Optimization for ConvNd if dropout=0.

src/peft/tuners/lora/dora.py

@@ -139,7 +139,7 @@ def get_weight_norm(self, weight, lora_weight, scaling) -> torch.Tensor:
         weight_norm = weight.norm(p=2, dim=dim, keepdim=True).transpose(1, 0)
         return weight_norm
 
-    def forward(self, x, *, lora_A, lora_B, scaling, base_layer):
+    def forward(self, x, *, lora_A, lora_B, scaling, base_layer, base_result=None):
         """
         For DoRA, calculate the extra output from LoRA with DoRA applied. This should be added on top of the base layer
         output.
@@ -157,8 +157,9 @@ def forward(self, x, *, lora_A, lora_B, scaling, base_layer):
         # during backpropagation"
         weight_norm = weight_norm.detach()
         mag_norm_scale = magnitude / weight_norm
-        result_dora = (mag_norm_scale - 1) * (
-            self.conv_fn(
+
+        if base_result is None:
+            base_result = self.conv_fn(
                 x,
                 weight,
                 bias=None,
@@ -167,8 +168,14 @@ def forward(self, x, *, lora_A, lora_B, scaling, base_layer):
                 dilation=base_layer.dilation,
                 groups=base_layer.groups,
             )
-        ) + mag_norm_scale * lora_B(lora_A(x)) * scaling
+        else:
+            bias = base_layer.bias
+            if bias is not None:
+                # reshape bias to (1, -1, 1, ...)
+                bias_shape = (1, -1) + (1,) * (base_result.dim() - 2)
+                base_result = base_result - bias.view(*bias_shape)
 
+        result_dora = (mag_norm_scale - 1) * base_result + mag_norm_scale * lora_B(lora_A(x)) * scaling
         return result_dora
 
     def __repr__(self) -> str:

src/peft/tuners/lora/layer.py

@@ -1272,6 +1272,7 @@ def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
             result = self._mixed_batch_forward(x, *args, adapter_names=adapter_names, **kwargs)
         elif self.merged:
             result = self.base_layer(x, *args, **kwargs)
+
         else:
             result = self.base_layer(x, *args, **kwargs)
             torch_result_dtype = result.dtype
@@ -1288,13 +1289,19 @@ def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
                 if not self.use_dora[active_adapter]:
                     result = result + lora_B(lora_A(dropout(x))) * scaling
                 else:
-                    x = dropout(x)
+                    if isinstance(dropout, nn.Identity) or not self.training:
+                        base_result = result
+                    else:
+                        x = dropout(x)
+                        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_result,
                     )
 
             result = result.to(torch_result_dtype)