model_training_utils.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.
# ==============================================================================
"""A light weight utilities to train NLP models."""

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

import json
import os

from absl import logging
import tensorflow as tf
from official.utils.misc import distribution_utils

_SUMMARY_TXT = 'training_summary.txt'
_MIN_SUMMARY_STEPS = 10


def _save_checkpoint(checkpoint, model_dir, checkpoint_prefix):
  """Saves model to with provided checkpoint prefix."""

  checkpoint_path = os.path.join(model_dir, checkpoint_prefix)
  saved_path = checkpoint.save(checkpoint_path)
  logging.info('Saving model as TF checkpoint: %s', saved_path)
  return


def _get_input_iterator(input_fn, strategy):
  """Returns distributed dataset iterator."""
  # When training with TPU pods, datasets needs to be cloned across
  # workers. Since Dataset instance cannot be cloned in eager mode, we instead
  # pass callable that returns a dataset.
  if not callable(input_fn):
    raise ValueError('`input_fn` should be a closure that returns a dataset.')
  iterator = iter(
      strategy.experimental_distribute_datasets_from_function(input_fn))
  return iterator


def _float_metric_value(metric):
  """Gets the value of a float-value keras metric."""
  return metric.result().numpy().astype(float)


def steps_to_run(current_step, steps_per_epoch, steps_per_loop):
  """Calculates steps to run on device."""
  if steps_per_loop <= 0:
    raise ValueError('steps_per_loop should be positive integer.')
  if steps_per_loop == 1:
    return steps_per_loop
  remainder_in_epoch = current_step % steps_per_epoch
  if remainder_in_epoch != 0:
    return min(steps_per_epoch - remainder_in_epoch, steps_per_loop)
  else:
    return steps_per_loop


def write_txt_summary(training_summary, summary_dir):
  """Writes a summary text file to record stats."""
  summary_path = os.path.join(summary_dir, _SUMMARY_TXT)
  with tf.io.gfile.GFile(summary_path, 'wb') as f:
    logging.info('Training Summary: \n%s', str(training_summary))
    f.write(json.dumps(training_summary, indent=4))


def _filter_grads(grads_and_vars):
  """Filter out iterable with grad equal to None."""
  grads_and_vars = tuple(grads_and_vars)
  if not grads_and_vars:
    return grads_and_vars
  filtered = []
  vars_with_empty_grads = []
  for grad, var in grads_and_vars:
    if grad is None:
      vars_with_empty_grads.append(var)
    else:
      filtered.append((grad, var))
  filtered = tuple(filtered)
  if not filtered:
    raise ValueError('No gradients provided for any variable: %s.' %
                     ([v.name for _, v in grads_and_vars],))
  if vars_with_empty_grads:
    logging.warning(
        ('Gradients do not exist for variables %s when minimizing the loss.'),
        ([v.name for v in vars_with_empty_grads]))
  return filtered


def _filter_and_allreduce_gradients(grads_and_vars,
                                    allreduce_precision='float32'):
  """Filter None grads and then allreduce gradients in specified precision.

  This utils function is used when users intent to explicitly allreduce
  gradients and customize gradients operations before and after allreduce.
  The allreduced gradients are then passed to optimizer.apply_gradients(
  all_reduce_sum_gradients=False).

  Arguments:
      grads_and_vars: gradients and variables pairs.
      allreduce_precision: Whether to allreduce gradients in float32 or float16.

  Returns:
      pairs of allreduced non-None gradients and variables.
  """
  filtered_grads_and_vars = _filter_grads(grads_and_vars)
  (grads, variables) = zip(*filtered_grads_and_vars)
  if allreduce_precision == 'float16':
    grads = [tf.cast(grad, 'float16') for grad in grads]
  allreduced_grads = tf.distribute.get_replica_context().all_reduce(
      tf.distribute.ReduceOp.SUM, grads)
  if allreduce_precision == 'float16':
    allreduced_grads = [tf.cast(grad, 'float32') for grad in allreduced_grads]
  return allreduced_grads, variables


def run_customized_training_loop(
    # pylint: disable=invalid-name
    _sentinel=None,
    # pylint: enable=invalid-name
    strategy=None,
    model_fn=None,
    loss_fn=None,
    model_dir=None,
    train_input_fn=None,
    steps_per_epoch=None,
    steps_per_loop=1,
    epochs=1,
    eval_input_fn=None,
    eval_steps=None,
    metric_fn=None,
    init_checkpoint=None,
    custom_callbacks=None,
    run_eagerly=False,
    sub_model_export_name=None,
    explicit_allreduce=False):
  """Run BERT pretrain model training using low-level API.

  Arguments:
      _sentinel: Used to prevent positional parameters. Internal, do not use.
      strategy: Distribution strategy on which to run low level training loop.
      model_fn: Function that returns a tuple (model, sub_model). Caller of this
        function should add optimizer to the `model` via calling
        `model.compile()` API or manually setting `model.optimizer` attribute.
        Second element of the returned tuple(sub_model) is an optional sub model
        to be used for initial checkpoint -- if provided.
      loss_fn: Function with signature func(labels, logits) and returns a loss
        tensor.
      model_dir: Model directory used during training for restoring/saving model
        weights.
      train_input_fn: Function that returns a tf.data.Dataset used for training.
      steps_per_epoch: Number of steps to run per epoch. At the end of each
        epoch, model checkpoint will be saved and evaluation will be conducted
        if evaluation dataset is provided.
      steps_per_loop: Number of steps per graph-mode loop. In order to reduce
        communication in eager context, training logs are printed every
        steps_per_loop.
      epochs: Number of epochs to train.
      eval_input_fn: Function that returns evaluation dataset. If none,
        evaluation is skipped.
      eval_steps: Number of steps to run evaluation. Required if `eval_input_fn`
        is not none.
      metric_fn: A metrics function that returns a Keras Metric object to record
        evaluation result using evaluation dataset or with training dataset
        after every epoch.
      init_checkpoint: Optional checkpoint to load to `sub_model` returned by
        `model_fn`.
      custom_callbacks: A list of Keras Callbacks objects to run during
        training. More specifically, `on_batch_begin()`, `on_batch_end()`,
        methods are invoked during training.
      run_eagerly: Whether to run model training in pure eager execution. This
        should be disable for TPUStrategy.
      sub_model_export_name: If not None, will export `sub_model` returned by
        `model_fn` into checkpoint files. The name of intermediate checkpoint
        file is {sub_model_export_name}_step_{step}.ckpt and the last
        checkpint's name is {sub_model_export_name}.ckpt;
        if None, `sub_model` will not be exported as checkpoint.
      explicit_allreduce: Whether to explicitly perform gradient allreduce,
        instead of relying on implicit allreduce in optimizer.apply_gradients().
        default is False. For now, if training using FP16 mixed precision,
        explicit allreduce will aggregate gradients in FP16 format. For TPU and
        GPU training using FP32, explicit allreduce will aggregate gradients in
        FP32 format.

  Returns:
      Trained model.

  Raises:
      ValueError: (1) When model returned by `model_fn` does not have optimizer
        attribute or when required parameters are set to none. (2) eval args are
        not specified correctly. (3) metric_fn must be a callable if specified.
        (4) sub_model_checkpoint_name is specified, but `sub_model` returned
        by `model_fn` is None.
  """

  if _sentinel is not None:
    raise ValueError('only call `run_customized_training_loop()` '
                     'with named arguments.')

  required_arguments = [
      strategy, model_fn, loss_fn, model_dir, steps_per_epoch, train_input_fn
  ]
  if [arg for arg in required_arguments if arg is None]:
    raise ValueError('`strategy`, `model_fn`, `loss_fn`, `model_dir`, '
                     '`steps_per_loop` and `steps_per_epoch` are required '
                     'parameters.')
  if steps_per_loop > steps_per_epoch:
    logging.error(
        'steps_per_loop: %d is specified to be greater than '
        ' steps_per_epoch: %d, we will use steps_per_epoch as'
        ' steps_per_loop.', steps_per_loop, steps_per_epoch)
    steps_per_loop = steps_per_epoch
  assert tf.executing_eagerly()

  if run_eagerly:
    if steps_per_loop > 1:
      raise ValueError(
          'steps_per_loop is used for performance optimization. When you want '
          'to run eagerly, you cannot leverage graph mode loop.')
    if isinstance(strategy, tf.distribute.experimental.TPUStrategy):
      raise ValueError(
          'TPUStrategy should not run eagerly as it heavily replies on graph'
          ' optimization for the distributed system.')

  if eval_input_fn and (eval_steps is None or metric_fn is None):
    raise ValueError(
        '`eval_step` and `metric_fn` are required when `eval_input_fn ` '
        'is not none.')
  if metric_fn and not callable(metric_fn):
    raise ValueError(
        'if `metric_fn` is specified, metric_fn must be a callable.')

  total_training_steps = steps_per_epoch * epochs

  # To reduce unnecessary send/receive input pipeline operation, we place input
  # pipeline ops in worker task.
  train_iterator = _get_input_iterator(train_input_fn, strategy)

  with distribution_utils.get_strategy_scope(strategy):
    # To correctly place the model weights on accelerators,
    # model and optimizer should be created in scope.
    model, sub_model = model_fn()
    if not hasattr(model, 'optimizer'):
      raise ValueError('User should set optimizer attribute to model '
                       'inside `model_fn`.')
    if sub_model_export_name and sub_model is None:
      raise ValueError('sub_model_export_name is specified as %s, but '
                       'sub_model is None.' % sub_model_export_name)

    optimizer = model.optimizer
    use_float16 = isinstance(
        optimizer, tf.keras.mixed_precision.experimental.LossScaleOptimizer)

    if init_checkpoint:
      logging.info(
          'Checkpoint file %s found and restoring from '
          'initial checkpoint for core model.', init_checkpoint)
      checkpoint = tf.train.Checkpoint(model=sub_model)
      checkpoint.restore(init_checkpoint).assert_existing_objects_matched()
      logging.info('Loading from checkpoint file completed')

    train_loss_metric = tf.keras.metrics.Mean(
        'training_loss', dtype=tf.float32)
    eval_metrics = [metric_fn()] if metric_fn else []
    # If evaluation is required, make a copy of metric as it will be used by
    # both train and evaluation.
    train_metrics = [
        metric.__class__.from_config(metric.get_config())
        for metric in eval_metrics
    ]

    # Create summary writers
    summary_dir = os.path.join(model_dir, 'summaries')
    eval_summary_writer = tf.summary.create_file_writer(
        os.path.join(summary_dir, 'eval'))
    if steps_per_loop >= _MIN_SUMMARY_STEPS:
      # Only writes summary when the stats are collected sufficiently over
      # enough steps.
      train_summary_writer = tf.summary.create_file_writer(
          os.path.join(summary_dir, 'train'))
    else:
      train_summary_writer = None

    # Collects training variables.
    training_vars = model.trainable_variables

    def _replicated_step(inputs):
      """Replicated training step."""

      inputs, labels = inputs
      with tf.GradientTape() as tape:
        model_outputs = model(inputs, training=True)
        loss = loss_fn(labels, model_outputs)
        if use_float16:
          scaled_loss = optimizer.get_scaled_loss(loss)

      if use_float16:
        scaled_grads = tape.gradient(scaled_loss, training_vars)
        if explicit_allreduce:
          (allreduced_scaled_grads,
           filtered_training_vars) = _filter_and_allreduce_gradients(
               zip(scaled_grads, training_vars), allreduce_precision='float16')
          allreduced_unscaled_grads = optimizer.get_unscaled_gradients(
              allreduced_scaled_grads)
          grads_and_vars = zip(allreduced_unscaled_grads,
                               filtered_training_vars)
        else:
          grads = optimizer.get_unscaled_gradients(scaled_grads)
          grads_and_vars = zip(grads, training_vars)
      else:
        # TPU or FP32 GPU code path
        grads = tape.gradient(loss, training_vars)
        if explicit_allreduce:
          (allreduced_grads,
           filtered_training_vars) = _filter_and_allreduce_gradients(
               zip(grads, training_vars), allreduce_precision='float32')
          grads_and_vars = zip(allreduced_grads, filtered_training_vars)
        else:
          grads_and_vars = zip(grads, training_vars)
      optimizer.apply_gradients(
          grads_and_vars, all_reduce_sum_gradients=not explicit_allreduce)

      # For reporting, the metric takes the mean of losses.
      train_loss_metric.update_state(loss)
      for metric in train_metrics:
        metric.update_state(labels, model_outputs)

    @tf.function
    def train_steps(iterator, steps):
      """Performs distributed training steps in a loop.

      Args:
        iterator: the distributed iterator of training datasets.
        steps: an tf.int32 integer tensor to specify number of steps to run
          inside host training loop.

      Raises:
        ValueError: Any of the arguments or tensor shapes are invalid.
      """
      if not isinstance(steps, tf.Tensor):
        raise ValueError('steps should be an Tensor. Python object may cause '
                         'retracing.')

      for _ in tf.range(steps):
        strategy.experimental_run_v2(_replicated_step, args=(next(iterator),))

    def train_single_step(iterator):
      """Performs a distributed training step.

      Args:
        iterator: the distributed iterator of training datasets.

      Raises:
        ValueError: Any of the arguments or tensor shapes are invalid.
      """
      strategy.experimental_run_v2(_replicated_step, args=(next(iterator),))

    def test_step(iterator):
      """Calculates evaluation metrics on distributed devices."""

      def _test_step_fn(inputs):
        """Replicated accuracy calculation."""

        inputs, labels = inputs
        model_outputs = model(inputs, training=False)
        for metric in eval_metrics:
          metric.update_state(labels, model_outputs)

      strategy.experimental_run_v2(_test_step_fn, args=(next(iterator),))

    if not run_eagerly:
      train_single_step = tf.function(train_single_step)
      test_step = tf.function(test_step)

    def _run_evaluation(current_training_step, test_iterator):
      """Runs validation steps and aggregate metrics."""
      for _ in range(eval_steps):
        test_step(test_iterator)

      with eval_summary_writer.as_default():
        for metric in eval_metrics + model.metrics:
          metric_value = _float_metric_value(metric)
          logging.info('Step: [%d] Validation %s = %f', current_training_step,
                       metric.name, metric_value)
          tf.summary.scalar(
              metric.name, metric_value, step=current_training_step)
        eval_summary_writer.flush()

    def _run_callbacks_on_batch_begin(batch):
      """Runs custom callbacks at the start of every step."""
      if not custom_callbacks:
        return
      for callback in custom_callbacks:
        callback.on_batch_begin(batch)

    def _run_callbacks_on_batch_end(batch, logs):
      """Runs custom callbacks at the end of every step."""
      if not custom_callbacks:
        return
      for callback in custom_callbacks:
        callback.on_batch_end(batch, logs)

    # Training loop starts here.
    checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
    sub_model_checkpoint = tf.train.Checkpoint(
        model=sub_model) if sub_model_export_name else None

    latest_checkpoint_file = tf.train.latest_checkpoint(model_dir)
    if latest_checkpoint_file:
      logging.info(
          'Checkpoint file %s found and restoring from '
          'checkpoint', latest_checkpoint_file)
      checkpoint.restore(latest_checkpoint_file)
      logging.info('Loading from checkpoint file completed')

    current_step = optimizer.iterations.numpy()
    checkpoint_name = 'ctl_step_{step}.ckpt'

    while current_step < total_training_steps:
      # Training loss/metric are taking average over steps inside micro
      # training loop. We reset the their values before each round.
      train_loss_metric.reset_states()
      for metric in train_metrics + model.metrics:
        metric.reset_states()

      _run_callbacks_on_batch_begin(current_step)
      # Runs several steps in the host while loop.
      steps = steps_to_run(current_step, steps_per_epoch, steps_per_loop)

      if tf.test.is_built_with_cuda():
        # TODO(zongweiz): merge with train_steps once tf.while_loop
        # GPU performance bugs are fixed.
        for _ in range(steps):
          train_single_step(train_iterator)
      else:
        # Converts steps to a Tensor to avoid tf.function retracing.
        train_steps(train_iterator,
                    tf.convert_to_tensor(steps, dtype=tf.int32))
      train_loss = _float_metric_value(train_loss_metric)
      _run_callbacks_on_batch_end(current_step, {'loss': train_loss})
      current_step += steps

      # Updates training logging.
      training_status = 'Train Step: %d/%d  / loss = %s' % (
          current_step, total_training_steps, train_loss)

      if train_summary_writer:
        with train_summary_writer.as_default():
          tf.summary.scalar(
              train_loss_metric.name, train_loss, step=current_step)
          for metric in train_metrics + model.metrics:
            metric_value = _float_metric_value(metric)
            training_status += '  %s = %f' % (metric.name, metric_value)
            tf.summary.scalar(metric.name, metric_value, step=current_step)
          train_summary_writer.flush()
      logging.info(training_status)

      # Saves model checkpoints and run validation steps at every epoch end.
      if current_step % steps_per_epoch == 0:
        # To avoid repeated model saving, we do not save after the last
        # step of training.
        if current_step < total_training_steps:
          _save_checkpoint(checkpoint, model_dir,
                           checkpoint_name.format(step=current_step))
          if sub_model_export_name:
            _save_checkpoint(
                sub_model_checkpoint, model_dir,
                '%s_step_%d.ckpt' % (sub_model_export_name, current_step))
        if eval_input_fn:
          logging.info('Running evaluation after step: %s.', current_step)
          _run_evaluation(current_step,
                          _get_input_iterator(eval_input_fn, strategy))
          # Re-initialize evaluation metric.
          for metric in eval_metrics + model.metrics:
            metric.reset_states()

    _save_checkpoint(checkpoint, model_dir,
                     checkpoint_name.format(step=current_step))
    if sub_model_export_name:
      _save_checkpoint(sub_model_checkpoint, model_dir,
                       '%s.ckpt' % sub_model_export_name)

    if eval_input_fn:
      logging.info('Running final evaluation after training is complete.')
      _run_evaluation(current_step,
                      _get_input_iterator(eval_input_fn, strategy))

    training_summary = {
        'total_training_steps': total_training_steps,
        'train_loss': _float_metric_value(train_loss_metric),
    }
    if eval_metrics:
      # TODO(hongkuny): Cleans up summary reporting in text.
      training_summary['last_train_metrics'] = _float_metric_value(
          train_metrics[0])
      training_summary['eval_metrics'] = _float_metric_value(eval_metrics[0])

    write_txt_summary(training_summary, summary_dir)

    return model