Integrate X-LoRA

src/peft/__init__.py

@@ -82,6 +82,8 @@
  LNTuningModel,
  VeraConfig,
  VeraModel,
+ XLoraConfig,
+ XLoraModel,
 )
 from .utils import (
  TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,

src/peft/mapping.py

@@ -19,6 +19,8 @@
 
 import torch
 
+from peft.tuners.xlora.model import XLoraModel
+
 from .config import PeftConfig
 from .mixed_model import PeftMixedModel
 from .peft_model import (
@@ -56,6 +58,7 @@
  PromptTuningConfig,
  VeraConfig,
  VeraModel,
+ XLoraConfig,
 )
 from .tuners.tuners_utils import BaseTuner as _BaseTuner
 from .utils import _prepare_prompt_learning_config
@@ -90,6 +93,7 @@
  "POLY": PolyConfig,
  "LN_TUNING": LNTuningConfig,
  "VERA": VeraConfig,
+ "XLORA": XLoraConfig,
 }
 
 PEFT_TYPE_TO_TUNER_MAPPING: dict[str, type[_BaseTuner]] = {
@@ -103,6 +107,7 @@
  "POLY": PolyModel,
  "LN_TUNING": LNTuningModel,
  "VERA": VeraModel,
+ "XLORA": XLoraModel,
 }
 
 

src/peft/mixed_model.py

@@ -23,8 +23,6 @@
 from torch import nn
 from transformers.utils import PushToHubMixin
 
-from peft.tuners.mixed import COMPATIBLE_TUNER_TYPES
-
 from .config import PeftConfig
 from .peft_model import PeftModel
 from .tuners import (
@@ -36,6 +34,7 @@
  MixedModel,
  OFTModel,
 )
+from .tuners.mixed import COMPATIBLE_TUNER_TYPES
 from .utils import PeftType, _set_adapter, _set_trainable
 
 

src/peft/peft_model.py

@@ -29,7 +29,7 @@
 from accelerate import dispatch_model, infer_auto_device_map
 from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules
 from accelerate.utils import get_balanced_memory, named_module_tensors
-from huggingface_hub import ModelCard, ModelCardData, hf_hub_download
+from huggingface_hub import HfFileSystem, ModelCard, ModelCardData, hf_hub_download
 from safetensors import safe_open
 from safetensors.torch import save_file as safe_save_file
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
@@ -55,6 +55,8 @@
  PromptEmbedding,
  PromptEncoder,
  VeraModel,
+ XLoraConfig,
+ XLoraModel,
 )
 from .tuners.tuners_utils import BaseTuner, BaseTunerLayer
 from .utils import (
@@ -91,6 +93,7 @@
  PeftType.POLY: PolyModel,
  PeftType.LN_TUNING: LNTuningModel,
  PeftType.VERA: VeraModel,
+ PeftType.XLORA: XLoraModel,
 }
 
 
@@ -479,13 +482,38 @@ def from_pretrained(
  raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
  else:
  config.inference_mode = not is_trainable
+ if isinstance(getattr(model, "base_model", None), XLoraModel):
+ if not isinstance(config, XLoraConfig):
+ raise TypeError(f"Expected 'XLoraConfig', got '{type(config)}' instead.")
+ if "adapters" in kwargs:
+ config.adapters = kwargs["adapters"]
+ else:
+ # If the path is on HF hub, then we get the adapter names to create a subfolders list which tells
+ # `load_adapter` where the adapters are.
+ if not os.path.exists(model_id):
+ s = HfFileSystem()
+
+ # The names of the adapters which must be in folders
+ adapter_names = [
+ file["name"][len(model_id) + 1 :] for file in s.ls(model_id) if file["type"] == "directory"
+ ]
+ # Prepare a dict of adapter paths, which really just point to the hf id; we will use the subfolders
+ adapter_paths = {}
+ for adapter_name in adapter_names:
+ adapter_paths[adapter_name] = os.path.join(model_id, model_id)
+ config.adapters = adapter_paths
+ config._subfolders = adapter_names
+ else:
+ if "adapters" not in kwargs:
+ raise ValueError("If model_id is a local path, then `adapters` must be passed in kwargs.")
 
  if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys():
  model = cls(model, config, adapter_name, autocast_adapter_dtype=autocast_adapter_dtype)
  else:
  model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](
  model, config, adapter_name, autocast_adapter_dtype=autocast_adapter_dtype
  )
+
  model.load_adapter(
  model_id, adapter_name, is_trainable=is_trainable, autocast_adapter_dtype=autocast_adapter_dtype, **kwargs
  )

src/peft/tuners/__init__.py

@@ -33,3 +33,4 @@
 from .poly import PolyConfig, PolyModel
 from .ln_tuning import LNTuningConfig, LNTuningModel
 from .vera import VeraConfig, VeraModel
+from .xlora import XLoraConfig, XLoraModel

src/peft/tuners/tuners_utils.py

@@ -30,6 +30,8 @@
 from transformers.pytorch_utils import Conv1D
 
 from peft.utils import INCLUDE_LINEAR_LAYERS_SHORTHAND
+from peft.utils.constants import DUMMY_TARGET_MODULES
+from peft.utils.peft_types import PeftType
 
 from ..config import PeftConfig
 from ..utils import ModulesToSaveWrapper, _get_submodules
@@ -141,7 +143,12 @@ class BaseTuner(nn.Module, ABC):
  double-check that the `config.target_modules` were specified correctly.
  """
 
- def __init__(self, model, peft_config: Union[PeftConfig, dict[str, PeftConfig]], adapter_name: str) -> None:
+ def __init__(
+ self,
+ model,
+ peft_config: Union[PeftConfig, dict[str, PeftConfig]],
+ adapter_name: str,
+ ) -> None:
  super().__init__()
 
  self.model = model
@@ -164,7 +171,8 @@ def __init__(self, model, peft_config: Union[PeftConfig, dict[str, PeftConfig]],
 
  self.active_adapter: str | list[str] = adapter_name
  self._pre_injection_hook(self.model, self.peft_config[adapter_name], adapter_name)
- self.inject_adapter(self.model, adapter_name)
+ if peft_config != PeftType.XLORA or peft_config[adapter_name] != PeftType.XLORA:
+ self.inject_adapter(self.model, adapter_name)
 
  # Copy the peft_config in the injected model.
  self.model.peft_config = self.peft_config
@@ -389,6 +397,11 @@ def inject_adapter(self, model: nn.Module, adapter_name: str, autocast_adapter_d
  is_target_modules_in_base_model = False
  key_list = [key for key, _ in model.named_modules()]
 
+ if getattr(peft_config, "target_modules", None) == DUMMY_TARGET_MODULES:
+ # dummy adapter, we allow not matching any module
+ key_list = []
+ is_target_modules_in_base_model = True
+
  # update peft_config.target_modules if required
  peft_config = _maybe_include_all_linear_layers(peft_config, model)
 
@@ -417,7 +430,8 @@ def inject_adapter(self, model: nn.Module, adapter_name: str, autocast_adapter_d
  parent, target, target_name = _get_submodules(model, key)
  self._create_and_replace(peft_config, adapter_name, target, target_name, parent, current_key=key)
 
- if not is_target_modules_in_base_model:
+ # Handle X-LoRA case.
+ if not is_target_modules_in_base_model and hasattr(peft_config, "target_modules"):
  raise ValueError(
  f"Target modules {peft_config.target_modules} not found in the base model. "
  f"Please check the target modules and try again."
@@ -776,6 +790,8 @@ def _maybe_include_all_linear_layers(peft_config: PeftConfig, model: nn.Module)
  Helper function to update `target_modules` to all linear/Conv1D layers if provided as 'all-linear'. Adapted from
  the QLoRA repository: https://github.com/artidoro/qlora/blob/main/qlora.py
  """
+ if not hasattr(peft_config, "target_modules"):
+ return peft_config
 
  # if `target_modules` is a string, convert to lower case and check if it matches "all-linear"
  if not (

src/peft/tuners/xlora/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+from .config import XLoraConfig
+from .model import XLoraModel
+
+
+__all__ = ["XLoraConfig", "XLoraModel"]