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run_race.py
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run_race.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from os.path import join
from absl import flags
import os
import csv
import collections
import numpy as np
import time
import math
import json
import random
from copy import copy
from collections import defaultdict as dd
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score
import absl.logging as _logging # pylint: disable=unused-import
import tensorflow as tf
import sentencepiece as spm
from data_utils import SEP_ID, VOCAB_SIZE, CLS_ID
import model_utils
import function_builder
from classifier_utils import PaddingInputExample
from classifier_utils import convert_single_example
from prepro_utils import preprocess_text, encode_ids
# Model
flags.DEFINE_string("model_config_path", default=None,
help="Model config path.")
flags.DEFINE_float("dropout", default=0.1,
help="Dropout rate.")
flags.DEFINE_float("dropatt", default=0.1,
help="Attention dropout rate.")
flags.DEFINE_integer("clamp_len", default=-1,
help="Clamp length")
flags.DEFINE_string("summary_type", default="last",
help="Method used to summarize a sequence into a compact vector.")
flags.DEFINE_bool("use_summ_proj", default=True,
help="Whether to use projection for summarizing sequences.")
flags.DEFINE_bool("use_bfloat16", default=False,
help="Whether to use bfloat16.")
# Parameter initialization
flags.DEFINE_enum("init", default="normal",
enum_values=["normal", "uniform"],
help="Initialization method.")
flags.DEFINE_float("init_std", default=0.02,
help="Initialization std when init is normal.")
flags.DEFINE_float("init_range", default=0.1,
help="Initialization std when init is uniform.")
# I/O paths
flags.DEFINE_bool("overwrite_data", default=False,
help="If False, will use cached data if available.")
flags.DEFINE_string("init_checkpoint", default=None,
help="checkpoint path for initializing the model. "
"Could be a pretrained model or a finetuned model.")
flags.DEFINE_string("output_dir", default="",
help="Output dir for TF records.")
flags.DEFINE_string("spiece_model_file", default="",
help="Sentence Piece model path.")
flags.DEFINE_string("model_dir", default="",
help="Directory for saving the finetuned model.")
flags.DEFINE_string("data_dir", default="",
help="Directory for input data.")
# TPUs and machines
flags.DEFINE_bool("use_tpu", default=False, help="whether to use TPU.")
flags.DEFINE_integer("num_hosts", default=1, help="How many TPU hosts.")
flags.DEFINE_integer("num_core_per_host", default=8,
help="8 for TPU v2 and v3-8, 16 for larger TPU v3 pod. In the context "
"of GPU training, it refers to the number of GPUs used.")
flags.DEFINE_string("tpu_job_name", default=None, help="TPU worker job name.")
flags.DEFINE_string("tpu", default=None, help="TPU name.")
flags.DEFINE_string("tpu_zone", default=None, help="TPU zone.")
flags.DEFINE_string("gcp_project", default=None, help="gcp project.")
flags.DEFINE_string("master", default=None, help="master")
flags.DEFINE_integer("iterations", default=1000,
help="number of iterations per TPU training loop.")
# Training
flags.DEFINE_bool("do_train", default=False, help="whether to do training")
flags.DEFINE_integer("train_steps", default=12000,
help="Number of training steps")
flags.DEFINE_integer("warmup_steps", default=0, help="number of warmup steps")
flags.DEFINE_float("learning_rate", default=2e-5, help="initial learning rate")
flags.DEFINE_float("lr_layer_decay_rate", 1.0,
"Top layer: lr[L] = FLAGS.learning_rate."
"Low layer: lr[l-1] = lr[l] * lr_layer_decay_rate.")
flags.DEFINE_float("min_lr_ratio", default=0.0,
help="min lr ratio for cos decay.")
flags.DEFINE_float("clip", default=1.0, help="Gradient clipping")
flags.DEFINE_integer("max_save", default=0,
help="Max number of checkpoints to save. Use 0 to save all.")
flags.DEFINE_integer("save_steps", default=None,
help="Save the model for every save_steps. "
"If None, not to save any model.")
flags.DEFINE_integer("train_batch_size", default=8,
help="Batch size for training. Note that batch size 1 corresponds to "
"4 sequences: one paragraph + one quesetion + 4 candidate answers.")
flags.DEFINE_float("weight_decay", default=0.00, help="weight decay rate")
flags.DEFINE_float("adam_epsilon", default=1e-6, help="adam epsilon")
flags.DEFINE_string("decay_method", default="poly", help="poly or cos")
# Evaluation
flags.DEFINE_bool("do_eval", default=False, help="whether to do eval")
flags.DEFINE_string("eval_split", default="dev",
help="could be dev or test")
flags.DEFINE_integer("eval_batch_size", default=32,
help="Batch size for evaluation.")
# Data config
flags.DEFINE_integer("max_seq_length", default=512,
help="Max length for the paragraph.")
flags.DEFINE_integer("max_qa_length", default=128,
help="Max length for the concatenated question and answer.")
flags.DEFINE_integer("shuffle_buffer", default=2048,
help="Buffer size used for shuffle.")
flags.DEFINE_bool("uncased", default=False,
help="Use uncased.")
flags.DEFINE_bool("high_only", default=False,
help="Evaluate on high school only.")
flags.DEFINE_bool("middle_only", default=False,
help="Evaluate on middle school only.")
FLAGS = flags.FLAGS
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4
class PaddingInputExample(object):
"""Fake example so the num input examples is a multiple of the batch size.
When running eval/predict on the TPU, we need to pad the number of examples
to be a multiple of the batch size, because the TPU requires a fixed batch
size. The alternative is to drop the last batch, which is bad because it means
the entire output data won't be generated.
We use this class instead of `None` because treating `None` as padding
battches could cause silent errors.
"""
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
input_ids,
input_mask,
segment_ids,
label_id,
is_real_example=True):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.is_real_example = is_real_example
def convert_single_example(example, tokenize_fn):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(
input_ids=[0] * FLAGS.max_seq_length * 4,
input_mask=[1] * FLAGS.max_seq_length * 4,
segment_ids=[0] * FLAGS.max_seq_length * 4,
label_id=0,
is_real_example=False)
input_ids, input_mask, all_seg_ids = [], [], []
tokens_context = tokenize_fn(example.context)
for i in range(len(example.qa_list)):
tokens_qa = tokenize_fn(example.qa_list[i])
if len(tokens_qa) > FLAGS.max_qa_length:
tokens_qa = tokens_qa[- FLAGS.max_qa_length:]
if len(tokens_context) + len(tokens_qa) > FLAGS.max_seq_length - 3:
tokens = tokens_context[: FLAGS.max_seq_length - 3 - len(tokens_qa)]
else:
tokens = tokens_context
segment_ids = [SEG_ID_A] * len(tokens)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_A)
tokens.extend(tokens_qa)
segment_ids.extend([SEG_ID_B] * len(tokens_qa))
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_B)
tokens.append(CLS_ID)
segment_ids.append(SEG_ID_CLS)
cur_input_ids = tokens
cur_input_mask = [0] * len(cur_input_ids)
if len(cur_input_ids) < FLAGS.max_seq_length:
delta_len = FLAGS.max_seq_length - len(cur_input_ids)
cur_input_ids = [0] * delta_len + cur_input_ids
cur_input_mask = [1] * delta_len + cur_input_mask
segment_ids = [SEG_ID_PAD] * delta_len + segment_ids
assert len(cur_input_ids) == FLAGS.max_seq_length
assert len(cur_input_mask) == FLAGS.max_seq_length
assert len(segment_ids) == FLAGS.max_seq_length
input_ids.extend(cur_input_ids)
input_mask.extend(cur_input_mask)
all_seg_ids.extend(segment_ids)
label_id = example.label
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=all_seg_ids,
label_id=label_id)
return feature
class InputExample(object):
def __init__(self, context, qa_list, label, level):
self.context = context
self.qa_list = qa_list
self.label = label
self.level = level
def get_examples(data_dir, set_type):
examples = []
for level in ["middle", "high"]:
if level == "middle" and FLAGS.high_only: continue
if level == "high" and FLAGS.middle_only: continue
cur_dir = os.path.join(data_dir, set_type, level)
for filename in tf.gfile.ListDirectory(cur_dir):
cur_path = os.path.join(cur_dir, filename)
with tf.gfile.Open(cur_path) as f:
cur_data = json.load(f)
answers = cur_data["answers"]
options = cur_data["options"]
questions = cur_data["questions"]
context = cur_data["article"]
for i in range(len(answers)):
label = ord(answers[i]) - ord("A")
qa_list = []
question = questions[i]
for j in range(4):
option = options[i][j]
if "_" in question:
qa_cat = question.replace("_", option)
else:
qa_cat = " ".join([question, option])
qa_list.append(qa_cat)
examples.append(InputExample(context, qa_list, label, level))
return examples
def file_based_convert_examples_to_features(examples, tokenize_fn, output_file):
if tf.gfile.Exists(output_file) and not FLAGS.overwrite_data:
return
tf.logging.info("Start writing tfrecord %s.", output_file)
writer = tf.python_io.TFRecordWriter(output_file)
for ex_index, example in enumerate(examples):
if ex_index % 10000 == 0:
tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
feature = convert_single_example(example, tokenize_fn)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
def create_float_feature(values):
f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return f
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_float_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["label_ids"] = create_int_feature([feature.label_id])
features["is_real_example"] = create_int_feature(
[int(feature.is_real_example)])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
def file_based_input_fn_builder(input_file, seq_length, is_training,
drop_remainder):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
name_to_features = {
"input_ids": tf.FixedLenFeature([seq_length * 4], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length * 4], tf.float32),
"segment_ids": tf.FixedLenFeature([seq_length * 4], tf.int64),
"label_ids": tf.FixedLenFeature([], tf.int64),
"is_real_example": tf.FixedLenFeature([], tf.int64),
}
tf.logging.info("Input tfrecord file {}".format(input_file))
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
def input_fn(params):
"""The actual input function."""
if FLAGS.use_tpu:
batch_size = params["batch_size"]
elif is_training:
batch_size = FLAGS.train_batch_size
elif FLAGS.do_eval:
batch_size = FLAGS.eval_batch_size
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.shuffle(buffer_size=FLAGS.shuffle_buffer)
d = d.repeat()
# d = d.shuffle(buffer_size=100)
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
return input_fn
def get_model_fn():
def model_fn(features, labels, mode, params):
#### Training or Evaluation
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
total_loss, per_example_loss, logits = function_builder.get_race_loss(
FLAGS, features, is_training)
#### Check model parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
#### load pretrained models
scaffold_fn = model_utils.init_from_checkpoint(FLAGS)
#### Evaluation mode
if mode == tf.estimator.ModeKeys.EVAL:
assert FLAGS.num_hosts == 1
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
eval_input_dict = {
'labels': label_ids,
'predictions': predictions,
'weights': is_real_example
}
accuracy = tf.metrics.accuracy(**eval_input_dict)
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
return {
'eval_accuracy': accuracy,
'eval_loss': loss}
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
#### Constucting evaluation TPUEstimatorSpec with new cache.
label_ids = tf.reshape(features['label_ids'], [-1])
metric_args = [per_example_loss, label_ids, logits, is_real_example]
if FLAGS.use_tpu:
eval_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=(metric_fn, metric_args),
scaffold_fn=scaffold_fn)
else:
eval_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=metric_fn(*metric_args))
return eval_spec
#### Configuring the optimizer
train_op, learning_rate, _ = model_utils.get_train_op(FLAGS, total_loss)
monitor_dict = {}
monitor_dict["lr"] = learning_rate
#### Constucting training TPUEstimatorSpec with new cache.
if FLAGS.use_tpu:
#### Creating host calls
host_call = None
train_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op, host_call=host_call,
scaffold_fn=scaffold_fn)
else:
train_spec = tf.estimator.EstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op)
return train_spec
return model_fn
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
#### Validate flags
if FLAGS.save_steps is not None:
FLAGS.iterations = min(FLAGS.iterations, FLAGS.save_steps)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MakeDirs(FLAGS.output_dir)
sp = spm.SentencePieceProcessor()
sp.Load(FLAGS.spiece_model_file)
def tokenize_fn(text):
text = preprocess_text(text, lower=FLAGS.uncased)
return encode_ids(sp, text)
# TPU Configuration
run_config = model_utils.configure_tpu(FLAGS)
model_fn = get_model_fn()
spm_basename = os.path.basename(FLAGS.spiece_model_file)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
if FLAGS.use_tpu:
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size)
else:
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config)
if FLAGS.do_train:
train_file_base = "{}.len-{}.train.tf_record".format(
spm_basename, FLAGS.max_seq_length)
train_file = os.path.join(FLAGS.output_dir, train_file_base)
if not tf.gfile.Exists(train_file) or FLAGS.overwrite_data:
train_examples = get_examples(FLAGS.data_dir, "train")
random.shuffle(train_examples)
file_based_convert_examples_to_features(
train_examples, tokenize_fn, train_file)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_steps)
if FLAGS.do_eval:
eval_examples = get_examples(FLAGS.data_dir, FLAGS.eval_split)
tf.logging.info("Num of eval samples: {}".format(len(eval_examples)))
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
#
# Modified in XL: We also adopt the same mechanism for GPUs.
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(PaddingInputExample())
eval_file_base = "{}.len-{}.{}.tf_record".format(
spm_basename, FLAGS.max_seq_length, FLAGS.eval_split)
if FLAGS.high_only:
eval_file_base = "high." + eval_file_base
elif FLAGS.middle_only:
eval_file_base = "middle." + eval_file_base
eval_file = os.path.join(FLAGS.output_dir, eval_file_base)
file_based_convert_examples_to_features(
eval_examples, tokenize_fn, eval_file)
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=True)
ret = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps)
# Log current result
tf.logging.info("=" * 80)
log_str = "Eval | "
for key, val in ret.items():
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
tf.logging.info("=" * 80)
if __name__ == "__main__":
tf.app.run()