EVOLUTION-MANAGER

Edit File: distribution_util.py

# Copyright 2020 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.
# ==============================================================================
"""Util for running models in a distribution setting.

Mostly from
https://github.com/tensorflow/models/blob/master/official/
utils/misc/distribution_utils.py.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import json

import tensorflow as tf

def _collective_communication(all_reduce_alg):
  """Return a CollectiveCommunication based on all_reduce_alg.

Args:
    all_reduce_alg: a string specifying which collective communication to pick,
      or None.

Returns:
    tf.distribute.experimental.CollectiveCommunication object

Raises:
    ValueError: if `all_reduce_alg` not in [None, "ring", "nccl"]
  """
  collective_communication_options = {
      None: tf.distribute.experimental.CollectiveCommunication.AUTO,
      "ring": tf.distribute.experimental.CollectiveCommunication.RING,
      "nccl": tf.distribute.experimental.CollectiveCommunication.NCCL
  }
  if all_reduce_alg not in collective_communication_options:
    raise ValueError(
        "When used with `multi_worker_mirrored`, valid values for "
        "all_reduce_alg are [`ring`, `nccl`].  Supplied value: {}".format(
            all_reduce_alg))
  return collective_communication_options[all_reduce_alg]

def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
  """Return a CrossDeviceOps based on all_reduce_alg and num_packs.

Args:
    all_reduce_alg: a string specifying which cross device op to pick, or None.
    num_packs: an integer specifying number of packs for the cross device op.

Returns:
    tf.distribute.CrossDeviceOps object or None.

Raises:
    ValueError: if `all_reduce_alg` not in [None, "nccl", "hierarchical_copy"].
  """
  if all_reduce_alg is None:
    return None
  mirrored_all_reduce_options = {
      "nccl": tf.distribute.NcclAllReduce,
      "hierarchical_copy": tf.distribute.HierarchicalCopyAllReduce
  }
  if all_reduce_alg not in mirrored_all_reduce_options:
    raise ValueError(
        "When used with `mirrored`, valid values for all_reduce_alg are "
        "[`nccl`, `hierarchical_copy`].  Supplied value: {}".format(
            all_reduce_alg))
  cross_device_ops_class = mirrored_all_reduce_options[all_reduce_alg]
  return cross_device_ops_class(num_packs=num_packs)

def get_distribution_strategy(distribution_strategy="mirrored",
                              num_gpus=0,
                              all_reduce_alg=None,
                              num_packs=1):
  """Return a DistributionStrategy for running the model.

Args:
    distribution_strategy: a string specifying which distribution strategy to
      use. Accepted values are "off", "one_device", "mirrored", and
      "multi_worker_mirrored" -- case insensitive. "off" means not to use
      Distribution Strategy.
    num_gpus: Number of GPUs to run this model.

Returns:
    tf.distribute.DistibutionStrategy object.
  Raises:
    ValueError: if `distribution_strategy` is "off" or "one_device" and
      `num_gpus` is larger than 1; or `num_gpus` is negative.
  """
  if num_gpus < 0:
    raise ValueError("`num_gpus` can not be negative.")

distribution_strategy = distribution_strategy.lower()

if distribution_strategy == "off":
    if num_gpus > 1:
      raise ValueError("When {} GPUs are specified, distribution_strategy "
                       "flag cannot be set to `off`.".format(num_gpus))
    return None

if distribution_strategy == "multi_worker_mirrored":
    return tf.distribute.experimental.MultiWorkerMirroredStrategy(
        communication=_collective_communication(all_reduce_alg))

if distribution_strategy == "one_device":
    if num_gpus == 0:
      return tf.distribute.OneDeviceStrategy("device:CPU:0")
    if num_gpus > 1:
      raise ValueError("`OneDeviceStrategy` can not be used for more than "
                       "one device.")
    return tf.distribute.OneDeviceStrategy("device:GPU:0")

if distribution_strategy == "mirrored":
    if num_gpus == 0:
      devices = ["device:CPU:0"]
    else:
      devices = ["device:GPU:%d" % i for i in range(num_gpus)]
    return tf.distribute.MirroredStrategy(
        devices=devices,
        cross_device_ops=_mirrored_cross_device_ops(all_reduce_alg, num_packs))

raise ValueError("Unrecognized Distribution Strategy: %r" %
                   distribution_strategy)

def configure_cluster(worker_hosts=None, task_index=-1):
  """Set multi-worker cluster spec in TF_CONFIG environment variable.

Args:
    worker_hosts: comma-separated list of worker ip:port pairs.

Returns:
    Number of workers in the cluster.
  """
  tf_config = json.loads(os.environ.get("TF_CONFIG", "{}"))
  if tf_config:
    num_workers = (
        len(tf_config["cluster"].get("chief", [])) +
        len(tf_config["cluster"].get("worker", [])))
  elif worker_hosts:
    workers = worker_hosts.split(",")
    num_workers = len(workers)
    if num_workers > 1 and task_index < 0:
      raise ValueError("Must specify task_index when number of workers > 1")
    task_index = 0 if num_workers == 1 else task_index
    os.environ["TF_CONFIG"] = json.dumps({
        "cluster": {
            "worker": workers
        },
        "task": {
            "type": "worker",
            "index": task_index
        }
    })
  else:
    num_workers = 1
  return num_workers

def get_strategy_scope(strategy):
  if strategy:
    strategy_scope = strategy.scope()
  else:
    strategy_scope = DummyContextManager()

return strategy_scope

class DummyContextManager(object):

def __enter__(self):
    pass

def __exit__(self, *args):
    pass