Removed support for non-per-tensor quantized relu (pytorch#14788)

Summary:

Not supporting quantized relu default, so removing it from ref_implementations

Differential Revision: D83874866

Author: Andrew Grebenisan

backends/cadence/aot/ref_implementations.py

@@ -1125,75 +1125,46 @@ def quantized_relu_common(
 
 
 def quantized_relu_variant(
-    per_tensor: bool,
     dtype: torch.dtype | None = None,
 ) -> Callable[[Callable[..., torch.Tensor]], Callable[..., torch.Tensor]]:
     """Create a quantized relu variant with type checking."""
 
     def decorator(_: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]:
         def variant(
             X: torch.Tensor,
-            X_zero_point: torch.Tensor | int,
+            X_zero_point: int,
             out_zero_point: int,
-            out_multiplier: torch.Tensor | int,
-            out_shift: torch.Tensor | int,
+            out_multiplier: int,
+            out_shift: int,
         ) -> torch.Tensor:
-            if per_tensor:
-                if dtype and X.dtype != dtype:
-                    raise ValueError(f"X dtype must be {dtype}. Got {X.dtype}")
-
-                assert isinstance(out_shift, int)
-                assert isinstance(out_multiplier, int)
-                _out_shift = out_shift
-                _out_multiplier = out_multiplier
-            else:
-                assert isinstance(out_multiplier, torch.Tensor)
-                if out_multiplier.numel() > 1:
-                    raise ValueError("Only scalar out_multiplier is supported")
-
-                assert isinstance(out_shift, torch.Tensor)
-                if out_shift.numel() > 1:
-                    raise ValueError("Only scalar out_shift is supported")
-
-                assert isinstance(X_zero_point, torch.Tensor)
-                if X_zero_point.shape != X.shape:
-                    raise ValueError(
-                        f"X_zero_point shape must be {X.shape}. Got {X_zero_point.shape}"
-                    )
-
-                _out_multiplier = int(out_multiplier.item())
-                _out_shift = int(out_shift.item())
+            if dtype and X.dtype != dtype:
+                raise ValueError(f"X dtype must be {dtype}. Got {X.dtype}")
 
             return quantized_relu_common(
                 X,
                 X_zero_point,
                 out_zero_point,
-                _out_multiplier,
-                _out_shift,
+                out_multiplier,
+                out_shift,
             )
 
         return variant
 
     return decorator
 
 
-@impl(m, "quantized_relu")
-@quantized_relu_variant(False)
-def quantized_relu() -> torch.Tensor: ...
-
-
 @impl(m, "quantized_relu.per_tensor")
-@quantized_relu_variant(True)
+@quantized_relu_variant()
 def quantized_relu_per_tensor() -> torch.Tensor: ...
 
 
 @impl(m, "quantized_relu_asym8s_asym8s.per_tensor")
-@quantized_relu_variant(True, torch.int8)
+@quantized_relu_variant(torch.int8)
 def quantized_relu_asym8s_asym8s_per_tensor() -> torch.Tensor: ...
 
 
 @impl(m, "quantized_relu_asym8u_asym8u.per_tensor")
-@quantized_relu_variant(True, torch.uint8)
+@quantized_relu_variant(torch.uint8)
 def quantized_relu_asym8u_asym8u_per_tensor() -> torch.Tensor: ...