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Add fmod to keras.ops.numpy #22369

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990588c
feat(ops): add fmod to keras.ops.numpy
rstar327 Mar 6, 2026
4d2c73d
style(ops): fix api init formatting for api_gen compatibility
rstar327 Mar 6, 2026
fed4e4a
fix(ops): add fmod to keras.ops exports and fix api formatting
rstar327 Mar 6, 2026
3efcb9f
style(ops): sync api init files with api_gen output
rstar327 Mar 6, 2026
78280a2
style: regenerate api files with api_gen
rstar327 Mar 6, 2026
14a3800
style: fix import sorting in api files
rstar327 Mar 6, 2026
ef470ad
fix(ops): cast truncated quotient to correct dtype in tf fmod
rstar327 Mar 6, 2026
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1 change: 1 addition & 0 deletions keras/api/_tf_keras/keras/ops/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,6 +196,7 @@
from keras.src.ops.numpy import flip as flip
from keras.src.ops.numpy import floor as floor
from keras.src.ops.numpy import floor_divide as floor_divide
from keras.src.ops.numpy import fmod as fmod
from keras.src.ops.numpy import full as full
from keras.src.ops.numpy import full_like as full_like
from keras.src.ops.numpy import gcd as gcd
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1 change: 1 addition & 0 deletions keras/api/_tf_keras/keras/ops/numpy/__init__.py
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Expand Up @@ -77,6 +77,7 @@
from keras.src.ops.numpy import flip as flip
from keras.src.ops.numpy import floor as floor
from keras.src.ops.numpy import floor_divide as floor_divide
from keras.src.ops.numpy import fmod as fmod
from keras.src.ops.numpy import full as full
from keras.src.ops.numpy import full_like as full_like
from keras.src.ops.numpy import gcd as gcd
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1 change: 1 addition & 0 deletions keras/api/ops/__init__.py
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Expand Up @@ -196,6 +196,7 @@
from keras.src.ops.numpy import flip as flip
from keras.src.ops.numpy import floor as floor
from keras.src.ops.numpy import floor_divide as floor_divide
from keras.src.ops.numpy import fmod as fmod
from keras.src.ops.numpy import full as full
from keras.src.ops.numpy import full_like as full_like
from keras.src.ops.numpy import gcd as gcd
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1 change: 1 addition & 0 deletions keras/api/ops/numpy/__init__.py
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Expand Up @@ -77,6 +77,7 @@
from keras.src.ops.numpy import flip as flip
from keras.src.ops.numpy import floor as floor
from keras.src.ops.numpy import floor_divide as floor_divide
from keras.src.ops.numpy import fmod as fmod
from keras.src.ops.numpy import full as full
from keras.src.ops.numpy import full_like as full_like
from keras.src.ops.numpy import gcd as gcd
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6 changes: 6 additions & 0 deletions keras/src/backend/jax/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1039,6 +1039,12 @@ def mod(x1, x2):
return jnp.mod(x1, x2)


def fmod(x1, x2):
x1 = convert_to_tensor(x1)
x2 = convert_to_tensor(x2)
return jnp.fmod(x1, x2)


def moveaxis(x, source, destination):
return jnp.moveaxis(x, source=source, destination=destination)

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11 changes: 11 additions & 0 deletions keras/src/backend/numpy/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -982,6 +982,17 @@ def mod(x1, x2):
return np.mod(x1, x2)


def fmod(x1, x2):
x1 = convert_to_tensor(x1)
x2 = convert_to_tensor(x2)
dtype = dtypes.result_type(x1.dtype, x2.dtype)
if dtype == "bool":
dtype = "int32"
x1 = x1.astype(dtype)
x2 = x2.astype(dtype)
return np.fmod(x1, x2)


def moveaxis(x, source, destination):
return np.moveaxis(x, source=source, destination=destination)

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4 changes: 4 additions & 0 deletions keras/src/backend/openvino/excluded_concrete_tests.txt
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Expand Up @@ -491,6 +491,7 @@ NNOpsDtypeTest::test_glu_
NNOpsDtypeTest::test_polar_
NNOpsDynamicShapeTest::test_glu
NumpyDtypeTest::test_array
NumpyDtypeTest::test_fmod
NumpyDtypeTest::test_maximum_python_types
NumpyDtypeTest::test_minimum_python_types
NumpyDtypeTest::test_nanargmax
Expand All @@ -510,6 +511,9 @@ NumpyOneInputOpsCorrectnessTest::test_vectorize
NumpyOneInputOpsCorrectnessTest::test_view
NumpyOneInputOpsDynamicShapeTest::test_view
NumpyOneInputOpsStaticShapeTest::test_view
NumpyTwoInputOpsCorrectnessTest::test_fmod
NumpyTwoInputOpsDynamicShapeTest::test_fmod
NumpyTwoInputOpsStaticShapeTest::test_fmod
OptimizerTest::test_constraints_are_applied
OptimizerTest::test_ema
OptimizerTest::test_gradient_accumulation
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4 changes: 4 additions & 0 deletions keras/src/backend/openvino/numpy.py
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Expand Up @@ -2827,6 +2827,10 @@ def mod(x1, x2):
return OpenVINOKerasTensor(ov_opset.floor_mod(x1, x2).output(0))


def fmod(x1, x2):
raise NotImplementedError("fmod is not supported by openvino backend.")


def moveaxis(x, source, destination):
x = get_ov_output(x)
if isinstance(source, int):
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14 changes: 14 additions & 0 deletions keras/src/backend/tensorflow/numpy.py
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Expand Up @@ -2168,6 +2168,20 @@ def mod(x1, x2):
return tf.math.mod(x1, x2)


def fmod(x1, x2):
x1 = convert_to_tensor(x1)
x2 = convert_to_tensor(x2)
dtype = dtypes.result_type(x1.dtype, x2.dtype)
if dtype == "bool":
dtype = "int32"
x1 = tf.cast(x1, dtype)
x2 = tf.cast(x2, dtype)
quotient = x1 / x2
truncated = tf.sign(quotient) * tf.math.floor(tf.math.abs(quotient))
truncated = tf.cast(truncated, dtype)
return x1 - truncated * x2
Comment on lines +2179 to +2182
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@gemini-code-assist gemini-code-assist bot Mar 6, 2026

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high

The current implementation can be simplified by using tf.trunc. More importantly, it returns a float tensor for integer inputs, which is inconsistent with other backends and numpy.fmod. The result should be cast back to the original integer dtype if the inputs were integers.

Suggested change
quotient = x1 / x2
truncated = tf.sign(quotient) * tf.math.floor(tf.math.abs(quotient))
return x1 - truncated * x2
quotient = x1 / x2
result = x1 - tf.trunc(quotient) * x2
if "int" in dtype:
return tf.cast(result, dtype)
return result

Comment on lines +2179 to +2182
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@hertschuh hertschuh Mar 11, 2026

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I think this would be a faster implementation:

return tf.sign(x1) * tf.math.floormod(tf.abs(x1), tf.abs(x2))



def moveaxis(x, source, destination):
x = convert_to_tensor(x)

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10 changes: 10 additions & 0 deletions keras/src/backend/torch/numpy.py
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Expand Up @@ -1320,6 +1320,16 @@ def mod(x1, x2):
return torch.remainder(x1, x2)


def fmod(x1, x2):
x1 = convert_to_tensor(x1)
x2 = convert_to_tensor(x2)
dtype = dtypes.result_type(x1.dtype, x2.dtype)
if dtype == "bool":
x1 = cast(x1, "int32")
x2 = cast(x2, "int32")
return torch.fmod(x1, x2)


def moveaxis(x, source, destination):
x = convert_to_tensor(x)
return torch.moveaxis(x, source=source, destination=destination)
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38 changes: 38 additions & 0 deletions keras/src/ops/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5255,6 +5255,44 @@ def mod(x1, x2):
return backend.numpy.mod(x1, x2)


class Fmod(Operation):
def call(self, x1, x2):
return backend.numpy.fmod(x1, x2)

def compute_output_spec(self, x1, x2):
x1_shape = getattr(x1, "shape", [])
x2_shape = getattr(x2, "shape", [])
output_shape = broadcast_shapes(x1_shape, x2_shape)
output_dtype = dtypes.result_type(
getattr(x1, "dtype", type(x1)),
getattr(x2, "dtype", type(x2)),
)
if output_dtype == "bool":
output_dtype = "int32"
return KerasTensor(output_shape, dtype=output_dtype)


@keras_export(["keras.ops.fmod", "keras.ops.numpy.fmod"])
def fmod(x1, x2):
"""Returns the element-wise remainder of division with truncation.

Computes the remainder complementary to the `floor_divide` function,
equivalent to the C library function ``fmod``. The result has the same
sign as the dividend ``x1``. This is different from `keras.ops.mod`
which has the same sign as the divisor ``x2``.

Args:
x1: First tensor, the dividend.
x2: Second tensor, the divisor.

Returns:
Output tensor, element-wise remainder with truncation.
"""
Comment on lines +5288 to +5290
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@gemini-code-assist gemini-code-assist bot Mar 6, 2026

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medium

Per the Keras API design guidelines, all docstrings should include code examples. Adding an example here would help users understand the function's behavior, especially its difference from keras.ops.mod.

    Returns:
        Output tensor, element-wise remainder with truncation.

    Examples:
    >>> x1 = keras.ops.array([-3., -2., -1., 1., 2., 3.])
    >>> x2 = keras.ops.array([2., 2., 2., 2., 2., 2.])
    >>> keras.ops.fmod(x1, x2)
    array([-1., -0., -1.,  1.,  0.,  1.], dtype=float32)

    >>> x1 = keras.ops.array([1, 2, 3, 4, 5])
    >>> x2 = keras.ops.array([-2, -2, -2, -2, -2])
    >>> keras.ops.fmod(x1, x2)
    array([1, 0, 1, 0, 1], dtype=int32)
    """
References
  1. All docstrings should include code examples. (link)

if any_symbolic_tensors((x1, x2)):
return Fmod().symbolic_call(x1, x2)
return backend.numpy.fmod(x1, x2)


class Moveaxis(Operation):
def __init__(self, source, destination, *, name=None):
super().__init__(name=name)
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50 changes: 50 additions & 0 deletions keras/src/ops/numpy_test.py
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Expand Up @@ -338,6 +338,11 @@ def test_mod(self):
y = KerasTensor((2, None))
self.assertEqual(knp.mod(x, y).shape, (2, 3))

def test_fmod(self):
x = KerasTensor((None, 3))
y = KerasTensor((2, None))
self.assertEqual(knp.fmod(x, y).shape, (2, 3))

def test_nextafter(self):
x = KerasTensor((None, 3))
y = KerasTensor((1, 3))
Expand Down Expand Up @@ -1002,6 +1007,19 @@ def test_mod(self):
y = KerasTensor((2, 3, 4))
knp.mod(x, y)

def test_fmod(self):
x = KerasTensor((2, 3))
y = KerasTensor((2, 3))
self.assertEqual(knp.fmod(x, y).shape, (2, 3))

x = KerasTensor((2, 3))
self.assertEqual(knp.fmod(x, 2).shape, (2, 3))

with self.assertRaises(ValueError):
x = KerasTensor((2, 3))
y = KerasTensor((2, 3, 4))
knp.fmod(x, y)

def test_nextafter(self):
x = KerasTensor((2, 3))
y = KerasTensor((2, 3))
Expand Down Expand Up @@ -3811,6 +3829,17 @@ def test_mod(self):
self.assertAllClose(knp.Mod()(x, 1), np.mod(x, 1))
self.assertAllClose(knp.Mod()(1, x), np.mod(1, x))

def test_fmod(self):
x = np.array([[-3, 7], [5, -2]])
y = np.array([[2, -3], [3, 4]])
self.assertAllClose(knp.fmod(x, y), np.fmod(x, y))
self.assertAllClose(knp.fmod(x, 2), np.fmod(x, 2))
self.assertAllClose(knp.fmod(1, x), np.fmod(1, x))

self.assertAllClose(knp.Fmod()(x, y), np.fmod(x, y))
self.assertAllClose(knp.Fmod()(x, 2), np.fmod(x, 2))
self.assertAllClose(knp.Fmod()(1, x), np.fmod(1, x))

def test_nextafter(self):
x = np.array([[1, 2, 3], [3, 2, 1]])
y = np.array([[4, 5, 6], [3, 2, 1]])
Expand Down Expand Up @@ -9520,6 +9549,27 @@ def test_mod(self, dtypes):
expected_dtype,
)

@parameterized.named_parameters(
named_product(dtypes=itertools.combinations(ALL_DTYPES, 2))
)
def test_fmod(self, dtypes):
import jax.numpy as jnp

dtype1, dtype2 = dtypes
x1 = knp.ones((), dtype=dtype1)
x2 = knp.ones((), dtype=dtype2)
x1_jax = jnp.ones((), dtype=dtype1)
x2_jax = jnp.ones((), dtype=dtype2)
expected_dtype = standardize_dtype(jnp.fmod(x1_jax, x2_jax).dtype)

self.assertEqual(
standardize_dtype(knp.fmod(x1, x2).dtype), expected_dtype
)
self.assertEqual(
standardize_dtype(knp.Fmod().symbolic_call(x1, x2).dtype),
expected_dtype,
)

@parameterized.named_parameters(named_product(dtype=ALL_DTYPES))
def test_moveaxis(self, dtype):
import jax.numpy as jnp
Expand Down