Workaround np.linalg.solve ambiguity #93
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NumPy's solve() does not handle the ambiguity around x2 being 1-D vector vs. an n-D stack of matrices in the way that the standard specifies. Namely, x2 should be treated as a 1-D vector iff it is 1-dimensional, and a stack of matrices in all other cases. This workaround is borrowed from array-api-strict. See numpy/numpy#15349 and data-apis/array-api#285. Note that this workaround only works for NumPy. CuPy currently does not support stacked vectors for solve() (see https://github.com/cupy/cupy/blob/main/cupy/cublas.py#L43), and the workaround in cupy.array_api.linalg does not seem to actually function.
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Test failure is an upstream issue data-apis/array-api-tests#238 |
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NumPy's solve() does not handle the ambiguity around x2 being 1-D vector vs. an n-D stack of matrices in the way that the standard specifies. Namely, x2 should be treated as a 1-D vector iff it is 1-dimensional, and a stack of matrices in all other cases. This workaround is borrowed from array-api-strict.
See numpy/numpy#15349 and data-apis/array-api#285.
Note that this workaround only works for NumPy. CuPy currently does not support stacked vectors for solve() (see
https://github.com/cupy/cupy/blob/main/cupy/cublas.py#L43), and the workaround in cupy.array_api.linalg does not seem to actually function.