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from __future__ import annotations
import pickle
import pytest
pytestmark = pytest.mark.gpu
from distributed.protocol import deserialize, serialize
cuda = pytest.importorskip("numba.cuda")
np = pytest.importorskip("numpy")
@pytest.mark.parametrize("shape", [(0,), (5,), (4, 6), (10, 11), (2, 3, 5)])
@pytest.mark.parametrize("dtype", ["u1", "u4", "u8", "f4"])
@pytest.mark.parametrize("order", ["C", "F"])
@pytest.mark.parametrize("serializers", [("cuda",), ("dask",)])
def test_serialize_numba(shape, dtype, order, serializers):
if not cuda.is_available():
pytest.skip("CUDA is not available")
ary = np.arange(np.product(shape), dtype=dtype)
ary = np.ndarray(shape, dtype=ary.dtype, buffer=ary.data, order=order)
x = cuda.to_device(ary)
header, frames = serialize(x, serializers=serializers)
y = deserialize(header, frames, deserializers=serializers)
if serializers[0] == "cuda":
assert all(hasattr(f, "__cuda_array_interface__") for f in frames)
elif serializers[0] == "dask":
assert all(isinstance(f, memoryview) for f in frames)
hx = x.copy_to_host()
hy = y.copy_to_host()
assert (hx == hy).all()
@pytest.mark.parametrize("size", [0, 3, 10])
def test_serialize_numba_from_rmm(size):
np = pytest.importorskip("numpy")
rmm = pytest.importorskip("rmm")
if not cuda.is_available():
pytest.skip("CUDA is not available")
x_np = np.arange(size, dtype="u1")
x_np_desc = x_np.__array_interface__
(x_np_ptr, _) = x_np_desc["data"]
(x_np_size,) = x_np_desc["shape"]
x = rmm.DeviceBuffer(ptr=x_np_ptr, size=x_np_size)
header, frames = serialize(x, serializers=("cuda", "dask", "pickle"))
header["type-serialized"] = pickle.dumps(cuda.devicearray.DeviceNDArray)
y = deserialize(header, frames, deserializers=("cuda", "dask", "pickle", "error"))
assert (x_np == y.copy_to_host()).all()
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