# Copyright (c) Microsoft Corporation # All rights reserved. # # MIT License # # Permission is hereby granted, free of charge, # to any person obtaining a copy of this software and associated # documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and # to permit persons to whom the Software is furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING # BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' Train the network combined by RNN and attention. ''' import tensorflow as tf from attention import DotAttention from rnn import XGRUCell from util import dropout from graph_to_tf import graph_to_network class GAGConfig: def __init__(self): self.batch_size = 128 self.dropout = 0.1 self.char_vcb_size = 1500 self.max_char_length = 20 self.char_embed_dim = 100 self.max_query_length = 40 self.max_passage_length = 800 self.att_is_vanilla = True self.att_need_padding = False self.att_is_id = False self.ptr_dim = 70 self.learning_rate = 0.1 self.labelsmoothing = 0.1 self.num_heads = 1 self.rnn_units = 256 class GAG: def __init__(self, cfg, embed, graph): self.cfg = cfg self.embed = embed self.graph = graph self.query_word = None self.query_mask = None self.query_lengths = None self.passage_word = None self.passage_mask = None self.passage_lengths = None self.answer_begin = None self.answer_end = None self.query_char_ids = None self.query_char_lengths = None self.passage_char_ids = None self.passage_char_lengths = None self.passage_states = None self.query_states = None self.query_init = None self.begin_prob = None self.end_prob = None self.loss = None self.train_op = None def build_net(self, is_training): cfg = self.cfg with tf.device('/cpu:0'): word_embed = tf.get_variable( name='word_embed', initializer=self.embed, dtype=tf.float32, trainable=False) char_embed = tf.get_variable(name='char_embed', shape=[cfg.char_vcb_size, cfg.char_embed_dim], dtype=tf.float32) # [query_length, batch_size] self.query_word = tf.placeholder(dtype=tf.int32, shape=[None, None], name='query_word') self.query_mask = tf.placeholder(dtype=tf.float32, shape=[None, None], name='query_mask') # [batch_size] self.query_lengths = tf.placeholder( dtype=tf.int32, shape=[None], name='query_lengths') # [passage_length, batch_size] self.passage_word = tf.placeholder( dtype=tf.int32, shape=[None, None], name='passage_word') self.passage_mask = tf.placeholder( dtype=tf.float32, shape=[None, None], name='passage_mask') # [batch_size] self.passage_lengths = tf.placeholder( dtype=tf.int32, shape=[None], name='passage_lengths') if is_training: self.answer_begin = tf.placeholder( dtype=tf.int32, shape=[None], name='answer_begin') self.answer_end = tf.placeholder( dtype=tf.int32, shape=[None], name='answer_end') self.query_char_ids = tf.placeholder(dtype=tf.int32, shape=[ self.cfg.max_char_length, None, None], name='query_char_ids') # sequence_length, batch_size self.query_char_lengths = tf.placeholder( dtype=tf.int32, shape=[None, None], name='query_char_lengths') self.passage_char_ids = tf.placeholder(dtype=tf.int32, shape=[ self.cfg.max_char_length, None, None], name='passage_char_ids') # sequence_length, batch_size self.passage_char_lengths = tf.placeholder(dtype=tf.int32, shape=[None, None], name='passage_char_lengths') query_char_states = self.build_char_states(char_embed=char_embed, is_training=is_training, reuse=False, char_ids=self.query_char_ids, char_lengths=self.query_char_lengths) passage_char_states = self.build_char_states(char_embed=char_embed, is_training=is_training, reuse=True, char_ids=self.passage_char_ids, char_lengths=self.passage_char_lengths) with tf.variable_scope("encoding") as scope: query_states = tf.concat([tf.nn.embedding_lookup( word_embed, self.query_word), query_char_states], axis=2) scope.reuse_variables() passage_states = tf.concat([tf.nn.embedding_lookup( word_embed, self.passage_word), passage_char_states], axis=2) passage_states = tf.transpose(passage_states, perm=[1, 0, 2]) query_states = tf.transpose(query_states, perm=[1, 0, 2]) self.passage_states = passage_states self.query_states = query_states output, output2 = graph_to_network(passage_states, query_states, self.passage_lengths, self.query_lengths, self.graph, self.cfg.dropout, is_training, num_heads=cfg.num_heads, rnn_units=cfg.rnn_units) passage_att_mask = self.passage_mask batch_size_x = tf.shape(self.query_lengths) answer_h = tf.zeros( tf.concat([batch_size_x, tf.constant([cfg.ptr_dim], dtype=tf.int32)], axis=0)) answer_context = tf.reduce_mean(output2, axis=1) query_init_w = tf.get_variable( 'query_init_w', shape=[output2.get_shape().as_list()[-1], cfg.ptr_dim]) self.query_init = query_init_w answer_context = tf.matmul(answer_context, query_init_w) output = tf.transpose(output, perm=[1, 0, 2]) with tf.variable_scope('answer_ptr_layer'): ptr_att = DotAttention('ptr', hidden_dim=cfg.ptr_dim, is_vanilla=self.cfg.att_is_vanilla, is_identity_transform=self.cfg.att_is_id, need_padding=self.cfg.att_need_padding) answer_pre_compute = ptr_att.get_pre_compute(output) ptr_gru = XGRUCell(hidden_dim=cfg.ptr_dim) begin_prob, begin_logits = ptr_att.get_prob(output, answer_context, passage_att_mask, answer_pre_compute, True) att_state = ptr_att.get_att(output, begin_prob) (_, answer_h) = ptr_gru.call(inputs=att_state, state=answer_h) answer_context = answer_h end_prob, end_logits = ptr_att.get_prob(output, answer_context, passage_att_mask, answer_pre_compute, True) self.begin_prob = tf.transpose(begin_prob, perm=[1, 0]) self.end_prob = tf.transpose(end_prob, perm=[1, 0]) begin_logits = tf.transpose(begin_logits, perm=[1, 0]) end_logits = tf.transpose(end_logits, perm=[1, 0]) if is_training: def label_smoothing(inputs, masks, epsilon=0.1): epsilon = cfg.labelsmoothing num_of_channel = tf.shape(inputs)[-1] # number of channels inputs = tf.cast(inputs, tf.float32) return (((1 - epsilon) * inputs) + (epsilon / tf.cast(num_of_channel, tf.float32))) * masks cost1 = tf.reduce_mean( tf.losses.softmax_cross_entropy(label_smoothing( tf.one_hot(self.answer_begin, depth=tf.shape(self.passage_word)[0]), tf.transpose(self.passage_mask, perm=[1, 0])), begin_logits)) cost2 = tf.reduce_mean( tf.losses.softmax_cross_entropy( label_smoothing(tf.one_hot(self.answer_end, depth=tf.shape(self.passage_word)[0]), tf.transpose(self.passage_mask, perm=[1, 0])), end_logits)) reg_ws = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) l2_loss = tf.reduce_sum(reg_ws) loss = cost1 + cost2 + l2_loss self.loss = loss optimizer = tf.train.AdamOptimizer(learning_rate=cfg.learning_rate) self.train_op = optimizer.minimize(self.loss) return tf.stack([self.begin_prob, self.end_prob]) def build_char_states(self, char_embed, is_training, reuse, char_ids, char_lengths): max_char_length = self.cfg.max_char_length inputs = dropout(tf.nn.embedding_lookup(char_embed, char_ids), self.cfg.dropout, is_training) inputs = tf.reshape( inputs, shape=[max_char_length, -1, self.cfg.char_embed_dim]) char_lengths = tf.reshape(char_lengths, shape=[-1]) with tf.variable_scope('char_encoding', reuse=reuse): cell_fw = XGRUCell(hidden_dim=self.cfg.char_embed_dim) cell_bw = XGRUCell(hidden_dim=self.cfg.char_embed_dim) _, (left_right, right_left) = tf.nn.bidirectional_dynamic_rnn( cell_fw=cell_fw, cell_bw=cell_bw, sequence_length=char_lengths, inputs=inputs, time_major=True, dtype=tf.float32 ) left_right = tf.reshape(left_right, shape=[-1, self.cfg.char_embed_dim]) right_left = tf.reshape(right_left, shape=[-1, self.cfg.char_embed_dim]) states = tf.concat([left_right, right_left], axis=1) out_shape = tf.shape(char_ids)[1:3] out_shape = tf.concat([out_shape, tf.constant( value=[self.cfg.char_embed_dim * 2], dtype=tf.int32)], axis=0) return tf.reshape(states, shape=out_shape)