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Rely on __cuda_array_interface__ for tensor conversions #26

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Rely on __cuda_array_interface__ for tensor conversions #26

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f6c4809
Rely on `__cuda_array_interface__` for tensor conversions
Jun 15, 2020
efad9a1
Update chainer_pytorch_migration/tensor.py
Jun 15, 2020
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31 changes: 2 additions & 29 deletions chainer_pytorch_migration/tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,20 +27,7 @@ def asarray(tensor):
return cupy.ndarray(
tuple(tensor.shape),
dtype=to_numpy_dtype(tensor.dtype))
itemsize = tensor.element_size()
storage = tensor.storage()
memptr = cupy.cuda.MemoryPointer(
cupy.cuda.UnownedMemory(
storage.data_ptr(), storage.size() * itemsize, tensor,
),
tensor.storage_offset() * itemsize,
)
return cupy.ndarray(
tuple(tensor.shape),
dtype=to_numpy_dtype(tensor.dtype),
memptr=memptr,
strides=tuple(s * itemsize for s in tensor.stride()),
)
return cupy.array(tensor.detach(), copy=False)
if dev_type == 'cpu':
return tensor.detach().numpy()
raise ValueError('tensor on device "{}" is not supported', dev_type)
Expand Down Expand Up @@ -78,28 +65,14 @@ def astensor(array):
device=array.device.id
)
return torch.as_tensor(
_ArrayWithCudaArrayInterfaceHavingStrides(array),
array,
device=array.device.id,
)
if isinstance(array, numpy.ndarray):
return torch.from_numpy(array)
raise TypeError('array of type {} is not supported'.format(type(array)))


# Workaround to avoid a bug in converting cupy.ndarray to torch.Tensor via
# __cuda_array_interface__. See: https://github.com/pytorch/pytorch/pull/24947
class _ArrayWithCudaArrayInterfaceHavingStrides:

def __init__(self, array):
self._array = array

@property
def __cuda_array_interface__(self):
d = self._array.__cuda_array_interface__
d['strides'] = self._array.strides
return d


def to_numpy_dtype(torch_dtype):
"""Convert PyTorch dtype to NumPy dtype.

Expand Down