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# 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.
# ==============================================================================
"""Helper functions for running models in a distributed setting."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
import string
from absl import logging
import tensorflow.compat.v2 as tf
from official.utils.misc import tpu_lib
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,
tpu_address=None):
"""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",
"parameter_server", "multi_worker_mirrored", and "tpu" -- case insensitive.
"off" means not to use Distribution Strategy; "tpu" means to use
TPUStrategy using `tpu_address`.
num_gpus: Number of GPUs to run this model.
all_reduce_alg: Optional. Specifies which algorithm to use when performing
all-reduce. For `MirroredStrategy`, valid values are "nccl" and
"hierarchical_copy". For `MultiWorkerMirroredStrategy`, valid values are
"ring" and "nccl". If None, DistributionStrategy will choose based on
device topology.
num_packs: Optional. Sets the `num_packs` in `tf.distribute.NcclAllReduce`
or `tf.distribute.HierarchicalCopyAllReduce` for `MirroredStrategy`.
tpu_address: Optional. String that represents TPU to connect to. Must not
be None if `distribution_strategy` is set to `tpu`.
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 or if
`distribution_strategy` is `tpu` but `tpu_address` is not specified.
"""
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 == "tpu":
# When tpu_address is an empty string, we communicate with local TPUs.
cluster_resolver = tpu_lib.tpu_initialize(tpu_address)
return tf.distribute.experimental.TPUStrategy(cluster_resolver)
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))
if distribution_strategy == "parameter_server":
return tf.distribute.experimental.ParameterServerStrategy()
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
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