[lora] add lora APIs for booster, support lora for TorchDDP (#4981)

* add apis and peft requirement

* add liscense and implement apis

* add checkpointio apis

* add torchddp fwd_bwd test

* add support_lora methods

* add checkpointio test and debug

* delete unneeded codes

* remove peft from LICENSE

* add concrete methods for enable_lora

* simplify enable_lora api

* fix requirements

colossalai/booster/booster.py

@@ -8,6 +8,14 @@ from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
 from torch.utils.data import DataLoader
 
+SUPPORT_PEFT = False
+try:
+    import peft
+
+    SUPPORT_PEFT = True
+except ImportError:
+    pass
+
 import colossalai.interface.pretrained as pretrained_utils
 from colossalai.checkpoint_io import GeneralCheckpointIO
 from colossalai.interface import ModelWrapper, OptimizerWrapper
@@ -221,6 +229,38 @@ class Booster:
         assert self.plugin.support_no_sync(), f"The plugin {self.plugin.__class__.__name__} does not support no_sync."
         return self.plugin.no_sync(model, optimizer)
 
+    def enable_lora(
+        self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: "peft.LoraConfig" = None
+    ) -> nn.Module:
+        """
+        Wrap the passed in model with LoRA modules for training. If pretrained directory is provided, lora configs and weights are loaded from that directory.
+        Lora in ColossalAI is implemented using Huggingface peft library, so the arguments for Lora configuration are same as those of peft.
+
+        Args:
+            model (nn.Module): The model to be appended with LoRA modules.
+            pretrained_dir(str, optional): The path to the pretrained directory, can be a local directory
+                or model_id of a PEFT configuration hosted inside a model repo on the Hugging Face Hub.
+                When set to None, create new lora configs and weights for the model using the passed in lora_config. Defaults to None.
+            lora_config: (peft.LoraConfig, optional): Passed in LoraConfig for peft. Defaults to None.
+        """
+        if not SUPPORT_PEFT:
+            raise ImportError("Please install Huggingface Peft library to enable lora features in ColossalAI!")
+
+        assert self.plugin is not None, f"Lora can only be enabled when a plugin is provided."
+        assert self.plugin.support_lora(), f"The plugin {self.plugin.__class__.__name__} does not support lora."
+        if pretrained_dir is None:
+            assert (
+                lora_config is not None
+            ), "Please provide configuration for Lora when pretrained directory path isn't passed in."
+            assert isinstance(
+                lora_config, peft.LoraConfig
+            ), "The passed in configuration should be an instance of peft.LoraConfig."
+        if lora_config is None:
+            assert (
+                pretrained_dir is not None
+            ), "Please provide pretrained directory path if not passing in lora configuration."
+        return self.plugin.enable_lora(model, pretrained_dir, lora_config)
+
     def load_model(self, model: Union[nn.Module, ModelWrapper], checkpoint: str, strict: bool = True) -> None:
         """Load model from checkpoint.
 
@@ -323,3 +363,20 @@ class Booster:
             checkpoint (str): Path to the checkpoint. It must be a local file path.
         """
         self.checkpoint_io.load_lr_scheduler(lr_scheduler, checkpoint)
+
+    def save_lora_as_pretrained(
+        self, model: Union[nn.Module, ModelWrapper], checkpoint: str, use_safetensors: bool = False
+    ) -> None:
+        """
+        Save the lora adapters and adapter configuration file to a pretrained checkpoint directory.
+
+        Args:
+            model (Union[nn.Module, ModelWrapper]): A model boosted by Booster.
+            checkpoint (str): Path to the checkpoint directory. It must be a local path.
+            use_safetensors (bool, optional): Whether to use safe tensors when saving. Defaults to False.
+        """
+        if not SUPPORT_PEFT:
+            raise ImportError("Please install Huggingface Peft library to enable lora features in ColossalAI!")
+        assert self.plugin is not None, f"Lora can only be enabled when a plugin is provided."
+        assert self.plugin.support_lora(), f"The plugin {self.plugin.__class__.__name__} does not support lora."
+        self.checkpoint_io.save_lora_as_pretrained(model, checkpoint, use_safetensors)