fix(factories): reject conversion in vector() and matrix() - #92
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All eight factory overloads -- native and complex, view and copy -- now
take nb::arg("a").noconvert(), so the dtype must match a registered one
exactly and the array must be C-contiguous.
The reported symptom was unregistered dtypes (f16, uint16, uint32,
uint64) coming back as DenseVector_f32 reporting is_view=True while
being a view of the converted temporary. Investigating it turned up
something worse. nanobind's converting pass repacks LAYOUT and DTYPE
together, and it takes the first overload that converts -- float32:
a = np.array([0.1, 1.0, 0.2, 2.0, 0.3, 3.0]) # float64
mtl5.vector(a[::2]) -> DenseVector_f32, is_view=True
v[0] == 0.10000000149011612
An ordinary float64 slice was silently downcast to float32, losing
precision, while claiming to be a view and aliasing nothing. The test
that covered this asserted values (1.0, 3.0, 5.0) exact in float32, which
is why it read as benign for so long.
A first attempt applying noconvert to only the native factories made
things worse rather than better: the fallback moved from float32 to
complex64, because the complex overloads had no noconvert. Measured
before and after rather than assumed -- all eight had to move together.
Verified dispatch: the nine registered dtypes (f32 f64 i8 i16 i32 i64 u8
c64 c128) resolve exactly; f16/u16/u32/u64 and any non-contiguous input
raise TypeError listing every accepted signature.
This is a BEHAVIOUR CHANGE. Callers passing a non-contiguous array, or a
dtype outside that set, now get a TypeError where they used to get a
quietly converted result. np.ascontiguousarray(a) is the deliberate way
through, and it keeps float64. mtl5.array.asarray has always worked this
way -- its .noconvert() carries the same reasoning, and the factories
simply never got it.
1411 passed, 3 skipped (from 1396).
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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📝 WalkthroughWalkthroughThe NumPy vector and matrix factories now require registered exact dtypes and C-contiguous arrays. Native and complex bindings reject implicit conversion, and tests cover invalid inputs and dtype preservation. ChangesNumPy factory validation
Estimated code review effort: 1 (Trivial) | ~5 minutes Merge Risk: 🔵 Low · up to The factories now reject implicit dtype and layout conversion, preventing silent precision loss and invalid views; however, the complex overload documentation should clearly state the accepted inputs and explicit conversion path. The change is mergeable with that bounded documentation follow-up. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 11.11% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 9 functions across 3 files. (1 skipped: 1 unsupported.) ✨ Finishing Touches 💡 1📝 Generate docstrings 💡
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Inline comments:
In `@python/src/mtl5_complex.cpp`:
- Line 319: Update the complex MTL5 vector factory documentation around the
shown argument declaration to state that each factory requires its matching
complex64 or complex128 dtype and a C-contiguous NumPy array, and that invalid
inputs raise TypeError. Document np.ascontiguousarray(a) or a.astype(...) as the
explicit conversion path.
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CHANGELOG.mdpython/src/mtl5_complex.cpppython/src/mtl5_module.cpptests/test_vector.py
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Addresses the review on #92. The complex overloads got .noconvert() in a20d574 but kept their one-line docstrings, so their behaviour changed while their documentation did not -- the native factories had been expanded and these had not. All four now state the same contract: exact complex64/complex128, a C-contiguous array, TypeError rather than conversion, and np.ascontiguousarray(a) / a.astype(...) as the deliberate path. The complex case has a failure mode of its own worth naming, which the native wording does not cover: without noconvert a REAL array converts and silently acquires an imaginary part. The copy variants drop the "neither zero-copy" clause, since zero-copy is not what they promise -- for them the reason is that copying the data does not copy the TYPE. Docstrings only; no behaviour change. 1411 passed, 3 skipped. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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All eight factory overloads — native and complex, view and copy — now take
nb::arg("a").noconvert(). 1411 passed, 3 skipped (from 1396).The reported bug was the small half of it
The symptom I set out to fix was unregistered dtypes coming back wrong:
mtl5.vector(np.arange(4, dtype=np.float16))returned aDenseVector_f32reportingis_view=Truewhile being a view of the converted temporary.Investigating it turned up something considerably worse. nanobind's converting pass repacks layout and dtype together, and it takes the first overload that converts — which is
float32:An ordinary float64 slice was silently downcast to float32. Not an exotic dtype —
a[::2], or a column of a 2-D array. It lost precision, claimedis_view=True, and aliased nothing.The test that covered this asserted values
1.0, 3.0, 5.0, all exact in float32, which is why it read as benign behaviour for so long rather than as a precision bug.One thing worth flagging about the fix
My first attempt applied
.noconvert()to only the native factories, which made it worse: the fallback moved fromfloat32tocomplex64, because the complex overloads inmtl5_complex.cpphad no.noconvert()of their own. A real float64 slice came back as a complex vector.All eight overloads have to move together. I found that by measuring the dispatch before and after rather than reasoning about it:
float16,uint16,uint32,uint64DenseVector_f32TypeErrorfloat64DenseVector_f32TypeErrornp.ascontiguousarray(...)DenseVector_f64The
TypeErrorlists every accepted signature includingorder='C', and the docstrings now namenp.ascontiguousarrayexplicitly.This is a behaviour change
Callers passing a non-contiguous array, or a dtype outside
{f32, f64, i8, i16, i32, i64, u8, c64, c128}, now get aTypeErrorwhere they previously got a quietly converted result.np.ascontiguousarray(a)is the deliberate way through, and it keeps float64 rather than downcasting.Only one existing test depended on the old behaviour; it's rewritten to assert the new contract, with the precision-loss demonstration in its docstring so the reason survives.
This also brings the factories in line with
mtl5.array.asarray, which has always used.noconvert()— its comment already spells out the identical reasoning ("a converted array would be a view of a temporary and neither zero-copy nor the dtype you asked for"). The factories simply never got it.🤖 Generated with Claude Code
Summary by CodeRabbit
TypeErrorinstead of being silently converted.