8000 MAINT: Fix computation of numpy.array_api.linalg.vector_norm by asmeurer · Pull Request #21084 · numpy/numpy · GitHub
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MAINT: Fix computation of numpy.array_api.linalg.vector_norm #21084

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Merged
merged 4 commits into from
Jun 16, 2022
Merged

MAINT: Fix computation of numpy.array_api.linalg.vector_norm #21084

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71fe19a
Fix computation of numpy.array_api.linalg.vector_norm
asmeurer Feb 18, 2022
6af507b
Fix the output dtype nonstandard vector norm()
asmeurer Feb 18, 2022
284a7c0
Clean up the numpy.array_api.linalg.vector_norm code a little bit
asmeurer Feb 19, 2022
54e1184
Merge branch 'main' into array-api-vector_norm-fix
asmeurer Jun 15, 2022
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44 changes: 35 additions & 9 deletions numpy/array_api/linalg.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
from __future__ import annotations

from ._dtypes import _floating_dtypes, _numeric_dtypes
from ._manipulation_functions import reshape
from ._array_object import Array

from typing import TYPE_CHECKING
Expand Down Expand Up @@ -391,18 +392,43 @@ def vector_norm(x: Array, /, *, axis: Optional[Union[int, Tuple[int, int]]] = No
if x.dtype not in _floating_dtypes:
raise TypeError('Only floating-point dtypes are allowed in norm')

# np.linalg.norm tries to do a matrix norm
a = x._array
if axis is None:
a = a.flatten()
axis = 0
# Note: np.linalg.norm() doesn't handle dimension 0 arrays
if a.ndim == 0:
a = a[None]
else:
a = a.flatten()
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@seberg seberg Feb 18, 2022 •
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I don't understand, flatten should always return a 1-D array?

EDIT: Actually, why not .ravel()? flatten forces a copy and that is really not necessary (ravel copies more often than you would expect too, but at least not always.)

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Oh I never realized that about flatten. We have to make it a 1-D array to force it to do a vector norm (this weird shape-based behavior is why we split norm into vector_norm and matrix_norm in the array API).

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Yes, but np.array(0).ravel() (or flatten) also is 1D, this is not <= 1 but == 1, so ravel() covers the a[None] path just as well.

_axis = 0
elif isinstance(axis, tuple):
# Note: The axis argument supports any number of axes, whereas norm()
# only supports a single axis for vector norm.
rest = tuple(i for i in range(a.ndim) if i not in axis)
# Note: The axis argument supports any number of axes, whereas
# np.linalg.norm() only supports a single axis for vector norm.
normalized_axis = [i if i >= 0 else i + a.ndim for i in axis]
rest = tuple(i for i in range(a.ndim) if i not in normalized_axis)
newshape = axis + rest
a = np.transpose(a, newshape).reshape((np.prod([a.shape[i] for i in axis]), *[a.shape[i] for i in rest]))
axis = 0
return Array._new(np.linalg.norm(a, axis=axis, keepdims=keepdims, ord=ord))

a = np.transpose(a, newshape).reshape(
(np.prod([a.shape[i] for i in axis], dtype=int), *[a.shape[i] for i in rest]))
_axis = 0
else:
_axis = axis
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We do have a normalize_axis_tuple helper in numpy/core/numeric.py, maybe worthwhile?

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Ah, I should have guessed there are probably already helpers in NumPy to do some of these things. I'll see if I can find anything that makes this simpler.


res = Array._new(np.linalg.norm(a, axis=_axis, ord=ord))

if keepdims:
# We can't reuse np.linalg.norm(keepdims) because of the hacks above
# to avoid matrix norm logic.
shape = list(x.shape)
if axis is None:
_axis = range(x.ndim)
elif isinstance(axis, int):
_axis = (axis,)
else:
_axis = axis
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Might put this together with the setup (i.e. figuring out the result shape there? But looks fine.

for i in _axis:
shape[i] = 1
res = reshape(res, tuple(shape))

return res

__all__ = ['cholesky', 'cross', 'det', 'diagonal', 'eigh', 'eigvalsh', 'inv', 'matmul', 'matrix_norm', 'matrix_power', 'matrix_rank', 'matrix_transpose', 'outer', 'pinv', 'qr', 'slogdet', 'solve', 'svd', 'svdvals', 'tensordot', 'trace', 'vecdot', 'vector_norm']
2 changes: 1 addition & 1 deletion 7DDA numpy/linalg/linalg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2558,7 +2558,7 @@ def norm(x, ord=None, axis=None, keepdims=False):
else:
absx = abs(x)
absx **= ord
ret = add.reduce(absx, axis=axis, keepdims=keepdims)
ret = asarray(add.reduce(absx, axis=axis, keepdims=keepdims))
ret **= (1 / ord)
return ret
elif len(axis) == 2:
Expand Down
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