# Copyright (c) 2019 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 numpy import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core from paddle.fluid.executor import Executor from paddle.fluid import framework from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell, BeamSearchDecoder, dynamic_decode from paddle.fluid.layers import rnn as dynamic_rnn from paddle.fluid import contrib from paddle.fluid.contrib.layers import basic_lstm import numpy as np class EncoderCell(RNNCell): def __init__(self, num_layers, hidden_size, dropout_prob=0.): self.num_layers = num_layers self.hidden_size = hidden_size self.dropout_prob = dropout_prob self.lstm_cells = [] for i in range(num_layers): self.lstm_cells.append(LSTMCell(hidden_size)) def call(self, step_input, states): new_states = [] for i in range(self.num_layers): out, new_state = self.lstm_cells[i](step_input, states[i]) step_input = layers.dropout( out, self.dropout_prob) if self.dropout_prob > 0 else out new_states.append(new_state) return step_input, new_states @property def state_shape(self): return [cell.state_shape for cell in self.lstm_cells] class DecoderCell(RNNCell): def __init__(self, num_layers, hidden_size, dropout_prob=0.): self.num_layers = num_layers self.hidden_size = hidden_size self.dropout_prob = dropout_prob self.lstm_cells = [] for i in range(num_layers): self.lstm_cells.append(LSTMCell(hidden_size)) def attention(self, hidden, encoder_output, encoder_padding_mask): query = layers.fc(hidden, size=encoder_output.shape[-1], bias_attr=False) attn_scores = layers.matmul( layers.unsqueeze(query, [1]), encoder_output, transpose_y=True) if encoder_padding_mask is not None: attn_scores = layers.elementwise_add(attn_scores, encoder_padding_mask) attn_scores = layers.softmax(attn_scores) attn_out = layers.squeeze( layers.matmul(attn_scores, encoder_output), [1]) attn_out = layers.concat([attn_out, hidden], 1) attn_out = layers.fc(attn_out, size=self.hidden_size, bias_attr=False) return attn_out def call(self, step_input, states, encoder_output, encoder_padding_mask=None): lstm_states, input_feed = states new_lstm_states = [] step_input = layers.concat([step_input, input_feed], 1) for i in range(self.num_layers): out, new_lstm_state = self.lstm_cells[i](step_input, lstm_states[i]) step_input = layers.dropout( out, self.dropout_prob) if self.dropout_prob > 0 else out new_lstm_states.append(new_lstm_state) out = self.attention(step_input, encoder_output, encoder_padding_mask) return out, [new_lstm_states, out] class TestDynamicDecode(unittest.TestCase): def setUp(self): self.batch_size = 4 self.input_size = 16 self.hidden_size = 16 self.seq_len = 4 def test_run(self): start_token = 0 end_token = 1 src_vocab_size = 10 trg_vocab_size = 10 num_layers = 1 hidden_size = self.hidden_size beam_size = 8 max_length = self.seq_len src = layers.data(name="src", shape=[-1, 1], dtype='int64') src_len = layers.data(name="src_len", shape=[-1], dtype='int64') trg = layers.data(name="trg", shape=[-1, 1], dtype='int64') trg_len = layers.data(name="trg_len", shape=[-1], dtype='int64') src_embeder = lambda x: fluid.embedding( x, size=[src_vocab_size, hidden_size], param_attr=fluid.ParamAttr(name="src_embedding")) trg_embeder = lambda x: fluid.embedding( x, size=[trg_vocab_size, hidden_size], param_attr=fluid.ParamAttr(name="trg_embedding")) # use basic_lstm encoder_cell = EncoderCell(num_layers, hidden_size) encoder_output, encoder_final_state = dynamic_rnn( cell=encoder_cell, inputs=src_embeder(src), sequence_length=src_len, is_reverse=False) src_mask = layers.sequence_mask( src_len, maxlen=layers.shape(src)[1], dtype='float32') encoder_padding_mask = (src_mask - 1.0) * 1000000000 encoder_padding_mask = layers.unsqueeze(encoder_padding_mask, [1]) decoder_cell = DecoderCell(num_layers, hidden_size) decoder_initial_states = [ encoder_final_state, decoder_cell.get_initial_states( batch_ref=encoder_output, shape=[hidden_size]) ] decoder_output, _ = dynamic_rnn( cell=decoder_cell, inputs=trg_embeder(trg), initial_states=decoder_initial_states, sequence_length=None, encoder_output=encoder_output, encoder_padding_mask=encoder_padding_mask) output_layer = lambda x: layers.fc(x, size=trg_vocab_size, num_flatten_dims=len(x.shape) - 1, param_attr=fluid.ParamAttr( name="output_w"), bias_attr=False) # inference encoder_output = BeamSearchDecoder.tile_beam_merge_with_batch( encoder_output, beam_size) encoder_padding_mask = BeamSearchDecoder.tile_beam_merge_with_batch( encoder_padding_mask, beam_size) beam_search_decoder = BeamSearchDecoder( decoder_cell, start_token, end_token, beam_size, embedding_fn=trg_embeder, output_fn=output_layer) outputs, _ = dynamic_decode( beam_search_decoder, inits=decoder_initial_states, max_step_num=max_length, encoder_output=encoder_output, encoder_padding_mask=encoder_padding_mask) if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() exe = Executor(place) exe.run(framework.default_startup_program()) src_np = np.random.randint( 0, src_vocab_size, (self.batch_size, max_length)).astype('int64') src_len_np = np.ones(self.batch_size, dtype='int64') * max_length trg_np = np.random.randint( 0, trg_vocab_size, (self.batch_size, max_length)).astype('int64') trg_len_np = np.ones(self.batch_size, dtype='int64') * max_length out = exe.run(feed={ 'src': src_np, 'src_len': src_len_np, 'trg': trg_np, 'trg_len': trg_len_np }, fetch_list=[outputs]) self.assertTrue(out[0].shape[0] == self.batch_size) self.assertTrue(out[0].shape[1] <= max_length + 1) self.assertTrue(out[0].shape[2] == beam_size) if __name__ == '__main__': unittest.main()