xref: /aosp_15_r20/external/tensorflow/tensorflow/python/autograph/lang/directives.py (revision b6fb3261f9314811a0f4371741dbb8839866f948)
1# Copyright 2017 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"""Directives are special no-op functions that serve as compilation markers.
16
17They provide static information like type hints, compilation and TensorFlow
18overrides.
19
20These serve as annotations in the compiled code, allowing the user some control
21over the compilation process. They have no functional role at runtime.
22"""
23
24from tensorflow.python.util.tf_export import tf_export
25
26UNSPECIFIED = object()
27
28
29def set_element_type(entity, dtype, shape=UNSPECIFIED):
30  """Indicates that the entity is expected hold items of specified type/shape.
31
32  The staged TensorFlow ops will reflect and assert this data type. Ignored
33  otherwise.
34
35  Args:
36    entity: The entity to annotate.
37    dtype: TensorFlow dtype value to assert for entity.
38    shape: Optional shape to assert for entity.
39  """
40  del entity
41  del dtype
42  del shape
43
44
45@tf_export('autograph.experimental.set_loop_options')
46def set_loop_options(
47    parallel_iterations=UNSPECIFIED,
48    swap_memory=UNSPECIFIED,
49    maximum_iterations=UNSPECIFIED,
50    shape_invariants=UNSPECIFIED):
51  """Specifies additional arguments to be passed to the enclosing while_loop.
52
53  The parameters apply to and only to the immediately enclosing loop. It only
54  has effect if the loop is staged as a TF while_loop; otherwise the parameters
55  have no effect.
56
57  Usage:
58
59    >>> @tf.function(autograph=True)
60    ... def f():
61    ...   n = 0
62    ...   for i in tf.range(10):
63    ...     tf.autograph.experimental.set_loop_options(maximum_iterations=3)
64    ...     n += 1
65    ...   return n
66
67    >>> @tf.function(autograph=True)
68    ... def f():
69    ...   v = tf.constant((0,))
70    ...   for i in tf.range(3):
71    ...     tf.autograph.experimental.set_loop_options(
72    ...         shape_invariants=[(v, tf.TensorShape([None]))]
73    ...     )
74    ...     v = tf.concat((v, [i]), 0)
75    ...   return v
76
77  Also see tf.while_loop.
78
79  Args:
80    parallel_iterations: The maximum number of iterations allowed to run in
81        parallel at any given time. Note that this does not guarantee parallel
82        execution.
83    swap_memory: Whether to store intermediate values needed for
84        gradients on the CPU instead of GPU.
85    maximum_iterations: Allows limiting the total number of iterations executed
86        by the loop.
87    shape_invariants: Allows controlling the argument with the same name passed
88        to tf.while_loop. Unlike tf.while_loop, this is a list of
89        `(tensor, shape)` pairs.
90  """
91  del parallel_iterations
92  del swap_memory
93  del maximum_iterations
94  del shape_invariants
95