Rearrange the weights directly in state dict before loading

bitsandbytes/nn/modules.py

@@ -306,6 +306,15 @@ class Int8Params(torch.nn.Parameter):
             return new_param
 
 
+def maybe_rearrange_weight(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+    weight = state_dict[f"{prefix}weight"]
+    weight_format = state_dict.pop(f"{prefix}weight_format", "row")
+
+    if weight_format != "row":
+        tile_indices = get_tile_inds(weight_format, weight.device)
+        state_dict[f"{prefix}weight"] = undo_layout(weight, tile_indices)
+
+
 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):
@@ -321,6 +330,7 @@ class Linear8bitLt(nn.Linear):
             self.state.use_pool = True
 
         self.weight = Int8Params(self.weight.data, has_fp16_weights=has_fp16_weights, requires_grad=has_fp16_weights)
+        self._register_load_state_dict_pre_hook(maybe_rearrange_weight)
 
     def _save_to_state_dict(self, destination, prefix, keep_vars):
         super()._save_to_state_dict(destination, prefix, keep_vars)
@@ -370,12 +380,6 @@ class Linear8bitLt(nn.Linear):
                     self.state.SCB = self.weight.SCB
 
                 unexpected_keys.remove(key)
-            if input_name == "weight_format":
-                input_param = state_dict[key]
-                if input_param != "row":
-                    tile_indices = get_tile_inds(input_param, self.weight.device)
-                    self.weight.data = self.weight.CB = undo_layout(self.weight.data, tile_indices)
-                unexpected_keys.remove(key)
 
     def init_8bit_state(self):
         self.state.CB = self.weight.CB