fix

bitsandbytes/nn/modules.py

@@ -257,7 +257,6 @@ class Linear4bit(nn.Linear):
     ```
     """
     def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, quant_type='fp4', quant_storage=torch.uint8, device=None):
-        super().__init__(input_features, output_features, bias, device)
         """
         Initialize Linear4bit class.
 
@@ -269,6 +268,7 @@ def __init__(self, input_features, output_features, bias=True, compute_dtype=Non
             bias (`bool`, defaults to `True`):
                 Whether the linear class uses the bias term as well.
         """
+        super().__init__(input_features, output_features, bias, device)
         self.weight = Params4bit(self.weight.data, requires_grad=False, compress_statistics=compress_statistics, quant_type=quant_type, quant_storage=quant_storage, module=self)
         # self.persistent_buffers = []  # TODO consider as way to save quant state
         self.compute_dtype = compute_dtype
@@ -473,7 +473,6 @@ class Linear8bitLt(nn.Linear):
     """
     def __init__(self, input_features, output_features, bias=True, has_fp16_weights=True,
                        memory_efficient_backward=False, threshold=0.0, index=None, device=None):
-        super().__init__(input_features, output_features, bias, device)
         """
         Initialize Linear8bitLt class.
 
@@ -484,7 +483,8 @@ def __init__(self, input_features, output_features, bias=True, has_fp16_weights=
                 Number of output features of the linear layer.
             bias (`bool`, defaults to `True`):
                 Whether the linear class uses the bias term as well.
-        """ 
+        """
+        super().__init__(input_features, output_features, bias, device)
         assert not memory_efficient_backward, "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
         self.state = bnb.MatmulLtState()
         self.index = index