[lora] add lora APIs for booster, support lora for TorchDDP

colossalai/booster/booster.py

@@ -8,6 +8,14 @@
 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 @@ def no_sync(self, model: nn.Module = None, optimizer: OptimizerWrapper = None) -
         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 @@ def load_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str) -> None:
             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)

colossalai/booster/plugin/gemini_plugin.py

@@ -2,7 +2,7 @@
 import logging
 import os
 from pathlib import Path
-from typing import Callable, Iterator, List, Optional, Tuple
+from typing import Callable, Dict, Iterator, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -360,6 +360,9 @@ def __init__(
     def support_no_sync(self) -> bool:
         return False
 
+    def support_lora(self) -> bool:
+        return False
+
     def control_precision(self) -> bool:
         return True
 
@@ -408,3 +411,8 @@ def get_checkpoint_io(self) -> CheckpointIO:
 
     def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
         raise NotImplementedError
+
+    def enable_lora(
+        self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
+    ) -> nn.Module:
+        raise NotImplementedError

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -3,7 +3,7 @@
 from contextlib import nullcontext
 from functools import partial
 from types import MethodType
-from typing import Any, Callable, Iterator, List, Optional, OrderedDict, Tuple, Union
+from typing import Any, Callable, Dict, Iterator, List, Optional, OrderedDict, Tuple, Union
 
 import numpy as np
 import torch
@@ -753,6 +753,9 @@ def control_precision(self) -> bool:
     def support_no_sync(self) -> bool:
         return False
 
+    def support_lora(self) -> bool:
+        return False
+
     def control_checkpoint_io(self) -> bool:
         return True
 
@@ -891,3 +894,8 @@ def get_checkpoint_io(self) -> CheckpointIO:
 
     def no_sync(self, model: Module) -> Iterator[None]:
         raise NotImplementedError
+
+    def enable_lora(
+        self, model: Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
+    ) -> Module:
+        raise NotImplementedError

colossalai/booster/plugin/low_level_zero_plugin.py

@@ -3,7 +3,7 @@
 from functools import partial
 from pathlib import Path
 from types import MethodType
-from typing import Callable, Iterator, List, Optional, Tuple
+from typing import Callable, Dict, Iterator, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -295,6 +295,9 @@ def __init__(
     def support_no_sync(self) -> bool:
         return self.stage == 1
 
+    def support_lora(self) -> bool:
+        return False
+
     def control_precision(self) -> bool:
         return True
 
@@ -336,3 +339,8 @@ def get_checkpoint_io(self) -> CheckpointIO:
     def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
         assert isinstance(optimizer, LowLevelZeroOptimizer)
         return optimizer.no_sync()
+
+    def enable_lora(
+        self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
+    ) -> nn.Module:
+        raise NotImplementedError

colossalai/booster/plugin/plugin_base.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Callable, Iterator, List, Optional, Tuple
+from typing import Callable, Dict, Iterator, List, Optional, Tuple
 
 import torch.nn as nn
 from torch.optim import Optimizer
@@ -33,6 +33,10 @@ def control_device(self) -> bool:
     def support_no_sync(self) -> bool:
         pass
 
+    @abstractmethod
+    def support_lora(self) -> bool:
+        pass
+
     @abstractmethod
     def configure(
         self,
@@ -63,6 +67,12 @@ def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[Non
         Context manager to disable gradient synchronization.
         """
 
+    @abstractmethod
+    def enable_lora(self, model: nn.Module, pretrained_dir: str, lora_config: Dict) -> nn.Module:
+        """
+        Add LoRA modules to the model passed in. Should only be called in booster.enable_lora().
+        """
+
     @abstractmethod
     def prepare_dataloader(
         self,

colossalai/booster/plugin/torch_ddp_plugin.py

@@ -1,4 +1,4 @@
-from typing import Callable, Iterator, List, Optional, Tuple
+from typing import Callable, Dict, Iterator, List, Optional, Tuple, Union
 
 import torch.nn as nn
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -116,6 +116,22 @@ def load_sharded_optimizer(
         assert isinstance(optimizer, OptimizerWrapper), "Please boost the optimizer before loading!"
         super().load_sharded_optimizer(optimizer.unwrap(), index_file_path, prefix)
 
+    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 checkpoint directory.
+        """
+        from peft import PeftModel
+
+        assert isinstance(model, ModelWrapper), "Please boost the model before saving!"
+        if self.coordinator.is_master():
+            peft_model = model.unwrap()
+            assert isinstance(
+                peft_model, PeftModel
+            ), "The model doesn't have lora adapters, please enable lora before saving."
+            peft_model.save_pretrained(save_directory=checkpoint, safe_serialization=use_safetensors)
+
 
 class TorchDDPModel(ModelWrapper):
     def __init__(self, module: nn.Module, *args, **kwargs) -> None:
@@ -173,6 +189,9 @@ def __init__(
     def support_no_sync(self) -> bool:
         return True
 
+    def support_lora(self) -> bool:
+        return True
+
     def control_precision(self) -> bool:
         return False
 
@@ -216,3 +235,14 @@ def get_checkpoint_io(self) -> CheckpointIO:
     def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
         assert isinstance(model, TorchDDPModel), "Model is not boosted by TorchDDPPlugin."
         return model.module.no_sync()
+
+    def enable_lora(
+        self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
+    ) -> nn.Module:
+        from peft import PeftModel, get_peft_model
+
+        assert not isinstance(model, TorchDDPModel), "Lora should be enabled before boosting the model."
+        if pretrained_dir is None:
+            return get_peft_model(model, lora_config)
+        else:
+            return PeftModel.from_pretrained(model, pretrained_dir, is_trainable=True)

colossalai/booster/plugin/torch_fsdp_plugin.py

@@ -1,6 +1,6 @@
 import warnings
 from pathlib import Path
-from typing import Callable, Iterable, Iterator, List, Optional, Tuple
+from typing import Callable, Dict, Iterable, Iterator, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -190,7 +190,10 @@ def __init__(
         raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
 
     def support_no_sync(self) -> bool:
-        False
+        return False
+
+    def support_lora(self) -> bool:
+        return False
 
     def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
         raise NotImplementedError("Torch fsdp no_sync func not supported yet.")
@@ -235,3 +238,8 @@ def control_checkpoint_io(self) -> bool:
 
     def get_checkpoint_io(self) -> CheckpointIO:
         return TorchFSDPCheckpointIO()
+
+    def enable_lora(
+        self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
+    ) -> nn.Module:
+        raise NotImplementedError

colossalai/checkpoint_io/checkpoint_io_base.py

@@ -327,3 +327,20 @@ def load_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
         """
         state_dict = torch.load(checkpoint)
         lr_scheduler.load_state_dict(state_dict)
+
+    # ================================================================================
+    # Abstract method for lora saving implementation.
+    # ================================================================================
+
+    @abstractmethod
+    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.
+        """

colossalai/checkpoint_io/general_checkpoint_io.py

@@ -228,3 +228,6 @@ def load_sharded_model(
                         self.__class__.__name__, "\n\t".join(error_msgs)
                     )
                 )
+
+    def save_lora_as_pretrained(self, model: nn.Module, checkpoint: str, use_safetensors: bool = False) -> None:
+        raise NotImplementedError

requirements/requirements-test.txt

@@ -7,7 +7,7 @@ torchvision
 transformers==4.33.0
 timm
 titans
-torchaudio
+torchaudio>=0.13.1
 torchx-nightly==2022.6.29 # torchrec 0.2.0 requires torchx-nightly. This package is updated every day. We fix the version to a specific date to avoid breaking changes.
 torchrec==0.2.0
 contexttimer
@@ -18,4 +18,5 @@ SentencePiece
 ninja
 flash_attn==2.0.5
 datasets
+peft
 #auto-gptq now not support torch1.12