# 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. import unittest import numpy as np from test_imperative_base import new_program_scope from utils import DyGraphProgramDescTracerTestHelper, is_equal_program import paddle import paddle.fluid as fluid import paddle.fluid.core as core import paddle.fluid.framework as framework from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph.nn import Embedding from paddle.fluid.framework import _in_legacy_dygraph, _test_eager_guard from paddle.fluid.optimizer import SGDOptimizer from paddle.jit import TracedLayer class SimpleLSTMRNN(fluid.Layer): def __init__( self, hidden_size, num_steps, num_layers=2, init_scale=0.1, dropout=None ): super().__init__() self._hidden_size = hidden_size self._num_layers = num_layers self._init_scale = init_scale self._dropout = dropout self._input = None self._num_steps = num_steps self.cell_array = [] self.hidden_array = [] self._create_parameter() def _create_parameter(self): self.weight_1_arr = [] self.weight_2_arr = [] self.bias_arr = [] self.mask_array = [] for i in range(self._num_layers): weight_1 = self.create_parameter( attr=fluid.ParamAttr( initializer=fluid.initializer.UniformInitializer( low=-self._init_scale, high=self._init_scale ) ), shape=[self._hidden_size * 2, self._hidden_size * 4], dtype="float32", default_initializer=fluid.initializer.UniformInitializer( low=-self._init_scale, high=self._init_scale ), ) self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1)) bias_1 = self.create_parameter( attr=fluid.ParamAttr( initializer=fluid.initializer.UniformInitializer( low=-self._init_scale, high=self._init_scale ) ), shape=[self._hidden_size * 4], dtype="float32", default_initializer=fluid.initializer.Constant(0.0), ) self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1)) def forward(self, input_embedding, init_hidden=None, init_cell=None): self.cell_array = [] self.hidden_array = [] for i in range(self._num_layers): pre_hidden = paddle.slice( init_hidden, axes=[0], starts=[i], ends=[i + 1] ) pre_cell = paddle.slice( init_cell, axes=[0], starts=[i], ends=[i + 1] ) pre_hidden = paddle.reshape( pre_hidden, shape=[-1, self._hidden_size] ) pre_cell = paddle.reshape(pre_cell, shape=[-1, self._hidden_size]) self.hidden_array.append(pre_hidden) self.cell_array.append(pre_cell) res = [] for index in range(self._num_steps): self._input = paddle.slice( input_embedding, axes=[1], starts=[index], ends=[index + 1] ) self._input = paddle.reshape( self._input, shape=[-1, self._hidden_size] ) for k in range(self._num_layers): pre_hidden = self.hidden_array[k] pre_cell = self.cell_array[k] weight_1 = self.weight_1_arr[k] bias = self.bias_arr[k] nn = fluid.layers.concat([self._input, pre_hidden], 1) gate_input = fluid.layers.matmul(x=nn, y=weight_1) gate_input = fluid.layers.elementwise_add(gate_input, bias) i, j, f, o = fluid.layers.split( gate_input, num_or_sections=4, dim=-1 ) c = pre_cell * paddle.nn.functional.sigmoid( f ) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j) m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o) self.hidden_array[k] = m self.cell_array[k] = c self._input = m if self._dropout is not None and self._dropout > 0.0: self._input = fluid.layers.dropout( self._input, dropout_prob=self._dropout, dropout_implementation='upscale_in_train', ) res.append( paddle.reshape(self._input, shape=[1, -1, self._hidden_size]) ) real_res = fluid.layers.concat(res, 0) real_res = paddle.transpose(x=real_res, perm=[1, 0, 2]) last_hidden = fluid.layers.concat(self.hidden_array, 1) last_hidden = paddle.reshape( last_hidden, shape=[-1, self._num_layers, self._hidden_size] ) last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2]) last_cell = fluid.layers.concat(self.cell_array, 1) last_cell = paddle.reshape( last_cell, shape=[-1, self._num_layers, self._hidden_size] ) last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2]) return real_res, last_hidden, last_cell class PtbModel(fluid.Layer): def __init__( self, hidden_size, vocab_size, num_layers=2, num_steps=20, init_scale=0.1, is_sparse=False, dropout=None, ): super().__init__() self.hidden_size = hidden_size self.vocab_size = vocab_size self.init_scale = init_scale self.num_layers = num_layers self.num_steps = num_steps self.dropout = dropout self.simple_lstm_rnn = SimpleLSTMRNN( hidden_size, num_steps, num_layers=num_layers, init_scale=init_scale, dropout=dropout, ) self.embedding = Embedding( size=[vocab_size, hidden_size], dtype='float32', is_sparse=is_sparse, param_attr=fluid.ParamAttr( name='embedding_para', initializer=fluid.initializer.UniformInitializer( low=-init_scale, high=init_scale ), ), ) self.softmax_weight = self.create_parameter( attr=fluid.ParamAttr(), shape=[self.hidden_size, self.vocab_size], dtype="float32", default_initializer=fluid.initializer.UniformInitializer( low=-self.init_scale, high=self.init_scale ), ) self.softmax_bias = self.create_parameter( attr=fluid.ParamAttr(), shape=[self.vocab_size], dtype="float32", default_initializer=fluid.initializer.UniformInitializer( low=-self.init_scale, high=self.init_scale ), ) def forward(self, input, label, init_hidden, init_cell): init_h = paddle.reshape( init_hidden, shape=[self.num_layers, -1, self.hidden_size] ) init_c = paddle.reshape( init_cell, shape=[self.num_layers, -1, self.hidden_size] ) x_emb = self.embedding(input) x_emb = paddle.reshape( x_emb, shape=[-1, self.num_steps, self.hidden_size] ) if self.dropout is not None and self.dropout > 0.0: x_emb = fluid.layers.dropout( x_emb, dropout_prob=self.drop_out, dropout_implementation='upscale_in_train', ) rnn_out, last_hidden, last_cell = self.simple_lstm_rnn( x_emb, init_h, init_c ) rnn_out = paddle.reshape( rnn_out, shape=[-1, self.num_steps, self.hidden_size] ) projection = fluid.layers.matmul(rnn_out, self.softmax_weight) projection = fluid.layers.elementwise_add(projection, self.softmax_bias) projection = paddle.reshape(projection, shape=[-1, self.vocab_size]) loss = fluid.layers.softmax_with_cross_entropy( logits=projection, label=label, soft_label=False ) loss = paddle.reshape(loss, shape=[-1, self.num_steps]) loss = paddle.mean(loss, axis=[0]) loss = paddle.sum(loss) return loss, last_hidden, last_cell class TestDygraphPtbRnn(unittest.TestCase): def func_test_ptb_rnn(self): for is_sparse in [True, False]: self.ptb_rnn_cpu_float32(is_sparse) def test_ptb_rnn(self): with _test_eager_guard(): self.func_test_ptb_rnn() self.func_test_ptb_rnn() def ptb_rnn_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 traced_layer = None with fluid.dygraph.guard(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # 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 helper = DyGraphProgramDescTracerTestHelper(self) program = 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') 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) if i % 5 == 0 and _in_legacy_dygraph(): outs, traced_layer = TracedLayer.trace( ptb_model, [x, y, init_hidden, init_cell] ) outs_static = traced_layer([x, y, init_hidden, init_cell]) helper.assertEachVar(outs, outs_static) if program is not None: self.assertTrue( is_equal_program(traced_layer.program, program) ) program = traced_layer.program traced_layer.save_inference_model( './infe_imperative_ptb_rnn', feed=list(range(4)) ) else: outs = ptb_model(x, y, init_hidden, init_cell) dy_loss, last_hidden, last_cell = outs 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(): paddle.seed(seed) paddle.framework.random._manual_program_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], 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] np.testing.assert_array_equal(static_loss_value, dy_loss_value) np.testing.assert_array_equal( static_last_cell_value, dy_last_cell_value ) np.testing.assert_array_equal( static_last_hidden_value, dy_last_hidden_value ) for key, value in static_param_init.items(): np.testing.assert_array_equal(value, dy_param_init[key]) for key, value in static_param_updated.items(): np.testing.assert_array_equal(value, dy_param_updated[key]) if __name__ == '__main__': unittest.main()