[PEFT] Support low_cpu_mem_usage option for PEFT loading adapters

src/transformers/integrations/peft.py

@@ -11,10 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import importlib
 import inspect
 import warnings
 from typing import Any, Dict, List, Optional, Union
 
+from packaging import version
+
 from ..utils import (
     check_peft_version,
     find_adapter_config_file,
@@ -77,6 +80,7 @@ def load_adapter(
         offload_index: Optional[int] = None,
         peft_config: Dict[str, Any] = None,
         adapter_state_dict: Optional[Dict[str, "torch.Tensor"]] = None,
+        low_cpu_mem_usage: bool = False,
         adapter_kwargs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """
@@ -129,12 +133,27 @@ def load_adapter(
             adapter_state_dict (`Dict[str, torch.Tensor]`, *optional*):
                 The state dict of the adapter to load. This argument is used in case users directly pass PEFT state
                 dicts
+            low_cpu_mem_usage (`bool`, *optional*, defaults to `False`):
+                Reduce memory usage while loading the PEFT adapter. This should also speed up the loading process.
+                Requires PEFT version 0.13.0 or higher.
             adapter_kwargs (`Dict[str, Any]`, *optional*):
                 Additional keyword arguments passed along to the `from_pretrained` method of the adapter config and
                 `find_adapter_config_file` method.
         """
         check_peft_version(min_version=MIN_PEFT_VERSION)
 
+        # peft only supports low_cpu_mem_usage starting from v0.13.0
+        peft_load_kwargs = {}
+        if low_cpu_mem_usage:
+            min_version_lcmu = "0.13.0"
+            if version.parse(importlib.metadata.version("peft")) >= version.parse(min_version_lcmu):
+                peft_load_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
+            else:
+                raise ValueError(
+                    "The version of PEFT you are using does not support `low_cpu_mem_usage` yet, "
+                    f"please install PEFT >= {min_version_lcmu}."
+                )
+
         adapter_name = adapter_name if adapter_name is not None else "default"
         if adapter_kwargs is None:
             adapter_kwargs = {}
@@ -192,7 +211,7 @@ def load_adapter(
             )
 
         # Create and add fresh new adapters into the model.
-        inject_adapter_in_model(peft_config, self, adapter_name)
+        inject_adapter_in_model(peft_config, self, adapter_name, **peft_load_kwargs)
 
         if not self._hf_peft_config_loaded:
             self._hf_peft_config_loaded = True
@@ -211,7 +230,9 @@ def load_adapter(
             processed_adapter_state_dict[new_key] = value
 
         # Load state dict
-        incompatible_keys = set_peft_model_state_dict(self, processed_adapter_state_dict, adapter_name)
+        incompatible_keys = set_peft_model_state_dict(
+            self, processed_adapter_state_dict, adapter_name, **peft_load_kwargs
+        )
 
         if incompatible_keys is not None:
             # check only for unexpected keys

tests/peft_integration/test_peft_integration.py

@@ -12,11 +12,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import importlib
 import os
 import tempfile
 import unittest
 
 from huggingface_hub import hf_hub_download
+from packaging import version
 
 from transformers import AutoModelForCausalLM, OPTForCausalLM
 from transformers.testing_utils import (
@@ -478,6 +480,48 @@ def test_peft_add_adapter_with_state_dict(self):
                 # dummy generation
                 _ = model.generate(input_ids=dummy_input)
 
+    def test_peft_add_adapter_with_state_dict_low_cpu_mem_usage(self):
+        """
+        Check the usage of low_cpu_mem_usage, which is supported in PEFT >= 0.13.0
+        """
+        from peft import LoraConfig
+
+        min_version_lcmu = "0.13.0"
+        is_lcmu_supported = version.parse(importlib.metadata.version("peft")) >= version.parse(min_version_lcmu)
+
+        for model_id, peft_model_id in zip(self.transformers_test_model_ids, self.peft_test_model_ids):
+            for transformers_class in self.transformers_test_model_classes:
+                model = transformers_class.from_pretrained(model_id).to(torch_device)
+
+                peft_config = LoraConfig()
+                state_dict_path = hf_hub_download(peft_model_id, "adapter_model.bin")
+                dummy_state_dict = torch.load(state_dict_path)
+
+                # this should always work
+                model.load_adapter(
+                    adapter_state_dict=dummy_state_dict, peft_config=peft_config, low_cpu_mem_usage=False
+                )
+
+                if is_lcmu_supported:
+                    # if supported, this should not raise an error
+                    model.load_adapter(
+                        adapter_state_dict=dummy_state_dict,
+                        adapter_name="other",
+                        peft_config=peft_config,
+                        low_cpu_mem_usage=True,
+                    )
+                    # after loading, no meta device should be remaining
+                    self.assertFalse(any((p.device.type == "meta") for p in model.parameters()))
+                else:
+                    err_msg = r"The version of PEFT you are using does not support `low_cpu_mem_usage` yet"
+                    with self.assertRaisesRegex(ValueError, err_msg):
+                        model.load_adapter(
+                            adapter_state_dict=dummy_state_dict,
+                            adapter_name="other",
+                            peft_config=peft_config,
+                            low_cpu_mem_usage=True,
+                        )
+
     def test_peft_from_pretrained_hub_kwargs(self):
         """
         Tests different combinations of PEFT model + from_pretrained + hub kwargs