746af76e4f
We used to have build_collective_* that launches collective on all replicas. However each replica is independent and it can simplify the utils if it only launches on one replica. This is mainly to prepare for switching to Collective V2 ops and reduce the use of merge_call in MirroredStrategy/MultiWorkerMirroredStrategy. For the former, it will be easier after this change. For the latter, we can directly call CollectiveReplicaLauncher.all_reduce from ReplicaContext.all_reduce without the need of merge_call in MS/MWMS. Note that the original CollecticeKeys is outdated. Now instance key is scoped under each group key, and is no longer global. PiperOrigin-RevId: 337007588 Change-Id: I111efad0ecbe829260c8e4d2f555c6b2dc1d4c05
167 lines
6.2 KiB
Python
167 lines
6.2 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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"""Tests for cross_device_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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from absl.testing import parameterized
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import cross_device_utils
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from tensorflow.python.distribute import device_util
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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 dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
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def _assert_values_equal(self, left, right):
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self.assertAllEqual(
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self.evaluate(ops.convert_to_tensor(left)),
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self.evaluate(ops.convert_to_tensor(right)))
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@test_util.run_in_graph_and_eager_modes
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def testAggregateTensors(self):
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t0 = constant_op.constant([[1., 2.], [0, 0], [3., 4.]])
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t1 = constant_op.constant([[0., 0.], [5, 6], [7., 8.]])
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total = constant_op.constant([[1., 2.], [5, 6], [10., 12.]])
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result = cross_device_utils.aggregate_tensors_or_indexed_slices([t0, t1])
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self._assert_values_equal(total, result)
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@test_util.run_in_graph_and_eager_modes
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def testAggregateIndexedSlices(self):
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t0 = math_ops._as_indexed_slices(
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constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
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t1 = math_ops._as_indexed_slices(
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constant_op.constant([[0., 0.], [5, 6], [7., 8.]]))
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total = constant_op.constant([[1., 2.], [5, 6], [10., 12.]])
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result = cross_device_utils.aggregate_tensors_or_indexed_slices([t0, t1])
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self.assertIsInstance(result, ops.IndexedSlices)
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self._assert_values_equal(total, result)
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@test_util.run_in_graph_and_eager_modes
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def testDivideTensor(self):
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t = constant_op.constant([[1., 2.], [0, 0], [3., 4.]])
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n = 2
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expected = constant_op.constant([[0.5, 1.], [0, 0], [1.5, 2.]])
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result = cross_device_utils.divide_by_n_tensors_or_indexed_slices(t, n)
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self._assert_values_equal(expected, result)
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@test_util.run_in_graph_and_eager_modes
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def testDivideIndexedSlices(self):
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t = math_ops._as_indexed_slices(
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constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
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n = 2
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expected = constant_op.constant([[0.5, 1.], [0, 0], [1.5, 2.]])
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result = cross_device_utils.divide_by_n_tensors_or_indexed_slices(t, n)
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self.assertIsInstance(result, ops.IndexedSlices)
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self._assert_values_equal(expected, result)
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@test_util.run_in_graph_and_eager_modes
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def testIsIndexedSlices(self):
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t = math_ops._as_indexed_slices(
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constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
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self.assertTrue(cross_device_utils.is_indexed_slices(t))
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@combinations.generate(combinations.combine(
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mode=["graph", "eager"],
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required_gpus=1))
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def testCopyTensor(self):
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with ops.device("/cpu:0"):
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t = constant_op.constant([[1., 2.], [0, 0], [3., 4.]])
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destination = "/gpu:0"
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result = cross_device_utils.copy_tensor_or_indexed_slices_to_device(
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t, destination)
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self._assert_values_equal(t, result)
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self.assertEqual(device_util.resolve(destination),
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device_util.resolve(result.device))
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@combinations.generate(combinations.combine(
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mode=["graph", "eager"],
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required_gpus=1))
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def testCopyIndexedSlices(self):
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with ops.device("/cpu:0"):
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t = math_ops._as_indexed_slices(
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constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
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destination = "/gpu:0"
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result = cross_device_utils.copy_tensor_or_indexed_slices_to_device(
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t, destination)
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self.assertIsInstance(result, ops.IndexedSlices)
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self._assert_values_equal(t, result)
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self.assertEqual(
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device_util.resolve(destination), device_util.resolve(result.device))
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class GroupBySizeTest(test.TestCase):
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def testPreferLargerPack(self):
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# Each packs except the last one should be equal or larger than
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# bytes_per_pack.
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values = [
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# size = 2 * 4 * 4 * 4 = 128
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array_ops.ones([2, 4, 4], dtype=dtypes.float32),
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# size = 8 * 4 = 32
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array_ops.ones([8], dtype=dtypes.int32),
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# size = 10 * 10 * 8 = 800
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array_ops.ones([10, 10], dtype=dtypes.int64),
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# size = 1 * 4 = 4
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array_ops.ones([1], dtype=dtypes.int32),
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]
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packs = cross_device_utils.group_by_size(values, bytes_per_pack=200)
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self.assertLen(packs, 2)
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self.assertLen(packs[0], 3)
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self.assertEqual(packs[0][0].shape, [2, 4, 4])
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self.assertEqual(packs[0][1].shape, [8])
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self.assertEqual(packs[0][2].shape, [10, 10])
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self.assertLen(packs[1], 1)
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self.assertEqual(packs[1][0].shape, [1])
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def testZeroBytesPerPack(self):
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values = [
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array_ops.ones([1], dtype=dtypes.float32),
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array_ops.ones([2], dtype=dtypes.float32),
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]
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packs = cross_device_utils.group_by_size(values, bytes_per_pack=0)
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self.assertLen(packs, 1)
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self.assertLen(packs[0], 2)
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self.assertEqual(packs[0][0].shape, [1])
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self.assertEqual(packs[0][1].shape, [2])
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def testUnknownShape(self):
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def create_placeholder(shape, dtype):
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with ops.Graph().as_default():
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return array_ops.placeholder(dtype=dtype, shape=shape)
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values = [
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array_ops.ones([10, 10], dtype=dtypes.float32),
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create_placeholder([None, 10], dtype=dtypes.float32),
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]
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packs = cross_device_utils.group_by_size(values, bytes_per_pack=1)
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self.assertLen(packs, 1)
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self.assertEqual(packs[0], values)
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if __name__ == "__main__":
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test.main()
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