cleanup commented out deletions

bitsandbytes/nn/modules.py

@@ -163,30 +163,6 @@ class Params4bit(torch.nn.Parameter):
         self.compress_statistics = self.quant_state.nested
         self.quant_type = self.quant_state.quant_type
         return self
-    
-    # @classmethod
-    # def from_state_dict(cls, state_dict, prefix="", requires_grad=False):
-    #     data = state_dict.pop(prefix.rstrip('.'))
-
-    #     # extracting components for QuantState from state_dict
-    #     qs_dict = {}
-    #     for k, v in state_dict.items():
-    #         if k.replace(prefix, '').split('.')[0] in QuantState.valid_qs_keys:
-    #             qs_dict[k] = v        
-    #     state_dict = {k: v for k, v in state_dict.items() if k not in qs_dict}
-    #     qs_dict = {k.replace(prefix, ''): v for k, v in qs_dict.items()}
-
-    #     if data.device.type != "cuda":
-    #         raise ValueError(f"`data.device.type` must be 'cuda', detected {data.device.type}")
-
-    #     cls.requires_grad = requires_grad
-    #     cls.quant_state = QuantState.from_dict(qs_dict=qs_dict, device=data.device)
-    #     cls.blocksize = cls.quant_state.blocksize             # this attribute can be deprecated - it duplicates same one in quant_state
-    #     cls.compress_statistics = cls.quant_state.nested      # this attribute can be deprecated - it duplicates quant_state.nested
-    #     cls.quant_type = cls.quant_state.quant_type           # this attribute can be deprecated - it duplicates same one in quant_state
-
-    #     self = torch.Tensor._make_subclass(cls, data=data.to(data.device))
-    #     return self, state_dict
 
     def cuda(self, device):
         w = self.data.contiguous().half().cuda(device)
@@ -227,7 +203,7 @@ class Params4bit(torch.nn.Parameter):
 
 class Linear4bit(nn.Linear):
 
-    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, quant_type='fp4',device=None):
+    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, quant_type='fp4', device=None):
         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)
         # self.persistent_buffers = []  # TODO consider as way to save quant state
@@ -261,18 +237,6 @@ class Linear4bit(nn.Linear):
             for k, v in self.weight.quant_state.as_dict(packed=True).items():
                 destination[prefix + "weight." + k] = v if keep_vars else v.detach()
 
-    # def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-    #                           missing_keys, unexpected_keys, error_msgs):
-    #     # Note: super()._load_from_state_dict() is not called here intentionally.
-    #     if self.bias is not None:
-    #         bias_data = state_dict.pop(prefix + "bias", None)
-    #         self.bias.data = bias_data.to(self.bias.data.device)
-
-    #     self.weight, state_dict = bnb.nn.Params4bit.from_state_dict(
-    #                     state_dict, prefix=prefix + "weight" + ".", requires_grad=False
-    #                 )
-    #     unexpected_keys.extend(state_dict.keys())
-
     def forward(self, x: torch.Tensor):
         # weights are cast automatically as Int8Params, but the bias has to be cast manually
         if self.bias is not None and self.bias.dtype != x.dtype:
@@ -295,10 +259,12 @@ class Linear4bit(nn.Linear):
 
         return out
 
+
 class LinearFP4(Linear4bit):
-    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True,device=None):
+    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, device=None):
         super().__init__(input_features, output_features, bias, compute_dtype, compress_statistics, 'fp4', device)
 
+
 class LinearNF4(Linear4bit):
     ''' Implements the NF4 data type.
 
@@ -310,7 +276,7 @@ class LinearNF4(Linear4bit):
         Implementation of the NF4 data type in bitsandbytes can be found in the `create_normal_map` function in
         the `functional.py` file: https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L236.
     '''
-    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True,device=None):
+    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, device=None):
         super().__init__(input_features, output_features, bias, compute_dtype, compress_statistics, 'nf4', device)