Custom where_Scalar op (#14470)

Summary:

Continued support of custom cadence ops

Reviewed By: hsharma35

Differential Revision: D82703256

Author: Andrew Grebenisan

backends/cadence/aot/ref_implementations.py

@@ -1092,3 +1092,15 @@ def rms_norm(
     eps: float,
 ) -> torch.Tensor:
     return W * nn.RMSNorm(list(normalized_shape), eps=eps, dtype=X.dtype)(X)
+
+
+@impl(m, "where_Scalar")
+def where_Scalar(
+    condition: torch.Tensor,
+    if_true: float,
+    if_false: float,
+) -> torch.Tensor:
+    if condition.dtype != torch.bool:
+        raise ValueError("condition must be a bool tensor")
+
+    return torch.where(condition, if_true, if_false)

backends/cadence/aot/tests/test_ref_implementations.py

@@ -1145,3 +1145,14 @@ def test_quantized_relu(
             torch.equal(output, expected_output),
             f"Output values don't match expected in {name}. Got {output}, expected {expected_output}",
         )
+
+    def test_where_Scalar(self) -> None:
+        input_tensor = torch.tensor([1, 2, 3, 4], dtype=torch.int8)
+        out = torch.ops.cadence.where_Scalar(input_tensor > 2, 1.0, 0.0)
+        self.assertTrue(
+            torch.equal(out, torch.tensor([0.0, 0.0, 1.0, 1.0], dtype=torch.float32))
+        )
+        with self.assertRaises(ValueError) as context:
+            torch.ops.cadence.where_Scalar(input_tensor, 1.0, 0.0)
+
+        self.assertIn("condition must be a bool tensor", str(context.exception))