models/official/nlp/bert/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
import tempfile

from absl import logging
import tensorflow as tf
from tensorflow.python.util import deprecation
from official.staging.training import grad_utils
from official.utils.misc import distribution_utils

_SUMMARY_TXT = 'training_summary.txt'
_MIN_SUMMARY_STEPS = 10


def _should_export_checkpoint(strategy):
  return (not strategy) or strategy.extended.should_checkpoint


def _should_export_summary(strategy):
  return (not strategy) or strategy.extended.should_save_summary


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

  if _should_export_checkpoint(strategy):
    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)
  else:
    # In multi worker training we need every worker to save checkpoint, because
    # variables can trigger synchronization on read and synchronization needs
    # all workers to participate. To avoid workers overriding each other we save
    # to a temporary directory on non-chief workers.
    tmp_dir = tempfile.mkdtemp()
    checkpoint.save(os.path.join(tmp_dir, 'ckpt'))
    tf.io.gfile.rmtree(tmp_dir)
  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."""
  if not tf.io.gfile.exists(summary_dir):
    tf.io.gfile.mkdir(summary_dir)
  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))


@deprecation.deprecated(
    None, 'This function is deprecated. Please use Keras compile/fit instead.')
def run_customized_training_loop(
    # pylint: disable=invalid-name
    _sentinel=None,
    # pylint: enable=invalid-name
    strategy=None,
    model_fn=None,
    loss_fn=None,
    scale_loss=True,
    model_dir=None,
    train_input_fn=None,
    steps_per_epoch=None,
    num_eval_per_epoch=1,
    steps_per_loop=None,
    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,
    pre_allreduce_callbacks=None,
    post_allreduce_callbacks=None,
    train_summary_interval=0):
  """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.
      scale_loss: Whether to divide the raw loss by number of replicas before
        gradients calculation.
      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.
      num_eval_per_epoch: Number of evaluations per epoch.
      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_train_begin(), on_train_end(),
        on_batch_begin()`, `on_batch_end()`, `on_epoch_begin()`,
        `on_epoch_end()` methods are invoked during training.
        Note that some metrics may be missing from `logs`.
      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.
      pre_allreduce_callbacks: A list of callback functions that takes gradients
        and model variables pairs as input, manipulate them, and returns a new
        gradients and model variables paris. The callback functions will be
        invoked in the list order and before gradients are allreduced. With
        mixed precision training, the pre_allreduce_allbacks will be applied on
        scaled_gradients. Default is no callbacks. Only used when
        explicit_allreduce=True.
      post_allreduce_callbacks: A list of callback functions that takes
        gradients and model variables pairs as input, manipulate them, and
        returns a new gradients and model variables paris. The callback
        functions will be invoked in the list order and right before gradients
        are applied to variables for updates. Default is no callbacks. Only used
        when explicit_allreduce=True.
      train_summary_interval: Step interval for training summaries. If the value
        is a negative number, then training summaries are not enabled.

  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
  ]

  steps_between_evals = int(steps_per_epoch / num_eval_per_epoch)
  if [arg for arg in required_arguments if arg is None]:
    raise ValueError('`strategy`, `model_fn`, `loss_fn`, `model_dir`, '
                     '`steps_per_epoch` and `train_input_fn` are required '
                     'parameters.')
  if not steps_per_loop:
    if tf.config.list_logical_devices('TPU'):
      # One can't fully utilize a TPU with steps_per_loop=1, so in this case
      # default users to a more useful value.
      steps_per_loop = min(1000, steps_between_evals)
    else:
      steps_per_loop = 1
    logging.info('steps_per_loop not specified. Using steps_per_loop=%d',
                 steps_per_loop)
  if steps_per_loop > steps_between_evals:
    logging.warning(
        'steps_per_loop: %d is specified to be greater than '
        ' steps_between_evals: %d, we will use steps_between_evals as'
        ' steps_per_loop.', steps_per_loop, steps_between_evals)
    steps_per_loop = steps_between_evals
  assert tf.executing_eagerly()

  if run_eagerly:
    if isinstance(strategy, tf.distribute.experimental.TPUStrategy):
      raise ValueError(
          'TPUStrategy should not run eagerly as it heavily relies on graph'
          ' optimization for the distributed system.')

  if eval_input_fn and eval_steps is None:
    raise ValueError(
        '`eval_step` is 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
  train_iterator = _get_input_iterator(train_input_fn, strategy)
  eval_loss_metric = tf.keras.metrics.Mean('training_loss', dtype=tf.float32)

  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)

    callback_list = tf.keras.callbacks.CallbackList(
        callbacks=custom_callbacks, model=model)

    optimizer = model.optimizer

    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
    if _should_export_summary(strategy):
      summary_dir = os.path.join(model_dir, 'summaries')
    else:
      # In multi worker training we need every worker to write summary, because
      # variables can trigger synchronization on read and synchronization needs
      # all workers to participate.
      summary_dir = tempfile.mkdtemp()
    eval_summary_writer = tf.summary.create_file_writer(
        os.path.join(summary_dir, 'eval'))
    last_summary_step = 0
    if steps_per_loop >= _MIN_SUMMARY_STEPS and train_summary_interval >= 0:
      # 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 = tf.summary.create_noop_writer()

    # 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)
        # Raw loss is used for reporting in metrics/logs.
        raw_loss = loss
        if scale_loss:
          # Scales down the loss for gradients to be invariant from replicas.
          loss = loss / strategy.num_replicas_in_sync

      if explicit_allreduce:
        grad_utils.minimize_using_explicit_allreduce(tape, optimizer, loss,
                                                     training_vars,
                                                     pre_allreduce_callbacks,
                                                     post_allreduce_callbacks)
      else:
        if isinstance(optimizer,
                      tf.keras.mixed_precision.experimental.LossScaleOptimizer):
          with tape:
            scaled_loss = optimizer.get_scaled_loss(loss)
          scaled_grads = tape.gradient(scaled_loss, training_vars)
          grads = optimizer.get_unscaled_gradients(scaled_grads)
        else:
          grads = tape.gradient(loss, training_vars)
        optimizer.apply_gradients(zip(grads, training_vars))
      # For reporting, the metric takes the mean of losses.
      train_loss_metric.update_state(raw_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.run(_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.run(_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)
        return model_outputs, labels

      outputs, labels = strategy.run(_test_step_fn, args=(next(iterator),))
      outputs = tf.nest.map_structure(strategy.experimental_local_results,
                                      outputs)
      labels = tf.nest.map_structure(strategy.experimental_local_results,
                                     labels)
      return outputs, labels

    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.

      Args:
        current_training_step: tf.int32 tensor containing the current step.
        test_iterator: distributed iterator of test datasets.

      Returns:
        A dict of metic names and values.
      """
      # The last batch of the evaluation is often smaller than previous ones.
      # Moreover, in some distributed pieces it might even be empty. Therefore,
      # different from the way training_loss is calculated, it is needed to
      # gather all the logits and labels here to calculate the evaluation loss
      # outside.
      loss_list, loss_weights = list(), list()
      for _ in range(eval_steps):
        outputs, labels = test_step(test_iterator)
        for cur_logits, cur_labels in zip(outputs, labels):
          # This is to handle cases when cur_labels is not a single tensor,
          # but a dict of tensors.
          cur_weight = tf.shape(tf.nest.flatten(cur_labels)[0])[0]
          if cur_weight != 0:
            loss_list.append(loss_fn(cur_labels, cur_logits).numpy())
            loss_weights.append(cur_weight)
      # The sample_weights are the actual number of examples in each batch,
      # a summation of numbers of examples in each replica if using
      # distributed training.
      eval_loss_metric.update_state(loss_list, sample_weight=loss_weights)

      logs = {}
      with eval_summary_writer.as_default():
        for metric in [eval_loss_metric] + eval_metrics + model.metrics:
          metric_value = _float_metric_value(metric)
          logs[metric.name] = metric_value
          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()

      return logs

    # Training loop starts here.
    checkpoint = tf.train.Checkpoint(
        model=model, optimizer=optimizer, global_step=optimizer.iterations)
    sub_model_checkpoint = tf.train.Checkpoint(
        model=sub_model,
        global_step=optimizer.iterations) 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'

    logs = {}
    callback_list.on_train_begin()
    while current_step < total_training_steps and not model.stop_training:
      if current_step % steps_per_epoch == 0:
        callback_list.on_epoch_begin(
            int(current_step / steps_per_epoch) + 1)

      # 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()

      callback_list.on_batch_begin(current_step)
      # Runs several steps in the host while loop.
      steps = steps_to_run(current_step, steps_between_evals, steps_per_loop)

      if tf.config.list_physical_devices('GPU'):
        # 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)
      current_step += steps

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

      if current_step >= last_summary_step + train_summary_interval:
        summary_writer = train_summary_writer
        last_summary_step = current_step
      else:
        summary_writer = tf.summary.create_noop_writer()

      with summary_writer.as_default():
        if callable(optimizer.learning_rate):
          tf.summary.scalar(
              'learning_rate',
              optimizer.learning_rate(current_step),
              step=current_step)
        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)
        summary_writer.flush()
      logging.info(training_status)

      # If no need for evaluation, we only call on_batch_end with train_loss,
      # this is to ensure we get granular global_step/sec on Tensorboard.
      if current_step % steps_between_evals:
        callback_list.on_batch_end(current_step - 1, {'loss': train_loss})
      else:
        # Save a submodel with the step in the file name after each epoch.
        if sub_model_export_name:
          _save_checkpoint(
              strategy, sub_model_checkpoint, model_dir,
              '%s_step_%d.ckpt' % (sub_model_export_name, current_step))

        # Save model checkpoints and run validation steps after each epoch
        # (with the exception of the final epoch which is handled after the
        # training loop).
        if current_step < total_training_steps:
          _save_checkpoint(strategy, checkpoint, model_dir,
                           checkpoint_name.format(step=current_step))
          if eval_input_fn:
            logging.info('Running evaluation after step: %s.', current_step)
            logs = _run_evaluation(current_step,
                                   _get_input_iterator(eval_input_fn, strategy))
            # Re-initialize evaluation metric.
            eval_loss_metric.reset_states()
            for metric in eval_metrics + model.metrics:
              metric.reset_states()
        # We add train_loss here rather than call on_batch_end twice to make
        # sure that no duplicated values are generated.
        logs['loss'] = train_loss
        callback_list.on_batch_end(current_step - 1, logs)

      # Calls on_epoch_end after each real epoch ends to prevent mis-calculation
      # of training steps.
      if current_step % steps_per_epoch == 0:
        callback_list.on_epoch_end(int(current_step / steps_per_epoch), logs)

    if sub_model_export_name:
      _save_checkpoint(strategy, sub_model_checkpoint, model_dir,
                       '%s.ckpt' % sub_model_export_name)

    _save_checkpoint(strategy, checkpoint, model_dir,
                     checkpoint_name.format(step=current_step))
    if eval_input_fn:
      logging.info('Running final evaluation after training is complete.')
      logs = _run_evaluation(current_step,
                             _get_input_iterator(eval_input_fn, strategy))
    callback_list.on_epoch_end(int(current_step / steps_per_epoch), logs)
    training_summary = {
        'total_training_steps': total_training_steps,
        'train_loss': _float_metric_value(train_loss_metric),
    }
    for metric in model.metrics:
      training_summary[metric.name] = _float_metric_value(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)

    if not _should_export_summary(strategy):
      tf.io.gfile.rmtree(summary_dir)

    callback_list.on_train_end()

    return model