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Support various filtering functions in OpenVINO #21836

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445b6c0
support filtering functions openvino
danielenricocahall Nov 9, 2025
162f6df
override `assertAllClose` to reduce duplication
danielenricocahall Nov 9, 2025
ec0b030
Update keras/src/backend/openvino/numpy.py
danielenricocahall Nov 9, 2025
315334d
Merge branch 'master' into support-filtering-functions-openvino
danielenricocahall Nov 11, 2025
958ef56
fix arg
danielenricocahall Nov 11, 2025
36d35de
Merge branch 'master' into support-filtering-functions-openvino
danielenricocahall Nov 15, 2025
f1e10e5
fix empty_like exclusion
danielenricocahall Nov 15, 2025
0b366b6
fix empty_like exclusion
danielenricocahall Nov 15, 2025
194c6f0
Merge branch 'keras-team:master' into support-filtering-functions-ope…
danielenricocahall Nov 23, 2025
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9 changes: 0 additions & 9 deletions keras/src/backend/openvino/excluded_concrete_tests.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -3,11 +3,8 @@ NumpyDtypeTest::test_add_
NumpyDtypeTest::test_angle
NumpyDtypeTest::test_argpartition
NumpyDtypeTest::test_array
NumpyDtypeTest::test_bartlett
NumpyDtypeTest::test_blackman
NumpyDtypeTest::test_empty_like
NumpyDtypeTest::test_gcd
NumpyDtypeTest::test_hamming
NumpyDtypeTest::test_hanning
NumpyDtypeTest::test_heaviside
NumpyDtypeTest::test_hypot
Expand Down Expand Up @@ -57,9 +54,6 @@ HistogramTest
NumpyOneInputOpsCorrectnessTest::test_angle
NumpyOneInputOpsCorrectnessTest::test_argpartition
NumpyOneInputOpsCorrectnessTest::test_array
NumpyOneInputOpsCorrectnessTest::test_bartlett
NumpyOneInputOpsCorrectnessTest::test_blackman
NumpyOneInputOpsCorrectnessTest::test_hamming
NumpyOneInputOpsCorrectnessTest::test_hanning
NumpyOneInputOpsCorrectnessTest::test_kaiser
NumpyOneInputOpsCorrectnessTest::test_bitwise_invert
Expand Down Expand Up @@ -112,12 +106,9 @@ NumpyTwoInputOpsCorrectnessTest::test_quantile
NumpyTwoInputOpsCorrectnessTest::test_tensordot
NumpyTwoInputOpsCorrectnessTest::test_vdot
NumpyOneInputOpsDynamicShapeTest::test_angle
NumpyOneInputOpsDynamicShapeTest::test_bartlett
NumpyOneInputOpsDynamicShapeTest::test_blackman
NumpyOneInputOpsDynamicShapeTest::test_empty_like
NumpyOneInputOpsDynamicShapeTest::test_cbrt
NumpyOneInputOpsDynamicShapeTest::test_corrcoef
NumpyOneInputOpsDynamicShapeTest::test_hamming
NumpyOneInputOpsDynamicShapeTest::test_hanning
NumpyOneInputOpsDynamicShapeTest::test_isreal
NumpyOneInputOpsDynamicShapeTest::test_kaiser
Expand Down
78 changes: 72 additions & 6 deletions keras/src/backend/openvino/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -545,16 +545,61 @@ def average(x, axis=None, weights=None):


def bartlett(x):
raise NotImplementedError(
"`bartlett` is not supported with openvino backend"
x = get_ov_output(x)
zero_const = ov_opset.constant(0, Type.i64)
one_const = ov_opset.constant(1, Type.i64)
two_const = ov_opset.constant(2, Type.i64)
two_const_f64 = ov_opset.constant(2.0, Type.f64)
if x.get_element_type() != Type.i64:
x = ov_opset.convert(x, Type.i64)
half = ov_opset.convert(
ov_opset.divide(ov_opset.subtract(x, one_const), two_const), Type.f64
)
n = ov_opset.range(zero_const, x, one_const, Type.f64)
condition = ov_opset.less_equal(n, half)
first_half = ov_opset.divide(
ov_opset.multiply(two_const_f64, n),
ov_opset.convert(ov_opset.subtract(x, one_const), Type.f64),
)
second_half = ov_opset.subtract(two_const_f64, first_half)
window = ov_opset.select(condition, first_half, second_half)
window = ov_opset.convert(window, OPENVINO_DTYPES[config.floatx()]).output(
0
)
return OpenVINOKerasTensor(window)


def hamming(x):
raise NotImplementedError(
"`hamming` is not supported with openvino backend"
m = get_ov_output(x)

m_i64 = (
m if m.get_element_type() == Type.i64 else ov_opset.convert(m, Type.i64)
)

start = ov_opset.constant(0, Type.i64)
step = ov_opset.constant(1, Type.i64)
n = ov_opset.range(start, m_i64, step, Type.f64)

one_i64 = ov_opset.constant(1, Type.i64)
denom_i64 = ov_opset.subtract(m_i64, one_i64)
denom = ov_opset.convert(denom_i64, Type.f64)

two_pi = ov_opset.constant(2.0 * np.pi, Type.f64)
two_pi_over_m_minus_1 = ov_opset.divide(two_pi, denom)

x = ov_opset.multiply(two_pi_over_m_minus_1, n)
c = ov_opset.cos(x)

# 0.54 - 0.46 * cos(...)
a = ov_opset.constant(0.54, Type.f64)
b = ov_opset.constant(0.46, Type.f64)
hamming_window = ov_opset.subtract(a, ov_opset.multiply(b, c))
hamming_window = ov_opset.convert(
hamming_window, OPENVINO_DTYPES[config.floatx()]
)

return OpenVINOKerasTensor(hamming_window.output(0))


def heaviside(x1, x2):
x1 = get_ov_output(x1)
Expand Down Expand Up @@ -624,9 +669,30 @@ def bincount(x, weights=None, minlength=0, sparse=False):


def blackman(x):
raise NotImplementedError(
"`blackman` is not supported with openvino backend"
x = get_ov_output(x)
zero_const = ov_opset.constant(0, Type.i64)
one_const = ov_opset.constant(1, Type.i64)
two_pi = ov_opset.constant(2.0 * np.pi, Type.f64)
term_1 = ov_opset.constant(0.42, Type.f64)
term_2 = ov_opset.constant(0.5, Type.f64)
term_3 = ov_opset.constant(0.08, Type.f64)
if x.get_element_type() != Type.i64:
x = ov_opset.convert(x, Type.i64)
n = ov_opset.range(zero_const, x, one_const, Type.f64)
n_minus_1 = ov_opset.subtract(
ov_opset.convert(x, Type.f64), ov_opset.constant(1.0, Type.f64)
).output(0)
angle_2pi = ov_opset.divide(ov_opset.multiply(two_pi, n), n_minus_1)
angle_4pi = ov_opset.multiply(angle_2pi, ov_opset.constant(2.0, Type.f64))
cos_2pi = ov_opset.cos(angle_2pi)
cos_4pi = ov_opset.cos(angle_4pi)
term_2_final = ov_opset.multiply(term_2, cos_2pi)
term_3_final = ov_opset.multiply(term_3, cos_4pi)
window = ov_opset.add(ov_opset.subtract(term_1, term_2_final), term_3_final)
window = ov_opset.convert(window, OPENVINO_DTYPES[config.floatx()]).output(
0
)
return OpenVINOKerasTensor(window)


def broadcast_to(x, shape):
Expand Down
9 changes: 9 additions & 0 deletions keras/src/ops/numpy_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3622,6 +3622,15 @@ def test_digitize(self):


class NumpyOneInputOpsCorrectnessTest(testing.TestCase):
def assertAllClose(self, x1, x2, atol=1e-6, rtol=1e-6, msg=None):
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@hertschuh hertschuh Nov 25, 2025

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Let's not do that, it reduces the precision for dozens of ops.

Which op(s) have the issue?

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@danielenricocahall danielenricocahall Nov 25, 2025

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@hertschuh Sure - with this change omitted:

-test_bartlett

E           Mismatched elements: 64 / 73 (87.7%)
E           Max absolute difference among violations: 0.00021701
E           Max relative difference among violations: 0.00035511
E            ACTUAL: array([0.      , 0.027771, 0.055542, 0.083313, 0.111084, 0.138916,
E                  0.166626, 0.194458, 0.222168, 0.25    , 0.277832, 0.305664,
E                  0.333252, 0.361084, 0.388916, 0.416748, 0.444336, 0.472168,...
E            DESIRED: array([0.      , 0.027778, 0.055556, 0.083333, 0.111111, 0.138889,
E                  0.166667, 0.194444, 0.222222, 0.25    , 0.277778, 0.305556,
E                  0.333333, 0.361111, 0.388889, 0.416667, 0.444444, 0.472222,...
  • test_blackman
E           Mismatched elements: 40 / 46 (87%)
E           Max absolute difference among violations: 0.0002353
E           Max relative difference among violations: 0.00032871
E            ACTUAL: array([-0.      ,  0.001767,  0.007214,  0.016754,  0.031052,  0.050873,
E                   0.077087,  0.110413,  0.151611,  0.200806,  0.258057,  0.322754,
E                   0.394043,  0.470215,  0.549805,  0.629883,  0.708496,  0.782227,...
E            DESIRED: array([-1.387779e-17,  1.766901e-03,  7.213000e-03,  1.675772e-02,
E                   3.104564e-02,  5.086963e-02,  7.707242e-02,  1.104350e-01,
E                   1.515615e-01,  2.007701e-01,  2.580005e-01,  3.227451e-01,
  • test_hamming
E           Mismatched elements: 10 / 11 (90.9%)
E           Max absolute difference among violations: 9.70361625e-05
E           Max relative difference among violations: 0.0002439
E            ACTUAL: array([0.080017, 0.167847, 0.397949, 0.682129, 0.912109, 1.      ,
E                  0.912109, 0.682129, 0.397949, 0.167847, 0.080017], dtype=float32)
E            DESIRED: array([0.08    , 0.167852, 0.397852, 0.682148, 0.912148, 1.      ,
E                  0.912148, 0.682148, 0.397852, 0.167852, 0.08    ])

Alternatively, there could be an "openvino" check in these tests before calling the assertion and updating the tolerance (such that it doesn't impact other operations to your point).

if backend.backend() == "openvino":
# OpenVINO seems to use lower precision for some operations,
# or employs some different algorithms that wind up with
# slightly different results. To address this, we relax
# the tolerances for OpenVINO backend.
atol = 1e-3
super().assertAllClose(x1, x2, atol=atol, rtol=rtol, msg=msg)

def test_mean(self):
x = np.array([[1, 2, 3], [3, 2, 1]])
self.assertAllClose(knp.mean(x), np.mean(x))
Expand Down
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