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fix_issue-2319 #2371

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Mar 20, 2025
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fix_issue-2319 #2371

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2b1ac6d
fix_issue-2319
vtavana Feb 18, 2025
79aca8f
update changelog
vtavana Mar 17, 2025
1d360ad
add a workaround
vtavana Mar 17, 2025
fb57ef2
address comments
vtavana Mar 18, 2025
63fc178
update tests
vtavana Mar 19, 2025
7ad964c
Merge branch 'master' into fix-issue-2319
vtavana Mar 19, 2025
3001a9e
add parametrize xp
vtavana Mar 19, 2025
adbf843
Merge branch 'master' into fix-issue-2319
vtavana Mar 19, 2025
f4c9628
Merge branch 'master' into fix-issue-2319
vtavana Mar 20, 2025
9edd4b7
Merge branch 'master' into fix-issue-2319
vtavana Mar 20, 2025
71d44f4
Merge branch 'master' into fix-issue-2319
vtavana Mar 20, 2025
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Changed

* Allowed input array of `uint64` dtype in `dpnp.bincount` [#2361](https://github.com/IntelPython/dpnp/pull/2361)
* The vector norms `ord={None, 1, 2, inf}` and the matrix norms `ord={None, 1, 2, inf, "fro", "nuc"}` now consistently return zero for empty arrays, which are arrays with at least one axis of size zero. This change affects `dpnp.linalg.norm`, `dpnp.linalg.vector_norm`, and `dpnp.linalg.matrix_norm`. Previously, dpnp would either raise errors or return zero depending on the parameters provided [#2371](https://github.com/IntelPython/dpnp/pull/2371)

### Fixed

Expand Down
7 changes: 7 additions & 0 deletions dpnp/linalg/dpnp_utils_linalg.py
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Expand Up @@ -1185,6 +1185,9 @@ def _norm_int_axis(x, ord, axis, keepdims):
"""

if ord == dpnp.inf:
if x.shape[axis] == 0:
x = dpnp.moveaxis(x, axis, -1)
return dpnp.zeros_like(x, shape=x.shape[:-1])
return dpnp.abs(x).max(axis=axis, keepdims=keepdims)
if ord == -dpnp.inf:
return dpnp.abs(x).min(axis=axis, keepdims=keepdims)
Expand Down Expand Up @@ -1220,6 +1223,10 @@ def _norm_tuple_axis(x, ord, row_axis, col_axis, keepdims):
"""

axis = (row_axis, col_axis)
flag = x.shape[row_axis] == 0 or x.shape[col_axis] == 0
if flag and ord in [1, 2, dpnp.inf]:
x = dpnp.moveaxis(x, axis, (-2, -1))
return dpnp.zeros_like(x, shape=x.shape[:-2])
if row_axis == col_axis:
raise ValueError("Duplicate axes given.")
if ord == 2:
Expand Down
172 changes: 105 additions & 67 deletions dpnp/tests/test_linalg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
assert_allclose,
assert_almost_equal,
assert_array_equal,
assert_equal,
assert_raises,
assert_raises_regex,
suppress_warnings,
Expand All @@ -24,6 +25,7 @@
has_support_aspect64,
is_cpu_device,
is_cuda_device,
numpy_version,
)
from .third_party.cupy import testing

Expand Down Expand Up @@ -2074,9 +2076,6 @@ def test_matrix_transpose():


class TestNorm:
def setup_method(self):
numpy.random.seed(42)

@pytest.mark.usefixtures("suppress_divide_numpy_warnings")
@pytest.mark.parametrize(
"shape", [(0,), (5, 0), (2, 0, 3)], ids=["(0,)", "(5, 0)", "(2, 0, 3)"]
Expand All @@ -2087,29 +2086,36 @@ def setup_method(self):
def test_empty(self, shape, ord, axis, keepdims):
a = numpy.empty(shape)
ia = dpnp.array(a)
kwarg = {"ord": ord, "axis": axis, "keepdims": keepdims}

if axis is None and a.ndim > 1 and ord in [0, 3]:
# Invalid norm order for matrices (a.ndim == 2) or
# Improper number of dimensions to norm (a.ndim>2)
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
elif axis is None and a.ndim > 2 and ord is not None:
# Improper number of dimensions to norm
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
elif (
axis is None
and ord is not None
and a.ndim != 1
and a.shape[-1] == 0
):
# reduction cannot be performed over zero-size axes
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
if ord in [-2, -1, 0, 3]:
# reduction cannot be performed over zero-size axes
assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
else:
# TODO: when similar changes in numpy are available, instead
# of assert_equal with zero, we should compare with numpy
# ord in [None, 1, 2]
assert_equal(dpnp.linalg.norm(ia, **kwarg), 0)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
else:
result = dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
expected = numpy.linalg.norm(
a, ord=ord, axis=axis, keepdims=keepdims
)
result = dpnp.linalg.norm(ia, **kwarg)
expected = numpy.linalg.norm(a, **kwarg)
assert_dtype_allclose(result, expected)

@pytest.mark.parametrize(
Expand All @@ -2121,11 +2127,11 @@ def test_0D(self, ord, axis):
ia = dpnp.array(a)
if axis is None and ord is not None:
# Improper number of dimensions to norm
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis)
assert_raises(ValueError, dpnp.linalg.norm, ia, ord=ord, axis=axis)
assert_raises(ValueError, numpy.linalg.norm, a, ord=ord, axis=axis)
elif axis is not None:
with pytest.raises(AxisError):
dpnp.linalg.norm(ia, ord=ord, axis=axis)
assert_raises(IndexError, dpnp.linalg.norm, ia, ord=ord, axis=axis)
assert_raises(AxisError, numpy.linalg.norm, a, ord=ord, axis=axis)
else:
result = dpnp.linalg.norm(ia, ord=ord, axis=axis)
expected = numpy.linalg.norm(a, ord=ord, axis=axis)
Expand Down Expand Up @@ -2158,24 +2164,21 @@ def test_1D(self, dtype, ord, axis, keepdims):
def test_2D(self, dtype, ord, axis, keepdims):
a = generate_random_numpy_array((3, 5), dtype)
ia = dpnp.array(a)
kwarg = {"ord": ord, "axis": axis, "keepdims": keepdims}

if (axis in [-1, 0, 1] and ord in ["nuc", "fro"]) or (
(isinstance(axis, tuple) or axis is None) and ord == 3
):
# Invalid norm order for vectors
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
else:
result = dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
expected = numpy.linalg.norm(
a, ord=ord, axis=axis, keepdims=keepdims
)
result = dpnp.linalg.norm(ia, **kwarg)
expected = numpy.linalg.norm(a, **kwarg)
assert_dtype_allclose(result, expected)

@pytest.mark.usefixtures("suppress_divide_numpy_warnings")
@pytest.mark.parametrize(
"dtype",
get_all_dtypes(no_none=True),
)
@pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
@pytest.mark.parametrize(
"ord", [None, -dpnp.inf, -2, -1, 1, 2, 3, dpnp.inf, "fro", "nuc"]
)
Expand All @@ -2188,21 +2191,21 @@ def test_2D(self, dtype, ord, axis, keepdims):
def test_ND(self, dtype, ord, axis, keepdims):
a = generate_random_numpy_array((2, 3, 4, 5), dtype)
ia = dpnp.array(a)
kwarg = {"ord": ord, "axis": axis, "keepdims": keepdims}

if (axis in [-1, 0, 1] and ord in ["nuc", "fro"]) or (
isinstance(axis, tuple) and ord == 3
):
# Invalid norm order for vectors
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
elif axis is None and ord is not None:
# Improper number of dimensions to norm
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
else:
result = dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
expected = numpy.linalg.norm(
a, ord=ord, axis=axis, keepdims=keepdims
)
result = dpnp.linalg.norm(ia, **kwarg)
expected = numpy.linalg.norm(a, **kwarg)
assert_dtype_allclose(result, expected)

@pytest.mark.usefixtures("suppress_divide_numpy_warnings")
Expand All @@ -2219,21 +2222,21 @@ def test_ND(self, dtype, ord, axis, keepdims):
def test_usm_ndarray(self, dtype, ord, axis, keepdims):
a = generate_random_numpy_array((2, 3, 4, 5), dtype)
ia = dpt.asarray(a)
kwarg = {"ord": ord, "axis": axis, "keepdims": keepdims}

if (axis in [-1, 0, 1] and ord in ["nuc", "fro"]) or (
isinstance(axis, tuple) and ord == 3
):
# Invalid norm order for vectors
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
elif axis is None and ord is not None:
# Improper number of dimensions to norm
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
else:
result = dpnp.linalg.norm(ia, ord=ord, axis=axis, keepdims=keepdims)
expected = numpy.linalg.norm(
a, ord=ord, axis=axis, keepdims=keepdims
)
result = dpnp.linalg.norm(ia, **kwarg)
expected = numpy.linalg.norm(a, **kwarg)
assert_dtype_allclose(result, expected)

@pytest.mark.parametrize("stride", [3, -1, -5])
Expand All @@ -2257,8 +2260,7 @@ def test_strided_2D(self, axis, stride):
A = numpy.random.rand(20, 30)
B = dpnp.asarray(A)
slices = tuple(slice(None, None, stride[i]) for i in range(A.ndim))
a = A[slices]
b = B[slices]
a, b = A[slices], B[slices]

result = dpnp.linalg.norm(b, axis=axis)
expected = numpy.linalg.norm(a, axis=axis)
Expand All @@ -2278,8 +2280,7 @@ def test_strided_ND(self, axis, stride):
A = numpy.random.rand(12, 16, 20, 24)
B = dpnp.asarray(A)
slices = tuple(slice(None, None, stride[i]) for i in range(A.ndim))
a = A[slices]
b = B[slices]
a, b = A[slices], B[slices]

result = dpnp.linalg.norm(b, axis=axis)
expected = numpy.linalg.norm(a, axis=axis)
Expand All @@ -2299,6 +2300,49 @@ def test_matrix_norm(self, ord, keepdims):
expected = numpy.linalg.matrix_norm(a, ord=ord, keepdims=keepdims)
assert_dtype_allclose(result, expected)

@pytest.mark.parametrize(
"xp",
[
dpnp,
pytest.param(
numpy,
marks=pytest.mark.skipif(
numpy_version() < "2.3.0",
reason="numpy raises an error",
),
),
],
)
@pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.int32])
@pytest.mark.parametrize(
"shape_axis", [[(2, 0), None], [(2, 0), (0, 1)], [(0, 2), (0, 1)]]
)
@pytest.mark.parametrize("ord", [None, "fro", "nuc", 1, 2, dpnp.inf])
def test_matrix_norm_empty(self, xp, dtype, shape_axis, ord):
shape, axis = shape_axis[0], shape_axis[1]
x = xp.zeros(shape, dtype=dtype)
assert_equal(xp.linalg.norm(x, axis=axis, ord=ord), 0)

@pytest.mark.parametrize(
"xp",
[
dpnp,
pytest.param(
numpy,
marks=pytest.mark.skipif(
numpy_version() < "2.3.0",
reason="numpy raises an error",
),
),
],
)
@pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.int32])
@pytest.mark.parametrize("axis", [None, 0])
@pytest.mark.parametrize("ord", [None, 1, 2, dpnp.inf])
def test_vector_norm_empty(self, xp, dtype, axis, ord):
x = xp.zeros(0, dtype=dtype)
assert_equal(xp.linalg.vector_norm(x, axis=axis, ord=ord), 0)

@testing.with_requires("numpy>=2.0")
@pytest.mark.parametrize(
"ord", [None, -dpnp.inf, -2, -1, 0, 1, 2, 3.5, dpnp.inf]
Expand All @@ -2320,13 +2364,10 @@ def test_vector_norm_0D(self, ord):
def test_vector_norm_1D(self, ord, axis, keepdims):
a = generate_random_numpy_array(10)
ia = dpnp.array(a)
kwarg = {"ord": ord, "axis": axis, "keepdims": keepdims}

result = dpnp.linalg.vector_norm(
ia, ord=ord, axis=axis, keepdims=keepdims
)
expected = numpy.linalg.vector_norm(
a, ord=ord, axis=axis, keepdims=keepdims
)
result = dpnp.linalg.vector_norm(ia, **kwarg)
expected = numpy.linalg.vector_norm(a, **kwarg)
assert_dtype_allclose(result, expected)

@testing.with_requires("numpy>=2.0")
Expand All @@ -2343,29 +2384,26 @@ def test_vector_norm_1D(self, ord, axis, keepdims):
def test_vector_norm_ND(self, ord, axis, keepdims):
a = numpy.arange(120).reshape(2, 3, 4, 5)
ia = dpnp.array(a)
kwarg = {"ord": ord, "axis": axis, "keepdims": keepdims}

result = dpnp.linalg.vector_norm(
ia, ord=ord, axis=axis, keepdims=keepdims
)
expected = numpy.linalg.vector_norm(
a, ord=ord, axis=axis, keepdims=keepdims
)
result = dpnp.linalg.vector_norm(ia, **kwarg)
expected = numpy.linalg.vector_norm(a, **kwarg)
assert_dtype_allclose(result, expected)

def test_error(self):
ia = dpnp.arange(120).reshape(2, 3, 4, 5)
a = numpy.arange(120).reshape(2, 3, 4, 5)
ia = dpnp.array(a)

# Duplicate axes given
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, axis=(2, 2))
assert_raises(ValueError, dpnp.linalg.norm, ia, axis=(2, 2))
assert_raises(ValueError, numpy.linalg.norm, a, axis=(2, 2))

#'axis' must be None, an integer or a tuple of integers
with pytest.raises(TypeError):
dpnp.linalg.norm(ia, axis=[2])
assert_raises(TypeError, dpnp.linalg.norm, ia, axis=[2])
assert_raises(TypeError, numpy.linalg.norm, a, axis=[2])

# Invalid norm order for vectors
with pytest.raises(ValueError):
dpnp.linalg.norm(ia, axis=1, ord=[3])
assert_raises(ValueError, dpnp.linalg.norm, ia, axis=1, ord=[3])


class TestQr:
Expand Down
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