laywerrobot/lib/python3.6/site-packages/tensorflow/contrib/distribute/python/values.py

# Copyright 2018 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.
# ==============================================================================
"""Various classes representing distributed values.

See go/tf-distribution-strategy.
"""

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

import collections
import weakref
import six

from tensorflow.contrib.distribute.python import input_ops
from tensorflow.contrib.distribute.python import prefetching_ops_v2
from tensorflow.python.eager import context
from tensorflow.python.framework import device as tf_device
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.training import device_util
from tensorflow.python.training import distribute as distribute_lib
from tensorflow.python.training import saver
from tensorflow.python.training.checkpointable import base as checkpointable
from tensorflow.python.util import nest


# pylint: disable=line-too-long
# TODO(josh11b): Should device values be strings or DeviceSpec objects?
# Not sure DeviceSpec objects are usable as a dict key.
class DistributedValues(object):
  """Holds a map from device to values. Either PerDevice or Mirrored."""

  def __init__(self, index):
    self._index = {device_util.canonicalize(key): value
                   for key, value in six.iteritems(index)}

  def get(self, device=None):
    """Returns the value for the current device or raises a ValueError."""
    if device is None:
      tower_context = distribute_lib.get_tower_context()
      if tower_context:
        device = tower_context.device
      else:
        device = distribute_lib.get_update_device()
        if device is None:
          return self._get_cross_tower()
    device = device_util.canonicalize(device)
    try:
      return self._index[device]
    except KeyError as e:
      six.raise_from(
          ValueError("Device %s not found in %s (current device %s)" %
                     (device, self._index.keys(), device_util.current())), e)

  def on_device(self, device):
    device = device_util.canonicalize(device)
    return device in self._index

  @property
  def devices(self):
    return list(self._index.keys())

  def __str__(self):
    return "%s:%s" % (self.__class__.__name__, self._index)

  def __repr__(self):
    return "%s(%r)" % (self.__class__.__name__, self._index)

  # TODO(josh11b): Possibly make an accessor for _index for use by
  # DistributionStrategy implementations.


class DistributedDelegate(DistributedValues):
  """A map from device to values; acts as the same type as the values."""

  def __init__(self, index):
    super(DistributedDelegate, self).__init__(index)

  def __getattr__(self, name):
    return getattr(self.get(), name)

  # pylint: disable=multiple-statements
  def __add__(self, o): return self.get() + o
  def __radd__(self, o): return o + self.get()
  def __sub__(self, o): return self.get() - o
  def __rsub__(self, o): return o - self.get()
  def __mul__(self, o): return self.get() * o
  def __rmul__(self, o): return o * self.get()
  def __truediv__(self, o): return self.get() / o
  def __rtruediv__(self, o): return o / self.get()
  def __floordiv__(self, o): return self.get() // o
  def __rfloordiv__(self, o): return o // self.get()
  def __mod__(self, o): return self.get() % o
  def __rmod__(self, o): return o % self.get()
  def __lt__(self, o): return self.get() < o
  def __le__(self, o): return self.get() <= o
  def __gt__(self, o): return self.get() > o
  def __ge__(self, o): return self.get() >= o
  def __and__(self, o): return self.get() & o
  def __rand__(self, o): return o & self.get()
  def __or__(self, o): return self.get() | o
  def __ror__(self, o): return o | self.get()
  def __xor__(self, o): return self.get() ^ o
  def __rxor__(self, o): return o ^ self.get()
  def __getitem__(self, o): return self.get()[o]
  def __pow__(self, o, modulo=None): return pow(self.get(), o, modulo)
  def __rpow__(self, o): return pow(o, self.get())
  def __invert__(self): return ~self.get()
  def __neg__(self): return -self.get()
  def __abs__(self): return abs(self.get())

  def __div__(self, o):
    try:
      return self.get().__div__(o)
    except AttributeError:
      # See https://docs.python.org/3/library/constants.html#NotImplemented
      return NotImplemented

  def __rdiv__(self, o):
    try:
      return self.get().__rdiv__(o)
    except AttributeError:
      # See https://docs.python.org/3/library/constants.html#NotImplemented
      return NotImplemented

  def __matmul__(self, o):
    try:
      return self.get().__matmul__(o)
    except AttributeError:
      # See https://docs.python.org/3/library/constants.html#NotImplemented
      return NotImplemented

  def __rmatmul__(self, o):
    try:
      return self.get().__rmatmul__(o)
    except AttributeError:
      # See https://docs.python.org/3/library/constants.html#NotImplemented
      return NotImplemented

  # TODO(josh11b): Even more operator overloads.


class PerDevice(DistributedValues):
  """Holds a map from device to unsynchronized values."""
  pass


# Note that unlike PerDevice, Mirrored values inherit from
# DistributedDelegate and so can be used directly in cross-tower mode.
class Mirrored(DistributedDelegate):
  """Holds a map from device to values which are kept in sync."""

  def _get_cross_tower(self):
    device = device_util.canonicalize(device_util.current())
    if device in self._index:
      return self._index[device]
    return list(self._index.values())[0]


def _assign_on_device(device, variable, tensor):
  with ops.device(device):
    return variable.assign(array_ops.identity(tensor))


DistributedVarOp = collections.namedtuple(
    "DistributedVarOp", ["name", "graph", "type"])


class DistributedVariable(DistributedDelegate):
  """Holds a map from device to variables."""
  # TODO(josh11b): Support changing the set of variables if e.g. if new
  # devices are joining or a device is to leave.

  def __init__(self, index):
    # Child class must set self._primary_var before calling
    # super(...).__init__(index).
    self._common_name = self._primary_var.name.split(":")[0]
    # Use a weakref to make it easy to map from the contained values
    # to the container without introducing a reference cycle.
    for v in six.itervalues(index):
      v._distributed_container = weakref.ref(self)  # pylint: disable=protected-access
    super(DistributedVariable, self).__init__(index)

  @property
  def initializer(self):
    return control_flow_ops.group([v.initializer for v in self._index.values()])

  @property
  def graph(self):
    return self._primary_var.graph

  @property
  def _shared_name(self):
    return self._common_name

  @property
  def _unique_id(self):
    return self._primary_var._unique_id   # pylint: disable=protected-access

  @property
  def name(self):
    return self._primary_var.name

  @property
  def dtype(self):
    return self._primary_var.dtype

  @property
  def shape(self):
    return self._primary_var.shape

  def get_shape(self):
    return self._primary_var.get_shape()

  def to_proto(self, export_scope=None):
    return self._primary_var.to_proto(export_scope=export_scope)

  @property
  def op(self):
    # We want cross-tower code that does some var.op.X calls
    # to work (even if the current device isn't in self.devices), but
    # other uses of var.op in a cross-tower context to fail.
    if distribute_lib.get_cross_tower_context():
      return DistributedVarOp(self._primary_var.op.name,
                              self._primary_var.op.graph,
                              self._primary_var.op.type)
    return self.get().op

  def _should_act_as_resource_variable(self):
    """Pass resource_variable_ops.is_resource_variable check."""
    pass


ops.register_dense_tensor_like_type(DistributedVariable)


def _get_update_device():
  """Validate we are in update/update_non_slot() and return current device.

  This is used in MirroredVariable.assign* members, to make sure they
  are only called via an update method, to make sure all components of the
  variable are being updated in a consistent way.

  Returns:
    A string device.

  Raises:
    RuntimeError: If not in distribution.update()/.update_non_slot().
  """
  device = distribute_lib.get_update_device()
  if device is None:
    raise RuntimeError(
        "Use DistributionStrategy.update() to modify a MirroredVariable.")
  return device


class _MirroredSaveable(saver.BaseSaverBuilder.ResourceVariableSaveable):
  """Class for defining how to restore a MirroredVariable."""

  def __init__(self, mirrored_variable, primary_variable, name):
    self._mirrored_variable = mirrored_variable
    super(_MirroredSaveable, self).__init__(primary_variable, "", name)

  def restore(self, restored_tensors, restored_shapes):
    """Restore the same value into all variables."""
    tensor, = restored_tensors
    return control_flow_ops.group([
        _assign_on_device(d, v, tensor)
        for d, v in six.iteritems(self._mirrored_variable._index)])  # pylint: disable=protected-access


class MirroredVariable(DistributedVariable, Mirrored,
                       checkpointable.CheckpointableBase):
  """Holds a map from device to variables whose values are kept in sync."""

  def __init__(self, index, primary_var, aggregation):
    # Use a weakref to make it easy to map from the contained values
    # to the container without introducing a reference cycle.
    for v in six.itervalues(index):
      v._mirrored_container = weakref.ref(self)  # pylint: disable=protected-access
    self._primary_var = primary_var
    # tf.keras keeps track of variables initialized using this attribute. When
    # tf.keras gets the default session, it initializes all uninitialized vars.
    # We need to make _keras_initialized a member of MirroredVariable because
    # without this it will use `__getattr__` which will delegate to a component
    # variable.
    self._keras_initialized = False
    self._aggregation = aggregation
    super(MirroredVariable, self).__init__(index)

  # The arguments to update() are automatically unwrapped so the update()
  # function would normally see regular variables, not MirroredVariables.
  # However, the update function can still operate on wrapped MirroredVariables
  # through object members, captured arguments, etc. This is more likely in an
  # update_non_slot() function (like OptimizerV2._finish), which can
  # update several non-slot variables in one call.
  def _assign_func(self, *args, **kwargs):
    f = kwargs.pop("f")
    if distribute_lib.get_cross_tower_context():
      update_device = distribute_lib.get_update_device()
      # We are calling update on the mirrored variable in cross tower context.
      if update_device is not None:
        # We are calling an assign function on the mirrored variable in cross
        # tower context.
        v = self.get(device=update_device)
        return f(v, *args, **kwargs)

      return distribute_lib.get_distribution_strategy().update(
          self, f, *args, **kwargs)
    else:
      # We are calling an assign function on the mirrored variable in tower
      # context.
      # We reduce the value we want to assign/add/sub. More details about how we
      # handle the different use cases can be found in the _reduce method.
      # We call the function on each of the mirrored variables with the reduced
      # value.
      if self._aggregation == vs.VariableAggregation.NONE:
        raise ValueError("You must specify an aggregation method to update a "
                         "MirroredVariable in Tower Context.")

      def merge_fn(strategy, value):
        return strategy.update(
            self, f,
            strategy.reduce(
                aggregation=self._aggregation, value=value, destinations=self))

      return distribute_lib.get_tower_context().merge_call(merge_fn, *args,
                                                           **kwargs)

  def assign_sub(self, *args, **kwargs):
    return self._assign_func(f=state_ops.assign_sub, *args, **kwargs)

  def assign_add(self, *args, **kwargs):
    return self._assign_func(f=state_ops.assign_add, *args, **kwargs)

  def assign(self, *args, **kwargs):
    return self._assign_func(f=state_ops.assign, *args, **kwargs)

  def is_initialized(self, name=None):
    # We have to cast the self._index.values() to a `list` because when we
    # use `model_to_estimator` to run tf.keras models, self._index.values() is
    # of type `dict_values` and not `list`.
    values_list = list(self._index.values())
    result = values_list[0].is_initialized()
    # We iterate through the list of values except the last one to allow us to
    # name the final `logical_and` op the same name that is passed by the user
    # to the `is_initialized` op. For mirrored variables, the `is_initialized`
    # op is a `logical_and` op.
    for v in values_list[1:-1]:
      result = math_ops.logical_and(result, v.is_initialized())
    result = math_ops.logical_and(result, values_list[-1].is_initialized(),
                                  name=name)
    return result

  @property
  def initializer(self):
    # return grouped ops of all the var initializations of component values of
    # the mirrored variable
    return control_flow_ops.group([v.initializer for v in self._index.values()])

  @property
  def aggregation(self):
    return self._aggregation

  def _get_cross_tower(self):
    device = device_util.canonicalize(device_util.current())
    if device in self._index:
      return array_ops.identity(self._index[device])
    return array_ops.identity(self._primary_var)

  def _as_graph_element(self):
    # pylint: disable=protected-access
    if distribute_lib.get_cross_tower_context():
      return self._primary_var._as_graph_element()
    return self.get()._as_graph_element()

  def _gather_saveables_for_checkpoint(self):
    """Overrides CheckpointableBase method.

    This allows both name-based and object-based save and restore of
    MirroredVariables.

    Returns:
      A dictionary mapping attribute names to `SaveableObject` factories.
    """
    def _saveable_factory(name=self._common_name):
      return _MirroredSaveable(self, self._primary_var, name)
    return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory}


# Register a conversion function which reads the value of the variable,
# allowing instances of the class to be used as tensors.
def _tensor_conversion_mirrored(var, dtype=None, name=None, as_ref=False):
  # Try to avoid assignments to and other mutations of MirroredVariable
  # state except through a DistributionStrategy.update() call.
  assert not as_ref
  return ops.internal_convert_to_tensor(
      var.get(), dtype=dtype, name=name, as_ref=as_ref)


ops.register_tensor_conversion_function(MirroredVariable,
                                        _tensor_conversion_mirrored)


class _TowerLocalSaveable(saver.BaseSaverBuilder.SaveableObject):
  """Class for defining how to restore a TowerLocalVariable."""

  def __init__(self, tower_local_variable, name):
    self._tower_local_variable = tower_local_variable
    # We use a callable so that we don't have to evaluate this expression
    # in the case where we are trying to restore instead of save.
    def tensor():
      return distribute_lib.get_distribution_strategy().read_var(
          tower_local_variable)
    spec = saver.BaseSaverBuilder.SaveSpec(
        tensor=tensor,
        slice_spec="",
        name=name,
        dtype=tower_local_variable.dtype)
    super(_TowerLocalSaveable, self).__init__(tensor, [spec], name)

  def restore(self, restored_tensors, restored_shapes):
    """Restore the same value into all variables."""
    tensor, = restored_tensors
    # To preserve the sum across save and restore, we have to divide the
    # total across all devices when restoring a variable that was summed
    # when saving.
    if self._tower_local_variable.aggregation == vs.VariableAggregation.SUM:
      tensor *= 1. / len(self._tower_local_variable.devices)
    return control_flow_ops.group([
        _assign_on_device(d, v, tensor)
        for d, v in six.iteritems(self._tower_local_variable._index)])  # pylint: disable=protected-access


def _assert_tower_context():
  if not distribute_lib.get_tower_context():
    raise RuntimeError(
        "Tower-local variables may only be assigned in a tower context.")


class TowerLocalVariable(DistributedVariable, PerDevice,
                         checkpointable.CheckpointableBase):
  """Holds a map from device to variables whose values are reduced on save."""

  def __init__(self, index, primary_var, aggregation):
    self._primary_var = primary_var
    self._aggregation = aggregation
    # tf.keras keeps track of variables initialized using this attribute. When
    # tf.keras gets the default session, it initializes all uninitialized vars.
    # We need to make _keras_initialized a member of TowerLocalVariable because
    # without this it will use `__getattr__` which will delegate to a component
    # variable.
    self._keras_initialized = False
    super(TowerLocalVariable, self).__init__(index)

  def assign_sub(self, *args, **kwargs):
    _assert_tower_context()
    return self.get().assign_sub(*args, **kwargs)

  def assign_add(self, *args, **kwargs):
    _assert_tower_context()
    return self.get().assign_add(*args, **kwargs)

  def assign(self, *args, **kwargs):
    _assert_tower_context()
    return self.get().assign(*args, **kwargs)

  def is_initialized(self, name=None):
    # We have to cast the self._index.values() to a `list` because when we
    # use `model_to_estimator` to run tf.keras models, self._index.values() is
    # of type `dict_values` and not `list`.
    values_list = list(self._index.values())
    result = values_list[0].is_initialized()
    # We iterate through the list of values except the last one to allow us to
    # name the final `logical_and` op the same name that is passed by the user
    # to the `is_initialized` op. For tower local variables, the
    # `is_initialized` op is a `logical_and` op.
    for v in values_list[1:-1]:
      result = math_ops.logical_and(result, v.is_initialized())
    result = math_ops.logical_and(result, values_list[-1].is_initialized(),
                                  name=name)
    return result

  @property
  def initializer(self):
    # return grouped ops of all the var initializations of component values of
    # the tower local variable
    return control_flow_ops.group([v.initializer for v in self._index.values()])

  @property
  def aggregation(self):
    return self._aggregation

  def _get_cross_tower(self):
    all_components = tuple(self._index.values())
    # TODO(josh11b): Use a strategy-specific method.
    total = math_ops.add_n(all_components)
    if self._aggregation == vs.VariableAggregation.MEAN:
      return total * (1./ len(all_components))
    return total

  def _as_graph_element(self):
    # pylint: disable=protected-access
    if distribute_lib.get_cross_tower_context():
      return self._get_cross_tower()
    return self.get()._as_graph_element()

  def _gather_saveables_for_checkpoint(self):
    """Overrides CheckpointableBase method.

    This allows both name-based and object-based save and restore of
    TowerLocalVariables.

    Returns:
      A dictionary mapping attribute names to `SaveableObject` factories.
    """
    def _saveable_factory(name=self._common_name):
      return _TowerLocalSaveable(self, name)
    return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory}


# Register a conversion function for TowerLocalVariable which allows as_ref to
# be true.
def _tensor_conversion_tower_local(var, dtype=None, name=None, as_ref=False):
  return ops.internal_convert_to_tensor(
      var.get(), dtype=dtype, name=name, as_ref=as_ref)


ops.register_tensor_conversion_function(TowerLocalVariable,
                                        _tensor_conversion_tower_local)


def _devices_match(d1, d2):
  return device_util.canonicalize(d1) == device_util.canonicalize(d2)


def regroup(per_device, wrap_class=PerDevice):
  """Makes device->nest map into a nest of PerDevice/Mirrored values."""
  items = list(per_device.items())
  assert items
  v0 = items[0][1]  # First value

  if isinstance(v0, list):
    for _, v in items[1:]:
      assert isinstance(v, list)
      assert len(v) == len(v0), ("len(v) == %d, len(v0) == %d, v: %s, v0: %s" %
                                 (len(v), len(v0), v, v0))
    return [regroup({k: v[i] for k, v in items}, wrap_class)
            for i in range(len(v0))]

  if isinstance(v0, tuple):
    for _, v in items[1:]:
      assert isinstance(v, tuple)
      assert len(v) == len(v0)
    regrouped_tuple = tuple(regroup({k: v[i] for k, v in items}, wrap_class)
                            for i in range(len(v0)))
    if hasattr(v0, "_fields"):
      # This tuple is in fact a namedtuple! Create a new namedtuple instance
      # and initialize it with the regrouped values:
      assert hasattr(type(v0), "_make")
      return type(v0)._make(regrouped_tuple)
    else:
      return regrouped_tuple

  if isinstance(v0, dict):
    v0keys = set(v0.keys())
    for _, v in items[1:]:
      assert isinstance(v, dict)
      assert set(v.keys()) == v0keys
    return {key: regroup({k: v[key] for k, v in items}, wrap_class)
            for key in v0keys}

  # If exactly the same object across all devices, return it unwrapped.
  same_id = True
  for _, v in items[1:]:
    if v is not v0:
      same_id = False
      break
  # Consider three cases where same_id is true:
  # * If v0 is a DistributedVariable (a MirroredVariable or
  #   TowerLocalVariable, and same_id means it is the same across all
  #   devices), we want to return it. We check DistributedVariable
  #   specifically since it can look like it has a
  #   _distributed_container member since its members do.
  # * If v0 is a member of a distributed variable, in which case
  #   hasattr(v0, "_distributed_container") is true, we want to
  #   return the DistributedVariable that contains it using the
  #   _distributed_container logic below. This case can trigger
  #   same_id when there is only one device.
  # * In any other situation, same_id means we return v0.
  if same_id and (isinstance(v0, DistributedVariable) or
                  not hasattr(v0, "_distributed_container")):
    return v0

  # Detect the case where each device has a parallel component of the
  # same MirroredVariable (or TowerLocalVariable). In this case we
  # want to return the containing MirroredVariable, after a bunch of
  # sanity checking. In particular, each component should have the
  # same container, and the devices of the variables should match the
  # keys of the per-device dictionary.
  if hasattr(v0, "_distributed_container"):
    # pylint: disable=protected-access
    assert not isinstance(v0, MirroredVariable), (
        "ids = %s, items = %s" % ([id(v[1]) for v in items], items))
    assert _devices_match(v0.device, items[0][0]), (
        "v0.device = %s, items = %s" % (v0.device, items))
    distributed_container = v0._distributed_container()
    assert distributed_container is not None
    for d, v in items[1:]:
      assert _devices_match(v.device, d), (
          "v.device = %s, d = %s, items = %s" % (v.device, d, items))
      assert distributed_container is v._distributed_container()
    return distributed_container
  # pylint: enable=protected-access

  return wrap_class(per_device)


def select_device(device, structured):
  """Specialize a nest of regular & per-device values for one device."""
  def _get(x):
    return x.get(device) if isinstance(x, DistributedValues) else x

  return nest.map_structure(_get, structured)


def select_device_mirrored(device, structured):
  """Specialize a nest of regular & mirrored values for one device."""
  def _get_mirrored(x):
    if isinstance(x, DistributedValues):
      if not isinstance(x, Mirrored):
        raise TypeError(
            "Expected value to be mirrored across towers: %s in %s." %
            (x, structured))
      return x.get(device)
    else:
      return x

  return nest.map_structure(_get_mirrored, structured)


class PerDeviceDataIterator(object):
  """An iterator (like `tf.data.Iterator`) into a `PerDeviceDataset`."""

  def __init__(self, iterator, devices, prefetch_on_device=None):
    self._iterator = iterator
    self._devices = devices
    self._prefetch_on_device = prefetch_on_device

  @property
  def initializer(self):
    return self._iterator.initializer

  def get_next(self, name=None):
    """Scatter the input across devices."""
    if self._prefetch_on_device:
      data_list = self._iterator.get_next(name=name)
      index = dict(zip(self._devices, data_list))
    else:
      batch = self._iterator.get_next(name=name)
      index = {}
      def get_ith(i):
        return lambda x: x[i]

      for i, d in enumerate(self._devices):
        index[d] = nest.map_structure(get_ith(i), batch)
        if context.executing_eagerly():
          with ops.device(d):
            index[d] = nest.map_structure(array_ops.identity, index[d])

    return regroup(index)


class PerDeviceDataset(object):
  """Like `tf.data.Dataset` split devices, producing `PerDevice` data."""

  def __init__(self, dataset, devices, prefetch_on_device=None):
    self._devices = devices

    # Default to using prefetching in graph mode, unless specified.
    # TODO(priyag): Enable prefetching in eager mode.
    self._prefetch_on_device = prefetch_on_device
    if self._prefetch_on_device is None:
      self._prefetch_on_device = not context.executing_eagerly()
    assert not (self._prefetch_on_device and context.executing_eagerly()), (
        "Prefetching is only supported in graph mode currently")

    if self._prefetch_on_device:
      self._dataset = dataset.apply(
          prefetching_ops_v2.prefetch_to_devices(self._devices))
    else:
      # TODO(priyag): If dropping remainder is not appropriate, find another
      # approach to distributing the dataset when not possible to divide evenly.
      # Possibly not an issue when we start using PartitionedDataset.
      self._dataset = dataset.batch(len(devices), drop_remainder=True)

  def make_one_shot_iterator(self):
    """Get a one time use iterator for the distributed PerDeviceDataset."""
    dataset_iterator = self._dataset.make_one_shot_iterator()
    return PerDeviceDataIterator(
        dataset_iterator, self._devices, self._prefetch_on_device)

  def make_initializable_iterator(self):
    """Get an initializable iterator for the distributed PerDeviceDataset."""
    dataset_iterator = self._dataset.make_initializable_iterator()
    return PerDeviceDataIterator(
        dataset_iterator, self._devices, self._prefetch_on_device)


class MultiWorkerDataIterator(object):
  """An iterator (like `tf.data.Iterator`) into a `MultiWorkerDataset`."""

  def __init__(self, iterators, worker_device_map):
    """Initialize the MultiWorkerDataIterator object.

    Args:
      iterators: a dict mapping from each worker to an iterator for
        that worker.
      worker_device_map: a dict mapping from each worker's devices to a list of
        devices that belong to this worker.

    Raises:
      ValueError: if iterators and worker_device_map are not compatible.
    """
    self._iterators = iterators
    self._worker_device_map = worker_device_map
    if set(self._iterators) != set(self._worker_device_map):
      raise ValueError("iterators and worker_device_map are not compatible.")

  @property
  def initializer(self):
    return control_flow_ops.group(
        [iterator.initializer for iterator in self._iterators.values()])

  def get_next(self, name=None):
    """Scatter the input across hosts and devices."""
    index = {}
    for worker, iterator in six.iteritems(self._iterators):
      if name is not None:
        d = tf_device.DeviceSpec.from_string(worker)
        new_name = "%s_%s_%d" % (name, d.job, d.task)
      else:
        new_name = None
      with ops.device(worker):
        data_per_worker = iterator.get_next(name=new_name)

      worker_devices = self._worker_device_map[worker]
      # Ungroup these per-device value so as to get a flat map from devices to
      # values.
      for d in worker_devices:
        v = select_device(d, data_per_worker)
        if d in index:
          raise ValueError("Duplicated devices in worker_device_map: %r" % v)
        index[d] = v

    return regroup(index)


class MultiWorkerDataset(object):
  """Like a `tf.data.Dataset` that distributes data to different workers.

  Each worker gets one shard of the input dataset. It is currently not working
  in
  eager mode.
  """

  def __init__(self, dataset_fn, worker_device_map, prefetch_on_device=None):
    """Initialize the MultiWorkerDataset object.

    Args:
      dataset_fn: a function that returns a `tf.data.Dataset`.
      worker_device_map: a dict mapping from each worker to a list of devices
        that belong to this worker.
      prefetch_on_device: whether to prefetch to devices.
    """
    self._worker_device_map = worker_device_map
    self._datasets = {}
    # TODO(yuefengz, priyag): support different set of jobs for input
    # processing.
    for i, (worker, worker_devices) in enumerate(
        six.iteritems(worker_device_map)):
      with ops.device(worker):
        worker_input = dataset_fn()
        worker_input = input_ops.auto_shard_dataset(
            worker_input, len(worker_device_map), i)
        self._datasets[worker] = PerDeviceDataset(
            worker_input, worker_devices, prefetch_on_device=prefetch_on_device)

  def make_one_shot_iterator(self):
    iterators = {}
    for worker, dataset in six.iteritems(self._datasets):
      with ops.device(worker):
        iterators[worker] = dataset.make_one_shot_iterator()
    return MultiWorkerDataIterator(iterators, self._worker_device_map)

  def make_initializable_iterator(self):
    iterators = {}
    for worker, dataset in six.iteritems(self._datasets):
      with ops.device(worker):
        iterators[worker] = dataset.make_initializable_iterator()
    return MultiWorkerDataIterator(iterators, self._worker_device_map)


class _PerKey(object):
  """Holds data associated by keys."""

  def __init__(self, *index):
    # pylint: disable=protected-access
    self._index = list(index)

  def get(self, iteration):
    return array_ops.gather(self._index, iteration)

  def get_shape(self):
    return self._index[-1][-1].get_shape()

  def get_dtype(self):
    return self._index[-1][-1].dtype

  def __str__(self):
    return "%s:%s" % (self.__class__.__name__, self._index)

  def __repr__(self):
    return "%s(%r)" % (self.__class__.__name__, self._index)


class PerIteration(_PerKey):
  """Holds input for multiple iterations at once."""

  def __init__(self, *index):
    # pylint: disable=protected-access
    super(PerIteration, self).__init__(*[batch._index for batch in index])


class Batches(_PerKey):
  pass


class MultiIterator(object):
  """Iterator that returns results of multiple get_next()s."""

  def __init__(self, dataset_iterator, iterations, batches_per_iteration):
    self._dataset_iterator = dataset_iterator
    self._iterations = iterations
    self._batches_per_iteration = batches_per_iteration

  def get_next(self, name=None):
    """Return PerIteration with `iterations x batches_per_iteration` inputs."""
    data = []
    for _ in range(self._batches_per_iteration):
      batch = []
      for _ in range(self._iterations):
        batch.append(self._dataset_iterator.get_next(name=name))
      data.append(batch)

    # Here is an example.  Suppose each get_next returns a tuple of two tensors.
    # For 3 `iterations` and 2 `batches_per_iteration`, the `data` is:
    # [[(a,z), (b,y), (c,x)], [(A,Z), (B,Y), (C,X)]]
    #
    # After the first `map_structure` it gets transformed to:
    #  [(Batches(a, A), Batches(z, Z)),
    #   (Batches(b, B), Batches(y, Y)),
    #   (Batches(c, C), Batches(x, X))]
    #
    # After the second `map_structure` it gets transformed to a tuple of:
    # (PerIteration([Batches(a, A), Batches(b, B), Batches(c, C)]),
    #  PerIteration([Batches(z, Z), Batches(y, Y), Batches(x, X)]))

    data = nest.map_structure(Batches, *data)
    data = nest.map_structure(PerIteration, *data)

    return data

  @property
  def initializer(self):
    return self._dataset_iterator.initializer


class PerIterationDataset(object):
  """A dataset that returns MultiIterators."""

  def __init__(self, dataset, iterations, batches_per_iteration):
    self._dataset = dataset
    self._iterations = iterations
    self._batches_per_iteration = batches_per_iteration

  def make_one_shot_iterator(self):
    iterator = self._dataset.make_one_shot_iterator()
    return MultiIterator(iterator, self._iterations,
                         self._batches_per_iteration)

  def make_initializable_iterator(self):
    iterator = self._dataset.make_initializable_iterator()
    return MultiIterator(iterator, self._iterations,
                         self._batches_per_iteration)


class MapOutput(object):
  """Map can result in multiple outputs per device."""

  def __init__(self, l):
    self._l = l

  def get(self):
    return self._l


class MultiStepContext(object):
  """A context object that can be used to capture things when running steps.

  This context object is useful when running multiple steps at a time using the
  `run_steps_on_dataset` API. For e.g. it allows the user's step function to
  specify which outputs to emit at what frequency. Currently it only supports
  capturing output from the last step, but will soon be augmented to support
  other use cases such as output each N steps.
  """

  def __init__(self, initial_loop_values=None):
    """Initializes an output context.

    Args:
      initial_loop_values: Initial values passed to the run steps
        while loop. The only purpose is to verify the shapes and types
        when the actual output is set. This will be removed once we
        automatically infer the output shapes and types (and do not need to
        check for user error in specifying them manually).
    Returns:
      A context object.
    """
    self._last_step_outputs = None
    self._non_tensor_outputs = None
    self._initial_loop_values = initial_loop_values

  @property
  def last_step_outputs(self):
    """Return the last step's outputs."""
    return self._last_step_outputs

  @last_step_outputs.setter
  def last_step_outputs(self, outputs):
    """Set the last step's outputs."""
    self._verify_structure_shapes_types(outputs, self._initial_loop_values)
    self._last_step_outputs = outputs

  @property
  def non_tensor_outputs(self):
    """Return the non tensor outputs."""
    return self._non_tensor_outputs

  @non_tensor_outputs.setter
  def non_tensor_outputs(self, outputs):
    """Set any non tensor outputs."""
    self._non_tensor_outputs = outputs

  def _verify_structure_shapes_types(self, left, right):
    """Verify that the structure, shapes and types of left are same as right."""
    nest.assert_same_structure(left, right)
    flat_left = nest.flatten(left)
    flat_right = nest.flatten(right)
    assert len(flat_left) == len(flat_right), (
        "Length of left {} and right {} should be same.".
        format(len(flat_left), len(flat_right)))

    for o, i in zip(flat_left, flat_right):
      # TODO(priyag): Add checks for other types like IndexedSlices.
      if isinstance(o, ops.Tensor):
        assert isinstance(i, ops.Tensor)
        assert o.shape == i.shape, (
            "Shape {} of left {} doesn't match shape {} of right {}.".
            format(o.shape, o, i.shape, i))
        assert o.dtype == i.dtype, (
            "Dtype {} of left {} doesn't match dtype {} of right {}.".
            format(o.dtype, o, i.dtype, i))