feat: improve structured dtype array support in StridedMemoryView
#1425
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/ok to test |
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mdboom
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Jan 5, 2026
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LGTM, other than improving the test a bit.
Leaning on Numpy for the conversion, as this now does, feels more future-proof as well.
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Jan 5, 2026
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Backport failed for Please cherry-pick the changes locally and resolve any conflicts. git fetch origin release/cuda-core-v0.5.1
git worktree add -d .worktree/backport-1425-to-release/cuda-core-v0.5.1 origin/release/cuda-core-v0.5.1
cd .worktree/backport-1425-to-release/cuda-core-v0.5.1
git switch --create backport-1425-to-release/cuda-core-v0.5.1
git cherry-pick -x 36742ddc5b91ed388f55d5ec7ade8d245835fb35 |
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Improve Support for Structured Dtype Arrays in StridedMemoryView
Summary
Extends
StridedMemoryViewto handle NumPy structured dtypes (e.g.,dtype([("field1", float64), ("field2", int64)])) instead of only built-in numeric types.Changes
cuda/core/_memoryview.pyx(30 lines, +20 -10)_typestr2dtypefrom static dict to function with lazy caching_typestr2itemsizefrom static dict to function with lazy cachingnumpy.dtype(typestr)for non-builtin types (e.g., structured dtypes)_typestr2dtype→_TYPESTR_TO_DTYPE,_typestr2itemsize→_TYPESTR_TO_ITEMSIZEtests/test_utils.py(+17 lines)test_struct_array()validating structured dtype handlingdtype=[("array1", np.float64), ("array2", np.int64)]Key Behavior
Structured dtypes now work but are represented as void types (e.g.,
V16) due to inherent lossiness in typestr encoding (dtype([("a", "int")]).str == "V8").