experimental LoRA support for NF4 Model

method may change later depending on result quality

backend/operations_bnb.py

@@ -4,6 +4,7 @@ import torch
 import bitsandbytes as bnb
 
 from bitsandbytes.nn.modules import Params4bit, QuantState
+from bitsandbytes.functional import dequantize_4bit
 
 
 def functional_linear_4bits(x, weight, bias):
@@ -12,6 +13,10 @@ def functional_linear_4bits(x, weight, bias):
     return out
 
 
+def functional_dequantize_4bit(weight):
+    return dequantize_4bit(weight, quant_state=weight.quant_state, blocksize=weight.blocksize, quant_type=weight.quant_type)
+
+
 def copy_quant_state(state: QuantState, device: torch.device = None) -> QuantState:
     if state is None:
         return None
@@ -119,3 +124,17 @@ class ForgeLoader4Bit(torch.nn.Module):
             del self.dummy
         else:
             super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+
+    def reload_weight(self, weight):
+        self.weight = ForgeParams4bit(
+            weight,
+            requires_grad=False,
+            compress_statistics=self.weight.compress_statistics,
+            blocksize=self.weight.blocksize,
+            quant_type=self.weight.quant_type,
+            quant_storage=self.weight.quant_storage,
+            module=self,
+            bnb_quantized=False
+        )
+        self.quant_state = self.weight.quant_state
+        return self