Source code for torch.utils.dlpack

from typing import Any

import torch
import enum

from torch._C import _from_dlpack
from torch._C import _to_dlpack as to_dlpack

class DLDeviceType(enum.IntEnum):
    # Enums as in DLPack specification (aten/src/ATen/dlpack.h)
    kDLCPU = 1,
    kDLGPU = 2,
    kDLCPUPinned = 3,
    kDLOpenCL = 4,
    kDLVulkan = 7,
    kDLMetal = 8,
    kDLVPI = 9,
    kDLROCM = 10,
    kDLExtDev = 12,

torch._C._add_docstr(to_dlpack, r"""to_dlpack(tensor) -> PyCapsule

Returns an opaque object (a "DLPack capsule") representing the tensor.

.. note::
  ``to_dlpack`` is a legacy DLPack interface. The capsule it returns
  cannot be used for anything in Python other than use it as input to
  ``from_dlpack``. The more idiomatic use of DLPack is to call
  ``from_dlpack`` directly on the tensor object - this works when that
  object has a ``__dlpack__`` method, which PyTorch and most other
  libraries indeed have now.

.. warning::
  Only call ``from_dlpack`` once per capsule produced with ``to_dlpack``.
  Behavior when a capsule is consumed multiple times is undefined.

    tensor: a tensor to be exported

The DLPack capsule shares the tensor's memory.

# TODO: add a typing.Protocol to be able to tell Mypy that only objects with
# __dlpack__ and __dlpack_device__ methods are accepted.
[docs]def from_dlpack(ext_tensor: Any) -> 'torch.Tensor': """from_dlpack(ext_tensor) -> Tensor Converts a tensor from an external library into a ``torch.Tensor``. The returned PyTorch tensor will share the memory with the input tensor (which may have come from another library). Note that in-place operations will therefore also affect the data of the input tensor. This may lead to unexpected issues (e.g., other libraries may have read-only flags or immutable data structures), so the user should only do this if they know for sure that this is fine. Args: ext_tensor (object with ``__dlpack__`` attribute, or a DLPack capsule): The tensor or DLPack capsule to convert. If ``ext_tensor`` is a tensor (or ndarray) object, it must support the ``__dlpack__`` protocol (i.e., have a ``ext_tensor.__dlpack__`` method). Otherwise ``ext_tensor`` may be a DLPack capsule, which is an opaque ``PyCapsule`` instance, typically produced by a ``to_dlpack`` function or method. Examples:: >>> import torch.utils.dlpack >>> t = torch.arange(4) # Convert a tensor directly (supported in PyTorch >= 1.10) >>> t2 = torch.from_dlpack(t) >>> t2[:2] = -1 # show that memory is shared >>> t2 tensor([-1, -1, 2, 3]) >>> t tensor([-1, -1, 2, 3]) # The old-style DLPack usage, with an intermediate capsule object >>> capsule = torch.utils.dlpack.to_dlpack(t) >>> capsule <capsule object "dltensor" at ...> >>> t3 = torch.from_dlpack(capsule) >>> t3 tensor([-1, -1, 2, 3]) >>> t3[0] = -9 # now we're sharing memory between 3 tensors >>> t3 tensor([-9, -1, 2, 3]) >>> t2 tensor([-9, -1, 2, 3]) >>> t tensor([-9, -1, 2, 3]) """ if hasattr(ext_tensor, '__dlpack__'): device = ext_tensor.__dlpack_device__() # device is either CUDA or ROCm, we need to pass the current # stream if device[0] in (DLDeviceType.kDLGPU, DLDeviceType.kDLROCM): stream = torch.cuda.current_stream('cuda:{}'.format(device[1])) # cuda_stream is the pointer to the stream and it is a public # attribute, but it is not documented # The array API specify that the default legacy stream must be passed # with a value of 1 for CUDA # # NOQA is_cuda = device[0] == DLDeviceType.kDLGPU # Since pytorch is not using PTDS by default, lets directly pass # the legacy stream stream_ptr = 1 if is_cuda and stream.cuda_stream == 0 else stream.cuda_stream dlpack = ext_tensor.__dlpack__(stream=stream_ptr) else: dlpack = ext_tensor.__dlpack__() else: # Old versions just call the converter dlpack = ext_tensor return _from_dlpack(dlpack)


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