8318ab26da
Fixes bug when taking gradients of nested functions. PiperOrigin-RevId: 346241008 Change-Id: I50fc01bd5d971d272058b13b5964f8cdc28b00d0
65 lines
2.6 KiB
Python
65 lines
2.6 KiB
Python
# Copyright 2017 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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"""Decorator to overrides the gradient for a function."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.client import pywrap_tf_session
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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get_resource_handle_data = ops.get_resource_handle_data
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def copy_handle_data(source_t, target_t):
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"""Copies HandleData for variant and resource type tensors if available.
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The CppShapeInferenceResult::HandleData proto contains information about the
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shapes and types of the element tensors of resource/variant type tensors.
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We need to copy this across function boundaries, i.e., when capturing a
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placeholder or when returning a function tensor as output. If we don't do this
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the element tensors will have unknown shapes, e.g., if a TensorList variant
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tensor is captured as a placeholder, elements popped from that list would have
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unknown shape.
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Args:
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source_t: The tensor to copy HandleData from.
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target_t: The tensor to copy HandleData to.
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"""
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if (target_t.dtype == dtypes.resource or
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target_t.dtype == dtypes.variant):
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if isinstance(source_t, ops.EagerTensor):
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handle_data = source_t._handle_data # pylint: disable=protected-access
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else:
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handle_data = get_resource_handle_data(source_t)
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if (handle_data is not None
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and handle_data.is_set
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and handle_data.shape_and_type):
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set_handle_data(target_t, handle_data)
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def set_handle_data(target_t, handle_data):
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# pylint: disable=protected-access
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if isinstance(target_t, ops.EagerTensor):
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target_t._handle_data = handle_data
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return
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pywrap_tf_session.SetHandleShapeAndType(target_t.graph._c_graph,
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target_t._as_tf_output(),
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handle_data.SerializeToString())
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# pylint: enable=protected-access
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