Returns a new array containing the data from another (array) object with a __dlpack__ method.
x (object) – input (array) object.
device (Optional[device]) –
device on which to place the created array. If device is None and x supports DLPack, the output array must be on the same device as x. Default: None.
The v2023.12 standard only mandates that a compliant library should offer a way for from_dlpack to return an array whose underlying memory is accessible to the Python interpreter, when the corresponding device is provided. If the array library does not support such cases at all, the function must raise BufferError. If a copy must be made to enable this support but copy is set to False, the function must raise ValueError.
Other device kinds will be considered for standardization in a future version of this API standard.
copy (Optional[bool]) – boolean indicating whether or not to copy the input. If True, the function must always copy. If False, the function must never copy, and raise BufferError in case a copy is deemed necessary (e.g. if a cross-device data movement is requested, and it is not possible without a copy). If None, the function must reuse the existing memory buffer if possible and copy otherwise. Default: None.
out (array) – an array containing the data in x.
BufferError – The __dlpack__ and __dlpack_device__ methods on the input array may raise BufferError when the data cannot be exported as DLPack (e.g., incompatible dtype, strides, or device). It may also raise other errors when export fails for other reasons (e.g., not enough memory available to materialize the data). from_dlpack must propagate such exceptions.
AttributeError – If the __dlpack__ and __dlpack_device__ methods are not present on the input array. This may happen for libraries that are never able to export their data with DLPack.
ValueError – If data exchange is possible via an explicit copy but copy is set to False.
Notes
See array.__dlpack__() for implementation suggestions for from_dlpack in order to handle DLPack versioning correctly.
A way to move data from two array libraries to the same device (assumed supported by both libraries) in a library-agnostic fashion is illustrated below:
def func(x, y):
xp_x = x.__array_namespace__()
xp_y = y.__array_namespace__()
# Other functions than `from_dlpack` only work if both arrays are from the same library. So if
# `y` is from a different one than `x`, let's convert `y` into an array of the same type as `x`:
if not xp_x == xp_y:
y = xp_x.from_dlpack(y, copy=True, device=x.device)
# From now on use `xp_x.xxxxx` functions, as both arrays are from the library `xp_x`
...
Changed in version 2023.12: Required exceptions to address unsupported use cases.
Changed in version 2023.12: Added device and copy support.
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