[Tests] Small Fixes

src/compressed_tensors/quantization/quant_args.py

@@ -407,14 +407,18 @@ def get_observer(self) -> str:
 
 
 def round_to_quantized_type_dtype(
-    tensor: torch.Tensor, dtype: torch.dtype
+    tensor: torch.Tensor,
+    dtype: torch.dtype,
+    cast_to_original_dtype: Optional[bool] = True,
 ) -> torch.Tensor:
     """
     Rounds an input tensor to the nearest quantized representation given a dtype.
     The original dtype is kept post-rounding.
 
     :param tensor: tensor to round
     :param dtype: dtype to use for rounding
+    :param cast_to_original_dtype: whether or not we cast the rounded tensor to
+        the original dtype
     :return: rounded tensor
     """
     original_dtype = tensor.dtype
@@ -425,14 +429,17 @@ def round_to_quantized_type_dtype(
         iinfo = torch.iinfo(dtype)
         rounded = torch.round(torch.clamp(tensor, iinfo.min, iinfo.max))
 
-    return rounded.to(original_dtype)
+    if cast_to_original_dtype:
+        return rounded.to(original_dtype)
+    return rounded
 
 
 def round_to_quantized_type_args(
     tensor: torch.Tensor,
     args: QuantizationArgs,
     min: torch.Tensor,
     max: torch.Tensor,
+    cast_to_original_dtype: Optional[bool] = True,
 ) -> torch.Tensor:
     """
     Rounds an input tensor to the nearest quantized representation given
@@ -442,6 +449,8 @@ def round_to_quantized_type_args(
     :param args: quantization args to use for rounding
     :param min: min value to use for clamping
     :param max: max value to use for clamping
+    :param cast_to_original_dtype: whether or not we cast the rounded tensor to
+        the original dtype
     :return: rounded tensor
     """
 
@@ -459,4 +468,6 @@ def round_to_quantized_type_args(
     else:
         raise ValueError(f"Invalid quantization type {args.type}")
 
-    return rounded.to(original_dtype)
+    if cast_to_original_dtype:
+        return rounded.to(original_dtype)
+    return rounded

src/compressed_tensors/quantization/utils/helpers.py

@@ -127,7 +127,7 @@ def calculate_qparams(
 
     # 5. Round the zp to zp_dtype
     zero_points = round_to_quantized_type_dtype(
-        zero_points, dtype=quantization_args.zp_dtype
+        zero_points, dtype=quantization_args.zp_dtype, cast_to_original_dtype=False
     )
 
     if scales.ndim == 0:

tests/test_quantization/lifecycle/test_enabled.py

@@ -26,8 +26,8 @@
 
 
 def test_quantization_enabled_disabled():
-    inp = torch.randn(16, dtype=torch.bfloat16)
-    model = Linear(16, 16, dtype=torch.bfloat16)
+    inp = torch.randn(16)
+    model = Linear(16, 16)
     quantized_model = deepcopy(model)
     apply_quantization_config(
         model=quantized_model,