update

Author: sayakpaul

tests/lora/test_lora_layers_wanvace.py

@@ -12,13 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import sys
 import tempfile
 import unittest
 
 import numpy as np
 import pytest
+import safetensors.torch
 import torch
+from peft.utils import get_peft_model_state_dict
 from PIL import Image
 from transformers import AutoTokenizer, T5EncoderModel
 
@@ -163,6 +166,7 @@ def test_layerwise_casting_inference_denoiser(self):
     @require_peft_version_greater("0.13.2")
     def test_lora_exclude_modules_wanvace(self):
         scheduler_cls = self.scheduler_classes[0]
+        exclude_module_name = "vace_blocks.0.proj_out"
         components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
         pipe = self.pipeline_class(**components).to(torch_device)
         _, _, inputs = self.get_dummy_inputs(with_generator=False)
@@ -172,22 +176,34 @@ def test_lora_exclude_modules_wanvace(self):
 
         # only supported for `denoiser` now
         denoiser_lora_config.target_modules = ["proj_out"]
-        denoiser_lora_config.exclude_modules = ["vace_blocks.0.proj_out"]
+        denoiser_lora_config.exclude_modules = [exclude_module_name]
         pipe, _ = self.add_adapters_to_pipeline(
             pipe, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
         )
+        # The state dict shouldn't contain the modules to be excluded from LoRA.
+        state_dict_from_model = get_peft_model_state_dict(pipe.transformer, adapter_name="default")
+        self.assertTrue(not any(exclude_module_name in k for k in state_dict_from_model))
+        self.assertTrue(any("proj_out" in k for k in state_dict_from_model))
         output_lora_exclude_modules = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
         with tempfile.TemporaryDirectory() as tmpdir:
             modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
             lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
-            lora_metadatas = self._get_lora_adapter_metadata(modules_to_save)
-            self.pipeline_class.save_lora_weights(save_directory=tmpdir, **lora_state_dicts, **lora_metadatas)
+            self.pipeline_class.save_lora_weights(save_directory=tmpdir, **lora_state_dicts)
             pipe.unload_lora_weights()
+
+            # Check in the loaded state dict.
+            loaded_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            self.assertTrue(not any(exclude_module_name in k for k in loaded_state_dict))
+            self.assertTrue(any("proj_out" in k for k in loaded_state_dict))
+
+            # Check in the state dict obtained after loading LoRA.
             pipe.load_lora_weights(tmpdir)
+            state_dict_from_model = get_peft_model_state_dict(pipe.transformer, adapter_name="default_0")
+            self.assertTrue(not any(exclude_module_name in k for k in state_dict_from_model))
+            self.assertTrue(any("proj_out" in k for k in state_dict_from_model))
 
             output_lora_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
-
             self.assertTrue(
                 not np.allclose(output_no_lora, output_lora_exclude_modules, atol=1e-3, rtol=1e-3),
                 "LoRA should change outputs.",