[OpenVino BackEnd] support np.diff for ov BackEnd

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -18,7 +18,6 @@ NumpyDtypeTest::test_cross
 NumpyDtypeTest::test_cumprod
 NumpyDtypeTest::test_cumsum_bool
 NumpyDtypeTest::test_diag
-NumpyDtypeTest::test_diff
 NumpyDtypeTest::test_digitize
 NumpyDtypeTest::test_einsum
 NumpyDtypeTest::test_empty
@@ -94,7 +93,6 @@ NumpyOneInputOpsCorrectnessTest::test_correlate
 NumpyOneInputOpsCorrectnessTest::test_cumprod
 NumpyOneInputOpsCorrectnessTest::test_diag
 NumpyOneInputOpsCorrectnessTest::test_diagonal
-NumpyOneInputOpsCorrectnessTest::test_diff
 NumpyOneInputOpsCorrectnessTest::test_exp
 NumpyOneInputOpsCorrectnessTest::test_expand_dims
 NumpyOneInputOpsCorrectnessTest::test_exp2

keras/src/backend/openvino/numpy.py

@@ -568,7 +568,76 @@ def diagonal(x, offset=0, axis1=0, axis2=1):
 
 
 def diff(a, n=1, axis=-1):
-    raise NotImplementedError("`diff` is not supported with openvino backend")
+    if n == 0:
+        return OpenVINOKerasTensor(get_ov_output(a))
+    if n < 0:
+        raise ValueError("order must be non-negative but got " + repr(n))
+    a = get_ov_output(a)
+    a_type = a.get_element_type()
+    if isinstance(a, np.ndarray):
+        rank = a.ndim
+    else:
+        rank = a.get_partial_shape().rank.get_length()
+    if axis < 0:
+        axis = axis + rank
+    result = a
+    for _ in range(n):
+        rank = result.get_partial_shape().rank.get_length()
+        strides = ov_opset.constant(
+            np.array([1] * rank, dtype=np.int64), Type.i64
+        ).output(0)
+
+        begin_upper_list = [0] * rank
+        begin_upper_list[axis] = 1
+        begin_upper = ov_opset.constant(
+            np.array(begin_upper_list, dtype=np.int64), Type.i64
+        ).output(0)
+        end_upper = ov_opset.constant(
+            np.array([0] * rank, dtype=np.int64), Type.i64
+        ).output(0)
+        begin_mask_upper = [1] * rank
+        begin_mask_upper[axis] = 0
+        end_mask_upper = [1] * rank
+        upper = ov_opset.strided_slice(
+            data=result,
+            begin=begin_upper,
+            end=end_upper,
+            strides=strides,
+            begin_mask=begin_mask_upper,
+            end_mask=end_mask_upper,
+            new_axis_mask=[],
+            shrink_axis_mask=[],
+            ellipsis_mask=[],
+        ).output(0)
+
+        begin_lower = ov_opset.constant(
+            np.array([0] * rank, dtype=np.int64), Type.i64
+        ).output(0)
+        end_lower_list = [0] * rank
+        end_lower_list[axis] = -1
+        end_lower = ov_opset.constant(
+            np.array(end_lower_list, dtype=np.int64), Type.i64
+        ).output(0)
+        begin_mask_lower = [1] * rank
+        end_mask_lower = [1] * rank
+        end_mask_lower[axis] = 0
+        lower = ov_opset.strided_slice(
+            data=result,
+            begin=begin_lower,
+            end=end_lower,
+            strides=strides,
+            begin_mask=begin_mask_lower,
+            end_mask=end_mask_lower,
+            new_axis_mask=[],
+            shrink_axis_mask=[],
+            ellipsis_mask=[],
+        ).output(0)
+
+        if a_type == Type.boolean:
+            result = ov_opset.not_equal(upper, lower).output(0)
+        else:
+            result = ov_opset.subtract(upper, lower).output(0)
+    return OpenVINOKerasTensor(result)
 
 
 def digitize(x, bins):