text: # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  # Modifications copyright (C) 2019 Uber Technologies, Inc.
  # Modifications copyright Microsoft
  #
  # 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.
  # =============================================================================
  """Inter-process communication using MPI."""
  
  import re
  import tensorflow as tf
  from tensorflow.python.framework import load_library
  from tensorflow.python.framework import ops
  from tensorflow.python.platform import resource_loader
  
  from horovod.common.util import get_ext_suffix, get_average_backwards_compatibility_fun, gpu_available, \
      num_rank_is_power_2
  from horovod.common.basics import HorovodBasics as _HorovodBasics
  from horovod.tensorflow.util import _executing_eagerly
  
  
  def _load_library(name):
      """Loads a .so file containing the specified operators.
  
      Args:
        name: The name of the .so file to load.
  
      Raises:
        NotFoundError if were not able to load .so file.
      """
      filename = resource_loader.get_path_to_datafile(name)
      library = load_library.load_op_library(filename)
      return library
  
  
  MPI_LIB = _load_library('mpi_lib' + get_ext_suffix())
  
  _basics = _HorovodBasics(__file__, 'mpi_lib')
  
  # import basic methods
  init = _basics.init
  shutdown = _basics.shutdown
  size = _basics.size
  local_size = _basics.local_size
  rank = _basics.rank
  local_rank = _basics.local_rank
  mpi_threads_supported = _basics.mpi_threads_supported
  mpi_enabled = _basics.mpi_enabled
  mpi_built = _basics.mpi_built
  gloo_enabled = _basics.gloo_enabled
  gloo_built = _basics.gloo_built
  nccl_built = _basics.nccl_built
  ddl_built = _basics.ddl_built
  ccl_built = _basics.ccl_built
  
  # import reduction op values
  Average = _basics.Average
  Sum = _basics.Sum
  Adasum = _basics.Adasum
  
  is_homogeneous = _basics.is_homogeneous
  
  handle_average_backwards_compatibility = get_average_backwards_compatibility_fun(_basics)
  
  check_num_rank_power_of_2 = num_rank_is_power_2
  
  
  # This function will create a default device map which includes all visible devices.
  # Please run this function in a subprocess
  def _check_has_gpu():
      import tensorflow as tf
      return tf.test.is_gpu_available()
  
  
  def _normalize_name(name):
      """Normalizes operation name to TensorFlow rules."""
      return re.sub('[^a-zA-Z0-9_]', '_', name)
  
  
  def _allreduce(tensor, name=None, op=Sum):
      """An op which reduces an input tensor over all the Horovod processes. The
      default reduction is a sum.
  
      The reduction operation is keyed by the name of the op. The tensor type and
      shape must be the same on all Horovod processes for a given name. The reduction
      will not start until all processes are ready to send and receive the tensor.
  
      Returns:
        A tensor of the same shape and type as `tensor`, summed across all
        processes.
      """
      if name is None and not _executing_eagerly():
          name = 'HorovodAllreduce_%s' % _normalize_name(tensor.name)
      return MPI_LIB.horovod_allreduce(tensor, name=name, reduce_op=op)
  
  
  @ops.RegisterGradient('HorovodAllreduce')
  def _allreduce_grad(op, grad):
      """Gradient for allreduce op.
  
      Args:
        op: An operation.
        grad: `Tensor` gradient with respect to the output of the op.
  
      Returns:
        The gradient with respect to the input of the op.
      """
      reduce_op = op.get_attr('reduce_op')
      return _allreduce(grad, op=reduce_op)
  
  
  def allgather(tensor, name=None):
      """An op which concatenates the input tensor with the same input tensor on
      all other Horovod processes.
  
      The concatenation is done on the first dimension, so the input tensors on the
      different processes must have the same rank and shape, except for the first
      dimension, which is allowed to be different.
  
      Returns:
        A tensor of the same type as `tensor`, concatenated on dimension zero
        across all processes. The shape is identical to the input shape, except for
        the first dimension, which may be greater and is the sum of all first
        dimensions of the tensors in different Horovod processes.
      """
      if name is None and not _executing_eagerly():
          name = 'HorovodAllgather_%s' % _normalize_name(tensor.name)
      return MPI_LIB.horovod_allgather(tensor, name=name)
  
  
  @ops.RegisterGradient('HorovodAllgather')
  def _allgather_grad(op, grad):
      """Gradient for allgather op.
  
      Args:
        op: An operation.
        grad: `Tensor` gradient with respect to the output of the op.
  
      Returns:
        The gradient with respect to the input of the op.
      """
      grad = _allreduce(grad)
  
      with tf.device('/cpu:0'):
          # Keep the tensor of split sizes on CPU.
          x = op.inputs[0]
          d = tf.shape(x)
          d = tf.reshape(d[0], [1])
  
          s = size()
          d = tf.reshape(allgather(d), [s])
  
      splits = tf.split(grad, num_or_size_splits=d, axis=0)
      return splits[rank()]
  
  
  def broadcast(tensor, root_rank, name=None):
      """An op which broadcasts the input tensor on root rank to the same input tensor
      on all other Horovod processes.
  
      The broadcast operation is keyed by the name of the op. The tensor type and
      shape must be the same on all Horovod processes for a given name. The broadcast
      will not start until all processes are ready to send and receive the tensor.
  
      Returns:
        A tensor of the same shape and type as `tensor`, with the value broadcasted
        from root rank.
      """
      if name is None and not _executing_eagerly():
          name = 'HorovodBroadcast_%s' % _normalize_name(tensor.name)
      return MPI_LIB.horovod_broadcast(tensor, name=name, root_rank=root_rank)
  
  
  @ops.RegisterGradient('HorovodBroadcast')
  def _broadcast_grad(op, grad):
      """Gradient for broadcast op.
  
      Args:
        op: An operation.
        grad: `Tensor` gradient with respect to the output of the op.
  
      Returns:
        The gradient with respect to the input of the op.
      """
      root_rank = op.get_attr('root_rank')
      grad_reduced = _allreduce(grad)
      if rank() != root_rank:
          return grad_reduced * 0
      return grad_reduced
  
  
  def join():
      return MPI_LIB.horovod_join()
  
  
  def size_op(name=None):
      """An op that returns the number of Horovod processes.
  
      This operation determines the return value at the graph execution time,
      rather than at the graph construction time, and so allows for a graph to be
      constructed in a different environment than where it will be executed.
  
      Returns:
        An integer scalar containing the number of Horovod processes.
      """
      return MPI_LIB.horovod_size(name=name)
  
  
  ops.NotDifferentiable('HorovodSize')
  
  
  def local_size_op(name=None):
      """An op that returns the number of Horovod processes within the
      node the current process is running on.
  
      This operation determines the return value at the graph execution time,
      rather than at the graph construction time, and so allows for a graph to be
      constructed in a different environment than where it will be executed.
  
      Returns:
        An integer scalar containing the number of local Horovod processes.
      """
      return MPI_LIB.horovod_local_size(name=name)
  
  
  ops.NotDifferentiable('HorovodLocalSize')
  
  
  def rank_op(name=None):
      """An op that returns the Horovod rank of the calling process.
  
      This operation determines the return value at the graph execution time,
      rather than at the graph construction time, and so allows for a graph to be
      constructed in a different environment than where it will be executed.
  
      Returns:
        An integer scalar with the Horovod rank of the calling process.
      """
      return MPI_LIB.horovod_rank(name=name)
  
  
  ops.NotDifferentiable('HorovodRank')
  
  
  def local_rank_op(name=None):
      """An op that returns the local Horovod rank of the calling process, within the
      node that it is running on. For example, if there are seven processes running
      on a node, their local ranks will be zero through six, inclusive.
  
      This operation determines the return value at the graph execution time,
      rather than at the graph construction time, and so allows for a graph to be
      constructed in a different environment than where it will be executed.
  
      Returns:
        An integer scalar with the local Horovod rank of the calling process.
      """
      return MPI_LIB.horovod_rank(name=name)
  
  
  ops.NotDifferentiable('HorovodLocalRank')
  