[LoRA depcrecation] LoRA depcrecation trilogy (huggingface#6450)

* edebug

* debug

* more debug

* more more debug

* remove tests for LoRAAttnProcessors.

* rename

Author: sayakpaul

tests/lora/test_lora_layers_old_backend.py

@@ -1496,7 +1496,7 @@ def prepare_init_args_and_inputs_for_common(self):
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
 
-    def test_lora_processors(self):
+    def test_lora_at_different_scales(self):
         # enable deterministic behavior for gradient checkpointing
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 
@@ -1514,9 +1514,6 @@ def test_lora_processors(self):
         model.load_attn_procs(lora_params)
         model.to(torch_device)
 
-        # test that attn processors can be set to itself
-        model.set_attn_processor(model.attn_processors)
-
         with torch.no_grad():
             sample2 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.0}).sample
             sample3 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
@@ -1595,7 +1592,7 @@ def test_lora_xformers_on_off(self, expected_max_diff=6e-4):
 
 
 @deprecate_after_peft_backend
-class UNet3DConditionModelTests(unittest.TestCase):
+class UNet3DConditionLoRAModelTests(unittest.TestCase):
     model_class = UNet3DConditionModel
     main_input_name = "sample"
 
@@ -1638,7 +1635,7 @@ def prepare_init_args_and_inputs_for_common(self):
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
 
-    def test_lora_processors(self):
+    def test_lora_at_different_scales(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 
         init_dict["attention_head_dim"] = 8
@@ -1655,9 +1652,6 @@ def test_lora_processors(self):
         model.load_attn_procs(unet_lora_params)
         model.to(torch_device)
 
-        # test that attn processors can be set to itself
-        model.set_attn_processor(model.attn_processors)
-
         with torch.no_grad():
             sample2 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.0}).sample
             sample3 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample