My fix branch

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -86,7 +86,6 @@ NumpyOneInputOpsCorrectnessTest::test_corrcoef
 NumpyOneInputOpsCorrectnessTest::test_correlate
 NumpyOneInputOpsCorrectnessTest::test_cumprod
 NumpyOneInputOpsCorrectnessTest::test_diag
-NumpyOneInputOpsCorrectnessTest::test_diagonal
 NumpyOneInputOpsCorrectnessTest::test_exp2
 NumpyOneInputOpsCorrectnessTest::test_flip
 NumpyOneInputOpsCorrectnessTest::test_floor_divide

keras/src/backend/openvino/numpy.py

@@ -1,6 +1,7 @@
 import numpy as np
 import openvino.opset14 as ov_opset
 from openvino import Type
+from openvino.runtime import opset13 as ov
 
 from keras.src.backend import config
 from keras.src.backend.common import dtypes
@@ -17,6 +18,109 @@
 from keras.src.backend.openvino.core import ov_to_keras_type
 
 
+def diagonal(x, offset=0, axis1=0, axis2=1):
+    x_node = ov.constant(x)  # -> ov.Node
+    offset_const = ov_opset.constant(int(offset), dtype="i64")
+
+    # rank & normalize axes
+    shape = ov_opset.shape_of(x_node)  # i64 vector
+    rank = ov_opset.shape_of(shape)  # scalar i64 (len of shape)
+    rank_val = ov_opset.squeeze(rank)  # [] -> scalar
+    axis1_node = ov_opset.floor_mod(
+        ov_opset.add(ov_opset.constant(int(axis1), dtype="i64"), rank_val),
+        rank_val,
+    )
+    axis2_node = ov_opset.floor_mod(
+        ov_opset.add(ov_opset.constant(int(axis2), dtype="i64"), rank_val),
+        rank_val,
+    )
+
+    arange = ov_opset.range(
+        ov_opset.constant(0, dtype="i64"),
+        rank_val,
+        ov_opset.constant(1, dtype="i64"),
+    )
+    mask1 = ov_opset.equal(arange, axis1_node)
+    mask2 = ov_opset.equal(arange, axis2_node)
+    not12 = ov_opset.logical_not(ov_opset.logical_or(mask1, mask2))
+    others = ov_opset.squeeze(
+        ov_opset.non_zero(not12), [1]
+    )  # gather positions != axis1, axis2
+    perm = ov_opset.concat(
+        [
+            others,
+            ov_opset.reshape(axis1_node, [1]),
+            ov_opset.reshape(axis2_node, [1]),
+        ],
+        0,
+    )
+
+    x_perm = ov_opset.transpose(x_node, perm)
+    permuted_shape = ov_opset.shape_of(x_perm)
+    d1 = ov_opset.gather(
+        permuted_shape,
+        ov_opset.constant([-2], dtype="i64"),
+        ov_opset.constant(0, dtype="i64"),
+    )
+    d2 = ov_opset.gather(
+        permuted_shape,
+        ov_opset.constant([-1], dtype="i64"),
+        ov_opset.constant(0, dtype="i64"),
+    )
+    d1 = ov_opset.squeeze(d1)  # scalar
+    d2 = ov_opset.squeeze(d2)  # scalar
+
+    # start1 = max(0, offset), start2 = max(0, -offset)
+    zero = ov_opset.constant(0, dtype="i64")
+    start1 = ov_opset.maximum(zero, offset_const)
+    start2 = ov_opset.maximum(zero, ov_opset.negative(offset_const))
+
+    # L = min(d1 - start1, d2 - start2)
+    l1 = ov_opset.subtract(d1, start1)
+    l2 = ov_opset.subtract(d2, start2)
+    L = ov_opset.minimum(l1, l2)
+
+    # r = range(0, L, 1)  -> shape [L]
+    r = ov_opset.range(zero, L, ov_opset.constant(1, dtype="i64"))
+    idx_row = ov_opset.add(r, start1)
+    idx_col = ov_opset.add(r, start2)
+    idx_row = ov_opset.unsqueeze(
+        idx_row, ov_opset.constant(1, dtype="i64")
+    )  # [L,1]
+    idx_col = ov_opset.unsqueeze(
+        idx_col, ov_opset.constant(1, dtype="i64")
+    )  # [L,1]
+    diag_idx = ov_opset.concat([idx_row, idx_col], 1)  # [L,2]
+
+    # Broadcast indices to batch dims: target shape = (*batch, L, 2)
+    # batch_rank = rank(x) - 2
+    two = ov_opset.constant(2, dtype="i64")
+    batch_rank = ov_opset.subtract(rank_val, two)
+    # build target shape: concat(permuted_shape[:batch_rank], [L, 2])
+    batch_shape = ov_opset.strided_slice(
+        permuted_shape,
+        begin=ov_opset.constant([0], dtype="i64"),
+        end=ov_opset.reshape(batch_rank, [1]),
+        strides=ov_opset.constant([1], dtype="i64"),
+        begin_mask=[0],
+        end_mask=[0],
+    )
+    target_shape = ov_opset.concat(
+        [
+            batch_shape,
+            ov_opset.reshape(L, [1]),
+            ov_opset.constant([2], dtype="i64"),
+        ],
+        0,
+    )
+    bcast_idx = ov_opset.broadcast(diag_idx, target_shape)
+
+    # GatherND with batch_dims = batch_rank
+    gathered = ov_opset.gather_nd(x_perm, bcast_idx, batch_rank)
+
+    return OpenVINOKerasTensor(gathered)
+
+
 def add(x1, x2):
     element_type = None
     if isinstance(x1, OpenVINOKerasTensor):
@@ -677,12 +781,6 @@ def diag(x, k=0):
     raise NotImplementedError("`diag` is not supported with openvino backend")
 
 
-def diagonal(x, offset=0, axis1=0, axis2=1):
-    raise NotImplementedError(
-        "`diagonal` is not supported with openvino backend"
-    )
-
-
 def diff(a, n=1, axis=-1):
     if n == 0:
         return OpenVINOKerasTensor(get_ov_output(a))