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biaffine_md.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import random
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import h5py
import util
from nn_md import NNMD
class BiaffineMD(NNMD):
def add_model_specific_valuables(self, config):
if config["lm_path"]:
self.lm_file = h5py.File(self.config["lm_path"], "r")
else:
self.lm_file = None
self.lm_layers = self.config["lm_layers"]
self.lm_size = self.config["lm_size"]
self.eval_data = None # Load eval data lazily.
input_props = []
input_props.append((tf.string, [None, None])) # Tokens.
input_props.append((tf.float32, [None, None, self.lm_size, self.lm_layers])) # LM embeddings.
input_props.append((tf.int32, [None])) # Text lengths.
input_props.append((tf.bool, [])) # Is training.
input_props.append((tf.int32, [None])) # Gold starts.
input_props.append((tf.int32, [None])) # Gold ends.
return input_props
def restore(self, session):
# Don't try to restore unused variables from the TF-Hub ELMo module.
vars_to_restore = [v for v in tf.global_variables() if "module/" not in v.name]
saver = tf.train.Saver(vars_to_restore)
checkpoint_path = os.path.join(self.config["log_dir"], "model.max.ckpt")
print("Restoring from {}".format(checkpoint_path))
session.run(tf.global_variables_initializer())
saver.restore(session, checkpoint_path)
def load_lm_embeddings(self, doc_key):
if self.lm_file is None:
return np.zeros([0, 0, self.lm_size, self.lm_layers])
file_key = doc_key.replace("/", ":")
if not file_key in self.lm_file and file_key[:-2] in self.lm_file:
file_key = file_key[:-2]
group = self.lm_file[file_key]
num_sentences = len(list(group.keys()))
sentences = [group[str(i)][...] for i in range(num_sentences)]
lm_emb = np.zeros([num_sentences, max(s.shape[0] for s in sentences), self.lm_size, self.lm_layers])
for i, s in enumerate(sentences):
lm_emb[i, :s.shape[0], :, :] = s
return lm_emb
def tensorize_example(self, example, is_training):
clusters = example["clusters"]
gold_mentions = sorted(tuple(m) for m in util.flatten(clusters))
sentences = example["sentences"]
num_words = sum(len(s) for s in sentences)
speakers = util.flatten(example["speakers"])
assert num_words == len(speakers)
max_sentence_length = max(len(s) for s in sentences)
text_len = np.array([len(s) for s in sentences])
tokens = [[""] * max_sentence_length for _ in sentences]
for i, sentence in enumerate(sentences):
for j, word in enumerate(sentence):
tokens[i][j] = word
tokens = np.array(tokens)
doc_key = example["doc_key"]
gold_starts, gold_ends = self.tensorize_mentions(gold_mentions)
lm_emb = self.load_lm_embeddings(doc_key)
example_tensors = (tokens, lm_emb, text_len, is_training, gold_starts, gold_ends)
if is_training and len(sentences) > self.config["max_training_sentences"]:
return self.truncate_example(*example_tensors)
else:
return example_tensors
def truncate_example(self, tokens, lm_emb, text_len, is_training, gold_starts, gold_ends):
max_training_sentences = self.config["max_training_sentences"]
num_sentences = tokens.shape[0]
assert num_sentences > max_training_sentences
sentence_offset = random.randint(0, num_sentences - max_training_sentences)
word_offset = text_len[:sentence_offset].sum()
num_words = text_len[sentence_offset:sentence_offset + max_training_sentences].sum()
tokens = tokens[sentence_offset:sentence_offset + max_training_sentences, :]
lm_emb = lm_emb[sentence_offset:sentence_offset + max_training_sentences, :, :, :]
text_len = text_len[sentence_offset:sentence_offset + max_training_sentences]
gold_spans = np.logical_and(gold_ends >= word_offset, gold_starts < word_offset + num_words)
gold_starts = gold_starts[gold_spans] - word_offset
gold_ends = gold_ends[gold_spans] - word_offset
return tokens, lm_emb, text_len, is_training, gold_starts, gold_ends
def get_predictions_and_loss(self, inputs):
tokens, lm_emb, text_len, is_training, gold_starts, gold_ends = inputs
self.dropout = self.get_dropout(self.config["dropout_rate"], is_training)
self.lexical_dropout = self.get_dropout(self.config["lexical_dropout_rate"], is_training)
self.lstm_dropout = self.get_dropout(self.config["lstm_dropout_rate"], is_training)
num_sentences = tf.shape(tokens)[0]
max_sentence_length = tf.shape(tokens)[1]
if not self.lm_file:
elmo_module = hub.Module("https://tfhub.dev/google/elmo/2")
lm_embeddings = elmo_module(
inputs={"tokens": tokens, "sequence_len": text_len},
signature="tokens", as_dict=True)
word_emb = lm_embeddings["word_emb"] # [num_sentences, max_sentence_length, 512]
lm_emb = tf.stack([tf.concat([word_emb, word_emb], -1),
lm_embeddings["lstm_outputs1"],
lm_embeddings["lstm_outputs2"]], -1) # [num_sentences, max_sentence_length, 1024, 3]
lm_emb_size = util.shape(lm_emb, 2)
lm_num_layers = util.shape(lm_emb, 3)
with tf.variable_scope("lm_aggregation"):
self.lm_weights = tf.nn.softmax(tf.get_variable("lm_scores", [lm_num_layers], initializer=tf.constant_initializer(0.0)))
self.lm_scaling = tf.get_variable("lm_scaling", [], initializer=tf.constant_initializer(1.0))
flattened_lm_emb = tf.reshape(lm_emb, [num_sentences * max_sentence_length * lm_emb_size, lm_num_layers])
flattened_aggregated_lm_emb = tf.matmul(flattened_lm_emb, tf.expand_dims(self.lm_weights, 1)) # [num_sentences * max_sentence_length * emb, 1]
aggregated_lm_emb = tf.reshape(flattened_aggregated_lm_emb, [num_sentences, max_sentence_length, lm_emb_size])
aggregated_lm_emb *= self.lm_scaling
context_emb = aggregated_lm_emb
context_emb = tf.nn.dropout(context_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length) # [num_sentence, max_sentence_length]
num_words = tf.reduce_sum(text_len)
sentence_indices = tf.tile(tf.expand_dims(tf.range(num_sentences), 1),
[1, max_sentence_length]) # [num_sentences, max_sentence_length]
flattened_sentence_indices = self.flatten_emb_by_sentence(sentence_indices, text_len_mask) # [num_words]
candidate_starts = tf.tile(tf.expand_dims(tf.range(num_words), 1),
[1, max_sentence_length]) # [num_words, max_sentence_length]
candidate_ends = candidate_starts + tf.expand_dims(tf.range(max_sentence_length), 0) # [num_words, max_sentence_length]
candidate_start_sentence_indices = tf.gather(flattened_sentence_indices,
candidate_starts) # [num_words, max_sentence_length]
candidate_end_sentence_indices = tf.gather(flattened_sentence_indices,
tf.minimum(candidate_ends, num_words - 1)) # [num_words, max_sentence_length]
candidate_mask = tf.logical_and(candidate_ends < num_words, tf.equal(candidate_start_sentence_indices,
candidate_end_sentence_indices)) # [num_words, max_sentence_length]
flattened_candidate_mask = tf.reshape(candidate_mask, [-1]) # [num_words * max_sentence_length]
candidate_starts = tf.boolean_mask(tf.reshape(candidate_starts, [-1]), flattened_candidate_mask) # [num_candidates]
candidate_ends = tf.boolean_mask(tf.reshape(candidate_ends, [-1]), flattened_candidate_mask) # [num_candidates]
candidate_labels = self.get_candidate_labels(candidate_starts, candidate_ends, gold_starts, gold_ends) # [num_candidates]
candidate_scores_mask = tf.logical_and(tf.expand_dims(text_len_mask,[1]),tf.expand_dims(text_len_mask,[2])) #[num_sentence, max_sentence_length,max_sentence_length]
sentence_ends_leq_starts = tf.tile(tf.expand_dims(tf.logical_not(tf.sequence_mask(tf.range(max_sentence_length),max_sentence_length)), 0),[num_sentences,1,1]) #[num_sentence, max_sentence_length,max_sentence_length]
candidate_scores_mask = tf.logical_and(candidate_scores_mask,sentence_ends_leq_starts)
flattened_candidate_scores_mask = tf.reshape(candidate_scores_mask,[-1]) #[num_sentence * max_sentence_length * max_sentence_length]
context_outputs = self.lstm_contextualize(context_emb, text_len, text_len_mask,self.lstm_dropout,False) # [num_sentence, max_sentence_length, emb]
with tf.variable_scope("candidate_starts_ffnn"):
candidate_starts_emb = util.projection(context_outputs,self.config["ffnn_size"]) #[num_sentences, max_sentences_length,emb]
with tf.variable_scope("candidate_ends_ffnn"):
candidate_ends_emb = util.projection(context_outputs,self.config["ffnn_size"]) #[num_sentences, max_sentences_length, emb]
candidate_mention_scores = util.bilinear_classifier(candidate_starts_emb,candidate_ends_emb,self.dropout)#[num_sentence, max_sentence_length,max_sentence_length]
candidate_mention_scores = tf.boolean_mask(tf.reshape(candidate_mention_scores,[-1]),flattened_candidate_scores_mask)
loss = self.sigmoid_loss(candidate_mention_scores, candidate_labels)
top_span_starts, top_span_ends = self.get_top_mentions(num_words,candidate_starts,candidate_ends,candidate_mention_scores)
return [top_span_starts, top_span_ends], loss