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# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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.
"""This file contains helper functions to run training in a distributed way."""
from typing import Text, Optional
import tensorflow as tf
def tpu_initialize(tpu_address: Text):
"""Initializes TPU for TF 2.x training.
Args:
tpu_address: string, bns address of master TPU worker.
Returns:
A TPUClusterResolver.
"""
cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=tpu_address)
if tpu_address not in ('', 'local'):
tf.config.experimental_connect_to_cluster(cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(cluster_resolver)
return cluster_resolver
def create_strategy(tpu_address: Optional[Text],
num_gpus: int = 0) -> tf.distribute.Strategy:
"""Creates a strategy based on the given parameters.
The strategies are created based on the following criteria and order:
1. If A tpu_address is not None, a TPUStrategy is used.
2. If num_gpus > 1, a MirrorStrategy is used which replicates the model on
each GPU.
3. If num_gpus == 1, a OneDevice strategy is used on the GPU.
4. If num_gpus == 0, a OneDevice strategy is used on the CPU.
Args:
tpu_address: The optional name or address of the TPU to connect to or None.
num_gpus: A non-negative integer specifying the number of GPUs.
Returns:
A tf.distribute.Strategy.
Raises:
ValueError: If `num_gpus` is negative and tpu_address is None.
"""
if tpu_address is not None:
resolver = tpu_initialize(tpu_address)
return tf.distribute.TPUStrategy(resolver)
else:
if num_gpus < 0:
raise ValueError('`num_gpus` must not be negative.')
elif num_gpus == 0:
devices = ['device:CPU:0']
else:
devices = ['device:GPU:%d' % i for i in range(num_gpus)]
if len(devices) == 1:
return tf.distribute.OneDeviceStrategy(devices[0])
return tf.distribute.MirroredStrategy(devices)
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