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# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Executes benchmark testing for bert pretraining."""
# pylint: disable=line-too-long
from __future__ import print_function
import json
import os
import time
from typing import Optional
from absl import flags
from absl import logging
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.benchmark import benchmark_wrappers
from official.benchmark import bert_benchmark_utils
from official.benchmark import owner_utils
from official.nlp.bert import run_pretraining
from official.utils.flags import core as flags_core
from official.utils.misc import distribution_utils
# Pretrain masked lanauge modeling accuracy range:
MIN_MLM_ACCURACY = 0.635
MAX_MLM_ACCURACY = 0.645
# Pretrain next sentence prediction accuracy range:
MIN_NSP_ACCURACY = 0.94
MAX_NSP_ACCURACY = 0.96
BERT_PRETRAIN_FILES_SEQ128 = 'gs://mlcompass-data/bert/pretraining_data/seq_128/wikipedia.tfrecord*,gs://mlcompass-data/bert/pretraining_data/seq_128/books.tfrecord*'
BERT_BASE_CONFIG_FILE = 'gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-12_H-768_A-12/bert_config.json'
FLAGS = flags.FLAGS
class BertPretrainAccuracyBenchmark(bert_benchmark_utils.BertBenchmarkBase):
"""Benchmark accuracy tests for BERT Pretraining."""
def __init__(self,
output_dir: Optional[str] = None,
tpu: Optional[str] = None,
**kwargs):
"""Inits BertPretrainAccuracyBenchmark class.
Args:
output_dir: Directory where to output e.g. log files
tpu: TPU name to use in a TPU benchmark.
**kwargs: Additional keyword arguments.
"""
super(BertPretrainAccuracyBenchmark, self).__init__(
output_dir=output_dir, tpu=tpu, **kwargs)
@benchmark_wrappers.enable_runtime_flags
def _run_and_report_benchmark(self, summary_path: str, report_accuracy: bool):
"""Runs and reports the benchmark given the provided configuration."""
distribution = distribution_utils.get_distribution_strategy(
distribution_strategy='tpu', tpu_address=self.tpu)
logging.info('Flags: %s', flags_core.get_nondefault_flags_as_str())
start_time_sec = time.time()
run_pretraining.run_bert_pretrain(
strategy=distribution, custom_callbacks=self.timer_callback)
wall_time_sec = time.time() - start_time_sec
with tf.io.gfile.GFile(summary_path, 'rb') as reader:
summary = json.loads(reader.read().decode('utf-8'))
self._report_benchmark(summary, start_time_sec, wall_time_sec,
report_accuracy)
def _report_benchmark(self, summary, start_time_sec, wall_time_sec,
report_accuracy):
metrics = [{
'name': 'train_loss',
'value': summary['train_loss'],
}, {
'name':
'exp_per_second',
'value':
self.timer_callback.get_examples_per_sec(FLAGS.train_batch_size *
FLAGS.steps_per_loop)
}, {
'name': 'startup_time',
'value': self.timer_callback.get_startup_time(start_time_sec)
}]
if report_accuracy:
metrics.extend([{
'name': 'masked_lm_accuracy',
'value': summary['masked_lm_accuracy'],
'min_value': MIN_MLM_ACCURACY,
'max_value': MAX_MLM_ACCURACY,
}, {
'name': 'next_sentence_accuracy',
'value': summary['next_sentence_accuracy'],
'min_value': MIN_NSP_ACCURACY,
'max_value': MAX_NSP_ACCURACY,
}])
self.report_benchmark(
iters=summary['total_training_steps'],
wall_time=wall_time_sec,
metrics=metrics,
extras={'flags': flags_core.get_nondefault_flags_as_str()})
def _specify_common_flags(self):
FLAGS.bert_config_file = BERT_BASE_CONFIG_FILE
FLAGS.train_batch_size = 512
FLAGS.learning_rate = 1e-4
FLAGS.warmup_steps = 10000
FLAGS.steps_per_loop = 10000
FLAGS.distribution_strategy = 'tpu'
FLAGS.input_files = BERT_PRETRAIN_FILES_SEQ128
FLAGS.max_seq_length = 128
FLAGS.max_predictions_per_seq = 20
FLAGS.dtype = 'bf16'
@owner_utils.Owner('tf-model-garden')
def benchmark_accuracy_8x8_tpu_bf16_seq128_500k_steps(self):
"""Test bert pretraining with 8x8 TPU for 500k steps."""
# This is used for accuracy test.
self._setup()
self._specify_common_flags()
FLAGS.num_steps_per_epoch = 500000
FLAGS.num_train_epochs = 1
FLAGS.model_dir = self._get_model_dir(
'benchmark_accuracy_8x8_tpu_bf16_seq128_500k_steps')
summary_path = os.path.join(FLAGS.model_dir,
'summaries/training_summary.txt')
# Set train_summary_interval to -1 to disable training summary, because
# writing summary to gcs may fail and summaries are not needed for this
# accuracy benchmark test.
FLAGS.train_summary_interval = -1
self._run_and_report_benchmark(summary_path=summary_path,
report_accuracy=True)
@owner_utils.Owner('tf-model-garden')
def benchmark_perf_4x4_tpu_bf16_seq128_10k_steps(self):
"""Test bert pretraining with 4x4 TPU for 10000 steps."""
self._setup()
self._specify_common_flags()
FLAGS.num_steps_per_epoch = 5000
FLAGS.num_train_epochs = 2
FLAGS.model_dir = self._get_model_dir(
'benchmark_perf_4x4_tpu_bf16_seq128_10k_steps')
summary_path = os.path.join(FLAGS.model_dir,
'summaries/training_summary.txt')
# Disable accuracy check.
self._run_and_report_benchmark(
summary_path=summary_path, report_accuracy=False)
@owner_utils.Owner('tf-model-garden')
def benchmark_perf_8x8_tpu_bf16_seq128_10k_steps(self):
"""Test bert pretraining with 8x8 TPU for 10000 steps."""
self._setup()
self._specify_common_flags()
FLAGS.num_steps_per_epoch = 5000
FLAGS.num_train_epochs = 2
FLAGS.model_dir = self._get_model_dir(
'benchmark_perf_8x8_tpu_bf16_seq128_10k_steps')
summary_path = os.path.join(FLAGS.model_dir,
'summaries/training_summary.txt')
# Disable accuracy check.
self._run_and_report_benchmark(summary_path=summary_path,
report_accuracy=False)
if __name__ == '__main__':
tf.test.main()
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