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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.
# ==============================================================================
"""Experimental API for controlling optimizations in `tf.data` pipelines."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.util import options
from tensorflow.python.util.tf_export import tf_export
@tf_export("data.experimental.MapVectorizationOptions")
class MapVectorizationOptions(options.OptionsBase):
"""Represents options for the MapVectorization optimization."""
# TODO(rachelim): Other configuration parameters can go here, for example,
# how many "experiments" to run with ChooseFastestBranchDataset.
enabled = options.create_option(
name="enabled",
ty=bool,
docstring=
"Whether to vectorize map transformations. If None, defaults to False."
)
use_choose_fastest = options.create_option(
name="use_choose_fastest",
ty=bool,
docstring="Whether to use ChooseFastestBranchDataset with this "
"transformation. If True, the pipeline picks between the vectorized and "
"original segment at runtime based on their iterations speed. If None, "
"defaults to False.")
def _static_optimizations(self):
if self.enabled:
return ["map_vectorization"]
return []
def _static_optimization_configs(self):
if self.use_choose_fastest:
return ["map_vectorization:use_choose_fastest:true"]
else:
return ["map_vectorization:use_choose_fastest:false"]
@tf_export("data.experimental.OptimizationOptions")
class OptimizationOptions(options.OptionsBase):
"""Represents options for dataset optimizations.
You can set the optimization options of a dataset through the
`experimental_optimization` property of `tf.data.Options`; the property is
an instance of `tf.data.experimental.OptimizationOptions`.
```python
options = tf.data.Options()
options.experimental_optimization.noop_elimination = True
options.experimental_optimization.map_vectorization.enabled = True
options.experimental_optimization.apply_default_optimizations = False
dataset = dataset.with_options(options)
```
"""
apply_default_optimizations = options.create_option(
name="apply_default_optimizations",
ty=bool,
docstring=
"Whether to apply default static optimizations. If False, only static "
"optimizations that have been explicitly enabled will be applied.")
autotune = options.create_option(
name="autotune",
ty=bool,
docstring=
"Whether to automatically tune performance knobs. If None, defaults to "
"True.")
autotune_cpu_budget = options.create_option(
name="autotune_cpu_budget",
ty=int,
docstring=
"When autotuning is enabled (through `autotune`), determines the CPU "
"budget to use. Values greater than the number of schedulable CPU cores "
"are allowed but may result in CPU contention. If None, defaults to the "
"number of schedulable CPU cores.")
filter_fusion = options.create_option(
name="filter_fusion",
ty=bool,
docstring=
"Whether to fuse filter transformations. If None, defaults to False.")
filter_with_random_uniform_fusion = options.create_option(
name="filter_with_random_uniform_fusion",
ty=bool,
docstring=
"Whether to fuse filter dataset that predicts random_uniform < rate into "
"a sampling dataset. If None, defaults to False.")
hoist_random_uniform = options.create_option(
name="hoist_random_uniform",
ty=bool,
docstring=
"Whether to hoist `tf.random_uniform()` ops out of map transformations. "
"If None, defaults to False.")
map_and_batch_fusion = options.create_option(
name="map_and_batch_fusion",
ty=bool,
docstring=
"Whether to fuse map and batch transformations. If None, defaults to "
"True.")
map_and_filter_fusion = options.create_option(
name="map_and_filter_fusion",
ty=bool,
docstring=
"Whether to fuse map and filter transformations. If None, defaults to "
"False.")
map_fusion = options.create_option(
name="map_fusion",
ty=bool,
docstring="Whether to fuse map transformations. If None, defaults to "
"False.")
map_parallelization = options.create_option(
name="map_parallelization",
ty=bool,
docstring=
"Whether to parallelize stateless map transformations. If None, defaults "
"to False.")
map_vectorization = options.create_option(
name="map_vectorization",
ty=MapVectorizationOptions,
docstring=
"The map vectorization options associated with the dataset. See "
"`tf.data.experimental.MapVectorizationOptions` for more details.",
default_factory=MapVectorizationOptions)
noop_elimination = options.create_option(
name="noop_elimination",
ty=bool,
docstring=
"Whether to eliminate no-op transformations. If None, defaults to True.")
parallel_batch = options.create_option(
name="parallel_batch",
ty=bool,
docstring="Whether to parallelize copying of batch elements. If None, "
"defaults to False.")
shuffle_and_repeat_fusion = options.create_option(
name="shuffle_and_repeat_fusion",
ty=bool,
docstring="Whether to fuse shuffle and repeat transformations. If None, "
"defaults to True.")
def _static_optimizations(self):
"""Produces the list of enabled static optimizations."""
result = set()
all_optimizations = [
"filter_fusion",
"filter_with_random_uniform_fusion",
"hoist_random_uniform",
"map_and_batch_fusion",
"map_and_filter_fusion",
"map_parallelization",
"map_fusion",
"noop_elimination",
"parallel_batch",
"shuffle_and_repeat_fusion",
]
for optimization in all_optimizations:
if getattr(self, optimization):
result.add(optimization)
if self.apply_default_optimizations is not False:
# The following optimizations are turned on by default, unless the user
# explicitly disables them.
optimizations_to_disable = [
"map_and_batch_fusion",
"noop_elimination",
"shuffle_and_repeat_fusion",
]
for optimization in optimizations_to_disable:
if getattr(self, optimization) is not False:
result.add(optimization)
if self.map_vectorization is not None:
result.update(self.map_vectorization._static_optimizations()) # pylint: disable=protected-access
return sorted(list(result))
def _static_optimization_configs(self):
if self.map_vectorization is not None:
return self.map_vectorization._static_optimization_configs() # pylint: disable=protected-access
return []