ENH LoRA ConvNd layers using the groups argument. (#2403)

Conv layers with groups>1 are supported, but not merging.

Author: gslama12

src/peft/tuners/lora/layer.py

@@ -1028,6 +1028,9 @@ def __init__(
         super().__init__()
         LoraLayer.__init__(self, base_layer)
 
+        if base_layer.groups > 1:
+            warnings.warn("LoRA adapter added to ConvNd layer with groups > 1. Merging is not supported.")
+
         self._active_adapter = adapter_name
         self._kernel_dim = base_layer.weight.dim()
 
@@ -1064,7 +1067,9 @@ def update_layer(
         conv_layer = type(base_layer)
         out_kernel = out_stride = (1,) * (self._kernel_dim - 2)
         self.lora_A[adapter_name] = conv_layer(self.in_features, r, kernel_size, stride, padding, bias=False)
-        self.lora_B[adapter_name] = conv_layer(r, self.out_features, out_kernel, out_stride, bias=lora_bias)
+        self.lora_B[adapter_name] = conv_layer(
+            r, self.out_features // base_layer.groups, out_kernel, out_stride, bias=lora_bias
+        )
         self.lora_bias[adapter_name] = lora_bias
 
         if use_rslora:
@@ -1129,6 +1134,11 @@ def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = N
         for active_adapter in adapter_names:
             if active_adapter in self.lora_A.keys():
                 base_layer = self.get_base_layer()
+
+                if base_layer.groups > 1:
+                    # https://github.com/huggingface/peft/pull/2403
+                    raise NotImplementedError("Merging is not supported for _ConvNd layers with groups > 1!")
+
                 if safe_merge:
                     # Note that safe_merge will be slower than the normal merge
                     # because of the copy operation.
@@ -1246,13 +1256,12 @@ def get_delta_weight(self, adapter) -> torch.Tensor:
                 3
             ) * self.scaling[adapter]
         else:
-            output_tensor = (
-                self.conv_fn(
-                    weight_A.transpose(0, 1),
-                    weight_B,
-                ).transpose(0, 1)
-                * self.scaling[adapter]
-            )
+            output_tensor = self.conv_fn(weight_A.transpose(0, 1), weight_B)
+
+            if self.get_base_layer().groups > 1:
+                output_tensor = output_tensor * self.scaling[adapter]
+            else:
+                output_tensor = output_tensor.transpose(0, 1) * self.scaling[adapter]
 
         if cast_to_fp32:
             output_tensor = output_tensor.to(dtype=dtype)

tests/test_custom_models.py

@@ -115,6 +115,8 @@
     ("Conv2d 2 LoRA", "Conv2d", LoraConfig, {"target_modules": ["conv2d", "lin0"]}),
     ("Conv2d 1 LoRA with DoRA", "Conv2d", LoraConfig, {"target_modules": ["conv2d"], "use_dora": True}),
     ("Conv2d 2 LoRA with DoRA", "Conv2d", LoraConfig, {"target_modules": ["conv2d", "lin0"], "use_dora": True}),
+    ("Conv2d Groups LoRA", "Conv2dGroups", LoraConfig, {"target_modules": ["conv2d"]}),
+    ("Conv2d Groups LoRA with DoRA", "Conv2dGroups", LoraConfig, {"target_modules": ["conv2d"], "use_dora": True}),
     ("Conv3d 1 LoRA", "Conv3d", LoraConfig, {"target_modules": ["conv3d"]}),
     ("Conv3d 2 LoRA", "Conv3d", LoraConfig, {"target_modules": ["conv3d", "lin0"]}),
     ("Conv3d 1 LoRA with DoRA", "Conv3d", LoraConfig, {"target_modules": ["conv3d"], "use_dora": True}),
@@ -903,6 +905,25 @@ def forward(self, X):
         return X
 
 
+class ModelConv2DGroups(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv2d = nn.Conv2d(5, 5, 3, groups=5)
+        self.relu = nn.ReLU()
+        self.flat = nn.Flatten()
+        self.lin0 = nn.Linear(5, 2)
+        self.sm = nn.LogSoftmax(dim=-1)
+
+    def forward(self, X):
+        X = X.float().reshape(-1, 5, 3, 3)
+        X = self.conv2d(X)
+        X = self.relu(X)
+        X = self.flat(X)
+        X = self.lin0(X)
+        X = self.sm(X)
+        return X
+
+
 class ModelConv3D(nn.Module):
     def __init__(self):
         super().__init__()
@@ -967,6 +988,9 @@ def from_pretrained(cls, model_id, torch_dtype=None):
         if model_id == "Conv2d":
             return ModelConv2D().to(torch_dtype)
 
+        if model_id == "Conv2dGroups":
+            return ModelConv2DGroups().to(torch_dtype)
+
         if model_id == "Conv3d":
             return ModelConv3D().to(torch_dtype)
 
@@ -1038,6 +1062,12 @@ def test_load_multiple_adapters(self, test_name, model_id, config_cls, config_kw
 
     @parameterized.expand(TEST_CASES)
     def test_merge_layers(self, test_name, model_id, config_cls, config_kwargs):
+        # https://github.com/huggingface/peft/pull/2403
+        if model_id in ["Conv2dGroups"]:
+            pytest.skip(
+                f"Skipping test for {model_id} as merging is not supported. (See https://github.com/huggingface/peft/pull/2403 for details)"
+            )
+
         config_kwargs = config_kwargs.copy()
         if issubclass(config_cls, LoraConfig):
             config_kwargs["init_lora_weights"] = False
@@ -1055,6 +1085,12 @@ def test_merge_layers(self, test_name, model_id, config_cls, config_kwargs):
 
     @parameterized.expand(TEST_CASES)
     def test_merge_layers_fp16(self, test_name, model_id, config_cls, config_kwargs):
+        # https://github.com/huggingface/peft/pull/2403
+        if model_id in ["Conv2dGroups"]:
+            pytest.skip(
+                f"Skipping test for {model_id} as merging is not supported. (See https://github.com/huggingface/peft/pull/2403 for details)"
+            )
+
         config_kwargs = config_kwargs.copy()
         if issubclass(config_cls, LoraConfig):
             config_kwargs["init_lora_weights"] = False
@@ -1064,6 +1100,12 @@ def test_merge_layers_fp16(self, test_name, model_id, config_cls, config_kwargs)
 
     @parameterized.expand(TEST_CASES)
     def test_merge_layers_is_idempotent(self, test_name, model_id, config_cls, config_kwargs):
+        # https://github.com/huggingface/peft/pull/2403
+        if model_id in ["Conv2dGroups"]:
+            pytest.skip(
+                f"Skipping test for {model_id} as merging is not supported. (See https://github.com/huggingface/peft/pull/2403 for details)"
+            )
+
         # calling merge twice with the same arguments should not change the output
         config_kwargs = config_kwargs.copy()
         if issubclass(config_cls, LoraConfig):
@@ -1074,6 +1116,12 @@ def test_merge_layers_is_idempotent(self, test_name, model_id, config_cls, confi
 
     @parameterized.expand(TEST_CASES)
     def test_safe_merge(self, test_name, model_id, config_cls, config_kwargs):
+        # https://github.com/huggingface/peft/pull/2403
+        if model_id in ["Conv2dGroups"]:
+            pytest.skip(
+                f"Skipping test for {model_id} as merging is not supported. (See https://github.com/huggingface/peft/pull/2403 for details)"
+            )
+
         # calling merge twice with the same arguments should not change the output
         config_kwargs = config_kwargs.copy()
         if issubclass(config_cls, LoraConfig):
@@ -1291,6 +1339,12 @@ def test_disable_adapters(self, test_name, model_id, config_cls, config_kwargs):
 
     @parameterized.expand(TEST_CASES)
     def test_disable_adapters_with_merging(self, test_name, model_id, config_cls, config_kwargs):
+        # https://github.com/huggingface/peft/pull/2403
+        if model_id in ["Conv2dGroups"]:
+            pytest.skip(
+                f"Skipping test for {model_id} as merging is not supported. (See https://github.com/huggingface/peft/pull/2403 for details)"
+            )
+
         # same as test_disable_adapters, but with merging
         X = self.prepare_inputs_for_testing()
         model = self.transformers_class.from_pretrained(model_id).to(self.torch_device)