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run_pretraining_test.py
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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.
"""Tests for run_pretraining."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import random
import tempfile
from absl.testing import flagsaver
from albert import modeling
from albert import run_pretraining
import tensorflow.compat.v1 as tf
FLAGS = tf.app.flags.FLAGS
def _create_config_file(filename, max_seq_length, vocab_size):
"""Creates an AlbertConfig and saves it to file."""
albert_config = modeling.AlbertConfig(
vocab_size,
embedding_size=5,
hidden_size=14,
num_hidden_layers=3,
num_hidden_groups=1,
num_attention_heads=2,
intermediate_size=19,
inner_group_num=1,
down_scale_factor=1,
hidden_act="gelu",
hidden_dropout_prob=0,
attention_probs_dropout_prob=0,
max_position_embeddings=max_seq_length,
type_vocab_size=2,
initializer_range=0.02)
with tf.gfile.Open(filename, "w") as outfile:
outfile.write(albert_config.to_json_string())
def _create_record(max_predictions_per_seq, max_seq_length, vocab_size):
"""Returns a tf.train.Example containing random data."""
example = tf.train.Example()
example.features.feature["input_ids"].int64_list.value.extend(
[random.randint(0, vocab_size - 1) for _ in range(max_seq_length)])
example.features.feature["input_mask"].int64_list.value.extend(
[random.randint(0, 1) for _ in range(max_seq_length)])
example.features.feature["masked_lm_positions"].int64_list.value.extend([
random.randint(0, max_seq_length - 1)
for _ in range(max_predictions_per_seq)
])
example.features.feature["masked_lm_ids"].int64_list.value.extend([
random.randint(0, vocab_size - 1) for _ in range(max_predictions_per_seq)
])
example.features.feature["masked_lm_weights"].float_list.value.extend(
[1. for _ in range(max_predictions_per_seq)])
example.features.feature["segment_ids"].int64_list.value.extend(
[0 for _ in range(max_seq_length)])
example.features.feature["next_sentence_labels"].int64_list.value.append(
random.randint(0, 1))
return example
def _create_input_file(filename,
max_predictions_per_seq,
max_seq_length,
vocab_size,
size=1000):
"""Creates an input TFRecord file of specified size."""
with tf.io.TFRecordWriter(filename) as writer:
for _ in range(size):
ex = _create_record(max_predictions_per_seq, max_seq_length, vocab_size)
writer.write(ex.SerializeToString())
class RunPretrainingTest(tf.test.TestCase):
def _verify_output_file(self, basename):
self.assertTrue(tf.gfile.Exists(os.path.join(FLAGS.output_dir, basename)))
def _verify_checkpoint_files(self, name):
self._verify_output_file(name + ".meta")
self._verify_output_file(name + ".index")
self._verify_output_file(name + ".data-00000-of-00001")
@flagsaver.flagsaver
def test_pretraining(self):
# Set up required flags.
vocab_size = 97
FLAGS.max_predictions_per_seq = 7
FLAGS.max_seq_length = 13
FLAGS.output_dir = tempfile.mkdtemp("output_dir")
FLAGS.albert_config_file = os.path.join(
tempfile.mkdtemp("config_dir"), "albert_config.json")
FLAGS.input_file = os.path.join(
tempfile.mkdtemp("input_dir"), "input_data.tfrecord")
FLAGS.do_train = True
FLAGS.do_eval = True
FLAGS.num_train_steps = 1
FLAGS.save_checkpoints_steps = 1
# Construct requisite input files.
_create_config_file(FLAGS.albert_config_file, FLAGS.max_seq_length,
vocab_size)
_create_input_file(FLAGS.input_file, FLAGS.max_predictions_per_seq,
FLAGS.max_seq_length, vocab_size)
# Run the pretraining.
run_pretraining.main(None)
# Verify output.
self._verify_checkpoint_files("model.ckpt-best")
self._verify_checkpoint_files("model.ckpt-1")
self._verify_output_file("eval_results.txt")
self._verify_output_file("checkpoint")
if __name__ == "__main__":
tf.test.main()