2c6f7e24dd
It is possible that v0 is a _TupleWrapper object wrapping a namedtuple instead of a namedtuple itself. The class _TupleWrapper does not have the class method _make, but it should have the instance method _make which calls the namedtuple class method. Instances of a namedtuple also have the instance method _make which calls the relevant class method. This will allow the code to work for both cases. PiperOrigin-RevId: 325815594 Change-Id: I209f8ec5a8617f72183e4c12937b4429321e7b4f
227 lines
9.3 KiB
Python
227 lines
9.3 KiB
Python
# Copyright 2020 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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"""Tests for utility functions in distribute_utils."""
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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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import collections
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from absl.testing import parameterized
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import wrapt
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import distribute_utils
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from tensorflow.python.distribute import strategy_combinations
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from tensorflow.python.distribute import values
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from tensorflow.python.eager import context
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from tensorflow.python.eager import test
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import variable_scope
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from tensorflow.python.saved_model.model_utils import mode_keys
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def _nested_value(d):
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return ("a" + d, ["b" + d, {"c": "d" + d, "e": "f" + d}, "g" + d], "h" + d)
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class RegroupAndSelectDeviceTest(test.TestCase, parameterized.TestCase):
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def _is_per_replica(self, result, expected, klass=values.PerReplica):
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self.assertIsInstance(result, klass)
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for i, exp in enumerate(expected):
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self.assertEqual(exp, result.values[i])
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def testNested(self):
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result = distribute_utils.regroup((_nested_value("1"), _nested_value("2")))
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self.assertIsInstance(result, tuple)
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self.assertLen(result, 3)
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self._is_per_replica(result[0], ["a1", "a2"])
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self._is_per_replica(result[2], ["h1", "h2"])
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self.assertIsInstance(result[1], list)
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self.assertLen(result[1], 3)
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self._is_per_replica(result[1][0], ["b1", "b2"])
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self._is_per_replica(result[1][2], ["g1", "g2"])
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self.assertIsInstance(result[1][1], dict)
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self.assertEqual(set(["c", "e"]), set(result[1][1].keys()))
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self._is_per_replica(result[1][1]["c"], ["d1", "d2"])
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self._is_per_replica(result[1][1]["e"], ["f1", "f2"])
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# Also test that we can undo the merge using select_replica()
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self.assertEqual(_nested_value("1"),
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distribute_utils.select_replica(0, result))
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self.assertEqual(_nested_value("2"),
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distribute_utils.select_replica(1, result))
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# select_device_mirrored() should fail due to non-mirrored values
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with self.assertRaises(TypeError):
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distribute_utils.select_replica_mirrored(0, result)
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with self.assertRaises(TypeError):
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distribute_utils.select_replica_mirrored(1, result)
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def testRegroupKeepsDictBasedClass(self):
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class DictBasedClass(dict):
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"""Dummy class inherited from a dict."""
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result = distribute_utils.regroup(
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(DictBasedClass(a="a1", b="b1"), DictBasedClass(a="a2", b="b2")))
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self.assertIsInstance(result, DictBasedClass)
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self._is_per_replica(result["a"], ["a1", "a2"])
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self._is_per_replica(result["b"], ["b1", "b2"])
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def testWrapClass(self):
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# Normally a mirrored value would be the same across devices, but
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# for a test it is convenient to be able to tell the values apart.
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result = distribute_utils.regroup((_nested_value("1"), _nested_value("2")),
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values.Mirrored)
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self.assertIsInstance(result, tuple)
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self.assertLen(result, 3)
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self._is_per_replica(result[0], ["a1", "a2"], values.Mirrored)
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self._is_per_replica(result[2], ["h1", "h2"], values.Mirrored)
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self.assertIsInstance(result[1], list)
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self.assertLen(result[1], 3)
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self._is_per_replica(result[1][0], ["b1", "b2"], values.Mirrored)
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self._is_per_replica(result[1][2], ["g1", "g2"], values.Mirrored)
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self.assertIsInstance(result[1][1], dict)
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self.assertEqual(set(["c", "e"]), set(result[1][1].keys()))
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self._is_per_replica(result[1][1]["c"], ["d1", "d2"], values.Mirrored)
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self._is_per_replica(result[1][1]["e"], ["f1", "f2"], values.Mirrored)
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# Also test that we can undo the merge using select_replica()
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self.assertEqual(_nested_value("1"),
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distribute_utils.select_replica(0, result))
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self.assertEqual(_nested_value("2"),
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distribute_utils.select_replica(1, result))
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# Values are marked as mirrored, so select_device_mirrored() is allowed.
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self.assertEqual(_nested_value("1"),
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distribute_utils.select_replica_mirrored(0, result))
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self.assertEqual(_nested_value("2"),
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distribute_utils.select_replica_mirrored(1, result))
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def testWrapAListOfTwoTuples(self):
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result = distribute_utils.regroup([("1", "2"), ("3", "4")])
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self.assertIsInstance(result, tuple)
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self.assertLen(result, 2)
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self._is_per_replica(result[0], ("1", "3"), values.PerReplica)
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self._is_per_replica(result[1], ("2", "4"), values.PerReplica)
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.mirrored_strategy_with_one_cpu,
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],
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mode=["graph", "eager"],
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))
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def testMirroredContainer(self, distribution):
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with distribution.scope():
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v = variable_scope.variable(
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1., aggregation=variable_scope.VariableAggregation.SUM)
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self.assertTrue(distribute_utils.is_distributed_variable(v))
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self.assertTrue(distribute_utils.is_distributed_variable(
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distribute_utils.regroup(v.values)))
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def testSameId(self):
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foo = object()
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result = distribute_utils.regroup((("a", foo), ("b", foo)))
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self.assertIsInstance(result, tuple)
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self.assertLen(result, 2)
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self._is_per_replica(result[0], ["a", "b"])
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self.assertIs(foo, result[1])
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# Test select_replica(), should undo the merge done by regroup().
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result_0 = distribute_utils.select_replica(0, result)
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self.assertIsInstance(result_0, tuple)
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self.assertLen(result_0, 2)
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self.assertEqual("a", result_0[0])
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self.assertIs(foo, result_0[1])
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result_1 = distribute_utils.select_replica(1, result)
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self.assertIsInstance(result_1, tuple)
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self.assertLen(result_1, 2)
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self.assertEqual("b", result_1[0])
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self.assertIs(foo, result_1[1])
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def testOneDevice(self):
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result = distribute_utils.regroup((_nested_value("1"),))
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# On one device regroup() and select_replica() are basically identity.
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self.assertEqual(_nested_value("1"), result)
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self.assertEqual(_nested_value("1"),
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distribute_utils.select_replica(0, result))
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def testNamedTuple(self):
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# We include toy implementations of Scaffold and EstimatorSpec to
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# avoid a dependency on Estimator here.
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class Scaffold(object):
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pass
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class EstimatorSpec(collections.namedtuple(
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"EstimatorSpec", ["mode", "loss", "train_op", "scaffold"])):
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def __new__(cls, mode, loss, train_op, scaffold=None):
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return super(EstimatorSpec, cls).__new__(
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cls, mode=mode, loss=loss, train_op=train_op,
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scaffold=scaffold or Scaffold())
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with context.graph_mode(), ops.Graph().as_default():
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created_estimator_specs = []
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for device_id in range(3):
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spec = EstimatorSpec(
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mode=mode_keys.EstimatorModeKeys.TRAIN,
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loss=constant_op.constant(device_id / 2),
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train_op=array_ops.identity(constant_op.constant(device_id)))
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created_estimator_specs.append(spec)
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merged_estimator_spec = distribute_utils.regroup(created_estimator_specs)
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self.assertIsInstance(merged_estimator_spec, EstimatorSpec)
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self.assertEqual(mode_keys.EstimatorModeKeys.TRAIN,
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merged_estimator_spec.mode)
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for device_id in range(3):
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self.assertEqual(created_estimator_specs[device_id].loss,
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merged_estimator_spec.loss.values[device_id])
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self.assertEqual(created_estimator_specs[device_id].train_op,
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merged_estimator_spec.train_op.values[device_id])
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# Scaffold is populated by `EstimatorSpec.__new__`.
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self.assertEqual(created_estimator_specs[device_id].scaffold,
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merged_estimator_spec.scaffold.values[device_id])
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self.assertIsInstance(created_estimator_specs[device_id].scaffold,
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Scaffold)
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# Also test that we can undo the merge using select_replica()
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self.assertEqual(created_estimator_specs[device_id],
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distribute_utils.select_replica(
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device_id, merged_estimator_spec))
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def testWrappedNamedTuple(self):
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Point = collections.namedtuple("Point", ["x", "y"])
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point1 = Point(x=0, y=2)
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point2 = Point(x=1, y=3)
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wrapped1 = wrapt.ObjectProxy(point1)
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wrapped2 = wrapt.ObjectProxy(point2)
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result = distribute_utils.regroup([wrapped1, wrapped2])
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self.assertEqual(result.x.values, (0, 1))
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self.assertEqual(result.y.values, (2, 3))
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if __name__ == "__main__":
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test.main()
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