standard_runner.py

# Copyright 2020 The Orbit Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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"""AbstractTrainer/Evaluator subclasses with added functionality.

The classes in this module provide some additional structure to the bare
`AbstractTrainer`/`AbstractEvaluator` APIs.

Both `StandardTrainer` and `StandardEvaluator` split the train/eval loops into
"begin", "step", and "end" methods, and provide an implementation of the loop
itself that makes calls to the relevant step method.

`StandardTrainer` supports running the loop using the TF while loop construct
for added performance (particularly on TPUs). It additionally provides some
functionality to make writing summaries from inside a model more performant when
running on TPUs.

These classes are intended to work well in common settings, however there may
be use cases these classes don't support (for instance, `StandardEvaluator` in
particular doesn't support running full evaluations over multiple different eval
datasets). Users are encouraged to simply fall back to custom `AbstractTrainer`
and `AbstractEvaluator` subclasses in these cases.
"""

import abc

from typing import Any, Optional

import dataclasses

from orbit import runner
from orbit.utils import loop_fns

import tensorflow as tf


@dataclasses.dataclass(frozen=True)
class StandardTrainerOptions:
  """Advanced options for `orbit.StandardTrainer`.

  Attributes:
    use_tf_function: A boolean indicating whether to apply `tf.function` to the
      training loop. This will only affect the body of the loop (involving
      `train_step`); `train_loop_begin` and `train_loop_end` will always be run
      in eager mode.
    use_tf_while_loop: A boolean indicating whether to run the training loop
      using a `tf.while_loop`. If `True`, `use_tf_function` must also be `True`.
    use_tpu_summary_optimization: A boolean indicating whether to enable a
      performance optimization for summaries in TPUs. Writing summaries
      conditionally with outside compilation on TPUs can be extremely slow. If
      `True`, this optimization creates two `tf.function`s with two XLA programs
      (one with summary calls, and one without). The program with summaries runs
      only for one step when summaries should be recorded.
  """
  use_tf_function: bool = True
  use_tf_while_loop: bool = True
  use_tpu_summary_optimization: bool = False


def _create_train_loop_fn(train_step_fn, options: StandardTrainerOptions):
  """Creates a training loop from the given step function and options."""
  if options.use_tf_while_loop:
    loop_fn = loop_fns.create_tf_while_loop_fn(train_step_fn)
    if options.use_tpu_summary_optimization:
      loop_fn = loop_fns.LoopFnWithSummaries(loop_fn)
    else:
      loop_fn = tf.function(loop_fn)
  else:
    if options.use_tf_function:
      train_step_fn = tf.function(train_step_fn)
    loop_fn = loop_fns.create_loop_fn(train_step_fn)
  return loop_fn


class StandardTrainer(runner.AbstractTrainer, metaclass=abc.ABCMeta):
  """Implements standard functionality on top of the AbstractTrainer API.

  This class structures the training "inner loop" roughly as follows:

      train_loop_begin()
      for _ in range(num_steps):
        train_step(train_iterator)
      return train_loop_end()

  Calls to `train_loop_begin` and `train_loop_end` are always done in eager
  mode, while the loop/`train_step` may be implemented using `tf.while` and/or
  `tf.function`, as determined by the `options` passed to `__init__`.
  """

  def __init__(self, train_dataset, options: StandardTrainerOptions = None):
    """Initializes the `StandardTrainer` instance.

    Args:
      train_dataset: A `tf.nest`-compatible structure of `tf.data.Dataset` or
        `DistributedDataset`.
      options: An `orbit.StandardTrainerOptions` instance.
    """
    options = options or StandardTrainerOptions()
    if options.use_tf_while_loop and not options.use_tf_function:
      raise ValueError("`use_tf_while_loop=True` and `use_tf_function=False` "
                       "is not supported")
    if options.use_tpu_summary_optimization and not options.use_tf_while_loop:
      raise ValueError("`use_tpu_summary_optimization=True` and "
                       "`use_tf_while_loop=False` is not supported")

    self._train_options = options
    self._train_dataset = train_dataset
    self._train_iter = None
    self._train_loop_fn = None

  def train(self, num_steps: tf.Tensor) -> Optional[runner.Output]:
    """Implements `num_steps` steps of training.

    Args:
      num_steps: The number of training steps to run. This corresponds directly
        to the number of calls made to `train_step`.

    Returns:
      The output of `train_loop_end`.
    """
    self.train_loop_begin()

    if self._train_loop_fn is None:
      self._train_loop_fn = _create_train_loop_fn(
          self.train_step, options=self._train_options)

    if self._train_iter is None:
      self._train_iter = tf.nest.map_structure(iter, self.train_dataset)

    self._train_loop_fn(self._train_iter, num_steps)
    return self.train_loop_end()

  def train_loop_begin(self):
    """Called once at the beginning of the training loop.

    This method is always called in eager mode, and is a good place to reset
    metrics that accumulate values over multiple steps of training.

    Note that this method is called before dataset iterator creation.
    """
    pass

  @abc.abstractmethod
  def train_step(self, iterator):
    """Implements one step of training.

    What a "step" consists of is up to the implementer. When using distribution
    strategies, the call to this method takes place in the "cross-replica
    context" for generality, to allow e.g. multiple iterator dequeues and calls
    to `strategy.run`.

    Note that if `use_tf_function=True`, all the code inside `train_step` should
    be compatible with `tf.function` tracing (and in particular, any state
    modifications involving `self` should be avoided). In some cases, non-
    `tf.function` compatible code can be moved to `train_loop_begin` or
    `train_loop_end`, which always execute eagerly.

    Args:
      iterator: A `tf.nest`-compatible structure of `tf.data.Iterator` or
        `DistributedIterator`. The structure of this input matches the structure
        of `train_dataset` as passed to `__init__`.
    """
    pass

  def train_loop_end(self) -> Optional[runner.Output]:
    """Called once at the end of the training loop.

    This method is always called in eager mode, and is a good place to get
    metric results. The value returned from this function will be returned as-is
    from the `train` method implementation provided by `StandardTrainer`.

    Returns:
      The function may return a dictionary of `Tensors`, which will be
      written to logs and as TensorBoard summaries. It can also be a
      nested dictionary, yielding a hierarchy of summary directories.
    """
    pass

  @property
  def train_dataset(self):
    """The current training dataset."""
    return self._train_dataset

  @train_dataset.setter
  def train_dataset(self, train_dataset):
    """Sets a new training dataset, replacing the current one.

    Any unprocessed examples in the current dataset are discarded.

    Args:
      train_dataset: A `tf.nest`-compatible structure of `tf.data.Dataset` or
        `DistributedDataset`.
    """
    self._train_dataset = train_dataset
    self._train_iter = None


@dataclasses.dataclass(frozen=True)
class StandardEvaluatorOptions:
  """Advanced options for the `orbit.StandardEvaluator`.

  Attributes:
    use_tf_function: A boolean indicating whether to apply `tf.function` to the
      training loop. This will only affect the body of the loop (involving
      `train_step`); `train_loop_begin` and `train_loop_end` will always be run
      in eager mode.
    use_tf_while_loop: A boolean indicating whether to run the training loop
      using a `tf.while_loop`. If `True`, `use_tf_function` must also be `True`.
  """
  use_tf_function: bool = True
  use_tf_while_loop: bool = False


def _create_eval_loop_fn(eval_step_fn, has_state: bool,
                         options: StandardEvaluatorOptions):
  """Create evaluation loop function."""
  if options.use_tf_while_loop:
    # TODO(b/176126742): tf.while_loop doesn't support `None` as a loop input
    # even when it is not used inside the loop. To workaround this limitation,
    # we have to build two tf.functions for it.
    if has_state:
      loop_fn = loop_fns.create_tf_while_loop_fn_with_state(eval_step_fn)
    else:
      loop_fn = loop_fns.create_tf_while_loop_fn(eval_step_fn)
    loop_fn = tf.function(loop_fn)
  else:
    if options.use_tf_function:
      eval_step_fn = tf.function(eval_step_fn)
    loop_fn = loop_fns.create_loop_fn(eval_step_fn)
  return loop_fn


class StandardEvaluator(runner.AbstractEvaluator, metaclass=abc.ABCMeta):
  """Implements the standard functionality of AbstractEvaluator APIs.

  This class structures evaluation roughly as follows:

      state = eval_begin()
      for _ in range(num_steps):
        step_outputs = eval_step(eval_iterator)
        state = eval_reduce(state, step_outputs)
      return eval_end(state)

  Calls to `eval_begin`, `eval_reduce`, and `eval_end` are always done in eager
  mode, while `eval_step` may be compiled with `tf.function` as determined by
  the `options` passed to `__init__`.

  This class does not support completely evaluating multiple different datasets
  (i.e., where every example of each dataset should be processed, as opposed to
  running for a fixed number of evaluation steps). A custom `AbstractEvaluator`
  is recommended in this case.
  """

  def __init__(self, eval_dataset, options: StandardEvaluatorOptions = None):
    """Initializes the `StandardEvaluator` instance.

    Args:
      eval_dataset: A `tf.nest`-compatible structure of `tf.data.Dataset` or
        `DistributedDataset`.
      options: An `orbit.StandardEvaluatorOptions` instance.
    """
    options = options or StandardEvaluatorOptions()
    if options.use_tf_while_loop and not options.use_tf_function:
      raise ValueError("`use_tf_while_loop=True` and `use_tf_function=False` "
                       "is not supported")

    self._eval_options = options
    self._eval_dataset = eval_dataset
    self._eval_loop_fn = None

  def evaluate(self, num_steps: tf.Tensor) -> Optional[runner.Output]:
    """Implements `num_steps` steps of evaluation.

    Args:
      num_steps: The number of evaluation steps to run. When this is -1,
        evaluation proceeds until a call to `eval_step` raises a `StopIteration`
        or `tf.errors.OutOfRangeError`.

    Returns:
      The output of `self.eval_end()`.

    Raises:
      ValueError: If `options.use_tf_while_loop` is `True` and `num_steps` is
        unspecified.
    """
    if self._eval_options.use_tf_while_loop and num_steps == -1:
      raise ValueError("Looping until exhausted is not supported if "
                       "`options.use_tf_while_loop` is `True`")

    outputs = self.eval_begin()  # pylint: disable=assignment-from-no-return

    has_state = outputs is not None
    if self._eval_loop_fn is None:
      self._eval_loop_fn = _create_eval_loop_fn(
          self.eval_step, has_state=has_state, options=self._eval_options)

    eval_iter = tf.nest.map_structure(iter, self.eval_dataset)
    if self._eval_options.use_tf_while_loop and not has_state:
      self._eval_loop_fn(eval_iter, num_steps)
    else:
      outputs = self._eval_loop_fn(
          eval_iter, num_steps, state=outputs, reduce_fn=self.eval_reduce)

    if outputs is None:
      return self.eval_end()
    else:
      return self.eval_end(outputs)

  def eval_begin(self) -> Any:
    """Called once at the beginning of the evaluation.

    This method is always called in eager mode, and is a good place to reset
    metrics that accumulate values over the course of evaluation.

    Note that this method is called before dataset iterator creation.

    Returns:
      An value to pass as the `state` argument to `eval_reduce`.
    """
    pass

  @abc.abstractmethod
  def eval_step(self, iterator) -> Any:
    """Implements one step of evaluation.

    What a "step" consists of is up to the implementer. When using distribution
    strategies, the call to this method takes place in the "cross-replica
    context" for generality, to allow e.g. multiple iterator dequeues and calls
    to `strategy.run`.

    Note that if `use_tf_function=True`, all the code inside `eval_step` should
    be compatible with `tf.function` tracing (and in particular, any state
    modifications involving `self` should be avoided). In some cases, non-
    `tf.function` compatible code can be moved to `eval_loop_begin`,
    `eval_reduce`, or `eval_loop_end`, which always execute eagerly.

    Args:
      iterator: A `tf.nest`-compatible structure of `tf.data.Iterator` or
        `DistributedIterator`.

    Returns:
      An output which is passed as `step_outputs` argument into `eval_reduce`
      function.
    """
    pass

  def eval_end(self, *args) -> Optional[runner.Output]:
    """Called at the end of the evaluation.

    Called once at the end of evaluation.

    This method is always called in eager mode, and is a good place to get
    metric results. The value returned from this function will be returned as-is
    from the `evaluate` method implementation provided by `StandardEvaluator`.

    Args:
      *args: The outputs from `eval_reduce` for the last eval step, if they are
        non-`None` (if they are `None`, nothing is passed).

    Returns:
      The function may return a dictionary of `Tensors`, which will be
      written to logs and as TensorBoard summaries. It can also be a
      nested dictionary, yielding a hierarchy of summary directories.
    """
    pass

  def eval_reduce(self,
                  state: Any = None,
                  step_outputs: Optional[runner.Output] = None) -> Any:
    """A function to perform per-step reduction on the evaluation outputs.

    This is useful for passing state throughout evaluation, especially in cases
    where maintaining or accumulating state is hard to accomplish using
    `tf.metrics.Metric` or other `tf.Variable`-based approaches. For instance,
    it can be used to easily accumulate all per-example losses from the full
    evaluation for subsequent processing in `eval_end()`.

    Args:
      state: A state being mainted throughout the evaluation.
      step_outputs: Outputs from the current evaluation step.

    Returns:
      An output which is passed as the `state` argument to this function for the
      next step. After evaluation is finished, the output from last step will be
      passed to `eval_end`.
    """
    pass

  @property
  def eval_dataset(self):
    """The current evaluation dataset."""
    return self._eval_dataset

  @eval_dataset.setter
  def eval_dataset(self, eval_dataset):
    """Sets a new eval dataset, replacing the current one.

    Any unprocessed examples in the current dataset are discarded.

    Args:
      eval_dataset: A `tf.nest`-compatible structure of `tf.data.Dataset` or
        `DistributedDataset`.
    """
    self._eval_dataset = eval_dataset