132 lines
4.6 KiB
Python
132 lines
4.6 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Device-related support functions."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.eager import context
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from tensorflow.python.framework import config
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from tensorflow.python.framework import device as tf_device
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from tensorflow.python.framework import ops
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def canonicalize(d, default=None):
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"""Canonicalize device string.
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If d has missing components, the rest would be deduced from the `default`
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argument or from '/replica:0/task:0/device:CPU:0'. For example:
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If d = '/cpu:0', default='/job:worker/task:1', it returns
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'/job:worker/replica:0/task:1/device:CPU:0'.
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If d = '/cpu:0', default='/job:worker', it returns
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'/job:worker/replica:0/task:0/device:CPU:0'.
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If d = '/gpu:0', default=None, it returns
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'/replica:0/task:0/device:GPU:0'.
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Note: This uses "job:localhost" as the default if executing eagerly.
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Args:
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d: a device string or tf.config.LogicalDevice
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default: a string for default device if d doesn't have all components.
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Returns:
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a canonicalized device string.
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"""
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if isinstance(d, context.LogicalDevice):
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d = tf_device.DeviceSpec.from_string(d.name)
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else:
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d = tf_device.DeviceSpec.from_string(d)
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assert d.device_type is None or d.device_type == d.device_type.upper(), (
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"Device type '%s' must be all-caps." % (d.device_type,))
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# Fill in missing device fields using defaults.
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result = tf_device.DeviceSpec(
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replica=0, task=0, device_type="CPU", device_index=0)
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if ops.executing_eagerly_outside_functions():
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# Try to deduce job, replica and task in case it's in a multi worker setup.
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# TODO(b/151452748): Using list_logical_devices is not always safe since it
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# may return remote devices as well, but we're already doing this elsewhere.
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host_cpu = tf_device.DeviceSpec.from_string(
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config.list_logical_devices("CPU")[0].name)
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if host_cpu.job:
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result = result.make_merged_spec(host_cpu)
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else:
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# The default job is localhost if eager execution is enabled
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result = result.replace(job="localhost")
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if default:
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# Overrides any defaults with values from the default device if given.
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result = result.make_merged_spec(
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tf_device.DeviceSpec.from_string(default))
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# Apply `d` last, so that it's values take precedence over the defaults.
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result = result.make_merged_spec(d)
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return result.to_string()
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def resolve(d):
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"""Canonicalize `d` with current device as default."""
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return canonicalize(d, default=current())
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class _FakeNodeDef(object):
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"""A fake NodeDef for _FakeOperation."""
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def __init__(self):
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self.op = ""
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self.name = ""
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class _FakeOperation(object):
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"""A fake Operation object to pass to device functions."""
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def __init__(self):
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self.device = ""
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self.type = ""
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self.name = ""
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self.node_def = _FakeNodeDef()
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def _set_device(self, device):
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self.device = ops._device_string(device) # pylint: disable=protected-access
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def _set_device_from_string(self, device_str):
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self.device = device_str
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def current():
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"""Return a string (not canonicalized) for the current device."""
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# TODO(josh11b): Work out how this function interacts with ops.colocate_with.
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if ops.executing_eagerly_outside_functions():
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d = context.context().device_name
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else:
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op = _FakeOperation()
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ops.get_default_graph()._apply_device_functions(op) # pylint: disable=protected-access
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d = op.device
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return d
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def get_host_for_device(device):
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"""Returns the corresponding host device for the given device."""
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spec = tf_device.DeviceSpec.from_string(device)
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return tf_device.DeviceSpec(
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job=spec.job, replica=spec.replica, task=spec.task,
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device_type="CPU", device_index=0).to_string()
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def local_devices_from_num_gpus(num_gpus):
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"""Returns device strings for local GPUs or CPU."""
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return (tuple("/device:GPU:%d" % i for i in range(num_gpus)) or
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("/device:CPU:0",))
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