FIX Setting device for DoRA parameters (#7655)

Fix a bug that causes the the call to set_lora_device to ignore the DoRA
parameters.

Author: BenjaminBossan

src/diffusers/loaders/lora.py

@@ -1267,6 +1267,10 @@ def set_lora_device(self, adapter_names: List[str], device: Union[torch.device,
                 for adapter_name in adapter_names:
                     unet_module.lora_A[adapter_name].to(device)
                     unet_module.lora_B[adapter_name].to(device)
+                    # this is a param, not a module, so device placement is not in-place -> re-assign
+                    unet_module.lora_magnitude_vector[adapter_name] = unet_module.lora_magnitude_vector[
+                        adapter_name
+                    ].to(device)
 
         # Handle the text encoder
         modules_to_process = []
@@ -1283,6 +1287,10 @@ def set_lora_device(self, adapter_names: List[str], device: Union[torch.device,
                     for adapter_name in adapter_names:
                         text_encoder_module.lora_A[adapter_name].to(device)
                         text_encoder_module.lora_B[adapter_name].to(device)
+                        # this is a param, not a module, so device placement is not in-place -> re-assign
+                        text_encoder_module.lora_magnitude_vector[
+                            adapter_name
+                        ] = text_encoder_module.lora_magnitude_vector[adapter_name].to(device)
 
 
 class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):

tests/lora/test_lora_layers_sd.py

@@ -150,6 +150,54 @@ def test_integration_move_lora_cpu(self):
             if ("adapter-1" in n or "adapter-2" in n) and not isinstance(m, (nn.Dropout, nn.Identity)):
                 self.assertTrue(m.weight.device != torch.device("cpu"))
 
+    @require_torch_gpu
+    def test_integration_move_lora_dora_cpu(self):
+        from peft import LoraConfig
+
+        path = "runwayml/stable-diffusion-v1-5"
+        unet_lora_config = LoraConfig(
+            init_lora_weights="gaussian",
+            target_modules=["to_k", "to_q", "to_v", "to_out.0"],
+            use_dora=True,
+        )
+        text_lora_config = LoraConfig(
+            init_lora_weights="gaussian",
+            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
+            use_dora=True,
+        )
+
+        pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+        pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
+
+        self.assertTrue(
+            check_if_lora_correctly_set(pipe.text_encoder),
+            "Lora not correctly set in text encoder",
+        )
+
+        self.assertTrue(
+            check_if_lora_correctly_set(pipe.unet),
+            "Lora not correctly set in text encoder",
+        )
+
+        for name, param in pipe.unet.named_parameters():
+            if "lora_" in name:
+                self.assertEqual(param.device, torch.device("cpu"))
+
+        for name, param in pipe.text_encoder.named_parameters():
+            if "lora_" in name:
+                self.assertEqual(param.device, torch.device("cpu"))
+
+        pipe.set_lora_device(["adapter-1"], torch_device)
+
+        for name, param in pipe.unet.named_parameters():
+            if "lora_" in name:
+                self.assertNotEqual(param.device, torch.device("cpu"))
+
+        for name, param in pipe.text_encoder.named_parameters():
+            if "lora_" in name:
+                self.assertNotEqual(param.device, torch.device("cpu"))
+
 
 @slow
 @require_torch_gpu