Update utils.py to remove dupe `replace_linear`

bitsandbytes/utils.py

@@ -99,45 +99,6 @@ def find_outlier_dims(weight, reduction_dim=0, zscore=4.0, topk=None, rdm=False)
 
     return idx
 
-def replace_linear(model, linear_replacement, skip_modules=["lm_head"], copy_weights=False, post_processing_function=None):
-    """
-    Replace linear modules with a new Linear module.
-
-    Parameters:
-        model (`torch.nn.Module`):
-            Input model or `torch.nn.Module` as the function is run recursively.
-        linear_replacement (`torch.nn.Module`):
-            The linear module that replaces the old one. Only expects standard arguments.
-            If other arguments need to be passed, use a lambda.
-        skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
-            List of modules names not to convert. Defaults to `lm_head`.
-        copy_weights (`bool`):
-            Copy the weights from the old linear module to the new one
-        post_processing_fun_name (`str`):
-            A function name of the replacement linear class that is called
-            after processing.
-    """
-    for name, module in model.named_children():
-        if len(list(module.children())) > 0:
-            replace_linear(module, linear_replacement, skip_modules, copy_weights, post_processing_function)
-
-        if isinstance(module, torch.nn.Linear) and name not in skip_modules:
-            old_module = model._modules[name]
-            model._modules[name] = linear_replacement(
-                module.in_features,
-                module.out_features,
-                module.bias is not None,
-            )
-            if copy_weights:
-                model._modules[name].weight = old_module.weight
-                model._modules[name].bias = old_module.bias
-
-            if post_processing_function is not None:
-               func = getattr(module, post_processing_function, None)
-               if func is not None: func(module)
-    return model
-
-
 
 def execute_and_return(command_string: str) -> Tuple[str, str]:
     def _decode(subprocess_err_out_tuple):