fa08cfd489
This contains all performance hints to the API. Currently there's only bytes_per_pack, which splits large batches into multiple packs allows overlapping communication and computation. Currently we can only pack if all Tensors in the batch have known shapes. PiperOrigin-RevId: 297269428 Change-Id: Iaf7d7d3adf7c6cad59aa6079fbcd36b31e92c4b5
223 lines
8.5 KiB
Python
223 lines
8.5 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.
|
|
# ==============================================================================
|
|
"""Tests for cross_device_utils."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from absl.testing import parameterized
|
|
|
|
from tensorflow.python.distribute import combinations
|
|
from tensorflow.python.distribute import cross_device_utils
|
|
from tensorflow.python.distribute import device_util
|
|
from tensorflow.python.distribute import values as value_lib
|
|
from tensorflow.python.eager import test
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.framework import test_util
|
|
from tensorflow.python.keras.engine import input_layer
|
|
from tensorflow.python.ops import array_ops
|
|
from tensorflow.python.ops import math_ops
|
|
|
|
|
|
class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
|
|
|
|
def _assert_values_equal(self, left, right):
|
|
self.assertAllEqual(
|
|
self.evaluate(ops.convert_to_tensor(left)),
|
|
self.evaluate(ops.convert_to_tensor(right)))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testAggregateTensors(self):
|
|
t0 = constant_op.constant([[1., 2.], [0, 0], [3., 4.]])
|
|
t1 = constant_op.constant([[0., 0.], [5, 6], [7., 8.]])
|
|
total = constant_op.constant([[1., 2.], [5, 6], [10., 12.]])
|
|
result = cross_device_utils.aggregate_tensors_or_indexed_slices([t0, t1])
|
|
self._assert_values_equal(total, result)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testAggregateIndexedSlices(self):
|
|
t0 = math_ops._as_indexed_slices(
|
|
constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
|
|
t1 = math_ops._as_indexed_slices(
|
|
constant_op.constant([[0., 0.], [5, 6], [7., 8.]]))
|
|
total = constant_op.constant([[1., 2.], [5, 6], [10., 12.]])
|
|
result = cross_device_utils.aggregate_tensors_or_indexed_slices([t0, t1])
|
|
self.assertIsInstance(result, ops.IndexedSlices)
|
|
self._assert_values_equal(total, result)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testDivideTensor(self):
|
|
t = constant_op.constant([[1., 2.], [0, 0], [3., 4.]])
|
|
n = 2
|
|
expected = constant_op.constant([[0.5, 1.], [0, 0], [1.5, 2.]])
|
|
result = cross_device_utils.divide_by_n_tensors_or_indexed_slices(t, n)
|
|
self._assert_values_equal(expected, result)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testDivideIndexedSlices(self):
|
|
t = math_ops._as_indexed_slices(
|
|
constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
|
|
n = 2
|
|
expected = constant_op.constant([[0.5, 1.], [0, 0], [1.5, 2.]])
|
|
result = cross_device_utils.divide_by_n_tensors_or_indexed_slices(t, n)
|
|
self.assertIsInstance(result, ops.IndexedSlices)
|
|
self._assert_values_equal(expected, result)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testIsIndexedSlices(self):
|
|
t = math_ops._as_indexed_slices(
|
|
constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
|
|
self.assertTrue(cross_device_utils.contains_indexed_slices(t))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testContainsIndexedSlices_List(self):
|
|
t0 = math_ops._as_indexed_slices(
|
|
constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
|
|
t1 = math_ops._as_indexed_slices(
|
|
constant_op.constant([[0., 0.], [5, 6], [7., 8.]]))
|
|
self.assertTrue(cross_device_utils.contains_indexed_slices([t0, t1]))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testContainsIndexedSlices_Tuple(self):
|
|
t0 = math_ops._as_indexed_slices(
|
|
constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
|
|
t1 = math_ops._as_indexed_slices(
|
|
constant_op.constant([[0., 0.], [5, 6], [7., 8.]]))
|
|
self.assertTrue(cross_device_utils.contains_indexed_slices((t0, t1)))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testContainsIndexedSlices_PerReplica(self):
|
|
t0 = math_ops._as_indexed_slices(
|
|
constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
|
|
t1 = math_ops._as_indexed_slices(
|
|
constant_op.constant([[0., 0.], [5, 6], [7., 8.]]))
|
|
per_replica = value_lib.PerReplica((t0, t1))
|
|
self.assertTrue(cross_device_utils.contains_indexed_slices(per_replica))
|
|
|
|
@combinations.generate(combinations.combine(
|
|
mode=["graph", "eager"],
|
|
required_gpus=1))
|
|
def testCopyTensor(self):
|
|
with ops.device("/cpu:0"):
|
|
t = constant_op.constant([[1., 2.], [0, 0], [3., 4.]])
|
|
destination = "/gpu:0"
|
|
result = cross_device_utils.copy_tensor_or_indexed_slices_to_device(
|
|
t, destination)
|
|
|
|
self._assert_values_equal(t, result)
|
|
self.assertEqual(device_util.resolve(destination),
|
|
device_util.resolve(result.device))
|
|
|
|
@combinations.generate(combinations.combine(
|
|
mode=["graph", "eager"],
|
|
required_gpus=1))
|
|
def testCopyIndexedSlices(self):
|
|
with ops.device("/cpu:0"):
|
|
t = math_ops._as_indexed_slices(
|
|
constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
|
|
destination = "/gpu:0"
|
|
result = cross_device_utils.copy_tensor_or_indexed_slices_to_device(
|
|
t, destination)
|
|
|
|
self.assertIsInstance(result, ops.IndexedSlices)
|
|
self._assert_values_equal(t, result)
|
|
self.assertEqual(
|
|
device_util.resolve(destination), device_util.resolve(result.device))
|
|
|
|
|
|
class PackBySizeTest(test.TestCase):
|
|
|
|
def assertShape(self, per_replica, shape):
|
|
for v in per_replica._values: # pylint: disable=protected-access
|
|
self.assertEqual(v.shape, shape)
|
|
|
|
def testPreferLargerPack(self):
|
|
# Each packs except the last one should be equal or larger than
|
|
# bytes_per_pack.
|
|
values = [
|
|
# size = 2 * 4 * 4 * 4 = 128
|
|
array_ops.ones([2, 4, 4], dtype=dtypes.float32),
|
|
# size = 8 * 4 = 32
|
|
array_ops.ones([8], dtype=dtypes.int32),
|
|
# size = 10 * 10 * 8 = 800
|
|
array_ops.ones([10, 10], dtype=dtypes.int64),
|
|
# size = 1 * 4 = 4
|
|
array_ops.ones([1], dtype=dtypes.int32),
|
|
]
|
|
per_replica_values = [value_lib.PerReplica([v, v]) for v in values]
|
|
packs = cross_device_utils.pack_by_size(
|
|
per_replica_values, bytes_per_pack=200)
|
|
self.assertLen(packs, 2)
|
|
self.assertLen(packs[0], 3)
|
|
self.assertShape(packs[0][0], [2, 4, 4])
|
|
self.assertShape(packs[0][1], [8])
|
|
self.assertShape(packs[0][2], [10, 10])
|
|
self.assertLen(packs[1], 1)
|
|
self.assertShape(packs[1][0], [1])
|
|
|
|
def testZeroBytesPerPack(self):
|
|
values = [
|
|
array_ops.ones([1], dtype=dtypes.float32),
|
|
array_ops.ones([2], dtype=dtypes.float32),
|
|
]
|
|
per_replica_values = [value_lib.PerReplica([v, v]) for v in values]
|
|
packs = cross_device_utils.pack_by_size(
|
|
per_replica_values, bytes_per_pack=0)
|
|
self.assertLen(packs, 1)
|
|
self.assertLen(packs[0], 2)
|
|
self.assertShape(packs[0][0], [1])
|
|
self.assertShape(packs[0][1], [2])
|
|
|
|
def testUnknownShape(self):
|
|
per_replica_values = [
|
|
value_lib.PerReplica([
|
|
array_ops.ones([10, 10], dtype=dtypes.float32),
|
|
array_ops.ones([10, 10], dtype=dtypes.float32),
|
|
]),
|
|
value_lib.PerReplica([
|
|
array_ops.ones([10, 10], dtype=dtypes.float32),
|
|
input_layer.Input(
|
|
shape=(10), batch_size=None, dtype=dtypes.float32),
|
|
]),
|
|
]
|
|
packs = cross_device_utils.pack_by_size(
|
|
per_replica_values, bytes_per_pack=1)
|
|
self.assertLen(packs, 1)
|
|
self.assertEqual(packs[0], per_replica_values)
|
|
|
|
def testInconsistentShape(self):
|
|
per_replica_values = [
|
|
value_lib.PerReplica([
|
|
array_ops.ones([10, 10], dtype=dtypes.float32),
|
|
array_ops.ones([10, 10], dtype=dtypes.float32),
|
|
]),
|
|
value_lib.PerReplica([
|
|
array_ops.ones([10, 10], dtype=dtypes.float32),
|
|
input_layer.Input(
|
|
shape=(10), batch_size=None, dtype=dtypes.float32),
|
|
]),
|
|
]
|
|
packs = cross_device_utils.pack_by_size(
|
|
per_replica_values, bytes_per_pack=1)
|
|
self.assertLen(packs, 1)
|
|
self.assertEqual(packs[0], per_replica_values)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|