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511d33983a
* Initial implementation of DLPack support. Unfortunately there are still a few bugs in the jaxlib DLPack support, so this code won't be ready to use until jaxlib 0.1.39. * Fix test failures. * Update XLA. Fix failing torch test.
48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
# Copyright 2020 Google LLC
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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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# https://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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from . import lazy
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from .interpreters import xla
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from .lib import xla_client
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from .lib import xla_bridge
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def to_dlpack(x):
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"""Returns a DLPack tensor that encapsulates a DeviceArray `x`.
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The DLPack shares memory with `x`.
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Args:
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x: a `DeviceArray`, on either CPU or GPU.
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"""
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if not isinstance(x, xla.DeviceArray):
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raise TypeError("Argument to to_dlpack must be a DeviceArray, got {}"
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.format(type(x)))
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buf = xla._force(x).device_buffer
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return xla_client._xla.BufferToDLPackManagedTensor(buf)
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def from_dlpack(dlpack, backend=None):
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"""Returns a `DeviceArray` representation of a DLPack tensor `dlpack`.
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The returned `DeviceArray` shares memory with `dlpack`.
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Args:
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dlpack: a DLPack tensor, on either CPU or GPU.
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backend: experimental, optional: the platform on which `dlpack` lives.
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"""
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# TODO(phawkins): ideally the user wouldn't need to provide a backend and we
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# would be able to figure it out from the DLPack.
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backend = backend or xla_bridge.get_backend()
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buf = xla_client._xla.DLPackManagedTensorToBuffer(dlpack, backend.client)
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aval = xla._aval_from_xla_shape(buf.shape())
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return xla.DeviceArray(aval, buf.device(), lazy.array(aval.shape), buf) |