chore: review suggestions

huggingface · Oct 8, 2024 · 09a1958 · 09a1958
1 parent c8a2700
commit 09a1958
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Showing 2 changed files with 11 additions and 19 deletions.
diff --git a/src/peft/tuners/lora/dora.py b/src/peft/tuners/lora/dora.py
@@ -97,9 +97,6 @@ def forward(self, x, *, lora_A, lora_B, scaling, base_layer, base_result=None):
 
         result_dora = (mag_norm_scale - 1) * base_result + mag_norm_scale * lora_result * scaling
 
-        if bias is not None:
-            result_dora = result_dora + bias
-
         return result_dora
 
     def __repr__(self) -> str:

diff --git a/src/peft/tuners/lora/layer.py b/src/peft/tuners/lora/layer.py
@@ -586,24 +586,19 @@ def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
                     result = result + lora_B(lora_A(dropout(x))) * scaling
                 else:
                     if isinstance(dropout, nn.Identity) or not self.training:
-                        result = self.lora_magnitude_vector[active_adapter](
-                            x,
-                            lora_A=lora_A,
-                            lora_B=lora_B,
-                            scaling=scaling,
-                            base_layer=self.get_base_layer(),
-                            base_result=result,
-                        )
+                        base_result = result
                     else:
                         x = dropout(x)
-                        result = result + self.lora_magnitude_vector[active_adapter](
-                            x,
-                            lora_A=lora_A,
-                            lora_B=lora_B,
-                            scaling=scaling,
-                            base_layer=self.get_base_layer(),
-                            result=None,
-                        )
+                        base_result = None
+
+                    result = result + self.lora_magnitude_vector[active_adapter](
+                        x,
+                        lora_A=lora_A,
+                        lora_B=lora_B,
+                        scaling=scaling,
+                        base_layer=self.get_base_layer(),
+                        base_result=base_layer,
+                    )
 
             result = result.to(torch_result_dtype)