gmarkall commented on issue #47128:
URL: https://github.com/apache/arrow/issues/47128#issuecomment-3085663610
Oops, I forgot to write up the remaining issue - it is:
```
______________________________________
test_numba_memalloc[float32-numba.cuda] _______________________________________
c = 1, dtype = dtype('float32')
@pytest.mark.parametrize("c", range(len(context_choice_ids)),
ids=context_choice_ids)
@pytest.mark.parametrize("dtype", dtypes, ids=dtypes)
def test_numba_memalloc(c, dtype):
ctx, nb_ctx = context_choices[c]
dtype = np.dtype(dtype)
# Allocate memory using numba context
# Warning: this will not be reflected in pyarrow context manager
# (e.g bytes_allocated does not change)
size = 10
mem = nb_ctx.memalloc(size * dtype.itemsize)
darr = DeviceNDArray((size,), (dtype.itemsize,), dtype, gpu_data=mem)
darr[:5] = 99
darr[5:] = 88
np.testing.assert_equal(darr.copy_to_host()[:5], 99)
np.testing.assert_equal(darr.copy_to_host()[5:], 88)
# wrap numba allocated memory with CudaBuffer
> cbuf = cuda.CudaBuffer.from_numba(mem)
pyarrow/tests/test_cuda_numba_interop.py:178:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> if mem.device_pointer.value is None and mem.size==0:
E AttributeError: 'cuda.bindings.driver.CUdeviceptr' object has no
attribute 'value'
```
`memalloc` returns `c_void_p` objects with the ctypes bindings, and
`CUdeviceptr` objects with the NVIDIA bindings. This is obviously an unpleasant
inconsistency, but I've not yet thought of a straightforward fix because other
libraries had adapted to this inconsistency (e.g.
https://github.com/rapidsai/rmm), so just straightforwardly making it
consistent will break them.
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