# 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.
# ==============================================================================
"""Class MirroredStrategy implementing DistributionStrategy."""

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

import contextlib
import copy
import threading

from tensorflow.python import pywrap_tensorflow
from tensorflow.python.distribute import cross_device_ops as cross_device_ops_lib
from tensorflow.python.distribute import device_util
from tensorflow.python.distribute import distribute_lib
from tensorflow.python.distribute import input_lib
from tensorflow.python.distribute import multi_worker_util
from tensorflow.python.distribute import numpy_dataset
from tensorflow.python.distribute import reduce_util
from tensorflow.python.distribute import shared_variable_creator
from tensorflow.python.distribute import values
from tensorflow.python.eager import context
from tensorflow.python.eager import tape
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import device as tf_device
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.training import coordinator
from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import tf_export


# TODO(josh11b): Replace asserts in this file with if ...: raise ...


@contextlib.contextmanager
def _enter_graph(g, eager, creator_stack=None):
  """Context manager for selecting a graph and maybe eager mode."""
  if eager:
    with g.as_default(), context.eager_mode():
      if creator_stack is not None:
        g._variable_creator_stack = creator_stack  # pylint: disable=protected-access
      yield
  else:
    with g.as_default():
      if creator_stack is not None:
        g._variable_creator_stack = creator_stack  # pylint: disable=protected-access
      yield


def _cpu_device(device):
  cpu_device = tf_device.DeviceSpec.from_string(device)
  cpu_device.merge_from(tf_device.DeviceSpec(device_type="CPU", device_index=0))
  return cpu_device.to_string()


class _RequestedStop(Exception):  # pylint: disable=g-bad-exception-name
  pass


# _call_for_each_replica is not a member of MirroredStrategy so that it is
# not allowed to use anything specific to MirroredStrategy and thus
# can be shared with other distribution strategies.


# TODO(yuefengz): maybe create a common class for those who need to call this
# _call_for_each_replica.
def _call_for_each_replica(distribution, device_map, fn, args, kwargs):
  """Run `fn` in separate threads, once per replica/worker device.

  Args:
    distribution: the DistributionStrategy object.
    device_map: the DeviceMap with the devices to run `fn` on.
    fn: function to run (will be run once per replica, each in its own thread).
    args: positional arguments for `fn`
    kwargs: keyword arguments for `fn`.

  Returns:
    Merged return value of `fn` across all replicas.

  Raises:
    RuntimeError: If fn() calls get_replica_context().merge_call() a different
        number of times from the available devices.
  """
  # TODO(josh11b): Add this option once we add synchronization to variable
  # creation. Until then, this is pretty unsafe to use.
  run_concurrently = False
  if not context.executing_eagerly():
    # Needed for per-thread device, etc. contexts in graph mode.
    ops.get_default_graph().switch_to_thread_local()

  coord = coordinator.Coordinator(clean_stop_exception_types=(_RequestedStop,))

  shared_variable_store = {}

  # TODO(isaprykin): Create these threads once instead of during every call.
  threads = []
  for index in range(device_map.num_replicas_in_graph):
    variable_creator_fn = shared_variable_creator.make_fn(
        shared_variable_store, index)
    t = _MirroredReplicaThread(
        distribution, coord, index, device_map, variable_creator_fn, fn,
        values.select_replica(index, args),
        values.select_replica(index, kwargs))
    threads.append(t)

  for t in threads:
    t.start()

  # When `fn` starts `should_run` event is set on _MirroredReplicaThread
  # (`MRT`) threads. The execution waits until
  # `MRT.has_paused` is set, which indicates that either `fn` is
  # complete or a `get_replica_context().merge_call()` is called.  If `fn` is
  # complete, then `MRT.done` is set to True.  Otherwise, arguments
  # of `get_replica_context().merge_call` from all paused threads are grouped
  # and the `merge_fn` is performed.  Results of the
  # `get_replica_context().merge_call` are then set to `MRT.merge_result`.
  # Each such `get_replica_context().merge_call` call returns the
  # `MRT.merge_result` for that thread when `MRT.should_run` event
  # is reset again. Execution of `fn` resumes.

  try:
    with coord.stop_on_exception():
      all_done = False
      while not all_done and not coord.should_stop():
        done = []
        if run_concurrently:
          for t in threads:
            t.should_run.set()
          for t in threads:
            t.has_paused.wait()
            t.has_paused.clear()
            if coord.should_stop():
              return None
            done.append(t.done)
        else:
          for t in threads:
            t.should_run.set()
            t.has_paused.wait()
            t.has_paused.clear()
            if coord.should_stop():
              return None
            done.append(t.done)
        if coord.should_stop():
          return None
        all_done = all(done)
        if not all_done:
          if any(done):
            raise RuntimeError("Some replicas made a different number of "
                               "replica_context().merge_call() calls.")
          # get_replica_context().merge_call() case
          merge_args = values.regroup(
              device_map, tuple(t.merge_args for t in threads))
          merge_kwargs = values.regroup(
              device_map, tuple(t.merge_kwargs for t in threads))
          # We capture the name_scope of the MRT when we call merge_fn
          # to ensure that if we have opened a name scope in the MRT,
          # it will be respected when executing the merge function. We only
          # capture the name_scope from the first MRT and assume it is
          # the same for all other MRTs.
          mtt_captured_name_scope = threads[0].captured_name_scope
          # Capture and merge the control dependencies from all the threads.
          mtt_captured_control_deps = set()
          for t in threads:
            mtt_captured_control_deps.update(t.captured_control_deps)
          with ops.name_scope(mtt_captured_name_scope),\
              ops.control_dependencies(mtt_captured_control_deps):
            merge_result = threads[0].merge_fn(distribution, *merge_args,
                                               **merge_kwargs)
          for r, t in enumerate(threads):
            t.merge_result = values.select_replica(r, merge_result)
  finally:
    for t in threads:
      t.should_run.set()
    coord.join(threads)

  return values.regroup(device_map, tuple(t.main_result for t in threads))


def _create_mirrored_variable(strategy, device_map, logical_device,  # pylint: disable=missing-docstring
                              real_mirrored_creator, *args, **kwargs):
  # Figure out what collections this variable should be added to.
  # We'll add the MirroredVariable to those collections instead.
  collections = kwargs.pop("collections", None)
  if collections is None:
    collections = [ops.GraphKeys.GLOBAL_VARIABLES]
  kwargs["collections"] = []

  # Get synchronization value
  synchronization = kwargs.get("synchronization",
                               variable_scope.VariableSynchronization.ON_WRITE)
  if synchronization == variable_scope.VariableSynchronization.NONE:
    raise ValueError("`NONE` variable synchronization mode is not "
                     "supported with `Mirrored` distribution strategy. Please"
                     " change the `synchronization` for variable: " +
                     kwargs["name"])
  elif synchronization == variable_scope.VariableSynchronization.ON_READ:
    # Variables that are to be synced on read are replica local.
    is_replica_local = True
    kwargs["trainable"] = False
  elif (synchronization == variable_scope.VariableSynchronization.ON_WRITE or
        synchronization == variable_scope.VariableSynchronization.AUTO):
    # `AUTO` synchronization for `MirroredStrategy` is `ON_WRITE`.
    is_replica_local = False
  else:
    raise ValueError("Invalid variable synchronization mode: " +
                     synchronization + " for variable: " + kwargs["name"])

  # Get aggregation value
  aggregation = kwargs.pop("aggregation",
                           variable_scope.VariableAggregation.NONE)
  if aggregation not in (
      variable_scope.VariableAggregation.NONE,
      variable_scope.VariableAggregation.SUM,
      variable_scope.VariableAggregation.MEAN,
      variable_scope.VariableAggregation.ONLY_FIRST_REPLICA
  ):
    raise ValueError("Invalid variable aggregation mode: " + aggregation +
                     " for variable: " + kwargs["name"])

  # Ignore user-specified caching device, not needed for mirrored variables.
  kwargs.pop("caching_device", None)

  # TODO(josh11b,apassos): It would be better if variable initialization
  # was never recorded on the tape instead of having to do this manually
  # here.
  with tape.stop_recording():
    devices = device_map.logical_to_actual_devices(logical_device)
    value_list = real_mirrored_creator(devices, *args, **kwargs)

    if is_replica_local:
      result = values.ReplicaLocalVariable(
          strategy, device_map, value_list, aggregation,
          logical_device=logical_device)
    else:
      result = values.MirroredVariable(
          strategy, device_map, value_list, aggregation,
          logical_device=logical_device)

  # Add the wrapped variable to the requested collections.
  # The handling of eager mode and the global step matches
  # ResourceVariable._init_from_args().
  if not context.executing_eagerly():
    g = ops.get_default_graph()
    # If "trainable" is True, next_creator() will add the member variables
    # to the TRAINABLE_VARIABLES collection, so we manually remove
    # them and replace with the MirroredVariable. We can't set
    # "trainable" to False for next_creator() since that causes functions
    # like implicit_gradients to skip those variables.
    if kwargs.get("trainable", True):
      collections.append(ops.GraphKeys.TRAINABLE_VARIABLES)
      l = g.get_collection_ref(ops.GraphKeys.TRAINABLE_VARIABLES)
      for v in value_list:
        if v in l:
          l.remove(v)
    g.add_to_collections(collections, result)
  elif ops.GraphKeys.GLOBAL_STEP in collections:
    ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, result)

  return result


def _is_device_list_local(devices):
  """Checks whether the devices list is for local or multi-worker.

  Args:
    devices: a list of device strings, either local for remote devices.

  Returns:
    a boolean indicating whether these device strings are for local or for
    remote.

  Raises:
    ValueError: if device strings are not consistent.
  """
  all_local = None
  for d in devices:
    d_spec = tf_device.DeviceSpec().parse_from_string(d)
    is_local = d_spec.job in (None, "localhost")

    if all_local is None:  # Determine all_local from first device.
      all_local = is_local

    if all_local:
      if not is_local:
        raise ValueError("Local device string cannot have job specified other "
                         "than 'localhost'")
    else:
      if is_local:
        raise ValueError("Remote device string must have job specified.")
      if d_spec.task is None:
        raise ValueError("Remote device string must have task specified.")
  return all_local


def _cluster_spec_to_device_list(cluster_spec, num_gpus_per_worker):
  """Returns a device list given a cluster spec."""
  cluster_spec = multi_worker_util.normalize_cluster_spec(cluster_spec)
  devices = []
  for task_type in ("chief", "worker"):
    for task_id in range(len(cluster_spec.as_dict().get(task_type, []))):
      if num_gpus_per_worker is 0:
        devices.append("/job:%s/task:%d" % (task_type, task_id))
      else:
        devices.extend([
            "/job:%s/task:%d/device:GPU:%i" % (task_type, task_id, gpu_id)
            for gpu_id in range(num_gpus_per_worker)
        ])
  return devices


def _group_device_list(devices):
  """Groups the devices list by task_type and task_id.

  Args:
    devices: a list of device strings for remote devices.

  Returns:
    a dict of list of device strings mapping from task_type to a list of devices
    for the task_type in the asceding order of task_id.
  """
  assert not _is_device_list_local(devices)
  device_dict = {}

  for d in devices:
    d_spec = tf_device.DeviceSpec().parse_from_string(d)

    # Create an entry for the task_type.
    if d_spec.job not in device_dict:
      device_dict[d_spec.job] = []

    # Fill the device list for task_type until it covers the task_id.
    while len(device_dict[d_spec.job]) <= d_spec.task:
      device_dict[d_spec.job].append([])

    device_dict[d_spec.job][d_spec.task].append(d)

  return device_dict


def _is_gpu_device(device):
  return tf_device.DeviceSpec().parse_from_string(device).device_type == "GPU"


def _infer_num_gpus_per_worker(devices):
  """Infers the number of GPUs on each worker.

  Currently to make multi-worker cross device ops work, we need all workers to
  have the same number of GPUs.

  Args:
    devices: a list of device strings, can be either local devices or remote
      devices.

  Returns:
    number of GPUs per worker.

  Raises:
    ValueError if workers have different number of GPUs or GPU indices are not
    consecutive and starting from 0.
  """
  if _is_device_list_local(devices):
    return sum(1 for d in devices if _is_gpu_device(d))
  else:
    device_dict = _group_device_list(devices)
    num_gpus = None
    for _, devices_in_task in device_dict.items():
      for device_in_task in devices_in_task:
        if num_gpus is None:
          num_gpus = sum(1 for d in device_in_task if _is_gpu_device(d))

        # Verify other workers have the same number of GPUs.
        elif num_gpus != sum(1 for d in device_in_task if _is_gpu_device(d)):
          raise ValueError("All workers should have the same number of GPUs.")

        for d in device_in_task:
          d_spec = tf_device.DeviceSpec().parse_from_string(d)
          if (d_spec.device_type == "GPU" and
              d_spec.device_index >= num_gpus):
            raise ValueError("GPU `device_index` on a worker should be "
                             "consecutive and start from 0.")
    return num_gpus


def all_local_devices(num_gpus=None):
  if num_gpus is None:
    num_gpus = context.num_gpus()
  return (tuple("/device:GPU:%d" % i for i in range(num_gpus)) or
          ("/device:CPU:0",))


@tf_export("distribute.MirroredStrategy")
class MirroredStrategy(distribute_lib.DistributionStrategy):
  """Mirrors vars to distribute across multiple devices and machines.

  This strategy uses one replica per device and sync replication for its
  multi-GPU version.

  The multi-worker version will be added in the future.

  Args:
    devices: a list of device strings.
    cross_device_ops: optional, a descedant of `CrossDeviceOps`. If this is not
      set, nccl will be use by default.
  """

  def __init__(self, devices=None, cross_device_ops=None):
    extended = MirroredExtended(
        self, devices=devices, cross_device_ops=cross_device_ops)
    super(MirroredStrategy, self).__init__(extended)


class MirroredExtended(distribute_lib.DistributionStrategyExtended):
  """Implementation of MirroredStrategy."""

  def __init__(self, container_strategy, devices=None, cross_device_ops=None):
    super(MirroredExtended, self).__init__(container_strategy)
    if devices is None:
      devices = all_local_devices()
    if not devices:
      raise ValueError("Got an empty `devices` list. Please make sure the "
                       "`devices` you pass in is not empty.")
    self._cross_device_ops = cross_device_ops
    self._initialize_strategy(devices)

  def _initialize_strategy(self, devices):
    # The _initialize_strategy method is intended to be used by distribute
    # coordinator as well.
    if _is_device_list_local(devices):
      self._initialize_local(devices)
    else:
      self._initialize_multi_worker(devices)

  def _initialize_local(self, devices):
    """Initializes the object for local training."""
    self._local_mode = True
    assert devices, "Must specify at least one device."
    devices = tuple(device_util.resolve(d) for d in devices)
    assert len(set(devices)) == len(devices), (
        "No duplicates allowed in `devices` argument: %s" % (devices,))
    # TODO(josh11b): Require at least 2 devices?
    self._device_map = values.ReplicaDeviceMap(devices)
    self._input_workers = input_lib.InputWorkers(self._device_map)
    self._inferred_cross_device_ops = cross_device_ops_lib.choose_the_best(
        devices)
    self._host_input_device = numpy_dataset.SingleDevice("/cpu:0")

  def _initialize_multi_worker(self, devices):
    """Initializes the object for multi-worker training."""
    self._local_mode = False

    assert devices, "Must specify at least one device."
    devices = tuple(device_util.resolve(d) for d in devices)
    assert len(set(devices)) == len(devices), (
        "No duplicates allowed in `devices` argument: %s" % devices)
    # TODO(josh11b): Require at least 2 devices?

    device_dict = _group_device_list(devices)
    workers = []
    worker_devices = []
    for job in ("chief", "worker"):
      for task in range(len(device_dict.get(job, []))):
        worker = "/job:%s/task:%d" % (job, task)
        workers.append(worker)
        worker_devices.append((worker, device_dict[job][task]))

    # Setting `_default_device` will add a device scope in the
    # distribution.scope. We set the default device to the first worker. When
    # users specify device under distribution.scope by
    #   with tf.device("/cpu:0"):
    #     ...
    # their ops will end up on the cpu device of its first worker, e.g.
    # "/job:worker/task:0/device:CPU:0". Note this is not used in replica mode.
    self._default_device = workers[0]
    self._host_input_device = numpy_dataset.SingleDevice(workers[0])

    self._device_map = values.ReplicaDeviceMap(devices)
    self._input_workers = input_lib.InputWorkers(
        self._device_map, worker_devices)
    self._inferred_cross_device_ops = cross_device_ops_lib.MultiWorkerAllReduce(
        workers, _infer_num_gpus_per_worker(devices))

  def _create_variable(self, next_creator, *args, **kwargs):
    """Create a mirrored variable. See `DistributionStrategy.scope`."""
    colocate_with = kwargs.pop("colocate_with", None)
    if colocate_with is None:
      device_map = self._device_map
      logical_device = 0  # TODO(josh11b): Get logical device from scope here.
    elif isinstance(colocate_with, numpy_dataset.SingleDevice):
      with ops.device(colocate_with.device):
        return next_creator(*args, **kwargs)
    else:
      device_map = colocate_with.device_map
      logical_device = colocate_with.logical_device

    def _real_mirrored_creator(devices, *args, **kwargs):  # pylint: disable=g-missing-docstring
      value_list = []
      for i, d in enumerate(devices):
        with ops.init_scope(), ops.device(d):
          if i > 0:
            # Give replicas meaningful distinct names:
            var0name = value_list[0].name.split(":")[0]
            # We append a / to variable names created on replicas with id > 0 to
            # ensure that we ignore the name scope and instead use the given
            # name as the absolute name of the variable.
            kwargs["name"] = "%s/replica_%d/" % (var0name, i)
            # Initialize replicas with the same value:
            def initial_value_fn(device=d):
              if context.executing_eagerly():
                init_value = value_list[0].value()
                return array_ops.identity(init_value)
              else:
                with ops.device(device):
                  init_value = value_list[0].initial_value
                  return array_ops.identity(init_value)
            kwargs["initial_value"] = initial_value_fn
          with context.context().device_policy(context.DEVICE_PLACEMENT_SILENT):
            # Don't record operations (e.g. other variable reads) during
            # variable creation.
            with tape.stop_recording():
              v = next_creator(*args, **kwargs)
          assert not isinstance(v, values.DistributedVariable)
          value_list.append(v)
      return value_list

    return _create_mirrored_variable(
        self._container_strategy(), device_map, logical_device,
        _real_mirrored_creator, *args, **kwargs)

  def _validate_colocate_with_variable(self, colocate_with_variable):
    values.validate_colocate_distributed_variable(colocate_with_variable, self)

  def _make_dataset_iterator(self, dataset):
    return input_lib.DatasetIterator(
        dataset, self._input_workers, self._num_replicas_in_sync)

  def _make_input_fn_iterator(
      self,
      input_fn,
      replication_mode=distribute_lib.InputReplicationMode.PER_WORKER):
    input_contexts = []
    num_workers = self._input_workers.num_workers
    for i in range(num_workers):
      input_contexts.append(distribute_lib.InputContext(
          num_input_pipelines=num_workers,
          input_pipeline_id=i,
          num_replicas_in_sync=self._num_replicas_in_sync))
    return input_lib.InputFunctionIterator(
        input_fn, self._input_workers, input_contexts)

  def _experimental_make_numpy_dataset(self, numpy_input, session):
    return numpy_dataset.one_host_numpy_dataset(
        numpy_input, self._host_input_device, session)

  # TODO(priyag): Deal with OutOfRange errors once b/111349762 is fixed.
  def _experimental_run_steps_on_iterator(self, fn, iterator, iterations,
                                          initial_loop_values=None):
    if initial_loop_values is None:
      initial_loop_values = {}
    initial_loop_values = nest.flatten(initial_loop_values)

    ctx = input_lib.MultiStepContext()
    def body(i, *args):
      """A wrapper around `fn` to create the while loop body."""
      del args
      fn_result = fn(ctx, iterator.get_next())
      for (name, output) in ctx.last_step_outputs.items():
        # Convert all outputs to tensors, potentially from `DistributedValues`.
        ctx.last_step_outputs[name] = self._unwrap(output)
      flat_last_step_outputs = nest.flatten(ctx.last_step_outputs)
      with ops.control_dependencies([fn_result]):
        return [i + 1] + flat_last_step_outputs

    # We capture the control_flow_context at this point, before we run `fn`
    # inside a while_loop. This is useful in cases where we might need to exit
    # these contexts and get back to the outer context to do some things, for
    # e.g. create an op which should be evaluated only once at the end of the
    # loop on the host. One such usage is in creating metrics' value op.
    self._outer_control_flow_context = (
        ops.get_default_graph()._get_control_flow_context())  # pylint: disable=protected-access

    cond = lambda i, *args: i < iterations
    i = constant_op.constant(0)
    loop_result = control_flow_ops.while_loop(