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1cb3507ed3
Summary: Partially Fixes https://github.com/pytorch/pytorch/issues/55090 Depends on https://github.com/pytorch/pytorch/issues/55365 Inspired by https://github.com/dmlc/dlpack/issues/57#issuecomment-774482973 Questions, in PyTorch we can't create streams or easily synchronize them from just an integer. Should we add an [`ExternalStream`](https://docs.cupy.dev/en/stable/reference/generated/cupy.cuda.ExternalStream.html) object like the one we have in CuPy? TODO: Add tests Would like some feedback as this design needs quite a few iterations rgommers leofang Pull Request resolved: https://github.com/pytorch/pytorch/pull/57110 Reviewed By: saketh-are Differential Revision: D30761481 Pulled By: mruberry fbshipit-source-id: e85d78df3c1f8defc2a698878da89cd843cb1209
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
from typing import Any
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import torch
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import enum
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from torch._C import _from_dlpack
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from torch._C import _to_dlpack as to_dlpack
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class DLDeviceType(enum.IntEnum):
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# Enums as in DLPack specification (aten/src/ATen/dlpack.h)
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kDLCPU = 1,
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kDLGPU = 2,
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kDLCPUPinned = 3,
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kDLOpenCL = 4,
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kDLVulkan = 7,
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kDLMetal = 8,
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kDLVPI = 9,
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kDLROCM = 10,
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kDLExtDev = 12,
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torch._C._add_docstr(to_dlpack, r"""to_dlpack(tensor) -> PyCapsule
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Returns a DLPack representing the tensor.
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Args:
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tensor: a tensor to be exported
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The DLPack shares the tensors memory.
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Note that each DLPack can only be consumed once.
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""")
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# TODO: add a typing.Protocol to be able to tell Mypy that only objects with
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# __dlpack__ and __dlpack_device__ methods are accepted.
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def from_dlpack(ext_tensor: Any) -> torch.Tensor:
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"""from_dlpack(ext_tensor) -> Tensor
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Convers a tensor from a external library into a ``torch.Tensor``
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by means of the ``__dlpack__`` protocol.
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The tensor will share the memory with the object represented
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in the DLPack.
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.. warning::
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Only call from_dlpack once per capsule. Its behavior when used
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on the same capsule multiple times is undefined.
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Args:
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ext_tensor (object with __dlpack__ attribute or DLPack capsule):
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The tensor or DLPack capsule to convert.
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"""
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if hasattr(ext_tensor, '__dlpack__'):
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device = ext_tensor.__dlpack_device__()
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# device is either CUDA or ROCm, we need to pass the current
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# stream
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if device[0] in (DLDeviceType.kDLGPU, DLDeviceType.kDLROCM):
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stream = torch.cuda.current_stream('cuda:{}'.format(device[1]))
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# cuda_stream is the pointer to the stream and it is a public
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# attribute, but it is not documented
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dlpack = ext_tensor.__dlpack__(stream=stream.cuda_stream)
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else:
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dlpack = ext_tensor.__dlpack__()
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else:
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# Old versions just call the converter
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dlpack = ext_tensor
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return _from_dlpack(dlpack)
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