make style linting

src/peft/tuners/loha/layer.py

@@ -136,7 +136,7 @@ def update_layer(
             shape = tuple(base_layer.weight.shape)
         elif isinstance(base_layer, nn.Conv2d):
             # For 1x1 convolutions, disable effective_conv2d to avoid unnecessary tensor reshaping overhead.
-            # Since 1x1 convolutions are essentially pointwise operations (matrix multiplications), 
+            # Since 1x1 convolutions are essentially pointwise operations (matrix multiplications),
             # they can be more efficiently handled with the flattened weight representation,
             # similar to how Linear layers work. This optimization reduces computational cost
             # without affecting the mathematical equivalence of the operation.
@@ -152,8 +152,8 @@ def update_layer(
                     base_layer.in_channels * base_layer.kernel_size[0] * base_layer.kernel_size[1],
                 )
         elif isinstance(base_layer, nn.Conv1d):
-            # For Conv1d with kernel_size=1, disable effective_conv2d for the same optimization reasons 
-            # as 1x1 Conv2d. Kernel size 1 means no spatial/temporal context, making it equivalent 
+            # For Conv1d with kernel_size=1, disable effective_conv2d for the same optimization reasons
+            # as 1x1 Conv2d. Kernel size 1 means no spatial/temporal context, making it equivalent
             # to a Linear layer applied across the channel dimension. Using flattened representation
             # avoids unnecessary reshaping and improves computational efficiency.
             if base_layer.kernel_size[0] == 1:
@@ -205,8 +205,7 @@ def get_delta_weight(self, adapter_name: str) -> torch.Tensor:
             )
 
         base_layer = self.get_base_layer()
-
-
+
         # Reshape to match base layer shape
         weight = weight.reshape(base_layer.weight.shape)
 

src/peft/tuners/lokr/layer.py

@@ -212,7 +212,7 @@ def update_layer(
             use_w1 = not (decompose_both and r < max(shape[0][0], shape[1][0]) / 2)
             use_w2 = r >= max(shape[0][1], shape[1][1]) / 2
             # For 1x1 convolutions, disable effective_conv2d to avoid unnecessary tensor reshaping overhead.
-            # Since 1x1 convolutions are essentially pointwise operations (matrix multiplications), 
+            # Since 1x1 convolutions are essentially pointwise operations (matrix multiplications),
             # they can be more efficiently handled with the flattened weight representation,
             # similar to how Linear layers work. This optimization reduces computational cost
             # without affecting the mathematical equivalence of the operation.
@@ -230,8 +230,8 @@ def update_layer(
 
             use_w1 = not (decompose_both and r < max(shape[0][0], shape[1][0]) / 2)
             use_w2 = r >= max(shape[0][1], shape[1][1]) / 2
-            # For Conv1d with kernel_size=1, disable effective_conv2d for the same optimization reasons 
-            # as 1x1 Conv2d. Kernel size 1 means no spatial/temporal context, making it equivalent 
+            # For Conv1d with kernel_size=1, disable effective_conv2d for the same optimization reasons
+            # as 1x1 Conv2d. Kernel size 1 means no spatial/temporal context, making it equivalent
             # to a Linear layer applied across the channel dimension. Using flattened representation
             # avoids unnecessary reshaping and improves computational efficiency.
             if base_layer.kernel_size[0] == 1:
@@ -273,11 +273,10 @@ def get_delta_weight(self, adapter_name: str) -> torch.Tensor:
 
         # Make weights with Kronecker product
         weight = make_kron(w1, w2, self.scaling[adapter_name])
-        
+
         # Get base layer for reshaping
         base_layer = self.get_base_layer()
-
-
+
         # Regular reshape to match base layer shape
         weight = weight.reshape(base_layer.weight.shape)
 

tests/test_custom_models.py

@@ -974,12 +974,12 @@ def __init__(self):
         self.relu = nn.ReLU()
         self.flat = nn.Flatten()
         self.lin0 = nn.Linear(10 * 5 * 5, 2)
-        self.sm = nn.LogSoftmax(dim=-1)   
-        self.dtype = torch.float          
+        self.sm = nn.LogSoftmax(dim=-1)
+        self.dtype = torch.float
 
     def forward(self, X):
         X = X.to(self.dtype)
-        X = X.reshape(-1, 1, 5, 5)  
+        X = X.reshape(-1, 1, 5, 5)
         X = self.conv2d(X)
         X = self.relu(X)
         X = self.flat(X)
@@ -1094,10 +1094,10 @@ def from_pretrained(cls, model_id, torch_dtype=None):
 
         if model_id == "Conv2d":
             return ModelConv2D().to(torch_dtype)
-            
+
         if model_id == "Conv2d1x1":
             return ModelConv2D1x1().to(torch_dtype)
-            
+
         if model_id == "Conv1d":
             return ModelConv1D().to(torch_dtype)