e</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_arrays</span> <span class="o">=</span> <span class="p">[[</span><span class="n">e</span><span class="o">.</span><span class="n">arg_arrays</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_idx</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad_arrays</span> <span class="o">=</span> <span class="p">[[</span><span class="n">e</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_idx</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span> <span class="o">=</span> <span class="p">[[</span><span class="n">e</span><span class="o">.</span><span class="n">aux_arrays</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">aux_names</span><span class="p">))]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">slices</span> <span class="o">=</span> <span class="n">slices</span>
<div class="viewcode-block" id="DataParallelExecutorGroup.load_data_batch"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorGroup.load_data_batch">[docs]</a> <span class="k">def</span> <span class="nf">load_data_batch</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="sd">"""Load data and labels into arrays."""</span>
<span class="n">_load_data</span><span class="p">(</span><span class="n">data_batch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_arrays</span><span class="p">)</span>
<span class="n">_load_label</span><span class="p">(</span><span class="n">data_batch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_arrays</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataParallelExecutorGroup.forward"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorGroup.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Perform a forward pass on each executor."""</span>
<span class="k">for</span> <span class="n">texec</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="p">:</span>
<span class="n">texec</span><span class="o">.</span><span class="n">forward</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="DataParallelExecutorGroup.backward"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorGroup.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="sd">"""Perform a backward pass on each executor."""</span>
<span class="k">for</span> <span class="n">texec</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="p">:</span>
<span class="n">texec</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span></div>
<div class="viewcode-block" id="DataParallelExecutorGroup.update_metric"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorGroup.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">metric</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="sd">"""Update evaluation metric with label and current outputs."""</span>
<span class="k">for</span> <span class="n">texec</span><span class="p">,</span> <span class="n">islice</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_execs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices</span><span class="p">):</span>
<span class="n">labels_slice</span> <span class="o">=</span> <span class="p">[</span><span class="n">label</span><span class="p">[</span><span class="n">islice</span><span class="p">]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels</span><span class="p">]</span>
<span class="n">metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels_slice</span><span class="p">,</span> <span class="n">texec</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="DataParallelExecutorManager"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorManager">[docs]</a><span class="k">class</span> <span class="nc">DataParallelExecutorManager</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">""" Helper class to manage multiple executors for data parallelism.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> symbol : Symbol</span>
<span class="sd"> Output symbol.</span>
<span class="sd"> ctx : list of Context</span>
<span class="sd"> Devices to run on.</span>
<span class="sd"> param_names: list of str</span>
<span class="sd"> Name of all trainable parameters of the network.</span>
<span class="sd"> arg_names: list of str</span>
<span class="sd"> Name of all arguments of the network.</span>
<span class="sd"> aux_names: list of str</span>
<span class="sd"> Name of all auxiliary states of the network.</span>
<span class="sd"> train_data : DataIter</span>
<span class="sd"> Training data iterator.</span>
<span class="sd"> work_load_list : list of float or int, optional</span>
<span class="sd"> The list of work load for different devices,</span>
<span class="sd"> in the same order as ctx.</span>
<span class="sd"> logger : logging logger</span>
<span class="sd"> When not specified, default logger will be used.</span>
<span class="sd"> sym_gen : A function that generate new Symbols depending on different</span>
<span class="sd"> input shapes. Used only for bucketing.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span>
<span class="n">arg_names</span><span class="p">,</span> <span class="n">param_names</span><span class="p">,</span> <span class="n">aux_names</span><span class="p">,</span>
<span class="n">work_load_list</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">logger</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">sym_gen</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">logger</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span>
<span class="c1"># preparation</span>
<span class="n">num_device</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Start training with </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">))</span>
<span class="k">if</span> <span class="n">work_load_list</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">work_load_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_device</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">work_load_list</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">work_load_list</span><span class="p">)</span> <span class="o">==</span> <span class="n">num_device</span><span class="p">,</span> \
<span class="s2">"Invalid settings for work load. "</span>
<span class="n">slices</span> <span class="o">=</span> <span class="n">_split_input_slice</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">work_load_list</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">slices</span> <span class="o">=</span> <span class="n">slices</span>
<span class="bp">self</span><span class="o">.</span><span class="n">arg_names</span> <span class="o">=</span> <span class="n">arg_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_names</span> <span class="o">=</span> <span class="n">param_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">aux_names</span> <span class="o">=</span> <span class="n">aux_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ctx</span> <span class="o">=</span> <span class="n">ctx</span>
<span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span> <span class="o">=</span> <span class="n">DataParallelExecutorGroup</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ctx</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">slices</span><span class="p">,</span> <span class="n">train_data</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">symbol</span> <span class="o">=</span> <span class="n">symbol</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="bp">self</span><span class="o">.</span><span class="n">curr_execgrp</span> <span class="o">=</span> <span class="bp">None</span> <span class="c1"># this is set when data is loaded</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">execgrp_bucket</span> <span class="o">=</span> <span class="p">{</span><span class="n">train_data</span><span class="o">.</span><span class="n">default_bucket_key</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span><span class="p">}</span>
<div class="viewcode-block" id="DataParallelExecutorManager.install_monitor"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorManager.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">monitor</span><span class="p">):</span>
<span class="sd">"""Install monitor on all executors."""</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"Monitoring is not implemented for bucketing"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">train_exec</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span><span class="o">.</span><span class="n">train_execs</span><span class="p">:</span>
<span class="n">monitor</span><span class="o">.</span><span class="n">install</span><span class="p">(</span><span class="n">train_exec</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataParallelExecutorManager.set_params"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorManager.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="sd">"""Set parameter and aux values.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> arg_params : list of NDArray</span>
<span class="sd"> Source parameter arrays</span>
<span class="sd"> aux_params : list of NDArray</span>
<span class="sd"> Source aux arrays.</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">texec</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span><span class="o">.</span><span class="n">train_execs</span><span class="p">:</span>
<span class="n">texec</span><span class="o">.</span><span class="n">copy_params_from</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="DataParallelExecutorManager.copy_to"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorManager.copy_to">[docs]</a> <span class="k">def</span> <span class="nf">copy_to</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="sd">""" Copy data from each executor to ```arg_params`` and ``aux_params``.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> arg_params : list of NDArray</span>
<span class="sd"> Target parameter arrays.</span>
<span class="sd"> aux_params : list of NDArray</span>
<span class="sd"> Target aux arrays.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> - This function will inplace update the NDArrays in arg_params and aux_params.</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_arrays</span><span class="p">):</span>
<span class="n">weight</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">cpu</span><span class="p">())</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">block</span><span class="p">)</span>
<span class="n">weight</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">arg_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">arg_params</span><span class="p">[</span><span class="n">name</span><span class="p">])</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">aux_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span><span class="p">):</span>
<span class="n">weight</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">cpu</span><span class="p">())</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">block</span><span class="p">)</span>
<span class="n">weight</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">aux_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">aux_params</span><span class="p">[</span><span class="n">name</span><span class="p">])</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">param_arrays</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Shared parameter arrays."""</span>
<span class="c1"># param arrays should be shared by all executor groups</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span><span class="o">.</span><span class="n">param_arrays</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">grad_arrays</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Shared gradient arrays."""</span>
<span class="c1"># grad arrays should be shared by all executor groups</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span><span class="o">.</span><span class="n">grad_arrays</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">aux_arrays</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Shared aux states."""</span>
<span class="c1"># aux arrays are also shared by all executor groups</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span><span class="o">.</span><span class="n">aux_arrays</span>
<div class="viewcode-block" id="DataParallelExecutorManager.load_data_batch"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorManager.load_data_batch">[docs]</a> <span class="k">def</span> <span class="nf">load_data_batch</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="sd">"""Load data and labels into arrays."""</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">key</span> <span class="o">=</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">bucket_key</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp_bucket</span><span class="p">:</span>
<span class="c1"># create new bucket entry</span>
<span class="n">symbol</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">key</span><span class="p">)</span>
<span class="n">execgrp</span> <span class="o">=</span> <span class="n">DataParallelExecutorGroup</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_names</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ctx</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">slices</span><span class="p">,</span> <span class="n">data_batch</span><span class="p">,</span>
<span class="n">shared_group</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">execgrp_bucket</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">execgrp</span>
<span class="bp">self</span><span class="o">.</span><span class="n">curr_execgrp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp_bucket</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">curr_execgrp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">execgrp</span>
<span class="bp">self</span><span class="o">.</span><span class="n">curr_execgrp</span><span class="o">.</span><span class="n">load_data_batch</span><span class="p">(</span><span class="n">data_batch</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataParallelExecutorManager.forward"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorManager.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Run forward on the current executor."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">curr_execgrp</span><span class="o">.</span><span class="n">forward</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="DataParallelExecutorManager.backward"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorManager.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="sd">"""Run backward on the current executor."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">curr_execgrp</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span></div>
<div class="viewcode-block" id="DataParallelExecutorManager.update_metric"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor_manager.DataParallelExecutorManager.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">metric</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="sd">"""Update metric with the current executor."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">curr_execgrp</span><span class="o">.</span><span class="n">update_metric</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span></div></div>
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