Implement cbrt function in keras.ops

keras/api/_tf_keras/keras/ops/__init__.py

@@ -149,6 +149,7 @@
 from keras.src.ops.numpy import bitwise_xor as bitwise_xor
 from keras.src.ops.numpy import blackman as blackman
 from keras.src.ops.numpy import broadcast_to as broadcast_to
+from keras.src.ops.numpy import cbrt as cbrt
 from keras.src.ops.numpy import ceil as ceil
 from keras.src.ops.numpy import clip as clip
 from keras.src.ops.numpy import concatenate as concatenate

keras/api/_tf_keras/keras/ops/numpy/__init__.py

@@ -38,6 +38,7 @@
 from keras.src.ops.numpy import bitwise_xor as bitwise_xor
 from keras.src.ops.numpy import blackman as blackman
 from keras.src.ops.numpy import broadcast_to as broadcast_to
+from keras.src.ops.numpy import cbrt as cbrt
 from keras.src.ops.numpy import ceil as ceil
 from keras.src.ops.numpy import clip as clip
 from keras.src.ops.numpy import concatenate as concatenate

keras/api/ops/__init__.py

@@ -149,6 +149,7 @@
 from keras.src.ops.numpy import bitwise_xor as bitwise_xor
 from keras.src.ops.numpy import blackman as blackman
 from keras.src.ops.numpy import broadcast_to as broadcast_to
+from keras.src.ops.numpy import cbrt as cbrt
 from keras.src.ops.numpy import ceil as ceil
 from keras.src.ops.numpy import clip as clip
 from keras.src.ops.numpy import concatenate as concatenate

keras/api/ops/numpy/__init__.py

@@ -38,6 +38,7 @@
 from keras.src.ops.numpy import bitwise_xor as bitwise_xor
 from keras.src.ops.numpy import blackman as blackman
 from keras.src.ops.numpy import broadcast_to as broadcast_to
+from keras.src.ops.numpy import cbrt as cbrt
 from keras.src.ops.numpy import ceil as ceil
 from keras.src.ops.numpy import clip as clip
 from keras.src.ops.numpy import concatenate as concatenate

keras/src/backend/jax/numpy.py

@@ -499,6 +499,11 @@ def broadcast_to(x, shape):
     return jnp.broadcast_to(x, shape)
 
 
+def cbrt(x):
+    x = convert_to_tensor(x)
+    return jnp.cbrt(x)
+
+
 @sparse.elementwise_unary(linear=False)
 def ceil(x):
     x = convert_to_tensor(x)

keras/src/backend/numpy/numpy.py

@@ -414,6 +414,18 @@ def broadcast_to(x, shape):
     return np.broadcast_to(x, shape)
 
 
+def cbrt(x):
+    x = convert_to_tensor(x)
+
+    dtype = standardize_dtype(x.dtype)
+    if dtype in ["bool", "int8", "int16", "int32", "uint8", "uint16", "uint32"]:
+        dtype = config.floatx()
+    elif dtype == "int64":
+        dtype = "float64"
+
+    return np.cbrt(x).astype(dtype)
+
+
 def ceil(x):
     x = convert_to_tensor(x)
     if standardize_dtype(x.dtype) == "int64":

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -14,6 +14,7 @@ NumpyDtypeTest::test_hamming
 NumpyDtypeTest::test_hanning
 NumpyDtypeTest::test_kaiser
 NumpyDtypeTest::test_bitwise
+NumpyDtypeTest::test_cbrt
 NumpyDtypeTest::test_ceil
 NumpyDtypeTest::test_concatenate
 NumpyDtypeTest::test_corrcoef
@@ -81,6 +82,7 @@ NumpyOneInputOpsCorrectnessTest::test_hamming
 NumpyOneInputOpsCorrectnessTest::test_hanning
 NumpyOneInputOpsCorrectnessTest::test_kaiser
 NumpyOneInputOpsCorrectnessTest::test_bitwise_invert
+NumpyOneInputOpsCorrectnessTest::test_cbrt
 NumpyOneInputOpsCorrectnessTest::test_conj
 NumpyOneInputOpsCorrectnessTest::test_corrcoef
 NumpyOneInputOpsCorrectnessTest::test_correlate
@@ -153,12 +155,14 @@ NumpyTwoInputOpsCorrectnessTest::test_vdot
 NumpyOneInputOpsDynamicShapeTest::test_angle
 NumpyOneInputOpsDynamicShapeTest::test_bartlett
 NumpyOneInputOpsDynamicShapeTest::test_blackman
+NumpyOneInputOpsDynamicShapeTest::test_cbrt
 NumpyOneInputOpsDynamicShapeTest::test_corrcoef
 NumpyOneInputOpsDynamicShapeTest::test_deg2rad
 NumpyOneInputOpsDynamicShapeTest::test_hamming
 NumpyOneInputOpsDynamicShapeTest::test_hanning
 NumpyOneInputOpsDynamicShapeTest::test_kaiser
 NumpyOneInputOpsStaticShapeTest::test_angle
+NumpyOneInputOpsStaticShapeTest::test_cbrt
 NumpyOneInputOpsStaticShapeTest::test_deg2rad
 CoreOpsBehaviorTests::test_associative_scan_invalid_arguments
 CoreOpsBehaviorTests::test_scan_invalid_arguments

keras/src/backend/openvino/numpy.py

@@ -545,6 +545,10 @@ def broadcast_to(x, shape):
     return OpenVINOKerasTensor(ov_opset.broadcast(x, target_shape).output(0))
 
 
+def cbrt(x):
+    raise NotImplementedError("`cbrt` is not supported with openvino backend")
+
+
 def ceil(x):
     x = get_ov_output(x)
     return OpenVINOKerasTensor(ov_opset.ceil(x).output(0))

keras/src/backend/tensorflow/numpy.py

@@ -1089,6 +1089,18 @@ def broadcast_to(x, shape):
     return tf.broadcast_to(x, shape)
 
 
+def cbrt(x):
+    x = convert_to_tensor(x)
+
+    dtype = standardize_dtype(x.dtype)
+    if dtype == "int64":
+        x = tf.cast(x, "float64")
+    elif dtype not in ["bfloat16", "float16", "float64"]:
+        x = tf.cast(x, config.floatx())
+
+    return tf.sign(x) * tf.pow(tf.abs(x), 1.0 / 3.0)
+
+
 @sparse.elementwise_unary
 def ceil(x):
     x = convert_to_tensor(x)

keras/src/backend/torch/numpy.py

@@ -540,6 +540,18 @@ def broadcast_to(x, shape):
     return torch.broadcast_to(x, shape)
 
 
+def cbrt(x):
+    x = convert_to_tensor(x)
+
+    dtype = standardize_dtype(x.dtype)
+    if dtype == "bool":
+        x = cast(x, "int32")
+    elif dtype == "int64":
+        x = cast(x, "float64")
+
+    return torch.sign(x) * torch.abs(x) ** (1.0 / 3.0)
+
+
 def ceil(x):
     x = convert_to_tensor(x)
     ori_dtype = standardize_dtype(x.dtype)

keras/src/ops/numpy.py

@@ -1779,6 +1779,29 @@ def broadcast_to(x, shape):
     return backend.numpy.broadcast_to(x, shape)
 
 
+class Cbrt(Operation):
+    def call(self, x):
+        return backend.numpy.cbrt(x)
+
+
+@keras_export(["keras.ops.cbrt", "keras.ops.numpy.cbrt"])
+def cbrt(x):
+    """Computes the cube root of the input tensor, element-wise.
+
+    This operation returns the real-valued cube root of `x`, handling
+    negative numbers properly in the real domain.
+
+    Args:
+        x: Input tensor.
+
+    Returns:
+        A tensor containing the cube root of each element in `x`.
+    """
+    if any_symbolic_tensors((x,)):
+        return Cbrt().symbolic_call(x)
+    return backend.numpy.cbrt(x)
+
+
 class Ceil(Operation):
     def call(self, x):
         return backend.numpy.ceil(x)

keras/src/ops/numpy_test.py

@@ -1255,6 +1255,10 @@ def test_broadcast_to(self):
             x = KerasTensor((3, 3))
             knp.broadcast_to(x, (2, 2, 3))
 
+    def test_cbrt(self):
+        x = KerasTensor((None, 3))
+        self.assertEqual(knp.cbrt(x).shape, (None, 3))
+
     def test_ceil(self):
         x = KerasTensor((None, 3))
         self.assertEqual(knp.ceil(x).shape, (None, 3))
@@ -1875,6 +1879,10 @@ def test_broadcast_to(self):
             x = KerasTensor((3, 3))
             knp.broadcast_to(x, (2, 2, 3))
 
+    def test_cbrt(self):
+        x = KerasTensor((2, 3))
+        self.assertEqual(knp.cbrt(x).shape, (2, 3))
+
     def test_ceil(self):
         x = KerasTensor((2, 3))
         self.assertEqual(knp.ceil(x).shape, (2, 3))
@@ -3737,6 +3745,17 @@ def test_broadcast_to(self):
             np.broadcast_to(x, [2, 2, 3]),
         )
 
+    def test_cbrt(self):
+        x = np.array([[-8, -1, 0], [1, 8, 27]], dtype="float32")
+        ref_y = np.sign(x) * np.abs(x) ** (1.0 / 3.0)
+        y = knp.cbrt(x)
+        self.assertEqual(standardize_dtype(y.dtype), "float32")
+        self.assertAllClose(y, ref_y)
+
+        y = knp.Cbrt()(x)
+        self.assertEqual(standardize_dtype(y.dtype), "float32")
+        self.assertAllClose(y, ref_y)
+
     def test_ceil(self):
         x = np.array([[1.2, 2.1, -2.5], [2.4, -11.9, -5.5]])
         self.assertAllClose(knp.ceil(x), np.ceil(x))
@@ -6479,6 +6498,20 @@ def test_broadcast_to(self, dtype):
             expected_dtype,
         )
 
+    @parameterized.named_parameters(named_product(dtype=ALL_DTYPES))
+    def test_cbrt(self, dtype):
+        import jax.numpy as jnp
+
+        x1 = knp.ones((1,), dtype=dtype)
+        x1_jax = jnp.ones((1,), dtype=dtype)
+        expected_dtype = standardize_dtype(jnp.cbrt(x1_jax).dtype)
+
+        self.assertEqual(standardize_dtype(knp.cbrt(x1).dtype), expected_dtype)
+        self.assertEqual(
+            standardize_dtype(knp.Cbrt().symbolic_call(x1).dtype),
+            expected_dtype,
+        )
+
     @parameterized.named_parameters(named_product(dtype=ALL_DTYPES))
     def test_ceil(self, dtype):
         import jax.numpy as jnp