Fix _quantize_affine_no_dtype_cast for FP8 types

torchao/quantization/quant_primitives.py

@@ -334,6 +334,7 @@ def _quantize_affine(
         zero_point,
         quant_min,
         quant_max,
+        output_dtype,
         zero_point_domain,
     ).to(output_dtype)
 
@@ -345,6 +346,7 @@ def _quantize_affine_no_dtype_cast(
     zero_point: Optional[torch.Tensor],
     quant_min: Union[int, float],
     quant_max: Union[int, float],
+    quant_dtype: Optional[torch.dtype],
     zero_point_domain: Optional[str] = ZeroPointDomain.INT.name,
 ) -> torch.Tensor:
     """
@@ -389,7 +391,7 @@ def _quantize_affine_no_dtype_cast(
         assert (
             zero_point is None
         ), "zero_point should be None when zero_point_domain is NONE"
-        if _is_float8_type(input.dtype):
+        if _is_float8_type(quant_dtype):
             quant = torch.clamp(input * scale.reciprocal(), quant_min, quant_max)
         else:
             quant = torch.clamp(torch.round(input * (1.0 / scale)), quant_min, quant_max)
@@ -661,6 +663,7 @@ def _do_fake_quantize_affine(
         zero_point,
         quant_min,
         quant_max,
+        quant_dtype,
         zero_point_domain.name,
     )
     dq = _dequantize_affine_no_dtype_check(