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run_pretraining_test.py
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# coding=utf-8
# Copyright 2018 The Google AI Team Authors.
#
# 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.
# Lint as: python2, python3
"""Run a small test for ALBERT pretraining."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import modeling
import run_pretraining
import numpy as np
from six.moves import range
import tensorflow as tf
from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver
flags = tf.flags
FLAGS = flags.FLAGS
def _add_float32_feature(example, feature, shape, minval, maxval):
values = np.random.random_sample(shape)
values = (maxval - minval) * values + minval
example.features.feature[feature].float_list.value.extend(values)
def _add_int64_feature(example, feature, shape, minval, maxval):
values = np.random.randint(low=minval, high=maxval+1, size=shape)
example.features.feature[feature].int64_list.value.extend(values)
def _make_dummy_input_files(num_files, num_per_file, vocab_size):
for i in range(num_files):
filename = os.path.join(FLAGS.output_dir, "input%d.tfrecord" % i)
with tf.io.TFRecordWriter(filename) as writer:
for _ in range(num_per_file):
example = tf.train.Example()
_add_int64_feature(example, "input_ids", [FLAGS.max_seq_length],
minval=0, maxval=vocab_size-1)
_add_int64_feature(example, "input_mask", [FLAGS.max_seq_length],
minval=0, maxval=1)
_add_int64_feature(example, "segment_ids", [FLAGS.max_seq_length],
minval=0, maxval=0)
_add_int64_feature(example, "next_sentence_labels", [1], minval=0,
maxval=1)
_add_int64_feature(example, "token_boundary", [FLAGS.max_seq_length],
minval=0, maxval=vocab_size)
_add_int64_feature(example, "masked_lm_positions",
[FLAGS.max_predictions_per_seq], minval=0,
maxval=FLAGS.max_seq_length-1)
_add_int64_feature(example, "masked_lm_ids",
[FLAGS.max_predictions_per_seq], minval=0,
maxval=vocab_size-1)
_add_float32_feature(example, "masked_lm_weights",
[FLAGS.max_predictions_per_seq], minval=0,
maxval=1)
record = example.SerializeToString()
writer.write(record)
yield filename
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
albert_config = modeling.AlbertConfig(
100,
embedding_size=7,
hidden_size=26,
num_hidden_layers=3,
num_hidden_groups=1,
num_attention_heads=13,
intermediate_size=29,
inner_group_num=1,
down_scale_factor=1,
hidden_act="gelu",
hidden_dropout_prob=0,
attention_probs_dropout_prob=0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02)
tf.io.gfile.makedirs(FLAGS.output_dir)
# Create some dummy input files instead of reading from actual data.
input_files = list(_make_dummy_input_files(2, 5, 100))
tf.logging.info("*** Input Files ***")
for input_file in input_files:
tf.logging.info(" %s" % input_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.estimator.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.estimator.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tf.estimator.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
model_fn = run_pretraining.model_fn_builder(
albert_config=albert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.num_train_steps,
num_warmup_steps=FLAGS.num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
optimizer=FLAGS.optimizer,
poly_power=FLAGS.poly_power,
start_warmup_step=FLAGS.start_warmup_step)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.estimator.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)
if FLAGS.do_train:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
train_input_fn = run_pretraining.input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=True)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)
if FLAGS.do_eval:
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
global_step = -1
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
writer = tf.io.gfile.GFile(output_eval_file, "w")
tf.io.gfile.makedirs(FLAGS.export_dir)
eval_input_fn = run_pretraining.input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False)
while global_step < FLAGS.num_train_steps:
if estimator.latest_checkpoint() is None:
tf.logging.info("No checkpoint found yet. Sleeping.")
time.sleep(1)
else:
result = estimator.evaluate(
input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if __name__ == "__main__":
flags.mark_flag_as_required("output_dir")
flags.mark_flag_as_required("export_dir")
tf.app.run()