122 lines
4.3 KiB
Python
122 lines
4.3 KiB
Python
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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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kDLOneAPI = 14,
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torch._C._add_docstr(to_dlpack, r"""to_dlpack(tensor) -> PyCapsule
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Returns an opaque object (a "DLPack capsule") representing the tensor.
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.. note::
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``to_dlpack`` is a legacy DLPack interface. The capsule it returns
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cannot be used for anything in Python other than use it as input to
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``from_dlpack``. The more idiomatic use of DLPack is to call
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``from_dlpack`` directly on the tensor object - this works when that
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object has a ``__dlpack__`` method, which PyTorch and most other
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libraries indeed have now.
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.. warning::
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Only call ``from_dlpack`` once per capsule produced with ``to_dlpack``.
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Behavior when a capsule is consumed multiple times is undefined.
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Args:
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tensor: a tensor to be exported
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The DLPack capsule shares the tensor's memory.
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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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Converts a tensor from an external library into a ``torch.Tensor``.
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The returned PyTorch tensor will share the memory with the input tensor
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(which may have come from another library). Note that in-place operations
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will therefore also affect the data of the input tensor. This may lead to
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unexpected issues (e.g., other libraries may have read-only flags or
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immutable data structures), so the user should only do this if they know
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for sure that this is fine.
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Args:
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ext_tensor (object with ``__dlpack__`` attribute, or a DLPack capsule):
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The tensor or DLPack capsule to convert.
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If ``ext_tensor`` is a tensor (or ndarray) object, it must support
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the ``__dlpack__`` protocol (i.e., have a ``ext_tensor.__dlpack__``
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method). Otherwise ``ext_tensor`` may be a DLPack capsule, which is
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an opaque ``PyCapsule`` instance, typically produced by a
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``to_dlpack`` function or method.
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Examples::
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>>> import torch.utils.dlpack
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>>> t = torch.arange(4)
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# Convert a tensor directly (supported in PyTorch >= 1.10)
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>>> t2 = torch.from_dlpack(t)
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>>> t2[:2] = -1 # show that memory is shared
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>>> t2
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tensor([-1, -1, 2, 3])
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>>> t
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tensor([-1, -1, 2, 3])
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# The old-style DLPack usage, with an intermediate capsule object
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>>> capsule = torch.utils.dlpack.to_dlpack(t)
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>>> capsule
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<capsule object "dltensor" at ...>
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>>> t3 = torch.from_dlpack(capsule)
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>>> t3
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tensor([-1, -1, 2, 3])
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>>> t3[0] = -9 # now we're sharing memory between 3 tensors
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>>> t3
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tensor([-9, -1, 2, 3])
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>>> t2
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tensor([-9, -1, 2, 3])
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>>> t
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tensor([-9, -1, 2, 3])
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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(f'cuda:{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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# The array API specify that the default legacy stream must be passed
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# with a value of 1 for CUDA
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# https://data-apis.org/array-api/latest/API_specification/array_object.html?dlpack-self-stream-none#dlpack-self-stream-none
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is_cuda = device[0] == DLDeviceType.kDLGPU
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# Since pytorch is not using PTDS by default, lets directly pass
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# the legacy stream
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stream_ptr = 1 if is_cuda and stream.cuda_stream == 0 else stream.cuda_stream
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dlpack = ext_tensor.__dlpack__(stream=stream_ptr)
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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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