feat(lokr, loha): Refine 1x1 Conv2d/Conv1d handling and test naming c…

…onsistency

Clarify existing optimal handling for 1x1 Conv2d and Conv1d layers;
confirmed  is already correctly disabled in  files,
addressing reviewer feedback on potential dead code.

Standardize test case naming in  for consistency:
- Updated 'LoHa' to 'LOHA' and 'LoKr' to 'LOKR' (all caps).
- Aligned Conv1D/Conv1d naming with PyTorch conventions for clarity.

src/peft/tuners/loha/layer.py

@@ -135,10 +135,12 @@ def update_layer(
         if isinstance(base_layer, nn.Linear):
             shape = tuple(base_layer.weight.shape)
         elif isinstance(base_layer, nn.Conv2d):
-            # Handle 1x1 convolutions differently
+            # For 1x1 convolutions, disable effective_conv2d to avoid unnecessary tensor reshaping overhead.
+            # 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.
             if base_layer.kernel_size == (1, 1):
-                # For 1x1 convolutions, use a more direct shape without using effective_conv2d
-                shape = (base_layer.out_channels, base_layer.in_channels, *base_layer.kernel_size)
                 use_effective_conv2d = False
             else:
                 use_effective_conv2d = use_effective_conv2d
@@ -150,10 +152,11 @@ def update_layer(
                     base_layer.in_channels * base_layer.kernel_size[0] * base_layer.kernel_size[1],
                 )
         elif isinstance(base_layer, nn.Conv1d):
-            # Handle kernel_size=1 Conv1d differently
+            # 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:
-                # For kernel_size=1, use a more direct shape without using effective_conv2d
-                shape = (base_layer.out_channels, base_layer.in_channels, base_layer.kernel_size[0])
                 use_effective_conv2d = False
             else:
                 use_effective_conv2d = use_effective_conv2d
@@ -203,9 +206,6 @@ def get_delta_weight(self, adapter_name: str) -> torch.Tensor:
 
         base_layer = self.get_base_layer()
 
-        # Special optimization for 1x1 convolutions and kernel_size=1 Conv1d
-        is_1x1_conv2d = isinstance(base_layer, nn.Conv2d) and base_layer.kernel_size == (1, 1)
-        is_1_conv1d = isinstance(base_layer, nn.Conv1d) and base_layer.kernel_size[0] == 1
 
         # Reshape to match base layer shape
         weight = weight.reshape(base_layer.weight.shape)

src/peft/tuners/lokr/layer.py

@@ -211,9 +211,12 @@ 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
-            # Handle 1x1 convolutions differently
+            # For 1x1 convolutions, disable effective_conv2d to avoid unnecessary tensor reshaping overhead.
+            # 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.
             if base_layer.kernel_size == (1, 1):
-                # For 1x1 convolutions, always disable use_effective_conv2d
                 use_effective_conv2d = False
             else:
                 use_effective_conv2d = use_effective_conv2d
@@ -227,9 +230,11 @@ 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
-            # Handle kernel_size=1 Conv1d differently
+            # 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:
-                # For kernel_size=1, always disable use_effective_conv2d
                 use_effective_conv2d = False
             else:
                 use_effective_conv2d = use_effective_conv2d
@@ -272,9 +277,6 @@ def get_delta_weight(self, adapter_name: str) -> torch.Tensor:
         # Get base layer for reshaping
         base_layer = self.get_base_layer()
 
-        # Special optimization for 1x1 convolutions and kernel_size=1 Conv1d
-        is_1x1_conv2d = isinstance(base_layer, nn.Conv2d) and base_layer.kernel_size == (1, 1)
-        is_1_conv1d = isinstance(base_layer, nn.Conv1d) and base_layer.kernel_size[0] == 1
 
         # Regular reshape to match base layer shape
         weight = weight.reshape(base_layer.weight.shape)

tests/test_custom_models.py

@@ -230,8 +230,8 @@
     ("Conv2d 2 LOHA", "Conv2d", LoHaConfig, {"target_modules": ["conv2d", "lin0"]}),
     ("Conv2d 3 LOHA", "Conv2d", LoHaConfig, {"target_modules": ["conv2d"], "use_effective_conv2d": True}),
     ("Conv2d 4 LOHA", "Conv2d", LoHaConfig, {"target_modules": ["conv2d", "lin0"], "use_effective_conv2d": True}),
-    ("Conv1D LoHa", "Conv1d", LoHaConfig, {"target_modules": ["conv1d"]}),
-    ("Conv2d 1x1 LoHa", "Conv2d1x1", LoHaConfig, {"target_modules": ["conv2d"]}),
+    ("Conv1d LOHA", "Conv1d", LoHaConfig, {"target_modules": ["conv1d"]}),
+    ("Conv2d 1x1 LOHA", "Conv2d1x1", LoHaConfig, {"target_modules": ["conv2d"]}),
     # LoKr
     ("Vanilla MLP 1 LOKR", "MLP", LoKrConfig, {"target_modules": "lin0"}),
     ("Vanilla MLP 2 LOKR", "MLP", LoKrConfig, {"target_modules": ["lin0"]}),
@@ -254,8 +254,8 @@
     ("Conv2d 2 LOKR", "Conv2d", LoKrConfig, {"target_modules": ["conv2d", "lin0"]}),
     ("Conv2d 3 LOKR", "Conv2d", LoKrConfig, {"target_modules": ["conv2d"], "use_effective_conv2d": True}),
     ("Conv2d 4 LOKR", "Conv2d", LoKrConfig, {"target_modules": ["conv2d", "lin0"], "use_effective_conv2d": True}),
-    ("Conv1D LoKr", "Conv1d", LoKrConfig, {"target_modules": ["conv1d"]}),
-    ("Conv2d 1x1 LoKr", "Conv2d1x1", LoKrConfig, {"target_modules": ["conv2d"]}),
+    ("Conv1d LOKR", "Conv1d", LoKrConfig, {"target_modules": ["conv1d"]}),
+    ("Conv2d 1x1 LOKR", "Conv2d1x1", LoKrConfig, {"target_modules": ["conv2d"]}),
     (
         "Conv2d 5 LOKR",
         "Conv2d",