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1# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Experimental API for controlling optimizations in `tf.data` pipelines."""
16from __future__ import absolute_import
17from __future__ import division
18from __future__ import print_function
19
20
21from tensorflow.python.data.util import options
22from tensorflow.python.util.tf_export import tf_export
23
24
25@tf_export("data.experimental.OptimizationOptions")
26class OptimizationOptions(options.OptionsBase):
27  """Represents options for dataset optimizations.
28
29  You can set the optimization options of a dataset through the
30  `experimental_optimization` property of `tf.data.Options`; the property is
31  an instance of `tf.data.experimental.OptimizationOptions`.
32
33  ```python
34  options = tf.data.Options()
35  options.experimental_optimization.map_vectorization = True
36  options.experimental_optimization.apply_default_optimizations = False
37  dataset = dataset.with_options(options)
38  ```
39  """
40  apply_default_optimizations = options.create_option(
41      name="apply_default_optimizations",
42      ty=bool,
43      docstring=
44      "Whether to apply default static optimizations. If False, only static "
45      "optimizations that have been explicitly enabled will be applied.")
46
47  autotune = options.create_option(
48      name="autotune",
49      ty=bool,
50      docstring=
51      "Whether to automatically tune performance knobs. If None, defaults to "
52      "True.")
53
54  autotune_cpu_budget = options.create_option(
55      name="autotune_cpu_budget",
56      ty=int,
57      docstring=
58      "When autotuning is enabled (through `autotune`), determines the CPU "
59      "budget to use. Values greater than the number of schedulable CPU cores "
60      "are allowed but may result in CPU contention. If None, defaults to the "
61      "number of schedulable CPU cores.")
62
63  filter_fusion = options.create_option(
64      name="filter_fusion",
65      ty=bool,
66      docstring=
67      "Whether to fuse filter transformations. If None, defaults to False.")
68
69  hoist_random_uniform = options.create_option(
70      name="hoist_random_uniform",
71      ty=bool,
72      docstring=
73      "Whether to hoist `tf.random_uniform()` ops out of map transformations. "
74      "If None, defaults to False.")
75
76  map_and_batch_fusion = options.create_option(
77      name="map_and_batch_fusion",
78      ty=bool,
79      docstring=
80      "Whether to fuse map and batch transformations. If None, defaults to "
81      "True.")
82
83  map_and_filter_fusion = options.create_option(
84      name="map_and_filter_fusion",
85      ty=bool,
86      docstring=
87      "Whether to fuse map and filter transformations. If None, defaults to "
88      "False.")
89
90  map_fusion = options.create_option(
91      name="map_fusion",
92      ty=bool,
93      docstring="Whether to fuse map transformations. If None, defaults to "
94      "False.")
95
96  map_parallelization = options.create_option(
97      name="map_parallelization",
98      ty=bool,
99      docstring=
100      "Whether to parallelize stateless map transformations. If None, defaults "
101      "to False.")
102
103  map_vectorization = options.create_option(
104      name="map_vectorization",
105      ty=bool,
106      docstring=
107      "Whether to vectorize map transformations. If None, defaults to False.")
108
109  noop_elimination = options.create_option(
110      name="noop_elimination",
111      ty=bool,
112      docstring=
113      "Whether to eliminate no-op transformations. If None, defaults to True.")
114
115  shuffle_and_repeat_fusion = options.create_option(
116      name="shuffle_and_repeat_fusion",
117      ty=bool,
118      docstring="Whether to fuse shuffle and repeat transformations. If None, "
119      "defaults to True.")
120
121  def _static_optimizations(self):
122    """Produces the list of enabled static optimizations."""
123    result = set()
124    all_optimizations = [
125        "filter_fusion",
126        "hoist_random_uniform",
127        "map_and_batch_fusion",
128        "map_and_filter_fusion",
129        "map_parallelization",
130        "map_fusion",
131        "map_vectorization",
132        "noop_elimination",
133        "shuffle_and_repeat_fusion",
134    ]
135    for optimization in all_optimizations:
136      if getattr(self, optimization):
137        result.add(optimization)
138
139    if self.apply_default_optimizations is not False:
140      # The following optimizations are turned on by default, unless the
141      # user explicitly disables them.
142      optimizations_to_disable = [
143          "map_and_batch_fusion",
144          "noop_elimination",
145          "shuffle_and_repeat_fusion",
146      ]
147      for optimization in optimizations_to_disable:
148        if getattr(self, optimization) is not False:
149          result.add(optimization)
150    return sorted(list(result))
151