132 lines
5.0 KiB
Python
132 lines
5.0 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Connects all half, float and double tensors to CheckNumericsOp."""
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from tensorflow.python.eager import context
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.util import deprecation
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from tensorflow.python.util import dispatch
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from tensorflow.python.util.tf_export import tf_export
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@tf_export(v1=["debugging.assert_all_finite", "verify_tensor_all_finite"])
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@dispatch.add_dispatch_support
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@deprecation.deprecated_endpoints("verify_tensor_all_finite")
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def verify_tensor_all_finite(t=None, msg=None, name=None, x=None, message=None):
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"""Assert that the tensor does not contain any NaN's or Inf's.
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Args:
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t: Tensor to check.
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msg: Message to log on failure.
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name: A name for this operation (optional).
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x: Alias for t.
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message: Alias for msg.
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Returns:
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Same tensor as `t`.
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"""
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x = deprecation.deprecated_argument_lookup("x", x, "t", t)
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message = deprecation.deprecated_argument_lookup(
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"message", message, "msg", msg)
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return verify_tensor_all_finite_v2(x, message, name)
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@tf_export("debugging.assert_all_finite", v1=[])
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@dispatch.add_dispatch_support
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def verify_tensor_all_finite_v2(x, message, name=None):
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"""Assert that the tensor does not contain any NaN's or Inf's.
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>>> @tf.function
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... def f(x):
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... x = tf.debugging.assert_all_finite(x, 'Input x must be all finite')
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... return x + 1
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>>> f(tf.constant([np.inf, 1, 2]))
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Traceback (most recent call last):
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...
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InvalidArgumentError: ...
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Args:
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x: Tensor to check.
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message: Message to log on failure.
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name: A name for this operation (optional).
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Returns:
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Same tensor as `x`.
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"""
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with ops.name_scope(name, "VerifyFinite", [x]) as name:
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x = ops.convert_to_tensor(x, name="x")
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with ops.colocate_with(x):
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verify_input = array_ops.check_numerics(x, message=message)
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out = control_flow_ops.with_dependencies([verify_input], x)
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return out
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@tf_export(v1=["add_check_numerics_ops"])
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def add_check_numerics_ops():
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"""Connect a `tf.debugging.check_numerics` to every floating point tensor.
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`check_numerics` operations themselves are added for each `half`, `float`,
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or `double` tensor in the current default graph. For all ops in the graph, the
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`check_numerics` op for all of its (`half`, `float`, or `double`) inputs
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is guaranteed to run before the `check_numerics` op on any of its outputs.
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Note: This API is not compatible with the use of `tf.cond` or
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`tf.while_loop`, and will raise a `ValueError` if you attempt to call it
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in such a graph.
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Returns:
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A `group` op depending on all `check_numerics` ops added.
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Raises:
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ValueError: If the graph contains any numeric operations in a control flow
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structure.
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RuntimeError: If called with eager execution enabled.
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@compatibility(eager)
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Not compatible with eager execution. To check for `Inf`s and `NaN`s under
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eager execution, call `tf.debugging.enable_check_numerics()` once before
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executing the checked operations.
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@end_compatibility
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"""
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if context.executing_eagerly():
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raise RuntimeError(
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"add_check_numerics_ops() is not compatible with eager execution. "
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"To check for Inf's and NaN's under eager execution, call "
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"tf.debugging.enable_check_numerics() once before executing the "
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"checked operations.")
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check_op = []
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# This code relies on the ordering of ops in get_operations().
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# The producer of a tensor always comes before that tensor's consumer in
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# this list. This is true because get_operations() returns ops in the order
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# added, and an op can only be added after its inputs are added.
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for op in ops.get_default_graph().get_operations():
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for output in op.outputs:
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if output.dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
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if op._get_control_flow_context() is not None: # pylint: disable=protected-access
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raise ValueError("`tf.add_check_numerics_ops() is not compatible "
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"with TensorFlow control flow operations such as "
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"`tf.cond()` or `tf.while_loop()`.")
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message = op.name + ":" + str(output.value_index)
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with ops.control_dependencies(check_op):
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check_op = [array_ops.check_numerics(output, message=message)]
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return control_flow_ops.group(*check_op)
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