3RNN/Lib/site-packages/tensorflow/python/ops/handle_data_util.py
2024-05-26 19:49:15 +02:00

90 lines
3.4 KiB
Python

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Decorator to overrides the gradient for a function."""
from tensorflow.python.client import pywrap_tf_session
from tensorflow.python.framework import cpp_shape_inference_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.types import core
from tensorflow.python.util import compat
def get_resource_handle_data(graph_op):
assert (isinstance(graph_op, core.Symbol)
and not isinstance(graph_op, core.Value))
with graph_op.graph._c_graph.get() as c_graph: # pylint: disable=protected-access
handle_data = pywrap_tf_session.GetHandleShapeAndType(
c_graph, graph_op._as_tf_output()) # pylint: disable=protected-access
return cpp_shape_inference_pb2.CppShapeInferenceResult.HandleData.FromString(
compat.as_bytes(handle_data))
def get_handle_data(source_t):
"""Obtains HandleData from a tensor."""
if isinstance(source_t, core.Value):
return source_t._handle_data # pylint: disable=protected-access
return get_resource_handle_data(source_t)
def copy_handle_data(source_t, target_t):
"""Copies HandleData for variant and resource type tensors if available.
The CppShapeInferenceResult::HandleData proto contains information about the
shapes and types of the element tensors of resource/variant type tensors.
We need to copy this across function boundaries, i.e., when capturing a
placeholder or when returning a function tensor as output. If we don't do this
the element tensors will have unknown shapes, e.g., if a TensorList variant
tensor is captured as a placeholder, elements popped from that list would have
unknown shape.
Args:
source_t: The tensor to copy HandleData from.
target_t: The tensor to copy HandleData to.
"""
if (target_t.dtype == dtypes.resource or
target_t.dtype == dtypes.variant):
handle_data = get_handle_data(source_t)
set_handle_data(target_t, handle_data)
def set_handle_data(target_t, handle_data):
"""Sets handle data on the giver tensor."""
if (
handle_data is None
or not handle_data.is_set
or not handle_data.shape_and_type
):
return
# pylint: disable=protected-access
if isinstance(target_t, core.Value):
target_t._handle_data = handle_data
return
with target_t.graph._c_graph.get() as c_graph:
pywrap_tf_session.SetHandleShapeAndType(c_graph, target_t._as_tf_output(),
handle_data.SerializeToString())
# pylint: enable=protected-access
def create_handle_data(shape, dtype):
handle_data = cpp_shape_inference_pb2.CppShapeInferenceResult.HandleData()
handle_data.is_set = True
handle_data.shape_and_type.append(
cpp_shape_inference_pb2.CppShapeInferenceResult.HandleShapeAndType(
shape=shape.as_proto(), dtype=dtype.as_datatype_enum))
return handle_data