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dlpack.py
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dlpack.py
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# Copyright 2020 Google LLC
#
# 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
#
# https://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.
from . import core
from . import lazy
from .interpreters import xla
from .lib import xla_client
from .lib import xla_bridge
def to_dlpack(x):
"""Returns a DLPack tensor that encapsulates a DeviceArray `x`.
The DLPack shares memory with `x`.
Args:
x: a `DeviceArray`, on either CPU or GPU.
"""
if not isinstance(x, xla.DeviceArray):
raise TypeError("Argument to to_dlpack must be a DeviceArray, got {}"
.format(type(x)))
buf = xla._force(x).device_buffer
return xla_client._xla.BufferToDLPackManagedTensor(buf)
def from_dlpack(dlpack, backend=None):
"""Returns a `DeviceArray` representation of a DLPack tensor `dlpack`.
The returned `DeviceArray` shares memory with `dlpack`.
Args:
dlpack: a DLPack tensor, on either CPU or GPU.
backend: experimental, optional: the platform on which `dlpack` lives.
"""
# TODO(phawkins): ideally the user wouldn't need to provide a backend and we
# would be able to figure it out from the DLPack.
backend = backend or xla_bridge.get_backend()
buf = xla_client._xla.DLPackManagedTensorToBuffer(dlpack, backend.client)
xla_shape = buf.shape()
assert not xla_shape.is_tuple()
aval = core.ShapedArray(xla_shape.dimensions(), xla_shape.numpy_dtype())
return xla.DeviceArray(aval, buf.device(), lazy.array(aval.shape), buf)