mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-03 01:55:12 +00:00
[booster] torch fsdp fix ckpt (#3788)
This commit is contained in:
wukong1992
GitHub
@@ -1,3 +1,4 @@
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from pathlib import Path
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from typing import Callable, Iterable, Iterator, List, Optional, Tuple, Union
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import torch
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@@ -5,30 +6,18 @@ import torch.nn as nn
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from packaging import version
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from torch.distributed import ProcessGroup
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if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
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torch.__version__) < version.parse('2.0.0'):
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if version.parse(torch.__version__) >= version.parse('1.12.0'):
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from torch.distributed.fsdp import FullStateDictConfig
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from torch.distributed.fsdp import StateDictType
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from torch.distributed.fsdp.fully_sharded_data_parallel import (
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BackwardPrefetch,
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CPUOffload,
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MixedPrecision,
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ShardingStrategy,
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)
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elif version.parse(torch.__version__) >= version.parse('2.0.0'):
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from torch.distributed.fsdp._init_utils import ProcessGroupType
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from torch.distributed.fsdp.api import (
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BackwardPrefetch,
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CPUOffload,
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FullOptimStateDictConfig,
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FullStateDictConfig,
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MixedPrecision,
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ShardingStrategy,
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StateDictType,
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)
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from torch.distributed.fsdp.wrap import _FSDPPolicy
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else:
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raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
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@@ -36,7 +25,7 @@ from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
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from torch.utils.data import DataLoader
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from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO
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from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO, utils
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from colossalai.cluster import DistCoordinator
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from colossalai.interface import ModelWrapper, OptimizerWrapper
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@@ -51,102 +40,71 @@ class TorchFSDPCheckpointIO(GeneralCheckpointIO):
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super().__init__()
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self.coordinator = DistCoordinator()
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def __set_model_optim_state(
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self,
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model,
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state_dict_type,
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state_dict_config,
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optim_state_dict_config,
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):
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return FSDP.set_state_dict_type(model, state_dict_type, state_dict_config, optim_state_dict_config)
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def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool):
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checkpoint = utils.load_state_dict(checkpoint)
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model.load_state_dict(checkpoint)
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def load_sharded_model(self, model: nn.Module, checkpoint: str):
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def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path):
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checkpoint = utils.load_state_dict(checkpoint)
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fsdp_model = optimizer.unwrap_model()
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sharded_osd = FSDP.scatter_full_optim_state_dict(checkpoint, fsdp_model)
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optimizer.load_state_dict(sharded_osd)
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# TODO(jishaomin): implement this method as it can be supported by Huggingface model
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raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.")
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def load_sharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str):
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# TODO(jishaomin): implement this method as it can be supported by Huggingface model
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raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.")
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def save_sharded_model(self, model: nn.Module, checkpoint: str):
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# TODO(jishaomin): implement this method as it can be supported by Huggingface model
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raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.")
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def save_sharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str):
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# TODO(jishaomin): implement this method as it can be supported by Huggingface model
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raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.")
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def load_unsharded_model(self, model: nn.Module, checkpoint: str):
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"""
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Load model from checkpoint with automatic unwrapping.
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"""
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# the model should be unwrapped in self.load_model via ModelWrapper.unwrap
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if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
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torch.__version__) < version.parse('2.0.0'):
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full_state_dict = self.load_state_dict(checkpoint)
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elif version.parse(torch.__version__) >= version.parse('2.0.0'):
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full_state_dict = self.load_state_dict(checkpoint)
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self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(rank0_only=True))
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full_state_dict = model.state_dict()
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else:
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raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
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model.load_state_dict(full_state_dict)
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def load_unsharded_optimizer(self, model: nn.Module, optim: Optimizer, checkpoint: str):
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"""
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Load Optimizer from checkpoint with automatic unwrapping.
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"""
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if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
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torch.__version__) < version.parse('2.0.0'):
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optim_full_state_dict = self.load_state_dict(checkpoint)
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elif version.parse(torch.__version__) >= version.parse('2.0.0'):
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optim_full_state_dict = self.load_state_dict(checkpoint)
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FSDP.full_optim_state_dict_to_load(optim_full_state_dict, model, optim)
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else:
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raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
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optim.load_state_dict(optim_full_state_dict)
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def save_unsharded_model(self, model: nn.Module, checkpoint: str):
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def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
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"""
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Save model to checkpoint but only on master process.
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"""
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# the model should be unwrapped in self.load_model via ModelWrapper.unwrap
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cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
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with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, cfg):
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full_model_state = model.state_dict()
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utils.save_state_dict(full_model_state, checkpoint_file_path=checkpoint, use_safetensors=use_safetensors)
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if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
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torch.__version__) < version.parse('2.0.0'):
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cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
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with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, cfg):
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model_state_dict = model.state_dict()
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elif version.parse(torch.__version__) >= version.parse('2.0.0'):
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self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(rank0_only=True))
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model_state_dict = model.state_dict()
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else:
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raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
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self.save_checkpoint(model_state_dict, checkpoint)
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def save_unsharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str):
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def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
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"""
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Save optimizer to checkpoint but only on master process.
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"""
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assert isinstance(optimizer, FSDPOptimizerWrapper)
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fsdp_model = optimizer.unwrap_model()
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full_optimizer_state = FSDP.full_optim_state_dict(fsdp_model, optim=optimizer, rank0_only=True)
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utils.save_state_dict(full_optimizer_state, checkpoint_file_path=checkpoint, use_safetensors=False)
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if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
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torch.__version__) < version.parse('2.0.0'):
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optim_state_dict = FSDP.full_optim_state_dict(model=model, optim=optimizer)
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elif version.parse(torch.__version__) >= version.parse('2.0.0'):
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self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT,
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FullOptimStateDictConfig(rank0_only=True))
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optim_state_dict = FSDP.optim_state_dict(model, optimizer)
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else:
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raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
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self.save_checkpoint(optim_state_dict, checkpoint)
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def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, variant: Optional[str],
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size_per_shard: int, use_safetensors: bool):
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"""
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Save model to checkpoint but only on master process.
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"""
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raise NotImplementedError("Sharded model checkpoint is not supported yet.")
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def load_sharded_model(self,
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model: nn.Module,
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checkpoint_index_file: Path,
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strict: bool = False,
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use_safetensors: bool = False,
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load_sub_module: bool = True):
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"""
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Load model to checkpoint but only on master process.
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"""
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raise NotImplementedError("Sharded model checkpoint is not supported yet.")
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def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
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"""
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Save optimizer to checkpoint but only on master process.
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"""
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raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.")
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def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str, size_per_shard: int):
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"""
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Load optimizer to checkpoint but only on master process.
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"""
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raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.")
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def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
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"""
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Save model to checkpoint but only on master process.
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"""
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if self.coordinator.is_master():
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super().save_lr_scheduler(lr_scheduler, checkpoint)
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class TorchFSDPModel(ModelWrapper):
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@@ -156,7 +114,17 @@ class TorchFSDPModel(ModelWrapper):
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self.module = FSDP(module, *args, **kwargs)
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def unwrap(self):
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return self.module.module
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return self.module
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class FSDPOptimizerWrapper(OptimizerWrapper):
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def __init__(self, optimizer: Optimizer, model: nn.Module):
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self.model = model
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super().__init__(optimizer)
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def unwrap_model(self) -> nn.Module:
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return self.model
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class TorchFSDPPlugin(DPPluginBase):
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@@ -178,8 +146,7 @@ class TorchFSDPPlugin(DPPluginBase):
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See https://pytorch.org/docs/stable/fsdp.html for details.
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"""
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if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
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torch.__version__) < version.parse('2.0.0'):
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if version.parse(torch.__version__) >= version.parse('1.12.0'):
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def __init__(
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self,
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@@ -191,7 +158,6 @@ class TorchFSDPPlugin(DPPluginBase):
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mixed_precision: Optional[MixedPrecision] = None,
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ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
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param_init_fn: Optional[Callable[[nn.Module], None]] = None,
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device_id: Optional[Union[int, torch.device]] = None,
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sync_module_states: bool = False,
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):
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super().__init__()
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@@ -203,42 +169,7 @@ class TorchFSDPPlugin(DPPluginBase):
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mixed_precision=mixed_precision,
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ignored_modules=ignored_modules,
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param_init_fn=param_init_fn,
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device_id=device_id,
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sync_module_states=sync_module_states)
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elif version.parse(torch.__version__) >= version.parse('2.0.0'):
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def __init__(
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self,
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process_group: ProcessGroupType = None,
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sharding_strategy: Optional[ShardingStrategy] = None,
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cpu_offload: Optional[CPUOffload] = None,
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auto_wrap_policy: Optional[Union[Callable, _FSDPPolicy]] = None,
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backward_prefetch: Optional[BackwardPrefetch] = BackwardPrefetch.BACKWARD_PRE,
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mixed_precision: Optional[MixedPrecision] = None,
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ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
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param_init_fn: Optional[Callable[[nn.Module], None]] = None,
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device_id: Optional[Union[int, torch.device]] = None,
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sync_module_states: bool = False,
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forward_prefetch: bool = False,
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limit_all_gathers: bool = False,
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use_orig_params: bool = False,
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ignored_parameters: Optional[Iterable[torch.nn.Parameter]] = None,
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):
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super().__init__()
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self.fsdp_kwargs = dict(process_group=process_group,
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sharding_strategy=sharding_strategy,
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cpu_offload=cpu_offload,
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auto_wrap_policy=auto_wrap_policy,
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backward_prefetch=backward_prefetch,
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mixed_precision=mixed_precision,
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ignored_modules=ignored_modules,
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param_init_fn=param_init_fn,
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device_id=device_id,
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sync_module_states=sync_module_states,
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forward_prefetch=forward_prefetch,
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limit_all_gathers=limit_all_gathers,
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use_orig_params=use_orig_params,
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ignored_parameters=ignored_parameters)
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else:
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raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
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@@ -269,14 +200,14 @@ class TorchFSDPPlugin(DPPluginBase):
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lr_scheduler: LRScheduler = None,
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) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]:
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model = model.cuda()
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# wrap the model with PyTorch FSDP
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model = TorchFSDPModel(model, **self.fsdp_kwargs)
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fsdp_model = TorchFSDPModel(model, device_id=torch.cuda.current_device(), **self.fsdp_kwargs)
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optimizer.__init__(fsdp_model.parameters(), **optimizer.defaults)
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if not isinstance(optimizer, OptimizerWrapper):
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optimizer = OptimizerWrapper(optimizer)
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if not isinstance(optimizer, FSDPOptimizerWrapper):
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optimizer = FSDPOptimizerWrapper(optimizer, fsdp_model)
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return model, optimizer, criterion, dataloader, lr_scheduler
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return fsdp_model, optimizer, criterion, dataloader, lr_scheduler
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def control_checkpoint_io(self) -> bool:
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return True
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