67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
# Copyright 2020 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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"""DLPack modules for Tensorflow."""
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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 import pywrap_tfe
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from tensorflow.python.eager import context
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("experimental.dlpack.to_dlpack", v1=[])
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def to_dlpack(tf_tensor):
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"""Returns the dlpack capsule representing the tensor.
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This operation ensures the underlying data memory is ready when returns.
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```python
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a = tf.tensor([1, 10])
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dlcapsule = tf.experimental.dlpack.to_dlpack(a)
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# dlcapsule represents the dlpack data structure
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```
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Args:
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tf_tensor: Tensorflow eager tensor, to be converted to dlpack capsule.
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Returns:
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A PyCapsule named as dltensor, which shares the underlying memory to other
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framework. This PyCapsule can be consumed only once.
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"""
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return pywrap_tfe.TFE_ToDlpackCapsule(tf_tensor)
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@tf_export("experimental.dlpack.from_dlpack", v1=[])
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def from_dlpack(dlcapsule):
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"""Returns the Tensorflow eager tensor.
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The returned tensor uses the memory shared by dlpack capsules from other
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framework.
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```python
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a = tf.experimental.dlpack.from_dlpack(dlcapsule)
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# `a` uses the memory shared by dlpack
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```
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Args:
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dlcapsule: A PyCapsule named as dltensor
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Returns:
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A Tensorflow eager tensor
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"""
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return pywrap_tfe.TFE_FromDlpackCapsule(dlcapsule, context.context()._handle)
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