Add int8/uint8 specialized variants of quantized_add_per_tensor

backends/cadence/aot/ref_implementations.py

@@ -193,6 +193,48 @@ def quantized_add_per_tensor(
     )
 
 
+@impl(m, "quantized_add_asym8sxasym8s_asym8s.per_tensor")
+def quantized_add_asym8sxasym8s_asym8s_per_tensor(
+    X: torch.Tensor,
+    X_scale: float,
+    X_zero_point: int,
+    Y: torch.Tensor,
+    Y_scale: float,
+    Y_zero_point: int,
+    out_scale: float,
+    out_zero_point: int,
+) -> torch.Tensor:
+    if X.dtype != torch.int8:
+        raise ValueError("X dtype must be torch.int8")
+    if Y.dtype != torch.int8:
+        raise ValueError("Y dtype must be torch.int8")
+
+    return quantized_add_per_tensor(
+        X, X_scale, X_zero_point, Y, Y_scale, Y_zero_point, out_scale, out_zero_point
+    )
+
+
+@impl(m, "quantized_add_asym8uxasym8u_asym8u.per_tensor")
+def quantized_add_asym8uxasym8u_asym8u_per_tensor(
+    X: torch.Tensor,
+    X_scale: float,
+    X_zero_point: int,
+    Y: torch.Tensor,
+    Y_scale: float,
+    Y_zero_point: int,
+    out_scale: float,
+    out_zero_point: int,
+) -> torch.Tensor:
+    if X.dtype != torch.uint8:
+        raise ValueError("X dtype must be torch.int8")
+    if Y.dtype != torch.uint8:
+        raise ValueError("Y dtype must be torch.int8")
+
+    return quantized_add_per_tensor(
+        X, X_scale, X_zero_point, Y, Y_scale, Y_zero_point, out_scale, out_zero_point
+    )
+
+
 def quantized_linear_common(
     src: torch.Tensor,
     weight: torch.Tensor,

backends/cadence/aot/tests/test_ref_implementations.py

@@ -100,7 +100,7 @@ def test_dequantize_per_tensor(
         [
             # Only these types need to be tested as per ET_FORALL_JARVIS_QUANTIZED_TYPES in
             # on_device_ai/Assistant/Jarvis/min_runtime/operators/generic/operators.h
-            ("int16", 5, 0.8, 4, 5, 0.8, 4, 0.8, 4, 6, torch.int8),
+            ("int8", 5, 0.8, 4, 5, 0.8, 4, 0.8, 4, 6, torch.int8),
             ("uint8", 5, 0.8, 4, 5, 0.8, 4, 0.8, 4, 6, torch.uint8),
         ]
     )
@@ -122,6 +122,27 @@ def test_quantized_add(
         Y_tensor = torch.tensor([Y], dtype=dtype)
         expected_output = torch.tensor([expected_value], dtype=dtype)
 
+        quantized_add = (
+            torch.ops.cadence.quantized_add_asym8sxasym8s_asym8s.per_tensor
+            if dtype == torch.int8
+            else torch.ops.cadence.quantized_add_asym8uxasym8u_asym8u.per_tensor
+        )
+        output = quantized_add(
+            X_tensor,
+            X_scale,
+            X_zero_point,
+            Y_tensor,
+            Y_scale,
+            Y_zero_point,
+            out_scale,
+            out_zero_point,
+        )
+
+        self.assertTrue(
+            torch.equal(output, expected_output),
+            f"Values don't match in {name}: got {output}, expected {expected_output}",
+        )
+
         output = torch.ops.cadence.quantized_add(
             X_tensor,
             X_scale,