models/official/benchmark/bert_squad_benchmark.py

# Copyright 2019 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 BERT SQuAD benchmarks and accuracy tests."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import json
import os
import time

# pylint: disable=g-bad-import-order
from absl import flags
from absl import logging
from absl.testing import flagsaver
import tensorflow as tf
# pylint: enable=g-bad-import-order

from official.benchmark import bert_benchmark_utils as benchmark_utils
from official.benchmark import owner_utils
from official.nlp.bert import run_squad
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
from official.benchmark import benchmark_wrappers


# pylint: disable=line-too-long
PRETRAINED_CHECKPOINT_PATH = 'gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16/bert_model.ckpt'
SQUAD_TRAIN_DATA_PATH = 'gs://tf-perfzero-data/bert/squad/squad_train.tf_record'
SQUAD_PREDICT_FILE = 'gs://tf-perfzero-data/bert/squad/dev-v1.1.json'
SQUAD_VOCAB_FILE = 'gs://tf-perfzero-data/bert/squad/vocab.txt'
SQUAD_MEDIUM_INPUT_META_DATA_PATH = 'gs://tf-perfzero-data/bert/squad/squad_medium_meta_data'
SQUAD_LONG_INPUT_META_DATA_PATH = 'gs://tf-perfzero-data/bert/squad/squad_long_meta_data'
SQUAD_FULL_INPUT_META_DATA_PATH = 'gs://tf-perfzero-data/bert/squad/squad_full_meta_data'
MODEL_CONFIG_FILE_PATH = 'gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16/bert_config.json'
# pylint: enable=line-too-long

TMP_DIR = os.getenv('TMPDIR')
FLAGS = flags.FLAGS


class BertSquadBenchmarkBase(benchmark_utils.BertBenchmarkBase):
  """Base class to hold methods common to test classes in the module."""

  def __init__(self, output_dir=None, tpu=None):
    super(BertSquadBenchmarkBase, self).__init__(output_dir=output_dir, tpu=tpu)

  def _read_training_summary_from_file(self):
    """Reads the training summary from a file."""
    summary_path = os.path.join(FLAGS.model_dir,
                                'summaries/training_summary.txt')
    with tf.io.gfile.GFile(summary_path, 'rb') as reader:
      return json.loads(reader.read().decode('utf-8'))

  def _read_input_meta_data_from_file(self):
    """Reads the input metadata from a file."""
    with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
      return json.loads(reader.read().decode('utf-8'))

  def _get_distribution_strategy(self, ds_type='mirrored'):
    """Gets the distribution strategy.

    Args:
      ds_type: String, the distribution strategy type to be used. Can be
      'mirrored', 'multi_worker_mirrored', 'tpu' and 'off'.

    Returns:
      A `tf.distribute.DistibutionStrategy` object.
    """
    if self.tpu or ds_type == 'tpu':
      return distribution_utils.get_distribution_strategy(
          distribution_strategy='tpu', tpu_address=self.tpu)
    elif ds_type == 'multi_worker_mirrored':
      # Configures cluster spec for multi-worker distribution strategy.
      _ = distribution_utils.configure_cluster(FLAGS.worker_hosts,
                                               FLAGS.task_index)
    return distribution_utils.get_distribution_strategy(
        distribution_strategy=ds_type,
        num_gpus=self.num_gpus,
        all_reduce_alg=FLAGS.all_reduce_alg)

  def _init_gpu_and_data_threads(self):
    """Set env variables before any TF calls."""
    if FLAGS.tf_gpu_thread_mode:
      keras_utils.set_gpu_thread_mode_and_count(
          per_gpu_thread_count=FLAGS.per_gpu_thread_count,
          gpu_thread_mode=FLAGS.tf_gpu_thread_mode,
          num_gpus=self.num_gpus,
          datasets_num_private_threads=FLAGS.datasets_num_private_threads)

  @flagsaver.flagsaver
  def _train_squad(self, run_eagerly=False, ds_type='mirrored'):
    """Runs BERT SQuAD training. Uses mirrored strategy by default."""
    self._init_gpu_and_data_threads()
    input_meta_data = self._read_input_meta_data_from_file()
    strategy = self._get_distribution_strategy(ds_type)

    run_squad.train_squad(
        strategy=strategy,
        input_meta_data=input_meta_data,
        run_eagerly=run_eagerly,
        custom_callbacks=[self.timer_callback])

  @flagsaver.flagsaver
  def _evaluate_squad(self, ds_type='mirrored'):
    """Runs BERT SQuAD evaluation. Uses mirrored strategy by default."""
    self._init_gpu_and_data_threads()
    input_meta_data = self._read_input_meta_data_from_file()
    strategy = self._get_distribution_strategy(ds_type)

    if input_meta_data.get('version_2_with_negative', False):
      logging.error('In memory evaluation result for SQuAD v2 is not accurate')
    eval_metrics = run_squad.eval_squad(strategy=strategy,
                                        input_meta_data=input_meta_data)
    # Use F1 score as reported evaluation metric.
    self.eval_metrics = eval_metrics['final_f1']


class BertSquadBenchmarkReal(BertSquadBenchmarkBase):
  """Short benchmark performance tests for BERT SQuAD model.

  Tests BERT SQuAD performance in different GPU configurations.
  The naming convention of below test cases follow
  `benchmark_(number of gpus)_gpu` format for GPUs and
  `benchmark_(topology)_tpu` format for TPUs.
  """

  def __init__(self, output_dir=TMP_DIR, tpu=None, **kwargs):
    super(BertSquadBenchmarkReal, self).__init__(output_dir=output_dir, tpu=tpu)

  def _setup(self):
    """Sets up the benchmark and SQuAD flags."""
    super(BertSquadBenchmarkReal, self)._setup()
    FLAGS.train_data_path = SQUAD_TRAIN_DATA_PATH
    FLAGS.predict_file = SQUAD_PREDICT_FILE
    FLAGS.vocab_file = SQUAD_VOCAB_FILE
    FLAGS.bert_config_file = MODEL_CONFIG_FILE_PATH
    FLAGS.num_train_epochs = 1
    FLAGS.steps_per_loop = 100

  @benchmark_wrappers.enable_runtime_flags
  def _run_and_report_benchmark(self,
                                run_eagerly=False,
                                ds_type='mirrored'):
    """Runs the benchmark and reports various metrics."""
    if FLAGS.train_batch_size <= 4 or run_eagerly:
      FLAGS.input_meta_data_path = SQUAD_MEDIUM_INPUT_META_DATA_PATH
    else:
      FLAGS.input_meta_data_path = SQUAD_LONG_INPUT_META_DATA_PATH
    start_time_sec = time.time()
    self._train_squad(run_eagerly=run_eagerly, ds_type=ds_type)
    wall_time_sec = time.time() - start_time_sec

    summary = self._read_training_summary_from_file()
    summary['start_time_sec'] = start_time_sec

    super(BertSquadBenchmarkReal, self)._report_benchmark(
        stats=summary,
        wall_time_sec=wall_time_sec,
        min_accuracy=0,
        max_accuracy=1)

  def benchmark_1_gpu(self):
    """Tests BERT SQuAD model performance with 1 GPU."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_squad')
    FLAGS.train_batch_size = 4

    self._run_and_report_benchmark()

  def benchmark_1_gpu_eager(self):
    """Tests BERT SQuAD model performance with 1 GPU."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_squad_eager')
    FLAGS.train_batch_size = 2

    self._run_and_report_benchmark(run_eagerly=True)

  def benchmark_1_gpu_xla(self):
    """Tests BERT SQuAD model performance with 1 GPU with XLA."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_xla_squad')
    # XLA runs out of memory when running with batch size 4.
    FLAGS.train_batch_size = 3
    FLAGS.enable_xla = True

    self._run_and_report_benchmark()

  def benchmark_1_gpu_no_dist_strat(self):
    """Tests BERT SQuAD model performance with 1 GPU without DS."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_no_dist_strat_squad')
    FLAGS.train_batch_size = 4

    self._run_and_report_benchmark(ds_type='off')

  def benchmark_1_gpu_eager_no_dist_strat(self):
    """Tests BERT SQuAD model performance with 1 GPU with eager execution."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir(
        'benchmark_1_gpu_eager_no_dist_strat_squad')
    FLAGS.train_batch_size = 4

    self._run_and_report_benchmark(ds_type='off', run_eagerly=True)

  @owner_utils.Owner('tf-model-garden')
  def benchmark_8_gpu(self):
    """Tests BERT SQuAD model performance with 8 GPUs."""

    self._setup()
    self.num_gpus = 8
    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_squad')
    FLAGS.train_batch_size = 24
    FLAGS.tf_gpu_thread_mode = 'gpu_private'

    self._run_and_report_benchmark()

  def benchmark_1_gpu_fp16_eager(self):
    """Tests BERT SQuAD model performance with 1 GPU and FP16."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_squad_fp16_eager')
    FLAGS.train_batch_size = 4
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 'dynamic'

    self._run_and_report_benchmark(run_eagerly=True)

  def benchmark_1_gpu_fp16(self):
    """Tests BERT SQuAD model performance with 1 GPU and FP16."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_squad_fp16')
    FLAGS.train_batch_size = 4
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 'dynamic'

    self._run_and_report_benchmark()

  def benchmark_1_gpu_xla_fp16(self):
    """Tests BERT SQuAD model performance with 1 GPU with XLA and FP16."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_xla_squad_fp16')
    FLAGS.train_batch_size = 4
    FLAGS.enable_xla = True
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 'dynamic'

    self._run_and_report_benchmark()

  def benchmark_8_gpu_fp16(self):
    """Tests BERT SQuAD model performance with 8 GPUs."""

    self._setup()
    self.num_gpus = 8
    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_squad_fp16')
    FLAGS.train_batch_size = 32
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 'dynamic'
    FLAGS.tf_gpu_thread_mode = 'gpu_private'

    self._run_and_report_benchmark()

  def benchmark_8_gpu_xla_fp16(self):
    """Tests BERT SQuAD model performance with 8 GPUs with XLA."""

    self._setup()
    self.num_gpus = 8
    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_squad_fp16')
    FLAGS.train_batch_size = 32
    FLAGS.enable_xla = True
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 'dynamic'

    self._run_and_report_benchmark()

  def benchmark_1_gpu_amp(self):
    """Tests BERT SQuAD model performance with 1 GPU with automatic mixed precision."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_amp_squad')
    FLAGS.train_batch_size = 4
    FLAGS.dtype = 'fp16'
    FLAGS.fp16_implementation = 'graph_rewrite'

    self._run_and_report_benchmark()

  def benchmark_8_gpu_amp(self):
    """Tests BERT SQuAD model performance with 1 GPU with automatic mixed precision."""

    self._setup()
    self.num_gpus = 8
    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_amp_squad')
    FLAGS.train_batch_size = 32
    FLAGS.dtype = 'fp16'
    FLAGS.fp16_implementation = 'graph_rewrite'
    FLAGS.tf_gpu_thread_mode = 'gpu_private'

    self._run_and_report_benchmark()

  @owner_utils.Owner('tf-model-garden')
  def benchmark_2x2_tpu(self):
    """Tests BERT SQuAD model performance with 2x2 TPU."""

    self._setup()
    FLAGS.model_dir = self._get_model_dir('benchmark_2x2_tpu')
    FLAGS.train_batch_size = 48
    FLAGS.predict_batch_size = 48
    FLAGS.mode = 'train'
    FLAGS.learning_rate = 8e-5
    FLAGS.num_train_epochs = 1
    FLAGS.steps_per_loop = 100
    FLAGS.do_lower_case = True
    FLAGS.init_checkpoint = PRETRAINED_CHECKPOINT_PATH
    self._run_and_report_benchmark()


class BertSquadAccuracy(BertSquadBenchmarkBase):
  """Short accuracy test for BERT SQuAD model.

  Tests BERT SQuAD accuracy. The naming convention of below test cases follow
  `benchmark_(number of gpus)_gpu` format for GPUs and
  `benchmark_(topology)_tpu` format for TPUs.
  """

  def __init__(self, output_dir=None, tpu=None, **kwargs):
    super(BertSquadAccuracy, self).__init__(output_dir=output_dir, tpu=tpu)

  def _setup(self):
    """Sets up the benchmark and SQuAD flags."""
    super(BertSquadAccuracy, self)._setup()
    FLAGS.train_data_path = SQUAD_TRAIN_DATA_PATH
    FLAGS.predict_file = SQUAD_PREDICT_FILE
    FLAGS.vocab_file = SQUAD_VOCAB_FILE
    FLAGS.input_meta_data_path = SQUAD_FULL_INPUT_META_DATA_PATH
    FLAGS.bert_config_file = MODEL_CONFIG_FILE_PATH
    FLAGS.init_checkpoint = PRETRAINED_CHECKPOINT_PATH
    FLAGS.num_train_epochs = 2
    FLAGS.steps_per_loop = 100

  @benchmark_wrappers.enable_runtime_flags
  def _run_and_report_benchmark(self,
                                run_eagerly=False,
                                ds_type='mirrored'):
    """Runs the benchmark and reports various metrics."""
    start_time_sec = time.time()
    self._train_squad(run_eagerly=run_eagerly, ds_type=ds_type)
    self._evaluate_squad(ds_type=ds_type)
    wall_time_sec = time.time() - start_time_sec

    summary = self._read_training_summary_from_file()
    summary['eval_metrics'] = self.eval_metrics
    summary['start_time_sec'] = start_time_sec

    super(BertSquadAccuracy, self)._report_benchmark(
        stats=summary,
        wall_time_sec=wall_time_sec,
        min_accuracy=0.900,
        max_accuracy=0.920)

  def benchmark_1_gpu_eager(self):
    """Tests BERT SQuAD model accuracy with 1 GPU with eager execution."""

    self._setup()
    self.num_gpus = 1
    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_squad_eager')
    FLAGS.train_batch_size = 4

    self._run_and_report_benchmark(ds_type='off', run_eagerly=True)

  @owner_utils.Owner('tf-model-garden')
  def benchmark_8_gpu(self):
    """Tests BERT SQuAD model accuracy with 8 GPUs."""

    self._setup()
    self.num_gpus = 8
    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_squad')
    FLAGS.train_batch_size = 24
    FLAGS.tf_gpu_thread_mode = 'gpu_private'

    self._run_and_report_benchmark()

  def benchmark_8_gpu_fp16(self):
    """Tests BERT SQuAD model accuracy with 8 GPUs and FP16."""

    self._setup()
    self.num_gpus = 8
    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_squad_fp16')
    FLAGS.train_batch_size = 32
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 'dynamic'
    FLAGS.tf_gpu_thread_mode = 'gpu_private'

    self._run_and_report_benchmark()

  def benchmark_8_gpu_xla(self):
    """Tests BERT SQuAD model accuracy with 8 GPUs."""

    self._setup()
    self.num_gpus = 8
    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_squad_xla')
    FLAGS.train_batch_size = 32
    FLAGS.enable_xla = True
    FLAGS.tf_gpu_thread_mode = 'gpu_private'

    self._run_and_report_benchmark()

  @owner_utils.Owner('tf-model-garden')
  def benchmark_2x2_tpu(self):
    """Tests BERT SQuAD model accuracy with 2x2 TPU."""

    self._setup()
    FLAGS.model_dir = self._get_model_dir('benchmark_2x2_tpu')
    FLAGS.train_batch_size = 48

    self._run_and_report_benchmark()


class BertSquadMultiWorkerAccuracy(BertSquadBenchmarkBase):
  """BERT SQuAD distributed accuracy tests with multiple workers."""

  def __init__(self, output_dir=None, tpu=None, **kwargs):
    super(BertSquadMultiWorkerAccuracy, self).__init__(
        output_dir=output_dir, tpu=tpu)

  def _setup(self):
    """Sets up the benchmark and SQuAD flags."""
    super(BertSquadMultiWorkerAccuracy, self)._setup()
    FLAGS.train_data_path = SQUAD_TRAIN_DATA_PATH
    FLAGS.predict_file = SQUAD_PREDICT_FILE
    FLAGS.vocab_file = SQUAD_VOCAB_FILE
    FLAGS.input_meta_data_path = SQUAD_FULL_INPUT_META_DATA_PATH
    FLAGS.bert_config_file = MODEL_CONFIG_FILE_PATH
    FLAGS.init_checkpoint = PRETRAINED_CHECKPOINT_PATH
    FLAGS.num_train_epochs = 2
    FLAGS.steps_per_loop = 100

  @benchmark_wrappers.enable_runtime_flags
  def _run_and_report_benchmark(self,
                                use_ds=True,
                                run_eagerly=False):
    """Runs the benchmark and reports various metrics."""
    start_time_sec = time.time()
    self._train_squad(run_eagerly=run_eagerly,
                      ds_type='multi_worker_mirrored')
    self._evaluate_squad(ds_type='multi_worker_mirrored')
    wall_time_sec = time.time() - start_time_sec

    summary = self._read_training_summary_from_file()
    summary['eval_metrics'] = self.eval_metrics

    super(BertSquadMultiWorkerAccuracy, self)._report_benchmark(
        stats=summary,
        wall_time_sec=wall_time_sec,
        min_accuracy=0.900,
        max_accuracy=0.920)

  def _benchmark_common(self, num_workers, all_reduce_alg):
    """Common to all benchmarks in this class."""
    self._setup()

    num_gpus = 8
    FLAGS.num_gpus = num_gpus
    FLAGS.dtype = 'fp16'
    FLAGS.enable_xla = False
    FLAGS.distribution_strategy = 'multi_worker_mirrored'
    FLAGS.tf_gpu_thread_mode = 'gpu_private'
    FLAGS.datasets_num_private_threads = 32
    FLAGS.model_dir = self._get_model_dir(
        'benchmark_8_gpu_{}_worker_fp16_{}_tweaked'.format(
            num_workers, all_reduce_alg))
    FLAGS.train_batch_size = 4 * num_gpus * num_workers
    FLAGS.all_reduce_alg = all_reduce_alg

    self._run_and_report_benchmark()

  def benchmark_eager_8_gpu_2_workers_fp16_ring_tweaked(self):
    """8 GPUs per worker, 2 workers, fp16, ring all-reduce."""
    self._benchmark_common(num_workers=2, all_reduce_alg='ring')

  def benchmark_eager_8_gpu_2_workers_fp16_nccl_tweaked(self):
    """8 GPUs per worker, 2 workers, fp16, nccl all-reduce."""
    self._benchmark_common(num_workers=2, all_reduce_alg='nccl')

  def benchmark_8_gpu_8_workers_fp16_ring_tweaked(self):
    """8 GPUs per worker, 8 workers, fp16, ring all-reduce."""
    self._benchmark_common(num_workers=8, all_reduce_alg='ring')

  def benchmark_8_gpu_8_workers_fp16_nccl_tweaked(self):
    """8 GPUs per worker, 8 workers, fp16, nccl all-reduce."""
    self._benchmark_common(num_workers=8, all_reduce_alg='nccl')


class BertSquadMultiWorkerBenchmark(BertSquadBenchmarkBase):
  """BERT SQuAD distributed benchmark tests with multiple workers."""

  def __init__(self, output_dir=TMP_DIR, tpu=None, **kwargs):
    super(BertSquadMultiWorkerBenchmark, self).__init__(
        output_dir=output_dir, tpu=tpu)

  def _setup(self):
    """Sets up the benchmark and SQuAD flags."""
    super(BertSquadMultiWorkerBenchmark, self)._setup()
    FLAGS.train_data_path = SQUAD_TRAIN_DATA_PATH
    FLAGS.predict_file = SQUAD_PREDICT_FILE
    FLAGS.vocab_file = SQUAD_VOCAB_FILE
    FLAGS.input_meta_data_path = SQUAD_FULL_INPUT_META_DATA_PATH
    FLAGS.bert_config_file = MODEL_CONFIG_FILE_PATH
    FLAGS.num_train_epochs = 1
    FLAGS.steps_per_loop = 100

  @benchmark_wrappers.enable_runtime_flags
  def _run_and_report_benchmark(self,
                                use_ds=True,
                                run_eagerly=False):
    """Runs the benchmark and reports various metrics."""
    if FLAGS.train_batch_size <= 4 * 8:
      FLAGS.input_meta_data_path = SQUAD_LONG_INPUT_META_DATA_PATH
    else:
      FLAGS.input_meta_data_path = SQUAD_FULL_INPUT_META_DATA_PATH
    start_time_sec = time.time()
    self._train_squad(run_eagerly=run_eagerly,
                      ds_type='multi_worker_mirrored')
    wall_time_sec = time.time() - start_time_sec

    summary = self._read_training_summary_from_file()
    summary['start_time_sec'] = start_time_sec

    super(BertSquadMultiWorkerBenchmark, self)._report_benchmark(
        stats=summary,
        wall_time_sec=wall_time_sec,
        min_accuracy=0,
        max_accuracy=1)

  def _benchmark_common(self, num_workers, all_reduce_alg):
    """Common to all benchmarks in this class."""
    self._setup()

    num_gpus = 8
    FLAGS.num_gpus = num_gpus
    FLAGS.dtype = 'fp16'
    FLAGS.enable_xla = False
    FLAGS.distribution_strategy = 'multi_worker_mirrored'
    FLAGS.tf_gpu_thread_mode = 'gpu_private'
    FLAGS.datasets_num_private_threads = 32
    FLAGS.model_dir = self._get_model_dir(
        'benchmark_8_gpu_{}_worker_fp16_{}_tweaked'.format(
            num_workers, all_reduce_alg))
    FLAGS.train_batch_size = 4 * num_gpus * num_workers
    FLAGS.all_reduce_alg = all_reduce_alg

    self._run_and_report_benchmark()

  def benchmark_8_gpu_1_worker_fp16_ring_tweaked(self):
    """8 GPUs per worker, 1 worker, fp16, ring all-reduce."""
    self._benchmark_common(num_workers=1, all_reduce_alg='ring')

  def benchmark_8_gpu_1_worker_fp16_nccl_tweaked(self):
    """8 GPUs per worker, 1 worker, fp16, nccl all-reduce."""
    self._benchmark_common(num_workers=1, all_reduce_alg='nccl')

  def benchmark_8_gpu_2_workers_fp16_ring_tweaked(self):
    """8 GPUs per worker, 2 workers, fp16, ring all-reduce."""
    self._benchmark_common(num_workers=2, all_reduce_alg='ring')

  def benchmark_8_gpu_2_workers_fp16_nccl_tweaked(self):
    """8 GPUs per worker, 2 workers, fp16, nccl all-reduce."""
    self._benchmark_common(num_workers=2, all_reduce_alg='nccl')

  def benchmark_8_gpu_8_workers_fp16_ring_tweaked(self):
    """8 GPUs per worker, 8 workers, fp16, ring all-reduce."""
    self._benchmark_common(num_workers=8, all_reduce_alg='ring')

  def benchmark_8_gpu_8_workers_fp16_nccl_tweaked(self):
    """8 GPUs per worker, 8 workers, fp16, nccl all-reduce."""
    self._benchmark_common(num_workers=8, all_reduce_alg='nccl')


if __name__ == '__main__':
  tf.test.main()