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| <h1>Source code for mxnet.executor</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=invalid-name, protected-access, too-many-locals, too-many-arguments</span> |
| <span class="sd">"""Symbolic Executor component of MXNet."""</span> |
| <span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span> |
| |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">copy</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">_LIB</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">mx_uint</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">ExecutorHandle</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">c_handle_array</span><span class="p">,</span> <span class="n">py_str</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="k">import</span> <span class="n">NDArray</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="k">import</span> <span class="n">_ndarray_cls</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="k">import</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">nd</span> |
| |
| <span class="c1"># those functions are not used here, we just import them to keep backward compatibility</span> |
| <span class="c1"># in case the end user calls them, as they originally lives here</span> |
| <span class="c1"># pylint: disable=unused-import</span> |
| <span class="kn">from</span> <span class="nn">.executor_manager</span> <span class="k">import</span> <span class="n">_split_input_slice</span><span class="p">,</span> <span class="n">_check_arguments</span><span class="p">,</span> <span class="n">_load_data</span><span class="p">,</span> <span class="n">_load_label</span> |
| |
| <span class="k">def</span> <span class="nf">_monitor_callback_wrapper</span><span class="p">(</span><span class="n">callback</span><span class="p">):</span> |
| <span class="sd">"""A wrapper for the user-defined handle."""</span> |
| <span class="k">def</span> <span class="nf">callback_handle</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span> |
| <span class="sd">""" ctypes function """</span> |
| <span class="n">callback</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">callback_handle</span> |
| |
| <div class="viewcode-block" id="Executor"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor.Executor">[docs]</a><span class="k">class</span> <span class="nc">Executor</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""Executor is the object providing efficient symbolic graph execution and optimization.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # typical approach to create an executor is to bind symbol</span> |
| <span class="sd"> >>> a = mx.sym.Variable('a')</span> |
| <span class="sd"> >>> b = mx.sym.Variable('b')</span> |
| <span class="sd"> >>> c = 2 * a + b</span> |
| <span class="sd"> >>> texec = c.bind(mx.cpu(), {'a': mx.nd.array([1,2]), 'b':mx.nd.array([2,3])})</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">handle</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">grad_req</span><span class="p">,</span> <span class="n">group2ctx</span><span class="p">):</span> |
| <span class="sd">"""Constructor, used Symbol.bind and Symbol.simple_bind instead.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> handle: ExecutorHandle</span> |
| <span class="sd"> ExecutorHandle generated by calling `bind`.</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> Symbol.bind : to create executor.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">ExecutorHandle</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Handle type error"</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span> <span class="o">=</span> <span class="n">handle</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">arg_arrays</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">grad_arrays</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_outputs</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">symbol</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ctx</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">grad_req</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">group2ctx</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorFree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">))</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">_get_dict</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">ndarrays</span><span class="p">):</span> |
| <span class="sd">"""Get the dictionary given name and ndarray pairs."""</span> |
| <span class="n">nset</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">nm</span> <span class="ow">in</span> <span class="n">names</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">nm</span> <span class="ow">in</span> <span class="n">nset</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Duplicate names detected, </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">names</span><span class="p">))</span> |
| <span class="n">nset</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nm</span><span class="p">)</span> |
| <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">ndarrays</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">_get_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""List all the output NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> A list of ndarray bound to the heads of executor.</span> |
| <span class="sd"> """</span> |
| <span class="n">out_size</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">()</span> |
| <span class="n">handles</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">)()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorOutputs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">out_size</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handles</span><span class="p">)))</span> |
| <span class="n">num_output</span> <span class="o">=</span> <span class="n">out_size</span><span class="o">.</span><span class="n">value</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">handles</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_output</span><span class="p">)]</span> |
| <span class="k">return</span> <span class="n">outputs</span> |
| |
| <div class="viewcode-block" id="Executor.forward"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor.Executor.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Calculate the outputs specified by the bound symbol.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> is_train: bool, optional</span> |
| <span class="sd"> Whether this forward is for evaluation purpose. If True,</span> |
| <span class="sd"> a backward call is expected to follow.</span> |
| |
| <span class="sd"> **kwargs</span> |
| <span class="sd"> Additional specification of input arguments.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # doing forward by specifying data</span> |
| <span class="sd"> >>> texec.forward(is_train=True, data=mydata)</span> |
| <span class="sd"> >>> # doing forward by not specifying things, but copy to the executor before hand</span> |
| <span class="sd"> >>> mydata.copyto(texec.arg_dict['data'])</span> |
| <span class="sd"> >>> texec.forward(is_train=True)</span> |
| <span class="sd"> >>> # doing forward by specifying data and get outputs</span> |
| <span class="sd"> >>> outputs = texec.forward(is_train=True, data=mydata)</span> |
| <span class="sd"> >>> print(outputs[0].asnumpy())</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">arg_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="p">(</span><span class="n">NDArray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'only accept keyword argument of NDArrays and numpy.ndarray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">arg_dict</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'Unknown argument </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Shape not match! Argument </span><span class="si">%s</span><span class="s1">, need: </span><span class="si">%s</span><span class="s1">, received: </span><span class="si">%s</span><span class="s1">'</span> |
| <span class="o">%</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span> |
| <span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">array</span> |
| |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorForward</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">is_train</span><span class="p">))))</span> |
| |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span></div> |
| |
| <div class="viewcode-block" id="Executor.backward"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor.Executor.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_grads</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="sd">"""Do backward pass to get the gradient of arguments.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> out_grads : NDArray or list of NDArray or dict of str to NDArray, optional</span> |
| <span class="sd"> Gradient on the outputs to be propagated back.</span> |
| <span class="sd"> This parameter is only needed when bind is called</span> |
| <span class="sd"> on outputs that are not a loss function.</span> |
| <span class="sd"> is_train : bool, default True</span> |
| <span class="sd"> Whether this backward is for training or inference. Note that in rare</span> |
| <span class="sd"> cases you want to call backward with is_train=False to get gradient</span> |
| <span class="sd"> during inference.</span> |
| |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # Example for binding on loss function symbol, which gives the loss value of the model.</span> |
| <span class="sd"> >>> # Equivalently it gives the head gradient for backward pass.</span> |
| <span class="sd"> >>> # In this example the built-in SoftmaxOutput is used as loss function.</span> |
| <span class="sd"> >>> # MakeLoss can be used to define customized loss function symbol.</span> |
| <span class="sd"> >>> net = mx.sym.Variable('data')</span> |
| <span class="sd"> >>> net = mx.sym.FullyConnected(net, name='fc', num_hidden=6)</span> |
| <span class="sd"> >>> net = mx.sym.Activation(net, name='relu', act_type="relu")</span> |
| <span class="sd"> >>> net = mx.sym.SoftmaxOutput(net, name='softmax')</span> |
| |
| <span class="sd"> >>> args = {'data': mx.nd.ones((1, 4)), 'fc_weight': mx.nd.ones((6, 4)),</span> |
| <span class="sd"> >>> 'fc_bias': mx.nd.array((1, 4, 4, 4, 5, 6)), 'softmax_label': mx.nd.ones((1))}</span> |
| <span class="sd"> >>> args_grad = {'fc_weight': mx.nd.zeros((6, 4)), 'fc_bias': mx.nd.zeros((6))}</span> |
| <span class="sd"> >>> texec = net.bind(ctx=mx.cpu(), args=args, args_grad=args_grad)</span> |
| <span class="sd"> >>> out = texec.forward(is_train=True)[0].copy()</span> |
| <span class="sd"> >>> print out.asnumpy()</span> |
| <span class="sd"> [[ 0.00378404 0.07600445 0.07600445 0.07600445 0.20660152 0.5616011 ]]</span> |
| <span class="sd"> >>> texec.backward()</span> |
| <span class="sd"> >>> print(texec.grad_arrays[1].asnumpy())</span> |
| <span class="sd"> [[ 0.00378404 0.00378404 0.00378404 0.00378404]</span> |
| <span class="sd"> [-0.92399555 -0.92399555 -0.92399555 -0.92399555]</span> |
| <span class="sd"> [ 0.07600445 0.07600445 0.07600445 0.07600445]</span> |
| <span class="sd"> [ 0.07600445 0.07600445 0.07600445 0.07600445]</span> |
| <span class="sd"> [ 0.20660152 0.20660152 0.20660152 0.20660152]</span> |
| <span class="sd"> [ 0.5616011 0.5616011 0.5616011 0.5616011 ]]</span> |
| <span class="sd"> >>></span> |
| <span class="sd"> >>> # Example for binding on non-loss function symbol.</span> |
| <span class="sd"> >>> # Here the binding symbol is neither built-in loss function</span> |
| <span class="sd"> >>> # nor customized loss created by MakeLoss.</span> |
| <span class="sd"> >>> # As a result the head gradient is not automatically provided.</span> |
| <span class="sd"> >>> a = mx.sym.Variable('a')</span> |
| <span class="sd"> >>> b = mx.sym.Variable('b')</span> |
| <span class="sd"> >>> # c is not a loss function symbol</span> |
| <span class="sd"> >>> c = 2 * a + b</span> |
| <span class="sd"> >>> args = {'a': mx.nd.array([1,2]), 'b':mx.nd.array([2,3])}</span> |
| <span class="sd"> >>> args_grad = {'a': mx.nd.zeros((2)), 'b': mx.nd.zeros((2))}</span> |
| <span class="sd"> >>> texec = c.bind(ctx=mx.cpu(), args=args, args_grad=args_grad)</span> |
| <span class="sd"> >>> out = texec.forward(is_train=True)[0].copy()</span> |
| <span class="sd"> >>> print(out.asnumpy())</span> |
| <span class="sd"> [ 4. 7.]</span> |
| <span class="sd"> >>> # out_grads is the head gradient in backward pass.</span> |
| <span class="sd"> >>> # Here we define 'c' as loss function.</span> |
| <span class="sd"> >>> # Then 'out' is passed as head gradient of backward pass.</span> |
| <span class="sd"> >>> texec.backward(out)</span> |
| <span class="sd"> >>> print(texec.grad_arrays[0].asnumpy())</span> |
| <span class="sd"> [ 8. 14.]</span> |
| <span class="sd"> >>> print(texec.grad_arrays[1].asnumpy())</span> |
| <span class="sd"> [ 4. 7.]</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">out_grads</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out_grads</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">out_grads</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out_grads</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">out_grads</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()]</span> |
| |
| <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">out_grads</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"inputs must be NDArray"</span><span class="p">)</span> |
| <span class="n">ndarray</span> <span class="o">=</span> <span class="n">c_handle_array</span><span class="p">(</span><span class="n">out_grads</span><span class="p">)</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorBackwardEx</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">out_grads</span><span class="p">)),</span> |
| <span class="n">ndarray</span><span class="p">,</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">is_train</span><span class="p">)))</span></div> |
| |
| <div class="viewcode-block" id="Executor.set_monitor_callback"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor.Executor.set_monitor_callback">[docs]</a> <span class="k">def</span> <span class="nf">set_monitor_callback</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">):</span> |
| <span class="sd">"""Install callback for monitor.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> callback : function</span> |
| <span class="sd"> Takes a string and an NDArrayHandle.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> def mon_callback(*args, **kwargs):</span> |
| <span class="sd"> >>> print("Do your stuff here.")</span> |
| <span class="sd"> >>></span> |
| <span class="sd"> >>> texe.set_monitor_callback(mon_callback)</span> |
| <span class="sd"> """</span> |
| <span class="n">cb_type</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">CFUNCTYPE</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span> <span class="o">=</span> <span class="n">cb_type</span><span class="p">(</span><span class="n">_monitor_callback_wrapper</span><span class="p">(</span><span class="n">callback</span><span class="p">))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorSetMonitorCallback</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">))</span></div> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">arg_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get dictionary representation of argument arrrays.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> arg_dict : dict of str to NDArray</span> |
| <span class="sd"> The dictionary that maps the names of arguments to NDArrays.</span> |
| |
| <span class="sd"> Raises</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> ValueError : if there are duplicated names in the arguments.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_arrays</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">grad_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get dictionary representation of gradient arrays.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> grad_dict : dict of str to NDArray</span> |
| <span class="sd"> The dictionary that maps name of arguments to gradient arrays.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">aux_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get dictionary representation of auxiliary states arrays.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> aux_dict : dict of str to NDArray</span> |
| <span class="sd"> The dictionary that maps name of auxiliary states to NDArrays.</span> |
| |
| <span class="sd"> Raises</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> ValueError : if there are duplicated names in the auxiliary states.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">output_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get dictionary representation of output arrays.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> output_dict : dict of str to NDArray</span> |
| <span class="sd"> The dictionary that maps name of output names to NDArrays.</span> |
| |
| <span class="sd"> Raises</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> ValueError : if there are duplicated names in the outputs.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> |
| |
| <div class="viewcode-block" id="Executor.copy_params_from"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor.Executor.copy_params_from">[docs]</a> <span class="k">def</span> <span class="nf">copy_params_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_extra_params</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="sd">"""Copy parameters from arg_params, aux_params into executor's internal array.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> arg_params : dict of str to NDArray</span> |
| <span class="sd"> Parameters, dict of name to NDArray of arguments.</span> |
| |
| <span class="sd"> aux_params : dict of str to NDArray, optional</span> |
| <span class="sd"> Parameters, dict of name to NDArray of auxiliary states.</span> |
| |
| <span class="sd"> allow_extra_params : boolean, optional</span> |
| <span class="sd"> Whether allow extra parameters that are not needed by symbol.</span> |
| <span class="sd"> If this is True, no error will be thrown when arg_params or aux_params</span> |
| <span class="sd"> contain extra parameters that is not needed by the executor.</span> |
| |
| <span class="sd"> Raises</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> ValueError</span> |
| <span class="sd"> If there is additional parameters in the dict but ``allow_extra_params=False``.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # set parameters with existing model checkpoint</span> |
| <span class="sd"> >>> model_prefix = 'mx_mlp'</span> |
| <span class="sd"> >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, 0)</span> |
| <span class="sd"> >>> texec.copy_params_from(arg_params, aux_params)</span> |
| <span class="sd"> """</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">:</span> |
| <span class="n">dst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="ow">not</span> <span class="n">allow_extra_params</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Find name </span><span class="se">\"</span><span class="si">%s</span><span class="se">\"</span><span class="s1"> that is not in the arguments'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">aux_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> |
| |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">aux_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">:</span> |
| <span class="n">dst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="ow">not</span> <span class="n">allow_extra_params</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Find name </span><span class="si">%s</span><span class="s1"> that is not in the auxiliary states'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Executor.reshape"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor.Executor.reshape">[docs]</a> <span class="k">def</span> <span class="nf">reshape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">partial_shaping</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_up_sizing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Return a new executor with the same symbol and shared memory,</span> |
| <span class="sd"> but different input/output shapes.</span> |
| <span class="sd"> For runtime reshaping, variable length sequences, etc.</span> |
| <span class="sd"> The returned executor shares state with the current one,</span> |
| <span class="sd"> and cannot be used in parallel with it.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> partial_shaping : bool</span> |
| <span class="sd"> Whether to allow changing the shape of unspecified arguments.</span> |
| <span class="sd"> allow_up_sizing : bool</span> |
| <span class="sd"> Whether to allow allocating new ndarrays that's larger than the original.</span> |
| <span class="sd"> kwargs : dict of string to tuple of int</span> |
| <span class="sd"> New shape for arguments.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> exec : Executor</span> |
| <span class="sd"> A new executor that shares memory with self.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a = mx.sym.Variable('a')</span> |
| <span class="sd"> >>> b = mx.sym.Variable('b')</span> |
| <span class="sd"> >>> c = 2 * a + b</span> |
| <span class="sd"> >>> texec = c.bind(mx.cpu(), {'a': mx.nd.zeros((2, 1)), 'b': mx.nd.ones((2,1))})</span> |
| <span class="sd"> >>> new_shape = {'a': (4, 2), 'b': (4, 2)}</span> |
| <span class="sd"> >>> texec.reshape(allow_up_sizing=True, **new_shape)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable=too-many-branches</span> |
| <span class="n">arg_shapes</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">aux_shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">infer_shape</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">arg_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Insufficient argument shapes provided."</span><span class="p">)</span> |
| |
| <span class="n">new_arg_dict</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="n">new_grad_dict</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">name</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">_symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()):</span> |
| <span class="n">new_shape</span> <span class="o">=</span> <span class="n">arg_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="n">arr</span> <span class="o">=</span> <span class="bp">self</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="n">darr</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_arrays</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</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">if</span> <span class="n">partial_shaping</span> <span class="ow">or</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">or</span> <span class="n">new_shape</span> <span class="o">==</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</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">new_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">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">allow_up_sizing</span><span class="p">,</span> <span class="s2">"New shape of arg:</span><span class="si">%s</span><span class="s2"> larger than original. "</span><span class="o">%</span><span class="n">name</span> <span class="o">+</span> \ |
| <span class="s2">"First making a big executor and then down sizing it "</span> <span class="o">+</span> \ |
| <span class="s2">"is more efficient than the reverse."</span> <span class="o">+</span> \ |
| <span class="s2">"If you really want to up size, set allow_up_sizing=True "</span> <span class="o">+</span> \ |
| <span class="s2">"to enable allocation of new arrays."</span> |
| <span class="n">new_arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">new_shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">darr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">new_grad_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">new_shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">darr</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">new_arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">darr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">new_grad_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">darr</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">"Shape of unspecified array arg:</span><span class="si">%s</span><span class="s2"> changed. "</span><span class="o">%</span><span class="n">name</span> <span class="o">+</span> \ |
| <span class="s2">"This can cause the new executor to not share parameters "</span> <span class="o">+</span> \ |
| <span class="s2">"with the old one. Please check for error in network."</span> <span class="o">+</span>\ |
| <span class="s2">"If this is intended, set partial_shaping=True to suppress this warning."</span><span class="p">)</span> |
| |
| <span class="n">new_aux_dict</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">new_shape</span><span class="p">,</span> <span class="n">arr</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">_symbol</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">(),</span> |
| <span class="n">aux_shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">partial_shaping</span> <span class="ow">or</span> <span class="n">new_shape</span> <span class="o">==</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</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">new_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">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">allow_up_sizing</span><span class="p">,</span> <span class="s2">"New shape of arg:</span><span class="si">%s</span><span class="s2"> larger than original. "</span><span class="o">%</span><span class="n">name</span> <span class="o">+</span> \ |
| <span class="s2">"First making a big executor and then down sizing it "</span> <span class="o">+</span> \ |
| <span class="s2">"is more efficient than the reverse."</span> <span class="o">+</span> \ |
| <span class="s2">"If you really want to up size, set allow_up_sizing=True "</span> <span class="o">+</span> \ |
| <span class="s2">"to enable allocation of new arrays."</span> |
| <span class="n">new_aux_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">new_shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">new_aux_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">"Shape of unspecified array aux:</span><span class="si">%s</span><span class="s2"> changed. "</span><span class="o">%</span><span class="n">name</span> <span class="o">+</span> \ |
| <span class="s2">"This can cause the new executor to not share parameters "</span> <span class="o">+</span> \ |
| <span class="s2">"with the old one. Please check for error in network."</span> <span class="o">+</span>\ |
| <span class="s2">"If this is intended, set partial_shaping=True to suppress this warning."</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">bind</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="n">args</span><span class="o">=</span><span class="n">new_arg_dict</span><span class="p">,</span> |
| <span class="n">args_grad</span><span class="o">=</span><span class="n">new_grad_dict</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="n">aux_states</span><span class="o">=</span><span class="n">new_aux_dict</span><span class="p">,</span> |
| <span class="n">group2ctx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span><span class="p">,</span> |
| <span class="n">shared_exec</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Executor.debug_str"><a class="viewcode-back" href="../../api/python/executor/executor.html#mxnet.executor.Executor.debug_str">[docs]</a> <span class="k">def</span> <span class="nf">debug_str</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Get a debug string about internal execution plan.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> debug_str : string</span> |
| <span class="sd"> Debug string of the executor.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a = mx.sym.Variable('a')</span> |
| <span class="sd"> >>> b = mx.sym.sin(a)</span> |
| <span class="sd"> >>> c = 2 * a + b</span> |
| <span class="sd"> >>> texec = c.bind(mx.cpu(), {'a': mx.nd.array([1,2]), 'b':mx.nd.array([2,3])})</span> |
| <span class="sd"> >>> print(texec.debug_str())</span> |
| <span class="sd"> Symbol Outputs:</span> |
| <span class="sd"> output[0]=_plus0(0)</span> |
| <span class="sd"> Variable:a</span> |
| <span class="sd"> --------------------</span> |
| <span class="sd"> Op:_mul_scalar, Name=_mulscalar0</span> |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> arg[0]=a(0) version=0</span> |
| <span class="sd"> Attrs:</span> |
| <span class="sd"> scalar=2</span> |
| <span class="sd"> --------------------</span> |
| <span class="sd"> Op:sin, Name=sin0</span> |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> arg[0]=a(0) version=0</span> |
| <span class="sd"> --------------------</span> |
| <span class="sd"> Op:elemwise_add, Name=_plus0</span> |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> arg[0]=_mulscalar0(0)</span> |
| <span class="sd"> arg[1]=sin0(0)</span> |
| <span class="sd"> Total 0 MB allocated</span> |
| <span class="sd"> Total 11 TempSpace resource requested</span> |
| <span class="sd"> """</span> |
| <span class="n">debug_str</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorPrint</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">debug_str</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">py_str</span><span class="p">(</span><span class="n">debug_str</span><span class="o">.</span><span class="n">value</span><span class="p">)</span></div></div> |
| </pre></div> |
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| Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
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