Add numpy_test for ldexp

Author: shashaka

keras/src/backend/jax/numpy.py

@@ -848,6 +848,13 @@ def lcm(x1, x2):
 def ldexp(x1, x2):
     x1 = convert_to_tensor(x1)
     x2 = convert_to_tensor(x2)
+
+    if standardize_dtype(x2.dtype) not in dtypes.INT_TYPES:
+        raise TypeError(
+            f"ldexp exponent must be an integer type. "
+            f"Received: x2 dtype={x2.dtype}"
+        )
+
     return jnp.ldexp(x1, x2)
 
 

keras/src/backend/numpy/numpy.py

@@ -776,7 +776,16 @@ def lcm(x1, x2):
 def ldexp(x1, x2):
     x1 = convert_to_tensor(x1)
     x2 = convert_to_tensor(x2)
-    dtype = dtypes.result_type(x1.dtype, x2.dtype)
+    dtype = dtypes.result_type(x1.dtype, x2.dtype, float)
+
+    if standardize_dtype(x2.dtype) not in dtypes.INT_TYPES:
+        raise TypeError(
+            f"ldexp exponent must be an integer type. "
+            f"Received: x2 dtype={x2.dtype}"
+        )
+
+    x1 = np.asarray(x1).astype(np.float32)
+    x2 = np.asarray(x2).astype(np.int32)
     return np.ldexp(x1, x2).astype(dtype)
 
 

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -29,6 +29,7 @@ NumpyDtypeTest::test_isin
 NumpyDtypeTest::test_isreal
 NumpyDtypeTest::test_kron
 NumpyDtypeTest::test_lcm
+NumpyDtypeTest::test_ldexp
 NumpyDtypeTest::test_logaddexp2
 NumpyDtypeTest::test_matmul_
 NumpyDtypeTest::test_maximum_python_types
@@ -108,6 +109,7 @@ NumpyTwoInputOpsCorrectnessTest::test_inner
 NumpyTwoInputOpsCorrectnessTest::test_isin
 NumpyTwoInputOpsCorrectnessTest::test_kron
 NumpyTwoInputOpsCorrectnessTest::test_lcm
+NumpyTwoInputOpsCorrectnessTest::test_ldexp
 NumpyTwoInputOpsCorrectnessTest::test_quantile
 NumpyTwoInputOpsCorrectnessTest::test_tensordot
 NumpyTwoInputOpsCorrectnessTest::test_vdot
@@ -131,11 +133,13 @@ NumpyTwoInputOpsDynamicShapeTest::test_hypot
 NumpyTwoInputOpsDynamicShapeTest::test_isin
 NumpyTwoInputOpsDynamicShapeTest::test_kron
 NumpyTwoInputOpsDynamicShapeTest::test_lcm
+NumpyTwoInputOpsDynamicShapeTest::test_ldexp
 NumpyTwoInputOpsStaticShapeTest::test_gcd
 NumpyTwoInputOpsStaticShapeTest::test_hypot
 NumpyTwoInputOpsStaticShapeTest::test_isin
 NumpyTwoInputOpsStaticShapeTest::test_kron
 NumpyTwoInputOpsStaticShapeTest::test_lcm
+NumpyTwoInputOpsStaticShapeTest::test_ldexp
 CoreOpsBehaviorTests::test_associative_scan_invalid_arguments
 CoreOpsBehaviorTests::test_scan_invalid_arguments
 CoreOpsCallsTests::test_associative_scan_basic_call

keras/src/backend/openvino/numpy.py

@@ -1149,6 +1149,10 @@ def lcm(x1, x2):
     raise NotImplementedError("`lcm` is not supported with openvino backend")
 
 
+def ldexp(x1, x2):
+    raise NotImplementedError("`ldexp` is not supported with openvino backend")
+
+
 def less(x1, x2):
     element_type = None
     if isinstance(x1, OpenVINOKerasTensor):

keras/src/backend/tensorflow/numpy.py

@@ -1847,7 +1847,14 @@ def lcm(x1, x2):
 def ldexp(x1, x2):
     x1 = convert_to_tensor(x1)
     x2 = convert_to_tensor(x2)
-    dtype = dtypes.result_type(x1.dtype, x2.dtype)
+    dtype = dtypes.result_type(x1.dtype, x2.dtype, float)
+
+    if standardize_dtype(x2.dtype) not in dtypes.INT_TYPES:
+        raise TypeError(
+            f"ldexp exponent must be an integer type. "
+            f"Received: x2 dtype={x2.dtype}"
+        )
+
     x1 = tf.cast(x1, tf.float32)
     x2 = tf.cast(x2, tf.float32)
     return tf.cast(x1 * tf.pow(2.0, x2), dtype)

keras/src/backend/torch/numpy.py

@@ -978,7 +978,15 @@ def lcm(x1, x2):
 def ldexp(x1, x2):
     x1 = convert_to_tensor(x1)
     x2 = convert_to_tensor(x2)
-    return torch.ldexp(x1, x2)
+    dtype = dtypes.result_type(x1.dtype, x2.dtype, float)
+
+    if standardize_dtype(x2.dtype) not in dtypes.INT_TYPES:
+        raise TypeError(
+            f"ldexp exponent must be an integer type. "
+            f"Received: x2 dtype={x2.dtype}"
+        )
+
+    return cast(torch.ldexp(x1, x2), dtype)
 
 
 def less(x1, x2):

keras/src/ops/numpy.py

@@ -4070,7 +4070,7 @@ def compute_output_spec(self, x1, x2):
 
         x1_type = backend.standardize_dtype(getattr(x1, "dtype", type(x1)))
         x2_type = backend.standardize_dtype(getattr(x2, "dtype", type(x2)))
-        dtype = dtypes.result_type(x1_type, x2_type)
+        dtype = dtypes.result_type(x1_type, x2_type, float)
         return KerasTensor(output_shape, dtype=dtype)
 
 
@@ -4081,19 +4081,12 @@ def ldexp(x1, x2):
     This function computes:
         ldexp(x1, x2) = x1 * 2**x2
 
-    Notes:
-    - TensorFlow does *not* provide a built-in `tf.math.ldexp`.
-    - The `backend.numpy.ldexp` implementation in TF NumPy is currently
-      unreliable due to a naming-collision bug and should not be used.
-    - This implementation provides correct broadcasting, dtype inference,
-      and gradient support.
-
     Args:
-        x1: Floating-point input tensor.
-        x2: Integer or floating-point exponent tensor.
+        x1: Float input tensor.
+        x2: Integer exponent tensor.
 
     Returns:
-        Output tensor, element-wise equal to `x1 * 2**x2`.
+        Output tensor
 
     Example:
     >>> x1 = keras.ops.convert_to_tensor([0.75, 1.5])

keras/src/ops/numpy_test.py

@@ -254,6 +254,11 @@ def test_lcm(self):
         y = KerasTensor((2, None))
         self.assertEqual(knp.lcm(x, y).shape, (2, 3))
 
+    def test_ldexp(self):
+        x = KerasTensor((None, 3))
+        y = KerasTensor((1, 3))
+        self.assertEqual(knp.ldexp(x, y).shape, (None, 3))
+
     def test_less(self):
         x = KerasTensor((None, 3))
         y = KerasTensor((2, None))
@@ -837,6 +842,15 @@ def test_lcm(self):
         y = KerasTensor((2, 3))
         self.assertEqual(knp.lcm(x, y).shape, (2, 3))
 
+    def test_ldexp(self):
+        x = KerasTensor((2, 3))
+        y = KerasTensor((2, 3))
+        self.assertEqual(knp.ldexp(x, y).shape, (2, 3))
+
+        x = KerasTensor((2, 3))
+        y = KerasTensor((1, 3))
+        self.assertEqual(knp.ldexp(x, y).shape, (2, 3))
+
     def test_less(self):
         x = KerasTensor((2, 3))
         y = KerasTensor((2, 3))
@@ -3114,6 +3128,12 @@ def test_lcm(self):
         self.assertAllClose(knp.lcm(x, y), np.lcm(x, y))
         self.assertAllClose(knp.Lcm()(x, y), np.lcm(x, y))
 
+    def test_ldexp(self):
+        x = np.array([[1, 2, 3], [3, 2, 1]])
+        y = np.array([[4, 5, 6], [3, 2, 1]])
+        self.assertAllClose(knp.ldexp(x, y), np.ldexp(x, y))
+        self.assertAllClose(knp.Ldexp()(x, y), np.ldexp(x, y))
+
     def test_less(self):
         x = np.array([[1, 2, 3], [3, 2, 1]])
         y = np.array([[4, 5, 6], [3, 2, 1]])
@@ -7884,6 +7904,27 @@ def test_lcm(self, dtypes):
             expected_dtype,
         )
 
+    @parameterized.named_parameters(
+        named_product(dtypes=list(itertools.product(ALL_DTYPES, INT_DTYPES)))
+    )
+    def test_ldexp(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.ldexp(x1_jax, x2_jax).dtype)
+
+        self.assertEqual(
+            standardize_dtype(knp.ldexp(x1, x2).dtype), expected_dtype
+        )
+        self.assertEqual(
+            standardize_dtype(knp.Ldexp().symbolic_call(x1, x2).dtype),
+            expected_dtype,
+        )
+
     @parameterized.named_parameters(
         named_product(dtypes=itertools.combinations(ALL_DTYPES, 2))
     )