feat: registry-based dispatch for resolve_lora_variant in Linear layer

src/peft/tuners/lora/config.py

@@ -709,7 +709,8 @@ class LoraConfig(PeftConfig):
                 "magnitude is handled by a separate learnable parameter. This can improve the performance of LoRA, "
                 "especially at low ranks. Right now, DoRA only supports linear and Conv2D layers. DoRA introduces a bigger"
                 "overhead than pure LoRA, so it is recommended to merge weights for inference."
-            )
+            ),
+            "is_lora_variant": True,
         },
     )
     velora_config: Optional[Union[VeloraConfig, dict]] = field(
@@ -718,7 +719,8 @@ class LoraConfig(PeftConfig):
             "help": (
                 "Enable VeLoRA as a LoRA variant by providing a VeloraConfig. VeLoRA swaps in a custom backward pass "
                 "for the LoRA A projection that stores compressed activations instead of the full input activations."
-            )
+            ),
+            "is_lora_variant": True,
         },
     )
     alora_invocation_tokens: Optional[list[int]] = field(
@@ -735,7 +737,8 @@ class LoraConfig(PeftConfig):
                 "operations. Overall adapter inference speedups of an order of magnitude or more can occur on vLLM, "
                 "depending on the length of the shared context. Note that merging is not possible due to the selective "
                 "application of the weights."
-            )
+            ),
+            "is_lora_variant": True,
         },
     )
     use_qalora: bool = field(
@@ -827,11 +830,14 @@ class LoraConfig(PeftConfig):
             "help": (
                 "Enable BD-LoRA (Block-Diagonal LoRA) by providing a BdLoraConfig. This technique uses block-diagonal matrices for LoRA-A or LoRA-B "
                 "factors to enable faster multi-LoRA serving by eliminating communication overheads in distributed settings."
-            )
+            ),
+            "is_lora_variant": True,
         },
     )
     arrow_config: Optional[ArrowConfig] = field(
-        default=None, metadata={"help": "The necessary config to apply arrow routing on the model."}
+        default=None, metadata={"help": "The necessary config to apply arrow routing on the model.",
+            "is_lora_variant": True,
+        },
     )
     ensure_weight_tying: bool = field(
         default=False,

src/peft/tuners/lora/layer.py

@@ -14,6 +14,7 @@
 from __future__ import annotations
 
 import copy
+import dataclasses
 import math
 import warnings
 from collections.abc import Callable
@@ -139,19 +140,51 @@ def __init__(self, base_layer: nn.Module, ephemeral_gpu_offload: bool = False, *
     def _get_in_out_features(self, module: nn.Module) -> tuple[int, int] | tuple[None, None]:
         return _get_in_out_features(module)
 
+    @property
+    def lora_variants(self):
+        """
+        A dictionary mapping the active LoRA variants to their respective classes.
+
+        To extend this, subclasses should override this property and return a dictionary 
+        where the keys are tuples of variant field names (from LoraConfig) and the 
+        values are the specific LoraVariant subclasses.
+
+        Tuples are used as keys because they are immutable and hashable, allowing us 
+        to safely map combinations of active variants (e.g., DoRA + another variant) 
+        to a specific composed variant class.
+        """
+        return {(): None}
+
     def resolve_lora_variant(self, *, config: LoraConfig, **kwargs) -> Optional[LoraVariant]:
-        """Return a matching LoRA variant for this layer type.
 
-        Given the init arguments of this layer, return the correct LoRA variant, if any. E.g., if `use_dora=True`, this
-        method should return the DoRA variant for the given layer. If `use_alora=True`, same for aLoRA.
+        # Safely fetch the dictionary (defaults to empty if a subclass forgot to define it)
+        layer_variants = getattr(self, "lora_variants", {(): None})
+        lora_variants_configs = [f for f in dataclasses.fields(config) if f.metadata.get("is_lora_variant")]
 
-        If there is no fitting variant, return None.
+        # 1. Gather all valid variant field names from the config
+        tagged_fields = { f.name for f in lora_variants_configs }
 
-        Note: If this layer type does not support the LoRA variant at all, please raise an error during __init__ as is
-        convention, and not here.
+        # 2. SANITY CHECK: Ensure all keys in the layer's dictionary actually exist in the config
+        for variant_keys in layer_variants.keys():
+            for variant_name in variant_keys:
+                if variant_name not in tagged_fields:
+                    raise ValueError(
+                        f"Variant '{variant_name}' found in lora_variants but it is not tagged with "
+                        f"'is_lora_variant' in LoraConfig."
+                    )
 
-        """
-        return None
+        # 3. Figure out which variants are currently active
+        active_variants = tuple(sorted(
+            f.name for f in lora_variants_configs
+            if getattr(config, f.name)
+        ))
+
+        # 4. Route to the correct variant class
+        if active_variants not in layer_variants:
+            raise ValueError(f"Invalid or unsupported variant combination: {active_variants}")
+
+        variant_class = layer_variants[active_variants]
+        return variant_class() if variant_class else None
 
     def update_layer(
         self,
@@ -795,36 +828,17 @@ def __init__(
         )
         self.is_target_conv_1d_layer = is_target_conv_1d_layer
 
-    def resolve_lora_variant(self, config: LoraConfig, **kwargs) -> Optional[LoraVariant]:
-        if config.velora_config is not None:
-            from .variants import VeloraLinearVariant
-
-            return VeloraLinearVariant()
-
-        if config.arrow_config is not None:
-            from .variants import ArrowLinearVariant
-
-            return ArrowLinearVariant()
-
-        if config.monteclora_config is not None:
-            from .variants import MontecloraLinearVariant
-
-            return MontecloraLinearVariant()
-        if config.use_bdlora is not None:
-            from .variants import BdLoraLinearVariant
-
-            return BdLoraLinearVariant()
-
-        use_alora = config.alora_invocation_tokens is not None
-        if not config.use_dora and not use_alora:
-            return None
-
-        from .variants import ALoraLinearVariant, DoraLinearVariant
-
-        if use_alora:
-            return ALoraLinearVariant()
-        else:
-            return DoraLinearVariant()
+    @property
+    def lora_variants(self):
+        from . import variants
+        return {
+            (): None,
+            ("use_dora",): variants.DoraLinearVariant,
+            ("arrow_config",): variants.ArrowLinearVariant,
+            ("use_bdlora",): variants.BdLoraLinearVariant,
+            ("alora_invocation_tokens",): variants.ALoraLinearVariant,
+            ("velora_config",): variants.VeloraLinearVariant,
+        }
 
     def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
         """
@@ -1061,15 +1075,13 @@ def __init__(
             config=config,
         )
 
-    def resolve_lora_variant(self, *, config: LoraConfig, **kwargs) -> Optional[LoraVariant]:
-        if config.velora_config is not None:
-            raise ValueError("VeLoRA does not support adapting embedding layers.")
-        if not config.use_dora:
-            return None
-
-        from .variants import DoraEmbeddingVariant
-
-        return DoraEmbeddingVariant()
+    @property
+    def lora_variants(self):
+        from . import variants
+        return {
+            (): None,
+            ("use_dora",): variants.DoraEmbeddingVariant,
+        }
 
     def update_layer(
         self,
@@ -1670,15 +1682,13 @@ def __init__(self, *args, **kwargs):
             raise ValueError(f"Conv2d layer kernel must have 4 dimensions, not {self._kernel_dim}")
         self.conv_fn = F.conv2d
 
-    def resolve_lora_variant(self, *, config: LoraConfig, **kwargs) -> Optional[LoraVariant]:
-        if config.velora_config is not None:
-            raise ValueError("VeLoRA does not support adapting conv layers.")
-        if not config.use_dora:
-            return None
-
-        from .variants import DoraConv2dVariant
-
-        return DoraConv2dVariant()
+    @property
+    def lora_variants(self):
+        from . import variants
+        return {
+            (): None,
+            ("use_dora",): variants.DoraConv2dVariant,
+        }
 
 
 class Conv1d(_ConvNd):
@@ -1689,15 +1699,13 @@ def __init__(self, *args, **kwargs):
             raise ValueError(f"Conv1d layer kernel must have 3 dimensions, not {self._kernel_dim}")
         self.conv_fn = F.conv1d
 
-    def resolve_lora_variant(self, *, config: LoraConfig, **kwargs) -> Optional[LoraVariant]:
-        if config.velora_config is not None:
-            raise ValueError("VeLoRA does not support adapting conv layers.")
-        if not config.use_dora:
-            return None
-
-        from .variants import DoraConv1dVariant
-
-        return DoraConv1dVariant()
+    @property
+    def lora_variants(self):
+        from . import variants
+        return {
+            (): None,
+            ("use_dora",): variants.DoraConv1dVariant,
+        }
 
 
 class Conv3d(_ConvNd):
@@ -1708,15 +1716,13 @@ def __init__(self, *args, **kwargs):
             raise ValueError(f"Conv3d layer kernel must have 5 dimensions, not {self._kernel_dim}")
         self.conv_fn = F.conv3d
 
-    def resolve_lora_variant(self, *, config: LoraConfig, **kwargs) -> Optional[LoraVariant]:
-        if config.velora_config is not None:
-            raise ValueError("VeLoRA does not support adapting conv layers.")
-        if not config.use_dora:
-            return None
-
-        from .variants import DoraConv3dVariant
-
-        return DoraConv3dVariant()
+    @property
+    def lora_variants(self):
+        from . import variants
+        return {
+            (): None,
+            ("use_dora",): variants.DoraConv3dVariant,
+        }
 
 
 class MultiheadAttention(nn.Module, LoraLayer):

tests/test_lora_variants.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from unittest.mock import PropertyMock, patch
+
 import pytest
 import torch
 from torch import nn
@@ -174,6 +176,34 @@ def test_dora_params_have_gradients(self):
         for layer in layer_names:
             assert getattr(peft_model.base_model.model, layer).lora_magnitude_vector["default"].weight.grad is not None
 
+    def test_unregistered_variant_raises_error(self):
+            # 1. Create a config and dummy linear layer
+            config = LoraConfig()
+            base_layer = nn.Linear(10, 10)
+            layer = LoraLinear(base_layer, "default", config, r=8, lora_alpha=8)
+
+            # 2. Monkey-patch the lora_variants property to include a fake variant
+            with patch("peft.tuners.lora.layer.Linear.lora_variants", new_callable=PropertyMock) as mock_variants:
+                mock_variants.return_value = {("fake_unregistered_variant",): None}
+
+                # 3. Assert that the sanity check catches it and throws the right error
+                with pytest.raises(ValueError, match="Variant 'fake_unregistered_variant' found in lora_variants but it is not tagged with 'is_lora_variant' in LoraConfig."):
+                    layer.resolve_lora_variant(config=config)
+
+    def test_invalid_variant_combination_raises_error(self):
+        # 1. Create a config with no variants active
+        config = LoraConfig()
+        base_layer = nn.Linear(10, 10)
+        layer = LoraLinear(base_layer, "default", config, r=8, lora_alpha=8)
+
+        # 2. Monkey-patch lora_variants to include a valid tagged combo that isn't active
+        with patch("peft.tuners.lora.layer.Linear.lora_variants", new_callable=PropertyMock) as mock_variants:
+            mock_variants.return_value = {
+                ("use_dora",): None,  # only use_dora is valid, empty combo not listed
+            }
+            # 3. Assert invalid combination error is raised
+            with pytest.raises(ValueError, match="Invalid or unsupported variant combination"):
+                layer.resolve_lora_variant(config=config)
 
 class TestActivatedLora:
     @pytest.mark.parametrize(
@@ -225,9 +255,8 @@ def test_alora_activation_matches_base_until_invocation(self):
 
         input_ids = torch.tensor([[0, 1, 2, 3]])
         start = 2
-        with lora_model.disable_adapter():
-            with torch.no_grad():
-                base_out = lora_model(X=input_ids)
+        with lora_model.disable_adapter(), torch.no_grad():
+            base_out = lora_model(X=input_ids)
 
         kwargs = get_alora_offsets_for_forward(lora_model, input_ids)
         with torch.no_grad():
@@ -272,9 +301,8 @@ def test_num_beams_error(self):
         lora_model.eval()
 
         input_ids = torch.tensor([[0, 1, 2, 3]])
-        with pytest.raises(ValueError) as e:
-            with torch.no_grad():
-                lora_out = lora_model(X=input_ids, num_beams=2, alora_offsets=[3])
+        with pytest.raises(ValueError) as e, torch.no_grad():
+            lora_out = lora_model(X=input_ids, num_beams=2, alora_offsets=[3])
         assert "Beam search not yet supported for aLoRA." in str(e.value)
 
     def test_gradient_checkpointing_double_forward_raises(self):