[Add] DoRA Embedding

src/peft/tuners/lora/dora.py

@@ -107,6 +107,32 @@ def __repr__(self) -> str:
         return "lora.dora." + rep
 
 
+class DoraEmbeddingLayer(DoraLinearLayer):
+    def forward(self, x, *, lora_A, lora_B, scaling, base_layer, embed_fn):
+        """
+        For DoRA, calculate the extra output from LoRA with DoRA applied. This should be added on top of the base layer
+        output.
+        """
+        lora_weight = (lora_A @ lora_B).T
+        magnitude = self.weight
+        weight = base_layer.weight
+        weight_norm = self.get_weight_norm(weight, lora_weight.detach(), scaling)
+        # see section 4.3 of DoRA (https://arxiv.org/abs/2402.09353)
+        # "[...] we suggest treating ||V +∆V ||_c in
+        # Eq. (5) as a constant, thereby detaching it from the gradient
+        # graph. This means that while ||V + ∆V ||_c dynamically
+        # reflects the updates of ∆V , it won’t receive any gradient
+        # during backpropagation"
+        weight_norm = weight_norm.detach()
+        mag_norm_scale = magnitude / weight_norm
+        result_dora = mag_norm_scale * (embed_fn(x, lora_A) @ lora_B) * scaling
+        return mag_norm_scale, result_dora
+
+    def __repr__(self) -> str:
+        rep = super().__repr__()
+        return "lora.dora." + rep
+
+
 class DoraConv2dLayer(DoraLinearLayer):
     def get_weight_norm(self, weight, lora_weight, scaling) -> torch.Tensor:
         # calculate L2 norm of weight matrix, column-wise

src/peft/tuners/lora/layer.py

@@ -29,7 +29,7 @@
 from peft.utils.other import transpose
 
 from .config import LoraConfig
-from .dora import DoraConv2dLayer, DoraLinearLayer
+from .dora import DoraConv2dLayer, DoraEmbeddingLayer, DoraLinearLayer
 
 
 class LoraLayer(BaseTunerLayer):
@@ -594,9 +594,6 @@ def __init__(
         super().__init__()
         LoraLayer.__init__(self, base_layer)
 
-        if use_dora:
-            raise ValueError(f"{self.__class__.__name__} does not support DoRA yet, please set it to False")
-
         self._active_adapter = adapter_name
         self.update_layer(
             adapter_name,
@@ -635,9 +632,31 @@ def update_layer(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weig
         elif init_lora_weights:
             self.reset_lora_parameters(adapter_name, init_lora_weights)
 
+        # call this before dora_init
         self._move_adapter_to_device_of_base_layer(adapter_name)
+
+        if use_dora:
+            self.dora_init(adapter_name)
+            self.use_dora[adapter_name] = True
+        else:
+            self.use_dora[adapter_name] = False
+
         self.set_adapter(self.active_adapters)
 
+    def dora_init(self, adapter_name: str) -> None:
+        if self.lora_magnitude_vector is None:
+            # first dora layer being added, add lora_magnitude_vector to the list of learnable parameters
+            self.adapter_layer_names = self.adapter_layer_names[:] + ("lora_magnitude_vector",)
+
+        dora_layer = DoraEmbeddingLayer(fan_in_fan_out=True)
+        lora_embedding_A = self.lora_embedding_A[adapter_name]
+        lora_embedding_B = self.lora_embedding_B[adapter_name]
+        scaling = self.scaling[adapter_name]
+        dora_layer.update_layer(
+            base_layer=self.get_base_layer(), lora_A=lora_embedding_A, lora_B=lora_embedding_B, scaling=scaling
+        )
+        self.lora_magnitude_vector[adapter_name] = dora_layer
+
     def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
         """
         Merge the active adapter weights into the base weights
@@ -785,8 +804,20 @@ def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
                 embedding_A = self.lora_embedding_A[active_adapter].T
                 embedding_B = self.lora_embedding_B[active_adapter].T
                 scaling = self.scaling[active_adapter]
-                after_A = self._embed(x, embedding_A)
-                result = result + (after_A @ embedding_B) * scaling
+
+                if not self.use_dora[active_adapter]:
+                    after_A = self._embed(x, embedding_A)
+                    result = result + (after_A @ embedding_B) * scaling
+                else:
+                    mag_norm_scale, dora_result = self.lora_magnitude_vector[active_adapter](
+                        x,
+                        lora_A=embedding_A,
+                        lora_B=embedding_B,
+                        scaling=scaling,
+                        base_layer=self.get_base_layer(),
+                        embed_fn=self._embed,
+                    )
+                    result = mag_norm_scale * result + dora_result
             result = result.to(torch_result_dtype)
 
         return result

tests/test_custom_models.py

@@ -88,6 +88,19 @@
     ("Embedding + transformers Conv1D 1 LoRA", "EmbConv1D", LoraConfig, {"target_modules": ["conv1d"]}),
     ("Embedding + transformers Conv1D 2 LoRA", "EmbConv1D", LoraConfig, {"target_modules": ["emb"]}),
     ("Embedding + transformers Conv1D 3 LoRA", "EmbConv1D", LoraConfig, {"target_modules": ["emb", "conv1d"]}),
+    (
+        "Embedding + transformers Conv1D 1 DoRA",
+        "EmbConv1D",
+        LoraConfig,
+        {"target_modules": ["conv1d"], "use_dora": True},
+    ),
+    ("Embedding + transformers Conv1D 2 DoRA", "EmbConv1D", LoraConfig, {"target_modules": ["emb"], "use_dora": True}),
+    (
+        "Embedding + transformers Conv1D 3 DoRA",
+        "EmbConv1D",
+        LoraConfig,
+        {"target_modules": ["emb", "conv1d"], "use_dora": True},
+    ),
     ("Conv2d 1 LoRA", "Conv2d", LoraConfig, {"target_modules": ["conv2d"]}),
     ("Conv2d 2 LoRA", "Conv2d", LoraConfig, {"target_modules": ["conv2d", "lin0"]}),
     ("Conv2d 1 LoRA with DoRA", "Conv2d", LoraConfig, {"target_modules": ["conv2d"], "use_dora": True}),
@@ -874,7 +887,9 @@ def test_only_params_are_updated(self, test_name, model_id, config_cls, config_k
         model_before = copy.deepcopy(model)
 
         model.train()
-        optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
+        # this high learning rate was found through testing to be necessary to avoid flakiness
+        lr = 100.0 if config_kwargs.get("use_dora") and model_id == "EmbConv1D" else 0.5
+        optimizer = torch.optim.SGD(model.parameters(), lr=lr)
 
         # train at least 3 steps for all parameters to be updated (probably this is required because of symmetry
         # breaking of some LoRA layers that are initialized with constants)
@@ -943,7 +958,6 @@ def test_parameters_after_loading_model(self, test_name, model_id, config_cls, c
     def test_disable_adapters(self, test_name, model_id, config_cls, config_kwargs):
         X = self.prepare_inputs_for_testing()
         model = self.transformers_class.from_pretrained(model_id).to(self.torch_device).eval()
-
         outputs_base = model(**X)
         if issubclass(config_cls, FourierFTConfig):
             config_kwargs = config_kwargs.copy()
@@ -1046,6 +1060,9 @@ def test_disable_adapters_with_merging(self, test_name, model_id, config_cls, co
         if issubclass(config_cls, IA3Config) and model_id == "Conv2d":  # more instability with Conv2d + IA3
             atol, rtol = 1e-3, 1e-3
 
+        if config_kwargs.get("use_dora") and model_id == "EmbConv1D":
+            atol, rtol = 1e-4, 1e-4
+
         # check that there is a difference in results after training
         assert not torch.allclose(outputs_before, outputs_after, atol=atol, rtol=rtol)