Add sinc to keras.ops.numpy (#22365)

rstar327 · web-flow · commit e65cfb8fc0e5 · 2026-03-10T20:40:39.000-07:00
* feat(ops): add sinc to keras.ops.numpy

* style(ops): fix api init formatting for api_gen compatibility

* fix(ops): add sinc to keras.ops exports and fix api formatting

* style(ops): sync api init files with api_gen output

* style: regenerate api files with api_gen

* style: fix import sorting in api files
diff --git a/keras/api/_tf_keras/keras/ops/__init__.py b/keras/api/_tf_keras/keras/ops/__init__.py
@@ -290,6 +290,7 @@
 from keras.src.ops.numpy import sign as sign
 from keras.src.ops.numpy import signbit as signbit
 from keras.src.ops.numpy import sin as sin
+from keras.src.ops.numpy import sinc as sinc
 from keras.src.ops.numpy import sinh as sinh
 from keras.src.ops.numpy import size as size
 from keras.src.ops.numpy import slogdet as slogdet
diff --git a/keras/api/_tf_keras/keras/ops/numpy/__init__.py b/keras/api/_tf_keras/keras/ops/numpy/__init__.py
@@ -171,6 +171,7 @@
 from keras.src.ops.numpy import sign as sign
 from keras.src.ops.numpy import signbit as signbit
 from keras.src.ops.numpy import sin as sin
+from keras.src.ops.numpy import sinc as sinc
 from keras.src.ops.numpy import sinh as sinh
 from keras.src.ops.numpy import size as size
 from keras.src.ops.numpy import slogdet as slogdet
diff --git a/keras/api/ops/__init__.py b/keras/api/ops/__init__.py
@@ -290,6 +290,7 @@
 from keras.src.ops.numpy import sign as sign
 from keras.src.ops.numpy import signbit as signbit
 from keras.src.ops.numpy import sin as sin
+from keras.src.ops.numpy import sinc as sinc
 from keras.src.ops.numpy import sinh as sinh
 from keras.src.ops.numpy import size as size
 from keras.src.ops.numpy import slogdet as slogdet
diff --git a/keras/api/ops/numpy/__init__.py b/keras/api/ops/numpy/__init__.py
@@ -171,6 +171,7 @@
 from keras.src.ops.numpy import sign as sign
 from keras.src.ops.numpy import signbit as signbit
 from keras.src.ops.numpy import sin as sin
+from keras.src.ops.numpy import sinc as sinc
 from keras.src.ops.numpy import sinh as sinh
 from keras.src.ops.numpy import size as size
 from keras.src.ops.numpy import slogdet as slogdet
diff --git a/keras/src/backend/jax/numpy.py b/keras/src/backend/jax/numpy.py
@@ -1249,6 +1249,16 @@ def sin(x):
     return jnp.sin(x)
 
 
+def sinc(x):
+    x = convert_to_tensor(x)
+    if standardize_dtype(x.dtype) == "int64":
+        dtype = config.floatx()
+    else:
+        dtype = dtypes.result_type(x.dtype, float)
+    x = cast(x, dtype)
+    return jnp.sinc(x)
+
+
 @sparse.elementwise_unary(linear=False)
 def sinh(x):
     x = convert_to_tensor(x)
diff --git a/keras/src/backend/numpy/numpy.py b/keras/src/backend/numpy/numpy.py
@@ -1233,6 +1233,16 @@ def sin(x):
     return np.sin(x)
 
 
+def sinc(x):
+    x = convert_to_tensor(x)
+    if standardize_dtype(x.dtype) == "int64":
+        dtype = config.floatx()
+    else:
+        dtype = dtypes.result_type(x.dtype, float)
+    x = x.astype(dtype)
+    return np.sinc(x).astype(dtype)
+
+
 def sinh(x):
     x = convert_to_tensor(x)
     if standardize_dtype(x.dtype) == "int64":
diff --git a/keras/src/backend/openvino/excluded_concrete_tests.txt b/keras/src/backend/openvino/excluded_concrete_tests.txt
@@ -357,6 +357,7 @@ NumpyDtypeTest::test_minimum_python_types
 NumpyDtypeTest::test_nanargmax
 NumpyDtypeTest::test_nanargmin
 NumpyDtypeTest::test_power
+NumpyDtypeTest::test_sinc
 NumpyDtypeTest::test_view
 NumpyOneInputOpsCorrectnessTest::test_array
 NumpyOneInputOpsCorrectnessTest::test_conj
@@ -366,10 +367,13 @@ NumpyOneInputOpsCorrectnessTest::test_nanargmax
 NumpyOneInputOpsCorrectnessTest::test_nanargmin
 NumpyOneInputOpsCorrectnessTest::test_real
 NumpyOneInputOpsCorrectnessTest::test_reshape
+NumpyOneInputOpsCorrectnessTest::test_sinc
 NumpyOneInputOpsCorrectnessTest::test_slogdet
 NumpyOneInputOpsCorrectnessTest::test_vectorize
 NumpyOneInputOpsCorrectnessTest::test_view
+NumpyOneInputOpsDynamicShapeTest::test_sinc
 NumpyOneInputOpsDynamicShapeTest::test_view
+NumpyOneInputOpsStaticShapeTest::test_sinc
 NumpyOneInputOpsStaticShapeTest::test_view
 OptimizerTest::test_constraints_are_applied
 OptimizerTest::test_ema
diff --git a/keras/src/backend/openvino/numpy.py b/keras/src/backend/openvino/numpy.py
@@ -3556,6 +3556,12 @@ def sin(x):
     return OpenVINOKerasTensor(ov_opset.sin(x).output(0))
 
 
+def sinc(x):
+    raise NotImplementedError(
+        "`sinc` is not supported with the OpenVINO backend."
+    )
+
+
 def sinh(x):
     x = get_ov_output(x)
     x_type = x.get_element_type()
diff --git a/keras/src/backend/tensorflow/numpy.py b/keras/src/backend/tensorflow/numpy.py
@@ -2707,6 +2707,21 @@ def sin(x):
     return tf.math.sin(x)
 
 
+def sinc(x):
+    x = convert_to_tensor(x)
+    if standardize_dtype(x.dtype) == "int64":
+        dtype = config.floatx()
+    else:
+        dtype = dtypes.result_type(x.dtype, float)
+    x = tf.cast(x, dtype)
+    pi_x = x * tf.constant(np.pi, dtype=x.dtype)
+    return tf.where(
+        tf.equal(x, 0),
+        tf.ones_like(x),
+        tf.math.sin(pi_x) / pi_x,
+    )
+
+
 @sparse.elementwise_unary
 def sinh(x):
     x = convert_to_tensor(x)
diff --git a/keras/src/backend/torch/numpy.py b/keras/src/backend/torch/numpy.py
@@ -1721,6 +1721,11 @@ def sin(x):
     return torch.sin(x)
 
 
+def sinc(x):
+    x = convert_to_tensor(x)
+    return torch.sinc(x)
+
+
 def sinh(x):
     x = convert_to_tensor(x)
     return torch.sinh(x)
diff --git a/keras/src/ops/numpy.py b/keras/src/ops/numpy.py
@@ -6923,6 +6923,43 @@ def sin(x):
     return backend.numpy.sin(x)
 
 
+class Sinc(Operation):
+    def call(self, x):
+        return backend.numpy.sinc(x)
+
+    def compute_output_spec(self, x):
+        dtype = backend.standardize_dtype(getattr(x, "dtype", backend.floatx()))
+        if dtype == "int64":
+            dtype = backend.floatx()
+        else:
+            dtype = dtypes.result_type(dtype, float)
+        return KerasTensor(x.shape, dtype=dtype)
+
+
+@keras_export(["keras.ops.sinc", "keras.ops.numpy.sinc"])
+def sinc(x):
+    """Return the normalized sinc function.
+
+    The sinc function is equal to `sin(pi*x) / (pi*x)` for any argument
+    `x != 0`, and `sinc(0)` takes the limit value 1, making `sinc` not
+    just everywhere continuous but also infinitely differentiable.
+
+    Arguments:
+        x: Input tensor.
+
+    Returns:
+        Output tensor of same shape as `x`.
+
+    Examples:
+    >>> x = keras.ops.convert_to_tensor([0.0, 1.0, 2.0])
+    >>> keras.ops.sinc(x)
+    array([1., 0., 0.], dtype=float32)
+    """
+    if any_symbolic_tensors((x,)):
+        return Sinc().symbolic_call(x)
+    return backend.numpy.sinc(x)
+
+
 class Sinh(Operation):
     def call(self, x):
         return backend.numpy.sinh(x)
diff --git a/keras/src/ops/numpy_test.py b/keras/src/ops/numpy_test.py
@@ -2036,6 +2036,10 @@ def test_sin(self):
         x = KerasTensor((None, 3))
         self.assertEqual(knp.sin(x).shape, (None, 3))
 
+    def test_sinc(self):
+        x = KerasTensor((None, 3))
+        self.assertEqual(knp.sinc(x).shape, (None, 3))
+
     def test_sinh(self):
         x = KerasTensor((None, 3))
         self.assertEqual(knp.sinh(x).shape, (None, 3))
@@ -2789,6 +2793,10 @@ def test_sin(self):
         x = KerasTensor((2, 3))
         self.assertEqual(knp.sin(x).shape, (2, 3))
 
+    def test_sinc(self):
+        x = KerasTensor((2, 3))
+        self.assertEqual(knp.sinc(x).shape, (2, 3))
+
     def test_sinh(self):
         x = KerasTensor((2, 3))
         self.assertEqual(knp.sinh(x).shape, (2, 3))
@@ -5534,6 +5542,11 @@ def test_sin(self):
         self.assertAllClose(knp.sin(x), np.sin(x))
         self.assertAllClose(knp.Sin()(x), np.sin(x))
 
+    def test_sinc(self):
+        x = np.array([[0, 1, -1], [0.5, -0.5, 2]])
+        self.assertAllClose(knp.sinc(x), np.sinc(x))
+        self.assertAllClose(knp.Sinc()(x), np.sinc(x))
+
     def test_sinh(self):
         x = np.array([[1, -2, 3], [-3, 2, -1]])
         self.assertAllClose(knp.sinh(x), np.sinh(x))
@@ -10217,6 +10230,22 @@ def test_sin(self, dtype):
             expected_dtype,
         )
 
+    @parameterized.named_parameters(named_product(dtype=ALL_DTYPES))
+    def test_sinc(self, dtype):
+        import jax.numpy as jnp
+
+        x = knp.ones((1,), dtype=dtype)
+        x_jax = jnp.ones((1,), dtype=dtype)
+        expected_dtype = standardize_dtype(jnp.sinc(x_jax).dtype)
+        if dtype == "int64":
+            expected_dtype = backend.floatx()
+
+        self.assertEqual(standardize_dtype(knp.sinc(x).dtype), expected_dtype)
+        self.assertEqual(
+            standardize_dtype(knp.Sinc().symbolic_call(x).dtype),
+            expected_dtype,
+        )
+
     @parameterized.named_parameters(named_product(dtype=ALL_DTYPES))
     def test_sinh(self, dtype):
         import jax.numpy as jnp
