FIX Missing low_cpu_mem_usage argument (huggingface#2156)

The newly introduced low_cpu_mem_usage argument was not propagated to
the add_adapter method of all PeftModel task types. This is now fixed
and tests were added.

src/peft/peft_model.py

@@ -1421,7 +1421,7 @@ def __init__(
         # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
         _set_trainable(self, adapter_name)
 
-    def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
+    def add_adapter(self, adapter_name: str, peft_config: PeftConfig, low_cpu_mem_usage: bool = False) -> None:
         """
         Add an adapter to the model based on the passed configuration.
 
@@ -1437,6 +1437,10 @@ def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
                 The name of the adapter to be added.
             peft_config ([`PeftConfig`]):
                 The configuration of the adapter to be added.
+            low_cpu_mem_usage (`bool`, `optional`, defaults to `False`):
+                Create empty adapter weights on meta device. Useful to speed up the process when loading saved
+                adapters. Don't use this option when creating a new PEFT adapter for training.
+
         """
         # ensure that additional adapters also add the classifier layer to modules_to_save
         if hasattr(peft_config, "modules_to_save"):
@@ -1446,7 +1450,7 @@ def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
             else:
                 peft_config.modules_to_save.extend(classifier_module_names)
 
-        return super().add_adapter(adapter_name, peft_config)
+        return super().add_adapter(adapter_name, peft_config, low_cpu_mem_usage=low_cpu_mem_usage)
 
     def forward(
         self,
@@ -2140,7 +2144,7 @@ def __init__(
         # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
         _set_trainable(self, adapter_name)
 
-    def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
+    def add_adapter(self, adapter_name: str, peft_config: PeftConfig, low_cpu_mem_usage: bool = False) -> None:
         """
         Add an adapter to the model based on the passed configuration.
 
@@ -2156,6 +2160,10 @@ def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
                 The name of the adapter to be added.
             peft_config ([`PeftConfig`]):
                 The configuration of the adapter to be added.
+            low_cpu_mem_usage (`bool`, `optional`, defaults to `False`):
+                Create empty adapter weights on meta device. Useful to speed up the process when loading saved
+                adapters. Don't use this option when creating a new PEFT adapter for training.
+
         """
         # ensure that additional adapters also add the classifier layer to modules_to_save
         if hasattr(peft_config, "modules_to_save"):
@@ -2165,7 +2173,7 @@ def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
             else:
                 peft_config.modules_to_save.extend(classifier_module_names)
 
-        return super().add_adapter(adapter_name, peft_config)
+        return super().add_adapter(adapter_name, peft_config, low_cpu_mem_usage=low_cpu_mem_usage)
 
     def forward(
         self,
@@ -2357,7 +2365,7 @@ def __init__(
         # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
         _set_trainable(self, adapter_name)
 
-    def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
+    def add_adapter(self, adapter_name: str, peft_config: PeftConfig, low_cpu_mem_usage: bool = False) -> None:
         """
         Add an adapter to the model based on the passed configuration.
 
@@ -2373,6 +2381,10 @@ def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
                 The name of the adapter to be added.
             peft_config ([`PeftConfig`]):
                 The configuration of the adapter to be added.
+            low_cpu_mem_usage (`bool`, `optional`, defaults to `False`):
+                Create empty adapter weights on meta device. Useful to speed up the process when loading saved
+                adapters. Don't use this option when creating a new PEFT adapter for training.
+
         """
         # ensure that additional adapters also add the classifier layer to modules_to_save
         if hasattr(peft_config, "modules_to_save"):
@@ -2382,7 +2394,7 @@ def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
             else:
                 peft_config.modules_to_save.extend(qa_module_names)
 
-        return super().add_adapter(adapter_name, peft_config)
+        return super().add_adapter(adapter_name, peft_config, low_cpu_mem_usage=low_cpu_mem_usage)
 
     def forward(
         self,

tests/test_decoder_models.py

@@ -200,6 +200,9 @@ def test_save_pretrained_selected_adapters(self, test_name, model_id, config_cls
     def test_save_pretrained_selected_adapters_pickle(self, test_name, model_id, config_cls, config_kwargs):
         self._test_save_pretrained_selected_adapters(model_id, config_cls, config_kwargs, safe_serialization=False)
 
+    def test_load_model_low_cpu_mem_usage(self):
+        self._test_load_model_low_cpu_mem_usage(PEFT_DECODER_MODELS_TO_TEST[0], LoraConfig, {})
+
     @parameterized.expand(
         PeftTestConfigManager.get_grid_parameters(FULL_GRID, filter_params_func=skip_oft_or_hra_and_gpt2)
     )

tests/test_encoder_decoder_models.py

@@ -82,6 +82,9 @@ def test_save_pretrained_selected_adapters(self, test_name, model_id, config_cls
     def test_save_pretrained_selected_adapters_pickle(self, test_name, model_id, config_cls, config_kwargs):
         self._test_save_pretrained_selected_adapters(model_id, config_cls, config_kwargs, safe_serialization=False)
 
+    def test_load_model_low_cpu_mem_usage(self):
+        self._test_load_model_low_cpu_mem_usage(PEFT_ENCODER_DECODER_MODELS_TO_TEST[0], LoraConfig, {})
+
     @parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
     def test_from_pretrained_config_construction(self, test_name, model_id, config_cls, config_kwargs):
         self._test_from_pretrained_config_construction(model_id, config_cls, config_kwargs)

tests/test_feature_extraction_models.py

@@ -17,7 +17,7 @@
 from parameterized import parameterized
 from transformers import AutoModel
 
-from peft import PrefixTuningConfig, PromptLearningConfig
+from peft import LoraConfig, PrefixTuningConfig, PromptLearningConfig
 
 from .testing_common import PeftCommonTester, PeftTestConfigManager
 
@@ -99,6 +99,9 @@ def test_save_pretrained(self, test_name, model_id, config_cls, config_kwargs):
     def test_save_pretrained_selected_adapters(self, test_name, model_id, config_cls, config_kwargs):
         self._test_save_pretrained_selected_adapters(model_id, config_cls, config_kwargs)
 
+    def test_load_model_low_cpu_mem_usage(self):
+        self._test_load_model_low_cpu_mem_usage(PEFT_FEATURE_EXTRACTION_MODELS_TO_TEST[0], LoraConfig, {})
+
     @parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
     def test_from_pretrained_config_construction(self, test_name, model_id, config_cls, config_kwargs):
         self._test_from_pretrained_config_construction(model_id, config_cls, config_kwargs)