infer device and dtype in LoraAdapter

README.md

@@ -183,7 +183,7 @@ from refiners.adapters.lora import LoraAdapter
 
 for layer in vit.layers(fl.Attention):
     for linear, parent in layer.walk(fl.Linear):
-        adapter = LoraAdapter(target=linear, rank=64, device=vit.device, dtype=vit.dtype)
+        adapter = LoraAdapter(target=linear, rank=64)
         adapter.inject(parent)
 
 # ... and load existing weights if the LoRAs are pretrained ...

scripts/training/finetune-ldm-lora.py

@@ -33,8 +33,6 @@ class LoraConfig(BaseModel):
                 adapter = LoraAdapter(
                     target=linear,
                     rank=self.rank,
-                    device=module.device,
-                    dtype=module.dtype,
                 )
                 adapter.inject(parent)
                 for linear in adapter.Lora.layers(fl.Linear):

src/refiners/adapters/lora.py

@@ -49,8 +49,6 @@ class LoraAdapter(fl.Sum, Adapter[fl.Linear]):
         target: fl.Linear,
         rank: int = 16,
         scale: float = 1.0,
-        device: Device | str | None = None,
-        dtype: DType | None = None,
     ) -> None:
         self.in_features = target.in_features
         self.out_features = target.out_features
@@ -63,8 +61,8 @@ class LoraAdapter(fl.Sum, Adapter[fl.Linear]):
                     in_features=target.in_features,
                     out_features=target.out_features,
                     rank=rank,
-                    device=device,
-                    dtype=dtype,
+                    device=target.device,
+                    dtype=target.dtype,
                 ),
             )
         self.Lora.set_scale(scale=scale)

src/refiners/foundationals/latent_diffusion/lora.py

@@ -49,8 +49,6 @@ def apply_loras_to_target(module: fl.Chain, target: LoraTarget, rank: int, scale
                 target=linear,
                 rank=rank,
                 scale=scale,
-                device=module.device,
-                dtype=module.dtype,
             )
             adapter.inject(parent)