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trial.py
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trial.py
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# 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.
import os
import logging
logger = logging.getLogger('ga_squad')
try:
import argparse
import heapq
import json
import numpy as np
import pickle
import graph
from util import Timer
import nni
import data
import evaluate
from train_model import *
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
except:
logger.exception('Catch exception in trial.py.')
raise
def get_config():
'''
Get config from argument parser.
'''
parser = argparse.ArgumentParser(
description='This program is using genetic algorithm to search architecture for SQuAD.')
parser.add_argument('--input_file', type=str,
default='./train-v1.1.json', help='input file')
parser.add_argument('--dev_file', type=str,
default='./dev-v1.1.json', help='dev file')
parser.add_argument('--embedding_file', type=str,
default='./glove.840B.300d.txt', help='dev file')
parser.add_argument('--root_path', default='./data/',
type=str, help='Root path of models')
parser.add_argument('--batch_size', type=int, default=64, help='batch size')
parser.add_argument('--save_path', type=str,
default='./save', help='save path dir')
parser.add_argument('--learning_rate', type=float, default=0.0001,
help='set half of original learning rate reload data and train.')
parser.add_argument('--max_epoch', type=int, default=30)
parser.add_argument('--dropout_rate', type=float,
default=0.1, help='dropout_rate')
parser.add_argument('--labelsmoothing', type=float,
default=0.1, help='labelsmoothing')
parser.add_argument('--num_heads', type=int, default=1, help='num_heads')
parser.add_argument('--rnn_units', type=int, default=256, help='rnn_units')
args = parser.parse_args()
return args
def get_id(word_dict, word):
'''
Return word id.
'''
if word in word_dict.keys():
return word_dict[word]
return word_dict['<unk>']
def load_embedding(path):
'''
return embedding for a specific file by given file path.
'''
EMBEDDING_DIM = 300
embedding_dict = {}
with open(path, 'r', encoding='utf-8') as file:
pairs = [line.strip('\r\n').split() for line in file.readlines()]
for pair in pairs:
if len(pair) == EMBEDDING_DIM + 1:
embedding_dict[pair[0]] = [float(x) for x in pair[1:]]
logger.debug('embedding_dict size: %d', len(embedding_dict))
return embedding_dict
class MaxQueue:
'''
Queue for max value.
'''
def __init__(self, capacity):
assert capacity > 0, 'queue size must be larger than 0'
self._capacity = capacity
self._entries = []
@property
def entries(self):
return self._entries
@property
def capacity(self):
return self._capacity
@property
def size(self):
return len(self._entries)
def clear(self):
self._entries = []
def push(self, item):
if self.size < self.capacity:
heapq.heappush(self.entries, item)
else:
heapq.heappushpop(self.entries, item)
def find_best_answer_span(left_prob, right_prob, passage_length, max_answer_length):
left = 0
right = 0
max_prob = left_prob[0] * right_prob[0]
for i in range(0, passage_length):
left_p = left_prob[i]
for j in range(i, min(i + max_answer_length, passage_length)):
total_prob = left_p * right_prob[j]
if max_prob < total_prob:
left, right, max_prob = i, j, total_prob
return [(max_prob, left, right)]
def write_prediction(path, position1_result, position2_result):
import codecs
with codecs.open(path, 'w', encoding='utf8') as file:
batch_num = len(position1_result)
for i in range(batch_num):
position1_batch = position1_result[i]
position2_batch = position2_result[i]
for j in range(position1_batch.shape[0]):
file.write(str(position1_batch[j]) +
'\t' + str(position2_batch[j]) + '\n')
def find_kbest_answer_span(k, left_prob, right_prob, passage_length, max_answer_length):
if k == 1:
return find_best_answer_span(left_prob, right_prob, passage_length, max_answer_length)
queue = MaxQueue(k)
for i in range(0, passage_length):
left_p = left_prob[i]
for j in range(i, min(i + max_answer_length, passage_length)):
total_prob = left_p * right_prob[j]
queue.push((total_prob, i, j))
return list(sorted(queue.entries, key=lambda x: -x[0]))
def run_epoch(batches, answer_net, is_training):
if not is_training:
position1_result = []
position2_result = []
contexts = []
ids = []
loss_sum = 0
timer = Timer()
count = 0
for batch in batches:
used = timer.get_elapsed(False)
count += 1
qps = batch['qp_pairs']
question_tokens = [qp['question_tokens'] for qp in qps]
passage_tokens = [qp['passage_tokens'] for qp in qps]
context = [(qp['passage'], qp['passage_tokens']) for qp in qps]
sample_id = [qp['id'] for qp in qps]
_, query, query_mask, query_lengths = data.get_word_input(
data=question_tokens, word_dict=word_vcb, embed=embed, embed_dim=cfg.word_embed_dim)
_, passage, passage_mask, passage_lengths = data.get_word_input(
data=passage_tokens, word_dict=word_vcb, embed=embed, embed_dim=cfg.word_embed_dim)
query_char, query_char_lengths = data.get_char_input(
data=question_tokens, char_dict=char_vcb, max_char_length=cfg.max_char_length)
passage_char, passage_char_lengths = data.get_char_input(
data=passage_tokens, char_dict=char_vcb, max_char_length=cfg.max_char_length)
if is_training:
answer_begin, answer_end = data.get_answer_begin_end(qps)
if is_training:
feed_dict = {answer_net.query_word: query,
answer_net.query_mask: query_mask,
answer_net.query_lengths: query_lengths,
answer_net.passage_word: passage,
answer_net.passage_mask: passage_mask,
answer_net.passage_lengths: passage_lengths,
answer_net.query_char_ids: query_char,
answer_net.query_char_lengths: query_char_lengths,
answer_net.passage_char_ids: passage_char,
answer_net.passage_char_lengths: passage_char_lengths,
answer_net.answer_begin: answer_begin,
answer_net.answer_end: answer_end}
loss, _, = sess.run(
[answer_net.loss, answer_net.train_op], feed_dict=feed_dict)
if count % 100 == 0:
logger.debug('%d %g except:%g, loss:%g' %
(count, used, used / count * len(batches), loss))
loss_sum += loss
else:
feed_dict = {answer_net.query_word: query,
answer_net.query_mask: query_mask,
answer_net.query_lengths: query_lengths,
answer_net.passage_word: passage,
answer_net.passage_mask: passage_mask,
answer_net.passage_lengths: passage_lengths,
answer_net.query_char_ids: query_char,
answer_net.query_char_lengths: query_char_lengths,
answer_net.passage_char_ids: passage_char,
answer_net.passage_char_lengths: passage_char_lengths}
position1, position2 = sess.run(
[answer_net.begin_prob, answer_net.end_prob], feed_dict=feed_dict)
position1_result += position1.tolist()
position2_result += position2.tolist()
contexts += context
ids = np.concatenate((ids, sample_id))
if count % 100 == 0:
logger.debug('%d %g except:%g' %
(count, used, used / count * len(batches)))
loss = loss_sum / len(batches)
if is_training:
return loss
return loss, position1_result, position2_result, ids, contexts
def generate_predict_json(position1_result, position2_result, ids, passage_tokens):
'''
Generate json by prediction.
'''
predict_len = len(position1_result)
logger.debug('total prediction num is %s', str(predict_len))
answers = {}
for i in range(predict_len):
sample_id = ids[i]
passage, tokens = passage_tokens[i]
kbest = find_best_answer_span(
position1_result[i], position2_result[i], len(tokens), 23)
_, start, end = kbest[0]
answer = passage[tokens[start]['char_begin']:tokens[end]['char_end']]
answers[sample_id] = answer
logger.debug('generate predict done.')
return answers
def generate_data(path, tokenizer, char_vcb, word_vcb, is_training=False):
'''
Generate data
'''
global root_path
qp_pairs = data.load_from_file(path=path, is_training=is_training)
tokenized_sent = 0
# qp_pairs = qp_pairs[:1000]1
for qp_pair in qp_pairs:
tokenized_sent += 1
data.tokenize(qp_pair, tokenizer, is_training)
for word in qp_pair['question_tokens']:
word_vcb.add(word['word'])
for char in word['word']:
char_vcb.add(char)
for word in qp_pair['passage_tokens']:
word_vcb.add(word['word'])
for char in word['word']:
char_vcb.add(char)
max_query_length = max(len(x['question_tokens']) for x in qp_pairs)
max_passage_length = max(len(x['passage_tokens']) for x in qp_pairs)
#min_passage_length = min(len(x['passage_tokens']) for x in qp_pairs)
cfg.max_query_length = max_query_length
cfg.max_passage_length = max_passage_length
return qp_pairs
def train_with_graph(graph, qp_pairs, dev_qp_pairs):
'''
Train a network from a specific graph.
'''
global sess
with tf.Graph().as_default():
train_model = GAG(cfg, embed, graph)
train_model.build_net(is_training=True)
tf.get_variable_scope().reuse_variables()
dev_model = GAG(cfg, embed, graph)
dev_model.build_net(is_training=False)
with tf.Session() as sess:
logger.debug('init variables')
init = tf.global_variables_initializer()
sess.run(init)
# writer = tf.summary.FileWriter('%s/graph/'%execution_path, sess.graph)
logger.debug('assign to graph')
saver = tf.train.Saver()
train_loss = None
bestacc = 0
patience = 5
patience_increase = 2
improvement_threshold = 0.995
for epoch in range(max_epoch):
logger.debug('begin to train')
train_batches = data.get_batches(qp_pairs, cfg.batch_size)
train_loss = run_epoch(train_batches, train_model, True)
logger.debug('epoch ' + str(epoch) +
' loss: ' + str(train_loss))
dev_batches = list(data.get_batches(
dev_qp_pairs, cfg.batch_size))
_, position1, position2, ids, contexts = run_epoch(
dev_batches, dev_model, False)
answers = generate_predict_json(
position1, position2, ids, contexts)
if save_path is not None:
with open(os.path.join(save_path, 'epoch%d.prediction' % epoch), 'w') as file:
json.dump(answers, file)
else:
answers = json.dumps(answers)
answers = json.loads(answers)
iter = epoch + 1
acc = evaluate.evaluate_with_predictions(
args.dev_file, answers)
logger.debug('Send intermediate acc: %s', str(acc))
nni.report_intermediate_result(acc)
logger.debug('Send intermediate result done.')
if acc > bestacc:
if acc * improvement_threshold > bestacc:
patience = max(patience, iter * patience_increase)
bestacc = acc
if save_path is not None:
saver.save(os.path.join(sess, save_path + 'epoch%d.model' % epoch))
with open(os.path.join(save_path, 'epoch%d.score' % epoch), 'wb') as file:
pickle.dump(
(position1, position2, ids, contexts), file)
logger.debug('epoch %d acc %g bestacc %g' %
(epoch, acc, bestacc))
if patience <= iter:
break
logger.debug('save done.')
return train_loss, bestacc
embed = None
char_vcb = None
tokenizer = None
word_vcb = None
def load_data():
global embed, char_vcb, tokenizer, word_vcb
logger.debug('tokenize data')
tokenizer = data.WhitespaceTokenizer()
char_set = set()
word_set = set()
logger.debug('generate train data')
qp_pairs = generate_data(input_file, tokenizer,
char_set, word_set, is_training=True)
logger.debug('generate dev data')
dev_qp_pairs = generate_data(
dev_file, tokenizer, char_set, word_set, is_training=False)
logger.debug('generate data done.')
char_vcb = {char: sample_id for sample_id, char in enumerate(char_set)}
word_vcb = {word: sample_id for sample_id, word in enumerate(word_set)}
timer.start()
logger.debug('read embedding table')
cfg.word_embed_dim = 300
embed = np.zeros((len(word_vcb), cfg.word_embed_dim), dtype=np.float32)
embedding = load_embedding(args.embedding_file)
for word, sample_id in enumerate(word_vcb):
if word in embedding:
embed[sample_id] = embedding[word]
# add UNK into dict
unk = np.zeros((1, cfg.word_embed_dim), dtype=np.float32)
embed = np.concatenate((unk, embed), axis=0)
word_vcb = {key: value + 1 for key, value in word_vcb.items()}
return qp_pairs, dev_qp_pairs
if __name__ == '__main__':
try:
args = get_config()
root_path = os.path.expanduser(args.root_path)
input_file = os.path.expanduser(args.input_file)
dev_file = os.path.expanduser(args.dev_file)
save_path = None
max_epoch = args.max_epoch
cfg = GAGConfig()
cfg.batch_size = args.batch_size
cfg.learning_rate = float(args.learning_rate)
cfg.dropout = args.dropout_rate
cfg.rnn_units = args.rnn_units
cfg.labelsmoothing = args.labelsmoothing
cfg.num_heads = args.num_heads
timer = Timer()
qp_pairs, dev_qp_pairs = load_data()
logger.debug('Init finish.')
original_params = nni.get_next_parameter()
'''
with open('data.json') as f:
original_params = json.load(f)
'''
try:
graph = graph.graph_loads(original_params)
except Exception:
logger.debug('Can\'t load graph.')
train_loss, best_acc = train_with_graph(graph, qp_pairs, dev_qp_pairs)
logger.debug('Send best acc: %s', str(best_acc))
nni.report_final_result(best_acc)
logger.debug('Send final result done')
except:
logger.exception('Catch exception in trial.py.')
raise