˲jddlmZddlZddlZddlmZddlmZGddejZ ej edded d fd Z dS) )AnyN) _from_dlpack) _to_dlpackc6eZdZdZdZdZdZdZdZdZ dZ d Z d Z d S) DLDeviceType))))))) ) ) )N) __name__ __module__ __qualname__kDLCPUkDLGPU kDLCPUPinned kDLOpenCL kDLVulkankDLMetalkDLVPIkDLROCM kDLExtDev kDLOneAPICC:\Projects\Historical\venv\Lib\site-packages\torch/utils/dlpack.pyrr s@ F FLIIH FGIIIIr rato_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. Args: tensor: a tensor to be exported The DLPack capsule shares the tensor's memory. ext_tensorreturnz torch.Tensorct|dr|}|dtjtjfvrkt jd|d}|dtjk}|r |jdkrdn|j}| |}n| }n|}t|S)aIfrom_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 >>> 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]) __dlpack__rzcuda:r)stream) hasattr__dlpack_device__rrrtorchcudacurrent_stream cuda_streamr%r)r"devicer&is_cuda stream_ptrdlpacks r! from_dlpackr11sfz<((--// !9,l.BC C CZ../Bvay/B/BCCF Qi<#66G&Y&*<*A*AvGYJ****==FF**,,FF   r ) typingrr)enumtorch._Crr to_dlpackIntEnumr_C _add_docstrr1rr r!r9s !!!!!!,,,,,,     4<   Y!2H CH NH H H H H H r