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# 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 the MapVectorization optimization."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
from absl.testing import parameterized
import numpy as np
from tensorflow.python.client import session
from tensorflow.python.data.experimental.ops import optimization
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class MapVectorizationTest(test_base.DatasetTestBase, parameterized.TestCase):
def _get_test_datasets(self,
base_dataset,
map_fn,
num_parallel_calls=None,
expect_optimized=True):
"""Given base dataset and map fn, creates test datasets.
Returns a tuple of (unoptimized, dataset, optimized dataset). The
unoptimized dataset has the assertion that Batch follows Map. The optimized
dataset has the assertion that Map follows Batch, and has the
"map_vectorization" optimization applied.
Args:
base_dataset: Input dataset to map->batch
map_fn: Map function to use
num_parallel_calls: (Optional.) num_parallel_calls argument for map
expect_optimized: (Optional.) Whether we expect the optimization to take
place, in which case we will assert that Batch is followed by Map,
otherwise Map followed by Batch. Defaults to True.
Returns:
Tuple of (unoptimized dataset, optimized dataset).
"""
map_node_name = "Map" if num_parallel_calls is None else "ParallelMap"
batch_size = 100
def _make_dataset(node_names):
return base_dataset.apply(optimization.assert_next(node_names)).map(
map_fn, num_parallel_calls=num_parallel_calls).batch(batch_size)
unoptimized = _make_dataset([map_node_name, "Batch"])
optimized = _make_dataset(["Batch", map_node_name]
if expect_optimized else [map_node_name, "Batch"])
options = dataset_ops.Options()
options.experimental_map_vectorization = True
optimized = optimized.with_options(options)
return unoptimized, optimized
@parameterized.named_parameters(
("Basic", lambda x: (x, x + 1), None),
("Const", lambda x: 2, 12),
("Parallel", lambda x: (x, x + 1), 12),
("Broadcast", lambda x: x + np.random.rand(5, 4, 3, 2), None),
("Gather", lambda x: array_ops.gather(x, 0), 12),
)
def testOptimization(self, map_fn, num_parallel_calls):
base_dataset = dataset_ops.Dataset.from_tensor_slices([[1, 2],
[3, 4]]).repeat(5)
unoptimized, optimized = self._get_test_datasets(base_dataset, map_fn,
num_parallel_calls)
self.assertDatasetsEqual(unoptimized, optimized)
def testOptimizationBadMapFn(self):
# Test map functions that give an error
def map_fn(x):
# x has leading dimension 5, this will raise an error
return array_ops.gather(x, 10)
base_dataset = dataset_ops.Dataset.range(5).repeat(5).batch(
5, drop_remainder=True)
_, optimized = self._get_test_datasets(base_dataset, map_fn)
nxt = optimized.make_one_shot_iterator().get_next()
with self.assertRaisesRegexp(errors.InvalidArgumentError,
r"indices = 10 is not in \[0, 5\)"):
self.evaluate(nxt)
def testOptimizationWithCapturedInputs(self):
# Tests that vectorization works with captured inputs
y = constant_op.constant(1, shape=(2,))
z = constant_op.constant(2, shape=(2,))
def map_fn(x):
return x, y, z
base_dataset = dataset_ops.Dataset.from_tensor_slices([[1, 2],
[3, 4]]).repeat(5)
unoptimized, optimized = self._get_test_datasets(
base_dataset, map_fn, expect_optimized=True)
self.assertDatasetsEqual(optimized, unoptimized)
def testOptimizationIgnoreStateful(self):
def map_fn(x):
with ops.control_dependencies([check_ops.assert_equal(x, 0)]):
return array_ops.identity(x)
base_dataset = dataset_ops.Dataset.from_tensor_slices([[1, 2],
[3, 4]]).repeat(5)
unoptimized, optimized = self._get_test_datasets(
base_dataset, map_fn, expect_optimized=False)
self.assertDatasetsRaiseSameError(
unoptimized, optimized, errors.InvalidArgumentError,
[("OneShotIterator", "OneShotIterator_1", 1),
("IteratorGetNext", "IteratorGetNext_1", 1)])
def testOptimizationIgnoreRagged(self):
# Make sure we ignore inputs that might not be uniformly sized
def map_fn(x):
return array_ops.gather(x, 0)
# output_shape = (?,)
base_dataset = dataset_ops.Dataset.range(20).batch(3, drop_remainder=False)
unoptimized, optimized = self._get_test_datasets(
base_dataset, map_fn, expect_optimized=False)
self.assertDatasetsEqual(unoptimized, optimized)
def testOptimizationIgnoreRaggedMap(self):
# Don't optimize when the output of the map fn shapes are unknown.
def map_fn(x):
return array_ops.tile(x, x)
base_dataset = dataset_ops.Dataset.range(20).batch(1, drop_remainder=True)
unoptimized, optimized = self._get_test_datasets(
base_dataset, map_fn, expect_optimized=False)
self.assertDatasetsRaiseSameError(
unoptimized, optimized, errors.InvalidArgumentError,
[("OneShotIterator", "OneShotIterator_1", 1),
("IteratorGetNext", "IteratorGetNext_1", 1)])
class MapVectorizationBenchmark(test.Benchmark):
# TODO(rachelim): Add a benchmark for more expensive transformations, such as
# vgg_preprocessing.
def _run(self, x, num_iters=100, name=None):
deltas = []
with session.Session() as sess:
for _ in range(5):
# Warm up session...
sess.run(x)
for _ in range(num_iters):
start = time.time()
sess.run(x)
end = time.time()
deltas.append(end - start)
median_time = np.median(deltas)
self.report_benchmark(iters=num_iters, wall_time=median_time, name=name)
return median_time
def _compare(self, input_dataset, map_fn, batch_size, input_size, str_id):
num_elems = np.prod(input_size)
name_template = "{}__batch_size_{}_input_size_{}_{}"
unoptimized = input_dataset.map(map_fn).batch(batch_size)
unoptimized_op = unoptimized.make_one_shot_iterator().get_next()
optimized = input_dataset.map(map_fn).batch(batch_size)
options = dataset_ops.Options()
options.experimental_map_vectorization = True
optimized = optimized.with_options(options)
optimized_op = optimized.make_one_shot_iterator().get_next()
unoptimized_time = self._run(
unoptimized_op,
name=name_template.format(str_id, batch_size, num_elems, "unoptimized"))
optimized_time = self._run(
optimized_op,
name=name_template.format(str_id, batch_size, num_elems, "optimized"))
print("Batch size: {}\n"
"Input size: {}\n"
"Transformation: {}\n"
"Speedup: {}\n".format(batch_size, input_size, str_id,
(unoptimized_time / optimized_time)))
# Known cheap functions
def benchmarkIdentity(self):
self._benchmark_helper(lambda *args: [array_ops.identity(x) for x in args],
"identity")
def benchmarkAddConst(self):
self._benchmark_helper(lambda *args: [x + 1 for x in args], "add_const")
def benchmarkReturnConst(self):
self._benchmark_helper(lambda *args: [constant_op.constant(2)], "ret_const")
def benchmarkSelect(self):
self._benchmark_helper(lambda *args: args[0], "select")
def benchmarkCast(self):
self._benchmark_helper(
lambda *args: [math_ops.cast(x, dtypes.float64) for x in args], "cast")
def _benchmark_helper(self, map_fn, str_id):
input_sizes = [(10, 10, 3), (10, 100, 300)]
batch_size = 1000
for input_size in input_sizes:
input_dataset = dataset_ops.Dataset.from_tensor_slices(
(np.random.rand(*input_size), np.random.rand(*input_size))).repeat()
self._compare(input_dataset, map_fn, batch_size, input_size, str_id)
if __name__ == "__main__":
test.main()
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