Add meshgrid op support in Keras OpenVINO backend

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -43,7 +43,6 @@ NumpyDtypeTest::test_matmul_
 NumpyDtypeTest::test_max
 NumpyDtypeTest::test_mean
 NumpyDtypeTest::test_median
-NumpyDtypeTest::test_meshgrid
 NumpyDtypeTest::test_minimum_python_types
 NumpyDtypeTest::test_multiply
 NumpyDtypeTest::test_power
@@ -100,7 +99,6 @@ NumpyOneInputOpsCorrectnessTest::test_logaddexp
 NumpyOneInputOpsCorrectnessTest::test_max
 NumpyOneInputOpsCorrectnessTest::test_mean
 NumpyOneInputOpsCorrectnessTest::test_median
-NumpyOneInputOpsCorrectnessTest::test_meshgrid
 NumpyOneInputOpsCorrectnessTest::test_pad_float16_constant_2
 NumpyOneInputOpsCorrectnessTest::test_pad_float32_constant_2
 NumpyOneInputOpsCorrectnessTest::test_pad_float64_constant_2

keras/src/backend/openvino/numpy.py

@@ -1097,9 +1097,47 @@ def median(x, axis=None, keepdims=False):
 
 
 def meshgrid(*x, indexing="xy"):
-    raise NotImplementedError(
-        "`meshgrid` is not supported with openvino backend"
-    )
+    if len(x) < 2:
+        raise ValueError(
+            "meshgrid requires at least 2 input arrays. "
+            f"Received: {len(x)} input array(s)."
+        )
+    if indexing not in ("xy", "ij"):
+        raise ValueError("indexing must be either 'xy' or 'ij'")
+
+    tensors = [get_ov_output(xi) for xi in x]
+    n = len(tensors)
+
+    shapes = [
+        ov_opset.shape_of(t, Type.i64).output(0) for t in tensors
+    ]  # each is [Ni]
+    one = ov_opset.constant([1], Type.i64).output(0)
+
+    if indexing == "xy":
+        shape_list = [shapes[1], shapes[0]] + shapes[2:]
+        out_shape = ov_opset.concat(shape_list, axis=0).output(0)
+    else:
+        out_shape = ov_opset.concat(shapes, axis=0).output(0)
+
+    outputs = []
+    for i, t in enumerate(tensors):
+        reshape_parts = [one] * n
+        if indexing == "xy":
+            if i == 0:
+                reshape_parts[1] = shapes[0]
+            elif i == 1:
+                reshape_parts[0] = shapes[1]
+            else:
+                reshape_parts[i] = shapes[i]
+        else:
+            reshape_parts[i] = shapes[i]
+
+        reshape_shape = ov_opset.concat(reshape_parts, axis=0).output(0)
+        reshaped = ov_opset.reshape(t, reshape_shape, False).output(0)
+        broadcasted = ov_opset.broadcast(reshaped, out_shape).output(0)
+        outputs.append(OpenVINOKerasTensor(broadcasted))
+
+    return outputs
 
 
 def min(x, axis=None, keepdims=False, initial=None):