STT-tensorflow/tensorflow/python/distribute/device_util.py

132 lines
4.6 KiB
Python

# 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.
# ==============================================================================
"""Device-related support functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.eager import context
from tensorflow.python.framework import config
from tensorflow.python.framework import device as tf_device
from tensorflow.python.framework import ops
def canonicalize(d, default=None):
"""Canonicalize device string.
If d has missing components, the rest would be deduced from the `default`
argument or from '/replica:0/task:0/device:CPU:0'. For example:
If d = '/cpu:0', default='/job:worker/task:1', it returns
'/job:worker/replica:0/task:1/device:CPU:0'.
If d = '/cpu:0', default='/job:worker', it returns
'/job:worker/replica:0/task:0/device:CPU:0'.
If d = '/gpu:0', default=None, it returns
'/replica:0/task:0/device:GPU:0'.
Note: This uses "job:localhost" as the default if executing eagerly.
Args:
d: a device string or tf.config.LogicalDevice
default: a string for default device if d doesn't have all components.
Returns:
a canonicalized device string.
"""
if isinstance(d, context.LogicalDevice):
d = tf_device.DeviceSpec.from_string(d.name)
else:
d = tf_device.DeviceSpec.from_string(d)
assert d.device_type is None or d.device_type == d.device_type.upper(), (
"Device type '%s' must be all-caps." % (d.device_type,))
# Fill in missing device fields using defaults.
result = tf_device.DeviceSpec(
replica=0, task=0, device_type="CPU", device_index=0)
if ops.executing_eagerly_outside_functions():
# Try to deduce job, replica and task in case it's in a multi worker setup.
# TODO(b/151452748): Using list_logical_devices is not always safe since it
# may return remote devices as well, but we're already doing this elsewhere.
host_cpu = tf_device.DeviceSpec.from_string(
config.list_logical_devices("CPU")[0].name)
if host_cpu.job:
result = result.make_merged_spec(host_cpu)
else:
# The default job is localhost if eager execution is enabled
result = result.replace(job="localhost")
if default:
# Overrides any defaults with values from the default device if given.
result = result.make_merged_spec(
tf_device.DeviceSpec.from_string(default))
# Apply `d` last, so that it's values take precedence over the defaults.
result = result.make_merged_spec(d)
return result.to_string()
def resolve(d):
"""Canonicalize `d` with current device as default."""
return canonicalize(d, default=current())
class _FakeNodeDef(object):
"""A fake NodeDef for _FakeOperation."""
def __init__(self):
self.op = ""
self.name = ""
class _FakeOperation(object):
"""A fake Operation object to pass to device functions."""
def __init__(self):
self.device = ""
self.type = ""
self.name = ""
self.node_def = _FakeNodeDef()
def _set_device(self, device):
self.device = ops._device_string(device) # pylint: disable=protected-access
def _set_device_from_string(self, device_str):
self.device = device_str
def current():
"""Return a string (not canonicalized) for the current device."""
# TODO(josh11b): Work out how this function interacts with ops.colocate_with.
if ops.executing_eagerly_outside_functions():
d = context.context().device_name
else:
op = _FakeOperation()
ops.get_default_graph()._apply_device_functions(op) # pylint: disable=protected-access
d = op.device
return d
def get_host_for_device(device):
"""Returns the corresponding host device for the given device."""
spec = tf_device.DeviceSpec.from_string(device)
return tf_device.DeviceSpec(
job=spec.job, replica=spec.replica, task=spec.task,
device_type="CPU", device_index=0).to_string()
def local_devices_from_num_gpus(num_gpus):
"""Returns device strings for local GPUs or CPU."""
return (tuple("/device:GPU:%d" % i for i in range(num_gpus)) or
("/device:CPU:0",))