# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # # 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 pd 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 word_dim = 16 IS_SPARSE = True batch_size = 2 max_length = 8 topk_size = 50 trg_dic_size = 10000 beam_size = 2 decoder_size = hidden_dim place = core.CPUPlace() def encoder(): # encoder src_word_id = pd.data( name="src_word_id", shape=[1], dtype='int64', lod_level=1) src_embedding = pd.embedding( input=src_word_id, size=[dict_size, word_dim], dtype='float32', is_sparse=IS_SPARSE, param_attr=fluid.ParamAttr(name='vemb')) fc1 = pd.fc(input=src_embedding, size=hidden_dim * 4, act='tanh') lstm_hidden0, lstm_0 = pd.dynamic_lstm(input=fc1, size=hidden_dim * 4) encoder_out = pd.sequence_last_step(input=lstm_hidden0) return encoder_out def decoder_train(context): # decoder trg_language_word = pd.data( name="target_language_word", shape=[1], dtype='int64', lod_level=1) trg_embedding = pd.embedding( input=trg_language_word, size=[dict_size, word_dim], dtype='float32', is_sparse=IS_SPARSE, param_attr=fluid.ParamAttr(name='vemb')) rnn = pd.DynamicRNN() with rnn.block(): current_word = rnn.step_input(trg_embedding) pre_state = rnn.memory(init=context) current_state = pd.fc(input=[current_word, pre_state], size=decoder_size, act='tanh') current_score = pd.fc(input=current_state, size=target_dict_dim, act='softmax') rnn.update_memory(pre_state, current_state) rnn.output(current_score) return rnn() def decoder_decode(context): init_state = context array_len = pd.fill_constant(shape=[1], dtype='int64', value=max_length) counter = pd.zeros(shape=[1], dtype='int64') # fill the first element with init_state state_array = pd.create_array('float32') pd.array_write(init_state, array=state_array, i=counter) # ids, scores as memory ids_array = pd.create_array('int64') scores_array = pd.create_array('float32') init_ids = pd.data(name="init_ids", shape=[1], dtype="int64", lod_level=2) init_scores = pd.data( name="init_scores", shape=[1], dtype="float32", lod_level=2) pd.array_write(init_ids, array=ids_array, i=counter) pd.array_write(init_scores, array=scores_array, i=counter) cond = pd.less_than(x=counter, y=array_len) while_op = pd.While(cond=cond) with while_op.block(): pre_ids = pd.array_read(array=ids_array, i=counter) pre_state = pd.array_read(array=state_array, i=counter) pre_score = pd.array_read(array=scores_array, i=counter) # expand the lod of pre_state to be the same with pre_score pre_state_expanded = pd.sequence_expand(pre_state, pre_score) pre_ids_emb = pd.embedding( input=pre_ids, size=[dict_size, word_dim], dtype='float32', is_sparse=IS_SPARSE) # use rnn unit to update rnn current_state = pd.fc(input=[pre_ids_emb, pre_state_expanded], size=decoder_size, act='tanh') # use score to do beam search current_score = pd.fc(input=current_state, size=target_dict_dim, act='softmax') topk_scores, topk_indices = pd.topk(current_score, k=50) selected_ids, selected_scores = pd.beam_search( pre_ids, topk_indices, topk_scores, beam_size, end_id=10, level=0) pd.increment(x=counter, value=1, in_place=True) # update the memories pd.array_write(current_state, array=state_array, i=counter) pd.array_write(selected_ids, array=ids_array, i=counter) pd.array_write(selected_scores, array=scores_array, i=counter) pd.less_than(x=counter, y=array_len, cond=cond) translation_ids, translation_scores = pd.beam_search_decode( ids=ids_array, scores=scores_array) # return init_ids, init_scores return translation_ids, translation_scores def set_init_lod(data, lod, place): res = core.LoDTensor() res.set(data, place) res.set_lod(lod) return res 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 train_main(): context = encoder() rnn_out = decoder_train(context) label = pd.data( name="target_language_next_word", shape=[1], dtype='int64', lod_level=1) cost = pd.cross_entropy(input=rnn_out, label=label) avg_cost = pd.mean(x=cost) 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) exe = Executor(place) exe.run(framework.default_startup_program()) batch_id = 0 for pass_id in xrange(1): 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={ 'src_word_id': word_data, 'target_language_word': trg_word, 'target_language_next_word': 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 batch_id > 3: break batch_id += 1 def decode_main(): context = encoder() translation_ids, translation_scores = decoder_decode(context) exe = Executor(place) exe.run(framework.default_startup_program()) init_ids_data = np.array([1 for _ in range(batch_size)], dtype='int64') init_scores_data = np.array( [1. for _ in range(batch_size)], dtype='float32') init_ids_data = init_ids_data.reshape((batch_size, 1)) init_scores_data = init_scores_data.reshape((batch_size, 1)) init_lod = [i for i in range(batch_size)] + [batch_size] init_lod = [init_lod, init_lod] train_data = paddle.batch( paddle.reader.shuffle( paddle.dataset.wmt14.train(dict_size), buf_size=1000), batch_size=batch_size) for _, data in enumerate(train_data()): init_ids = set_init_lod(init_ids_data, init_lod, place) init_scores = set_init_lod(init_scores_data, init_lod, place) src_word_data = to_lodtensor(map(lambda x: x[0], data), place) result_ids, result_scores = exe.run( framework.default_main_program(), feed={ 'src_word_id': src_word_data, 'init_ids': init_ids, 'init_scores': init_scores }, fetch_list=[translation_ids, translation_scores], return_numpy=False) print result_ids.lod() break if __name__ == '__main__': # train_main() decode_main()