Accept device for Int8DynActInt4WeightQuantizer (pytorch#475)

Before we migrate away from `Quantizer` APIs, we want to
align the `__init__` arguments between `Int8DynActInt4WeightQuantizer`
and `Int4WeightOnlyQuantizer` so it's easier for users to use.

torchao/quantization/GPTQ.py

@@ -942,12 +942,14 @@ def __init__(
         padding_allowed: bool = False,
         precision: torch.dtype = torch.float32,
         scales_precision: torch.dtype = torch.float32,
+        device: torch.device = torch.device("cpu"),
     ) -> None:
         super().__init__()
         self.groupsize: int = groupsize
         self.padding_allowed: bool = padding_allowed
         self.precision: torch.dtype = precision
         self.scales_precision: torch.dtype = scales_precision
+        self.device: torch.device = device
 
     @torch.no_grad()
     def _create_quantized_state_dict(
@@ -988,9 +990,9 @@ def _create_quantized_state_dict(
                     self.groupsize,
                     self.scales_precision,
                 )
-                cur_state_dict[f"{fqn}.weight"] = weight_int8.to("cpu")
-                cur_state_dict[f"{fqn}.scales"] = scales.to("cpu")
-                cur_state_dict[f"{fqn}.zeros"] = zeros.to("cpu")
+                cur_state_dict[f"{fqn}.weight"] = weight_int8.to(self.device)
+                cur_state_dict[f"{fqn}.scales"] = scales.to(self.device)
+                cur_state_dict[f"{fqn}.zeros"] = zeros.to(self.device)
                 # TODO: support bias?
 
         return cur_state_dict