FIX Revert optimization for LoRA scaling == 1 (#2416)

The PR #2404 introduced an optimization for LoRA in case that scaling ==
1 (see
#2404 (comment)).
This unfortunately leads to recompilation when the model is compiled, as
witnessed by the failing CI here:

https://github.com/huggingface/peft/actions/runs/13755365121/job/38461837691#step:6:157

For now, let's revert the optimization. If we have concrete numbers that
show that the optimization makes a significant difference, we can
start thinking about how to optimize this code path in a
compile-friendly way.

Author: BenjaminBossan

src/peft/tuners/lora/layer.py

@@ -489,13 +489,7 @@ def _mixed_batch_forward(
             # getting the sub-batch, passing it to LoRA layers and updating the corresponding indices of the linear
             # layer output
             sub_batch = x[sub_batch_indices_list[i]].to(lora_A.weight.dtype)
-
-            # Loras such as EoRA will always be scaling == 1 so we can skip the no-op math
-            if scaling == 1:
-                lora_output = lora_B(lora_A(dropout(sub_batch)))
-            else:
-                lora_output = lora_B(lora_A(dropout(sub_batch))) * scaling
-
+            lora_output = lora_B(lora_A(dropout(sub_batch))) * scaling
             result[sub_batch_indices_list[i]] += lora_output.to(torch_result_dtype)
 
         return result
@@ -730,11 +724,7 @@ def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
                 x = self._cast_input_dtype(x, lora_A.weight.dtype)
 
                 if not self.use_dora[active_adapter]:
-                    # Loras such as EoRA will always be scaling == 1 so we can skip the no-op math
-                    if scaling == 1:
-                        result = result + lora_B(lora_A(dropout(x)))
-                    else:
-                        result = result + lora_B(lora_A(dropout(x))) * scaling
+                    result = result + lora_B(lora_A(dropout(x))) * scaling
                 else:
                     if isinstance(dropout, nn.Identity) or not self.training:
                         base_result = result