common.py 19.7 KB
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
# 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.
# ==============================================================================
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"""Common util functions and classes used by both keras cifar and imagenet."""
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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import os
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from absl import flags
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import numpy as np
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import tensorflow as tf
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from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_v2
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import tensorflow_model_optimization as tfmot
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from official.utils.flags import core as flags_core
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from official.utils.misc import keras_utils
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FLAGS = flags.FLAGS
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BASE_LEARNING_RATE = 0.1  # This matches Jing's version.
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TRAIN_TOP_1 = 'training_accuracy_top_1'
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LR_SCHEDULE = [    # (multiplier, epoch to start) tuples
    (1.0, 5), (0.1, 30), (0.01, 60), (0.001, 80)
]


def learning_rate_schedule(current_epoch,
                           current_batch,
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                           steps_per_epoch,
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                           batch_size):
  """Handles linear scaling rule, gradual warmup, and LR decay.

  Scale learning rate at epoch boundaries provided in LR_SCHEDULE by the
  provided scaling factor.

  Args:
    current_epoch: integer, current epoch indexed from 0.
    current_batch: integer, current batch in the current epoch, indexed from 0.
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    steps_per_epoch: integer, number of steps in an epoch.
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    batch_size: integer, total batch sized.

  Returns:
    Adjusted learning rate.
  """
  initial_lr = BASE_LEARNING_RATE * batch_size / 256
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  epoch = current_epoch + float(current_batch) / steps_per_epoch
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  warmup_lr_multiplier, warmup_end_epoch = LR_SCHEDULE[0]
  if epoch < warmup_end_epoch:
    # Learning rate increases linearly per step.
    return initial_lr * warmup_lr_multiplier * epoch / warmup_end_epoch
  for mult, start_epoch in LR_SCHEDULE:
    if epoch >= start_epoch:
      learning_rate = initial_lr * mult
    else:
      break
  return learning_rate
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class LearningRateBatchScheduler(tf.keras.callbacks.Callback):
  """Callback to update learning rate on every batch (not epoch boundaries).

  N.B. Only support Keras optimizers, not TF optimizers.

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  Attributes:
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      schedule: a function that takes an epoch index and a batch index as input
          (both integer, indexed from 0) and returns a new learning rate as
          output (float).
  """

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  def __init__(self, schedule, batch_size, steps_per_epoch):
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    super(LearningRateBatchScheduler, self).__init__()
    self.schedule = schedule
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    self.steps_per_epoch = steps_per_epoch
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    self.batch_size = batch_size
    self.epochs = -1
    self.prev_lr = -1

  def on_epoch_begin(self, epoch, logs=None):
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    if not hasattr(self.model.optimizer, 'learning_rate'):
      raise ValueError('Optimizer must have a "learning_rate" attribute.')
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    self.epochs += 1

  def on_batch_begin(self, batch, logs=None):
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    """Executes before step begins."""
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    lr = self.schedule(self.epochs,
                       batch,
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                       self.steps_per_epoch,
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                       self.batch_size)
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    if not isinstance(lr, (float, np.float32, np.float64)):
      raise ValueError('The output of the "schedule" function should be float.')
    if lr != self.prev_lr:
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      self.model.optimizer.learning_rate = lr  # lr should be a float here
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      self.prev_lr = lr
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      tf.compat.v1.logging.debug(
          'Epoch %05d Batch %05d: LearningRateBatchScheduler '
          'change learning rate to %s.', self.epochs, batch, lr)
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class PiecewiseConstantDecayWithWarmup(
    tf.keras.optimizers.schedules.LearningRateSchedule):
  """Piecewise constant decay with warmup schedule."""

  def __init__(self, batch_size, epoch_size, warmup_epochs, boundaries,
               multipliers, compute_lr_on_cpu=True, name=None):
    super(PiecewiseConstantDecayWithWarmup, self).__init__()
    if len(boundaries) != len(multipliers) - 1:
      raise ValueError('The length of boundaries must be 1 less than the '
                       'length of multipliers')

    base_lr_batch_size = 256
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    steps_per_epoch = epoch_size // batch_size
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    self.rescaled_lr = BASE_LEARNING_RATE * batch_size / base_lr_batch_size
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    self.step_boundaries = [float(steps_per_epoch) * x for x in boundaries]
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    self.lr_values = [self.rescaled_lr * m for m in multipliers]
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    self.warmup_steps = warmup_epochs * steps_per_epoch
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    self.compute_lr_on_cpu = compute_lr_on_cpu
    self.name = name

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    self.learning_rate_ops_cache = {}
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  def __call__(self, step):
    if tf.executing_eagerly():
      return self._get_learning_rate(step)

    # In an eager function or graph, the current implementation of optimizer
    # repeatedly call and thus create ops for the learning rate schedule. To
    # avoid this, we cache the ops if not executing eagerly.
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    graph = tf.compat.v1.get_default_graph()
    if graph not in self.learning_rate_ops_cache:
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      if self.compute_lr_on_cpu:
        with tf.device('/device:CPU:0'):
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          self.learning_rate_ops_cache[graph] = self._get_learning_rate(step)
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      else:
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        self.learning_rate_ops_cache[graph] = self._get_learning_rate(step)
    return self.learning_rate_ops_cache[graph]
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  def _get_learning_rate(self, step):
    """Compute learning rate at given step."""
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    with tf.compat.v1.name_scope(self.name, 'PiecewiseConstantDecayWithWarmup',
                                 [self.rescaled_lr, self.step_boundaries,
                                  self.lr_values, self.warmup_steps,
                                  self.compute_lr_on_cpu]):
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      def warmup_lr(step):
        return self.rescaled_lr * (
            tf.cast(step, tf.float32) / tf.cast(self.warmup_steps, tf.float32))
      def piecewise_lr(step):
        return tf.compat.v1.train.piecewise_constant(
            step, self.step_boundaries, self.lr_values)
      return tf.cond(step < self.warmup_steps,
                     lambda: warmup_lr(step),
                     lambda: piecewise_lr(step))

  def get_config(self):
    return {
        'rescaled_lr': self.rescaled_lr,
        'step_boundaries': self.step_boundaries,
        'lr_values': self.lr_values,
        'warmup_steps': self.warmup_steps,
        'compute_lr_on_cpu': self.compute_lr_on_cpu,
        'name': self.name
    }


def get_optimizer(learning_rate=0.1):
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  """Returns optimizer to use."""
  # The learning_rate is overwritten at the beginning of each step by callback.
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  return gradient_descent_v2.SGD(learning_rate=learning_rate, momentum=0.9)
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# TODO(hongkuny,haoyuzhang): make cifar model use_tensor_lr to clean up code.
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def get_callbacks(
    steps_per_epoch,
    learning_rate_schedule_fn=None,
    pruning_method=None,
    enable_checkpoint_and_export=False,
    model_dir=None):
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  """Returns common callbacks."""
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  time_callback = keras_utils.TimeHistory(
      FLAGS.batch_size,
      FLAGS.log_steps,
      logdir=FLAGS.model_dir if FLAGS.enable_tensorboard else None)
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  callbacks = [time_callback]

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  if not FLAGS.use_tensor_lr and learning_rate_schedule_fn:
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    lr_callback = LearningRateBatchScheduler(
        learning_rate_schedule_fn,
        batch_size=FLAGS.batch_size,
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        steps_per_epoch=steps_per_epoch)
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    callbacks.append(lr_callback)
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  if FLAGS.enable_tensorboard:
    tensorboard_callback = tf.keras.callbacks.TensorBoard(
        log_dir=FLAGS.model_dir)
    callbacks.append(tensorboard_callback)

  if FLAGS.profile_steps:
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    profiler_callback = keras_utils.get_profiler_callback(
        FLAGS.model_dir,
        FLAGS.profile_steps,
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        FLAGS.enable_tensorboard,
        steps_per_epoch)
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    callbacks.append(profiler_callback)

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  is_pruning_enabled = pruning_method is not None
  if is_pruning_enabled:
    callbacks.append(tfmot.sparsity.keras.UpdatePruningStep())
    if model_dir is not None:
      callbacks.append(tfmot.sparsity.keras.PruningSummaries(
          log_dir=model_dir, profile_batch=0))

  if enable_checkpoint_and_export:
    if model_dir is not None:
      ckpt_full_path = os.path.join(model_dir, 'model.ckpt-{epoch:04d}')
      callbacks.append(
          tf.keras.callbacks.ModelCheckpoint(ckpt_full_path,
                                             save_weights_only=True))
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  return callbacks


def build_stats(history, eval_output, callbacks):
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  """Normalizes and returns dictionary of stats.

  Args:
    history: Results of the training step. Supports both categorical_accuracy
      and sparse_categorical_accuracy.
    eval_output: Output of the eval step. Assumes first value is eval_loss and
      second value is accuracy_top_1.
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    callbacks: a list of callbacks which might include a time history callback
      used during keras.fit.
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  Returns:
    Dictionary of normalized results.
  """
  stats = {}
  if eval_output:
    stats['accuracy_top_1'] = eval_output[1].item()
    stats['eval_loss'] = eval_output[0].item()
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  if history and history.history:
    train_hist = history.history
    # Gets final loss from training.
    stats['loss'] = train_hist['loss'][-1].item()
    # Gets top_1 training accuracy.
    if 'categorical_accuracy' in train_hist:
      stats[TRAIN_TOP_1] = train_hist['categorical_accuracy'][-1].item()
    elif 'sparse_categorical_accuracy' in train_hist:
      stats[TRAIN_TOP_1] = train_hist['sparse_categorical_accuracy'][-1].item()

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  if not callbacks:
    return stats

  # Look for the time history callback which was used during keras.fit
  for callback in callbacks:
    if isinstance(callback, keras_utils.TimeHistory):
      timestamp_log = callback.timestamp_log
      stats['step_timestamp_log'] = timestamp_log
      stats['train_finish_time'] = callback.train_finish_time
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      if callback.epoch_runtime_log:
        stats['avg_exp_per_second'] = callback.average_examples_per_second

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  return stats


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def define_keras_flags(
    dynamic_loss_scale=True,
    model=False,
    optimizer=False,
    pretrained_filepath=False):
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  """Define flags for Keras models."""
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  flags_core.define_base(clean=True, num_gpu=True, run_eagerly=True,
                         train_epochs=True, epochs_between_evals=True,
                         distribution_strategy=True)
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  flags_core.define_performance(num_parallel_calls=False,
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                                synthetic_data=True,
                                dtype=True,
                                all_reduce_alg=True,
                                num_packs=True,
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                                tf_gpu_thread_mode=True,
                                datasets_num_private_threads=True,
                                dynamic_loss_scale=dynamic_loss_scale,
                                loss_scale=True,
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                                fp16_implementation=True,
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                                tf_data_experimental_slack=True,
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                                enable_xla=True,
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                                force_v2_in_keras_compile=True,
                                training_dataset_cache=True)
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  flags_core.define_image()
  flags_core.define_benchmark()
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  flags_core.define_distribution()
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  flags.adopt_module_key_flags(flags_core)
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  flags.DEFINE_boolean(name='enable_eager', default=False, help='Enable eager?')
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  flags.DEFINE_boolean(name='skip_eval', default=False, help='Skip evaluation?')
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  # TODO(b/135607288): Remove this flag once we understand the root cause of
  # slowdown when setting the learning phase in Keras backend.
  flags.DEFINE_boolean(
      name='set_learning_phase_to_train', default=True,
      help='If skip eval, also set Keras learning phase to 1 (training).')
  flags.DEFINE_boolean(
      name='explicit_gpu_placement', default=False,
      help='If not using distribution strategy, explicitly set device scope '
      'for the Keras training loop.')
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  flags.DEFINE_boolean(name='use_trivial_model', default=False,
                       help='Whether to use a trivial Keras model.')
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  flags.DEFINE_boolean(name='report_accuracy_metrics', default=True,
                       help='Report metrics during training and evaluation.')
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  flags.DEFINE_boolean(name='use_tensor_lr', default=False,
                       help='Use learning rate tensor instead of a callback.')
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  flags.DEFINE_boolean(
      name='enable_tensorboard', default=False,
      help='Whether to enable Tensorboard callback.')
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  flags.DEFINE_integer(
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      name='train_steps', default=None,
      help='The number of steps to run for training. If it is larger than '
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      '# batches per epoch, then use # batches per epoch. This flag will be '
      'ignored if train_epochs is set to be larger than 1. ')
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  flags.DEFINE_string(
      name='profile_steps', default=None,
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      help='Save profiling data to model dir at given range of global steps. The '
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      'value must be a comma separated pair of positive integers, specifying '
      'the first and last step to profile. For example, "--profile_steps=2,4" '
      'triggers the profiler to process 3 steps, starting from the 2nd step. '
      'Note that profiler has a non-trivial performance overhead, and the '
      'output file can be gigantic if profiling many steps.')
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  flags.DEFINE_boolean(
      name='batchnorm_spatial_persistent', default=True,
      help='Enable the spacial persistent mode for CuDNN batch norm kernel.')
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  flags.DEFINE_boolean(
      name='enable_get_next_as_optional', default=False,
      help='Enable get_next_as_optional behavior in DistributedIterator.')
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  flags.DEFINE_boolean(
      name='enable_checkpoint_and_export', default=False,
      help='Whether to enable a checkpoint callback and export the savedmodel.')
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  flags.DEFINE_string(
      name='tpu', default='', help='TPU address to connect to.')
  flags.DEFINE_integer(
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      name='steps_per_loop',
      default=500,
      help='Number of steps per training loop. Only training step happens '
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      'inside the loop. Callbacks will not be called inside. Will be capped at '
      'steps per epoch.')
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  flags.DEFINE_boolean(
      name='use_tf_while_loop',
      default=True,
      help='Whether to build a tf.while_loop inside the training loop on the '
      'host. Setting it to True is critical to have peak performance on '
      'TPU.')
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  flags.DEFINE_boolean(
      name='use_tf_keras_layers', default=False,
      help='Whether to use tf.keras.layers instead of tf.python.keras.layers.'
      'It only changes imagenet resnet model layers for now. This flag is '
      'a temporal flag during transition to tf.keras.layers. Do not use this '
      'flag for external usage. this will be removed shortly.')
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  if model:
    flags.DEFINE_string('model', 'resnet50_v1.5',
                        'Name of model preset. (mobilenet, resnet50_v1.5)')
  if optimizer:
    flags.DEFINE_string('optimizer', 'resnet50_default',
                        'Name of optimizer preset. '
                        '(mobilenet_default, resnet50_default)')
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    # TODO(kimjaehong): Replace as general hyper-params not only for mobilenet.
    flags.DEFINE_float('initial_learning_rate_per_sample', 0.00007,
                       'Initial value of learning rate per sample for '
                       'mobilenet_default.')
    flags.DEFINE_float('lr_decay_factor', 0.94,
                       'Learning rate decay factor for mobilenet_default.')
    flags.DEFINE_float('num_epochs_per_decay', 2.5,
                       'Number of epochs per decay for mobilenet_default.')
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  if pretrained_filepath:
    flags.DEFINE_string('pretrained_filepath', '',
                        'Pretrained file path.')

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def get_synth_data(height, width, num_channels, num_classes, dtype):
  """Creates a set of synthetic random data.

  Args:
    height: Integer height that will be used to create a fake image tensor.
    width: Integer width that will be used to create a fake image tensor.
    num_channels: Integer depth that will be used to create a fake image tensor.
    num_classes: Number of classes that should be represented in the fake labels
      tensor
    dtype: Data type for features/images.

  Returns:
    A tuple of tensors representing the inputs and labels.

  """
  # Synthetic input should be within [0, 255].
  inputs = tf.random.truncated_normal([height, width, num_channels],
                                      dtype=dtype,
                                      mean=127,
                                      stddev=60,
                                      name='synthetic_inputs')
  labels = tf.random.uniform([1],
                             minval=0,
                             maxval=num_classes - 1,
                             dtype=tf.int32,
                             name='synthetic_labels')
  return inputs, labels


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def define_pruning_flags():
  """Define flags for pruning methods."""
  flags.DEFINE_string('pruning_method', None,
                      'Pruning method.'
                      'None (no pruning) or polynomial_decay.')
  flags.DEFINE_float('pruning_initial_sparsity', 0.0,
                     'Initial sparsity for pruning.')
  flags.DEFINE_float('pruning_final_sparsity', 0.5,
                     'Final sparsity for pruning.')
  flags.DEFINE_integer('pruning_begin_step', 0,
                       'Begin step for pruning.')
  flags.DEFINE_integer('pruning_end_step', 100000,
                       'End step for pruning.')
  flags.DEFINE_integer('pruning_frequency', 100,
                       'Frequency for pruning.')


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def get_synth_input_fn(height, width, num_channels, num_classes,
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                       dtype=tf.float32, drop_remainder=True):
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  """Returns an input function that returns a dataset with random data.

  This input_fn returns a data set that iterates over a set of random data and
  bypasses all preprocessing, e.g. jpeg decode and copy. The host to device
  copy is still included. This used to find the upper throughput bound when
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  tuning the full input pipeline.
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  Args:
    height: Integer height that will be used to create a fake image tensor.
    width: Integer width that will be used to create a fake image tensor.
    num_channels: Integer depth that will be used to create a fake image tensor.
    num_classes: Number of classes that should be represented in the fake labels
      tensor
    dtype: Data type for features/images.
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    drop_remainder: A boolean indicates whether to drop the remainder of the
      batches. If True, the batch dimension will be static.
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  Returns:
    An input_fn that can be used in place of a real one to return a dataset
    that can be used for iteration.
  """
  # pylint: disable=unused-argument
  def input_fn(is_training, data_dir, batch_size, *args, **kwargs):
    """Returns dataset filled with random data."""
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    inputs, labels = get_synth_data(height=height,
                                    width=width,
                                    num_channels=num_channels,
                                    num_classes=num_classes,
                                    dtype=dtype)
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    # Cast to float32 for Keras model.
    labels = tf.cast(labels, dtype=tf.float32)
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    data = tf.data.Dataset.from_tensors((inputs, labels)).repeat()
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    # `drop_remainder` will make dataset produce outputs with known shapes.
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    data = data.batch(batch_size, drop_remainder=drop_remainder)
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    data = data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
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    return data

  return input_fn
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def set_cudnn_batchnorm_mode():
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  """Set CuDNN batchnorm mode for better performance.

     Note: Spatial Persistent mode may lead to accuracy losses for certain
     models.
  """
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  if FLAGS.batchnorm_spatial_persistent:
    os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
  else:
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    os.environ.pop('TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT', None)