# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.dygraph.nn import Embedding import paddle.fluid.framework as framework from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid.dygraph.base import to_variable from test_imperative_base import new_program_scope from test_imperative_ptb_rnn import PtbModel import numpy as np import six class TestDygraphPtbRnnSortGradient(unittest.TestCase): def test_ptb_rnn_sort_gradient(self): for is_sparse in [True, False]: self.ptb_rnn_sort_gradient_cpu_float32(is_sparse) def ptb_rnn_sort_gradient_cpu_float32(self, is_sparse): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 with fluid.dygraph.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed fluid.set_flags({'FLAGS_sort_sum_gradient': True}) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, is_sparse=is_sparse) sgd = SGDOptimizer( learning_rate=1e-3, parameter_list=ptb_model.parameters()) dy_param_updated = dict() dy_param_init = dict() dy_loss = None last_hidden = None last_cell = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32') init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32') x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden, init_cell) if i == 0: for param in ptb_model.parameters(): dy_param_init[param.name] = param.numpy() dy_loss.backward() sgd.minimize(dy_loss) ptb_model.clear_gradients() if i == batch_num - 1: for param in ptb_model.parameters(): dy_param_updated[param.name] = param.numpy() dy_loss_value = dy_loss.numpy() dy_last_cell_value = last_cell.numpy() dy_last_hidden_value = last_hidden.numpy() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, is_sparse=is_sparse) exe = fluid.Executor(fluid.CPUPlace( ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) sgd = SGDOptimizer(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps, 1], dtype='int64') y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32') init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32') static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell) sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() static_param_name_list = list() for param in ptb_model.parameters(): static_param_name_list.append(param.name) out = exe.run(framework.default_startup_program(), fetch_list=static_param_name_list) for i in range(len(static_param_name_list)): static_param_init[static_param_name_list[i]] = out[i] static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32') init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32') fetch_list = [static_loss, static_last_hidden, static_last_cell] fetch_list.extend(static_param_name_list) out = exe.run(fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data }, fetch_list=fetch_list) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] if i == batch_num - 1: for k in range(3, len(out)): static_param_updated[static_param_name_list[k - 3]] = out[k] self.assertTrue(np.array_equal(static_loss_value, dy_loss_value)) self.assertTrue( np.array_equal(static_last_cell_value, dy_last_cell_value)) self.assertTrue( np.array_equal(static_last_hidden_value, dy_last_hidden_value)) for key, value in six.iteritems(static_param_init): self.assertTrue(np.array_equal(value, dy_param_init[key])) for key, value in six.iteritems(static_param_updated): self.assertTrue(np.array_equal(value, dy_param_updated[key])) if __name__ == '__main__': unittest.main()