# 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 numpy as np import paddle.v2 as paddle import paddle.v2.fluid as fluid import paddle.v2.fluid.core as core import paddle.v2.fluid.framework as framework import paddle.v2.fluid.layers as layers import contextlib import math import sys import unittest from paddle.v2.fluid.executor import Executor dict_size = 30000 source_dict_dim = target_dict_dim = dict_size src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size) hidden_dim = 32 embedding_dim = 16 batch_size = 10 max_length = 50 topk_size = 50 encoder_size = decoder_size = hidden_dim IS_SPARSE = True USE_PEEPHOLES = False def bi_lstm_encoder(input_seq, hidden_size): input_forward_proj = fluid.layers.fc(input=input_seq, size=hidden_size * 4, bias_attr=True) forward, _ = fluid.layers.dynamic_lstm( input=input_forward_proj, size=hidden_size * 4, use_peepholes=USE_PEEPHOLES) input_backward_proj = fluid.layers.fc(input=input_seq, size=hidden_size * 4, bias_attr=True) backward, _ = fluid.layers.dynamic_lstm( input=input_backward_proj, size=hidden_size * 4, is_reverse=True, use_peepholes=USE_PEEPHOLES) forward_last = fluid.layers.sequence_last_step(input=forward) backward_first = fluid.layers.sequence_first_step(input=backward) return forward_last, backward_first # FIXME(peterzhang2029): Replace this function with the lstm_unit_op. def lstm_step(x_t, hidden_t_prev, cell_t_prev, size): def linear(inputs): return fluid.layers.fc(input=inputs, size=size, bias_attr=True) forget_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t])) input_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t])) output_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t])) cell_tilde = fluid.layers.tanh(x=linear([hidden_t_prev, x_t])) cell_t = fluid.layers.sums(input=[ fluid.layers.elementwise_mul( x=forget_gate, y=cell_t_prev), fluid.layers.elementwise_mul( x=input_gate, y=cell_tilde) ]) hidden_t = fluid.layers.elementwise_mul( x=output_gate, y=fluid.layers.tanh(x=cell_t)) return hidden_t, cell_t def lstm_decoder_without_attention(target_embedding, decoder_boot, context, decoder_size): rnn = fluid.layers.DynamicRNN() cell_init = fluid.layers.fill_constant_batch_size_like( input=decoder_boot, value=0.0, shape=[-1, decoder_size], dtype='float32') cell_init.stop_gradient = False with rnn.block(): current_word = rnn.step_input(target_embedding) context = rnn.static_input(context) hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True) cell_mem = rnn.memory(init=cell_init) decoder_inputs = fluid.layers.concat( input=[context, current_word], axis=1) h, c = lstm_step(decoder_inputs, hidden_mem, cell_mem, decoder_size) rnn.update_memory(hidden_mem, h) rnn.update_memory(cell_mem, c) out = fluid.layers.fc(input=h, size=target_dict_dim, bias_attr=True, act='softmax') rnn.output(out) return rnn() def seq_to_seq_net(): """Construct a seq2seq network.""" src_word_idx = fluid.layers.data( name='source_sequence', shape=[1], dtype='int64', lod_level=1) src_embedding = fluid.layers.embedding( input=src_word_idx, size=[source_dict_dim, embedding_dim], dtype='float32') src_forward_last, src_backward_first = bi_lstm_encoder( input_seq=src_embedding, hidden_size=encoder_size) encoded_vector = fluid.layers.concat( input=[src_forward_last, src_backward_first], axis=1) decoder_boot = fluid.layers.fc(input=src_backward_first, size=decoder_size, bias_attr=False, act='tanh') trg_word_idx = fluid.layers.data( name='target_sequence', shape=[1], dtype='int64', lod_level=1) trg_embedding = fluid.layers.embedding( input=trg_word_idx, size=[target_dict_dim, embedding_dim], dtype='float32') prediction = lstm_decoder_without_attention(trg_embedding, decoder_boot, encoded_vector, decoder_size) label = fluid.layers.data( name='label_sequence', shape=[1], dtype='int64', lod_level=1) cost = fluid.layers.cross_entropy(input=prediction, label=label) avg_cost = fluid.layers.mean(x=cost) return avg_cost, prediction def to_lodtensor(data, place): seq_lens = [len(seq) for seq in data] cur_len = 0 lod = [cur_len] for l in seq_lens: cur_len += l lod.append(cur_len) flattened_data = np.concatenate(data, axis=0).astype("int64") flattened_data = flattened_data.reshape([len(flattened_data), 1]) res = core.LoDTensor() res.set(flattened_data, place) res.set_lod([lod]) return res def create_random_lodtensor(lod, place, low, high): data = np.random.random_integers(low, high, [lod[-1], 1]).astype("int64") res = fluid.LoDTensor() res.set(data, place) res.set_lod([lod]) return res def train(use_cuda, save_dirname=None): [avg_cost, prediction] = seq_to_seq_net() optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4) optimizer.minimize(avg_cost) train_data = paddle.batch( paddle.reader.shuffle( paddle.dataset.wmt14.train(dict_size), buf_size=1000), batch_size=batch_size) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = Executor(place) exe.run(framework.default_startup_program()) batch_id = 0 for pass_id in xrange(2): for data in train_data(): word_data = to_lodtensor(map(lambda x: x[0], data), place) trg_word = to_lodtensor(map(lambda x: x[1], data), place) trg_word_next = to_lodtensor(map(lambda x: x[2], data), place) outs = exe.run(framework.default_main_program(), feed={ 'source_sequence': word_data, 'target_sequence': trg_word, 'label_sequence': trg_word_next }, fetch_list=[avg_cost]) avg_cost_val = np.array(outs[0]) print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) + " avg_cost=" + str(avg_cost_val)) if math.isnan(float(avg_cost_val[0])): sys.exit("got NaN loss, training failed.") if batch_id > 3: if save_dirname is not None: fluid.io.save_inference_model( save_dirname, ['source_sequence', 'target_sequence'], [prediction], exe) return batch_id += 1 def infer(use_cuda, save_dirname=None): if save_dirname is None: return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) # Use fluid.io.load_inference_model to obtain the inference program desc, # the feed_target_names (the names of variables that will be feeded # data using feed operators), and the fetch_targets (variables that # we want to obtain data from using fetch operators). [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(save_dirname, exe) lod = [0, 4, 10] word_data = create_random_lodtensor(lod, place, low=0, high=1) trg_word = create_random_lodtensor(lod, place, low=0, high=1) # Construct feed as a dictionary of {feed_target_name: feed_target_data} # and results will contain a list of data corresponding to fetch_targets. assert feed_target_names[0] == 'source_sequence' assert feed_target_names[1] == 'target_sequence' results = exe.run(inference_program, feed={ feed_target_names[0]: word_data, feed_target_names[1]: trg_word, }, fetch_list=fetch_targets, return_numpy=False) print(results[0].lod()) np_data = np.array(results[0]) print("Inference shape: ", np_data.shape) print("Inference results: ", np_data) def main(use_cuda): if use_cuda and not fluid.core.is_compiled_with_cuda(): return # Directory for saving the trained model save_dirname = "rnn_encoder_decoder.inference.model" train(use_cuda, save_dirname) infer(use_cuda, save_dirname) class TestRnnEncoderDecoder(unittest.TestCase): def test_cuda(self): with self.scope_prog_guard(): main(use_cuda=True) def test_cpu(self): with self.scope_prog_guard(): main(use_cuda=False) @contextlib.contextmanager def scope_prog_guard(self): prog = fluid.Program() startup_prog = fluid.Program() scope = fluid.core.Scope() with fluid.scope_guard(scope): with fluid.program_guard(prog, startup_prog): yield if __name__ == '__main__': unittest.main()