[PEFT] Allow PEFT model dict to be loaded

src/transformers/integrations/peft.py

@@ -12,13 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import inspect
-from typing import Optional
+from typing import TYPE_CHECKING, Any, Dict, Optional
 
 from ..utils import (
  check_peft_version,
  find_adapter_config_file,
  is_accelerate_available,
  is_peft_available,
+ is_torch_available,
  logging,
 )
 
@@ -30,6 +31,11 @@
 # Minimum PEFT version supported for the integration
 MIN_PEFT_VERSION = "0.5.0"
 
+if TYPE_CHECKING:
+ if is_torch_available():
+ import torch
+
+
 logger = logging.get_logger(__name__)
 
 
@@ -61,14 +67,16 @@ class PeftAdapterMixin:
 
  def load_adapter(
  self,
- peft_model_id: str,
+ peft_model_id: Optional[str] = None,
  adapter_name: Optional[str] = None,
  revision: Optional[str] = None,
  token: Optional[str] = None,
  device_map: Optional[str] = "auto",
  max_memory: Optional[str] = None,
  offload_folder: Optional[str] = None,
  offload_index: Optional[int] = None,
+ peft_config: Dict[str, Any] = None,
+ adapter_state_dict: Optional[Dict[str, "torch.Tensor"]] = None,
  ) -> None:
  """
  Load adapter weights from file or remote Hub folder. If you are not familiar with adapters and PEFT methods, we
@@ -77,7 +85,7 @@ def load_adapter(
  Requires peft as a backend to load the adapter weights.
 
  Args:
- peft_model_id (`str`):
+ peft_model_id (`str`, *optional*):
  The identifier of the model to look for on the Hub, or a local path to the saved adapter config file
  and adapter weights.
  adapter_name (`str`, *optional*):
@@ -114,6 +122,12 @@ def load_adapter(
  If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
  offload_index (`int`, `optional`):
  `offload_index` argument to be passed to `accelerate.dispatch_model` method.
+ peft_config (`Dict[str, Any]`, *optional*):
+ The configuration of the adapter to add, supported adapters are non-prefix tuning and adaption prompts
+ methods. This argument is used in case users directly pass PEFT state dicts
+ 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
  """
  check_peft_version(min_version=MIN_PEFT_VERSION)
 
@@ -122,33 +136,41 @@ def load_adapter(
  from peft import PeftConfig, inject_adapter_in_model, load_peft_weights
  from peft.utils import set_peft_model_state_dict
 
- if not self._hf_peft_config_loaded:
- self._hf_peft_config_loaded = True
- elif adapter_name in self.peft_config:
+ if self._hf_peft_config_loaded and adapter_name in self.peft_config:
  raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
 
- adapter_config_file = find_adapter_config_file(
- peft_model_id,
- revision=revision,
- token=token,
- )
-
- if adapter_config_file is None:
+ if peft_model_id is None and (adapter_state_dict is None and peft_config is None):
  raise ValueError(
- f"adapter model file not found in {peft_model_id}. Make sure you are passing the correct path to the "
- "adapter model."
+ "You should either pass a `peft_model_id` or a `peft_config` and `adapter_state_dict` to load an adapter."
  )
 
- loaded_peft_config = PeftConfig.from_pretrained(
- peft_model_id,
- revision=revision,
- use_auth_token=token,
- )
+ if peft_config is None:
+ adapter_config_file = find_adapter_config_file(
+ peft_model_id,
+ revision=revision,
+ token=token,
+ )
+
+ if adapter_config_file is None:
+ raise ValueError(
+ f"adapter model file not found in {peft_model_id}. Make sure you are passing the correct path to the "
+ "adapter model."
+ )
+
+ peft_config = PeftConfig.from_pretrained(
+ peft_model_id,
+ revision=revision,
+ use_auth_token=token,
+ )
 
  # Create and add fresh new adapters into the model.
- inject_adapter_in_model(loaded_peft_config, self, adapter_name)
+ inject_adapter_in_model(peft_config, self, adapter_name)
+
+ if not self._hf_peft_config_loaded:
+ self._hf_peft_config_loaded = True
 
- adapter_state_dict = load_peft_weights(peft_model_id, revision=revision, use_auth_token=token)
+ if peft_model_id is not None:
+ adapter_state_dict = load_peft_weights(peft_model_id, revision=revision, use_auth_token=token)
 
  # We need to pre-process the state dict to remove unneeded prefixes - for backward compatibility
  processed_adapter_state_dict = {}

tests/peft_integration/test_peft_integration.py

@@ -16,6 +16,8 @@
 import tempfile
 import unittest
 
+from huggingface_hub import hf_hub_download
+
 from transformers import AutoModelForCausalLM, OPTForCausalLM
 from transformers.testing_utils import require_peft, require_torch, require_torch_gpu, slow, torch_device
 from transformers.utils import is_torch_available
@@ -300,3 +302,32 @@ def test_peft_pipeline(self):
  for model_id in self.peft_test_model_ids:
  pipe = pipeline("text-generation", model_id)
  _ = pipe("Hello")
+
+ def test_peft_add_adapter_with_state_dict(self):
+ """
+ Simple test that tests the basic usage of PEFT model through `from_pretrained`. This test tests if
+ add_adapter works as expected with a state_dict being passed.
+ """
+ from peft import LoraConfig
+
+ dummy_input = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device)
+
+ 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(init_lora_weights=False)
+
+ with self.assertRaises(ValueError):
+ model.load_adapter(peft_model_id=None)
+
+ state_dict_path = hf_hub_download(peft_model_id, "adapter_model.bin")
+
+ dummy_state_dict = torch.load(state_dict_path)
+
+ model.load_adapter(adapter_state_dict=dummy_state_dict, peft_config=peft_config)
+
+ self.assertTrue(self._check_lora_correctly_converted(model))
+
+ # dummy generation
+ _ = model.generate(input_ids=dummy_input)