# Copyright 2024 Bytedance Ltd. and/or its affiliates # # 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. import os import shutil import warnings from typing import Optional, Union import torch import torch.distributed from torch.distributed.fsdp import FullStateDictConfig, ShardedOptimStateDictConfig, ShardedStateDictConfig, StateDictType from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from transformers import GenerationConfig, PreTrainedTokenizer, ProcessorMixin from verl.utils.fs import copy_to_local, is_non_local from verl.utils.fsdp_utils import fsdp_version, get_fsdp_state_ctx from .checkpoint_manager import BaseCheckpointManager class FSDPCheckpointManager(BaseCheckpointManager): """ A checkpoint manager that saves and loads - model - optimizer - lr_scheduler - extra_states in a SPMD way. We save - sharded model states and optimizer states - full lr_scheduler states - huggingface tokenizer/processor and config for ckpt merge """ def __init__( self, model: FSDP, optimizer: torch.optim.Optimizer, lr_scheduler: torch.optim.lr_scheduler.LRScheduler, processing_class: Union[PreTrainedTokenizer, ProcessorMixin] = None, checkpoint_contents: Optional[list] = None, **kwargs, ): if checkpoint_contents is None: checkpoint_contents = ["model", "optimizer", "extra"] if processing_class is None: assert "tokenizer" in kwargs, "tokenizer or processor must be provided" warnings.warn("`tokenizer` is deprecated. use `processing_class` instead.", DeprecationWarning, stacklevel=2) processing_class = kwargs.pop("tokenizer") assert "model" in checkpoint_contents and "optimizer" in checkpoint_contents and "extra" in checkpoint_contents, f"FSDPCheckpointManager must include ['model', 'optimizer', 'extra'], got {checkpoint_contents}" super().__init__( model, optimizer, lr_scheduler=lr_scheduler, processing_class=processing_class, checkpoint_contents=checkpoint_contents, ) def load_checkpoint(self, local_path: str, hdfs_path: str = None, del_local_after_load=False): if local_path is None: return # every rank download its own checkpoint remote_model_path = os.path.join(local_path, f"model_world_size_{self.world_size}_rank_{self.rank}.pt") remote_optim_path = os.path.join(local_path, f"optim_world_size_{self.world_size}_rank_{self.rank}.pt") remote_extra_state_path = os.path.join(local_path, f"extra_state_world_size_{self.world_size}_rank_{self.rank}.pt") print(f"[rank-{self.rank}]: Loading from {remote_model_path} and {remote_optim_path} and {remote_extra_state_path}") local_model_path = copy_to_local(remote_model_path) local_optim_path = copy_to_local(remote_optim_path) local_extra_state_path = copy_to_local(remote_extra_state_path) model_state_dict = torch.load(local_model_path, weights_only=False) optimizer_state_dict = torch.load(local_optim_path, weights_only=False) extra_state_dict = torch.load(local_extra_state_path, weights_only=False) if del_local_after_load: try: os.remove(local_model_path) if is_non_local(local_model_path) else None os.remove(local_optim_path) if is_non_local(local_optim_path) else None os.remove(local_extra_state_path) if is_non_local(local_extra_state_path) else None except Exception as e: print(f"[rank-{self.rank}]: remove local resume ckpt file after loading failed, exception {e} will be ignored") lr_scheduler_state_dict = extra_state_dict["lr_scheduler"] state_dict_cfg = ShardedStateDictConfig(offload_to_cpu=True) optim_cfg = ShardedOptimStateDictConfig(offload_to_cpu=True) with get_fsdp_state_ctx(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg): self.model.load_state_dict(model_state_dict) if self.optimizer is not None: self.optimizer.load_state_dict(optimizer_state_dict) # recover random state if "rng" in extra_state_dict: # 'rng' may not exist for backward compatibility self.load_rng_state(extra_state_dict["rng"]) if self.lr_scheduler is not None: self.lr_scheduler.load_state_dict(lr_scheduler_state_dict) def save_checkpoint(self, local_path: str, hdfs_path: str = None, global_step: int = 0, max_ckpt_to_keep=None): # record the previous global step self.previous_global_step = global_step # only support save and load ckpt for actor from torch.distributed.fsdp import FullyShardedDataParallel as FSDP experiment_dir = os.path.dirname(local_path) if self.rank == 0: if os.path.exists(experiment_dir): subdirs = [name for name in os.listdir(experiment_dir) if os.path.isdir(os.path.join(experiment_dir, name))] for name in subdirs: full_path = os.path.join(experiment_dir, name) shutil.rmtree(full_path) os.makedirs(local_path, exist_ok=True) from torch.distributed.fsdp import FullStateDictConfig, StateDictType torch.distributed.barrier() with warnings.catch_warnings(): warnings.simplefilter("ignore") with FSDP.state_dict_type( self.model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(offload_to_cpu=True, rank0_only=True) ): state_dict = self.model.state_dict() model_path = os.path.join(local_path, f'model.pt') if self.rank == 0: torch.save(state_dict, model_path) print("\n" + "="*60) print(f"✅✅✅ SUCCESS: Model saved ✅✅✅") print("="*60 + "\n") torch.distributed.barrier()