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1# Copyright 2015 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
16"""Connects all half, float and double tensors to CheckNumericsOp."""
17
18from tensorflow.python.eager import context
19from tensorflow.python.framework import dtypes
20from tensorflow.python.framework import ops
21from tensorflow.python.ops import array_ops
22from tensorflow.python.ops import control_flow_ops
23from tensorflow.python.util import deprecation
24from tensorflow.python.util import dispatch
25from tensorflow.python.util.tf_export import tf_export
26
27
28@tf_export(v1=["debugging.assert_all_finite", "verify_tensor_all_finite"])
29@dispatch.add_dispatch_support
30@deprecation.deprecated_endpoints("verify_tensor_all_finite")
31def verify_tensor_all_finite(t=None, msg=None, name=None, x=None, message=None):
32  """Assert that the tensor does not contain any NaN's or Inf's.
33
34  Args:
35    t: Tensor to check.
36    msg: Message to log on failure.
37    name: A name for this operation (optional).
38    x: Alias for t.
39    message: Alias for msg.
40
41  Returns:
42    Same tensor as `t`.
43  """
44  x = deprecation.deprecated_argument_lookup("x", x, "t", t)
45  message = deprecation.deprecated_argument_lookup(
46      "message", message, "msg", msg)
47  return verify_tensor_all_finite_v2(x, message, name)
48
49
50@tf_export("debugging.assert_all_finite", v1=[])
51@dispatch.add_dispatch_support
52def verify_tensor_all_finite_v2(x, message, name=None):
53  """Assert that the tensor does not contain any NaN's or Inf's.
54
55  Args:
56    x: Tensor to check.
57    message: Message to log on failure.
58    name: A name for this operation (optional).
59
60  Returns:
61    Same tensor as `x`.
62  """
63  with ops.name_scope(name, "VerifyFinite", [x]) as name:
64    x = ops.convert_to_tensor(x, name="x")
65    with ops.colocate_with(x):
66      verify_input = array_ops.check_numerics(x, message=message)
67      out = control_flow_ops.with_dependencies([verify_input], x)
68  return out
69
70
71@tf_export(v1=["add_check_numerics_ops"])
72def add_check_numerics_ops():
73  """Connect a `tf.debugging.check_numerics` to every floating point tensor.
74
75  `check_numerics` operations themselves are added for each `half`, `float`,
76  or `double` tensor in the current default graph. For all ops in the graph, the
77  `check_numerics` op for all of its (`half`, `float`, or `double`) inputs
78  is guaranteed to run before the `check_numerics` op on any of its outputs.
79
80  Note: This API is not compatible with the use of `tf.cond` or
81  `tf.while_loop`, and will raise a `ValueError` if you attempt to call it
82  in such a graph.
83
84  Returns:
85    A `group` op depending on all `check_numerics` ops added.
86
87  Raises:
88    ValueError: If the graph contains any numeric operations in a control flow
89      structure.
90    RuntimeError: If called with eager execution enabled.
91
92  @compatibility(eager)
93  Not compatible with eager execution. To check for `Inf`s and `NaN`s under
94  eager execution, call `tf.debugging.enable_check_numerics()` once before
95  executing the checked operations.
96  @end_compatibility
97  """
98  if context.executing_eagerly():
99    raise RuntimeError(
100        "add_check_numerics_ops() is not compatible with eager execution. "
101        "To check for Inf's and NaN's under eager execution, call "
102        "tf.debugging.enable_check_numerics() once before executing the "
103        "checked operations.")
104
105  check_op = []
106  # This code relies on the ordering of ops in get_operations().
107  # The producer of a tensor always comes before that tensor's consumer in
108  # this list. This is true because get_operations() returns ops in the order
109  # added, and an op can only be added after its inputs are added.
110  for op in ops.get_default_graph().get_operations():
111    for output in op.outputs:
112      if output.dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
113        if op._get_control_flow_context() is not None:  # pylint: disable=protected-access
114          raise ValueError("`tf.add_check_numerics_ops() is not compatible "
115                           "with TensorFlow control flow operations such as "
116                           "`tf.cond()` or `tf.while_loop()`.")
117
118        message = op.name + ":" + str(output.value_index)
119        with ops.control_dependencies(check_op):
120          check_op = [array_ops.check_numerics(output, message=message)]
121  return control_flow_ops.group(*check_op)
122