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import os
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import os.path as osp
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import re
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from typing import Tuple
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import torch
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from colossalai.context import Config
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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__all__ = [
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'get_checkpoint_path',
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'get_latest_checkpoint_path',
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'get_latest_checkpoint_pattern',
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'save_checkpoint',
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'load_checkpoint'
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]
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def unwrap_config(config: Config):
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'''
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unwrap Config objects to normal dicts
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'''
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config_dict = dict()
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for k, v in config.items():
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if isinstance(v, dict):
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config_dict[k] = unwrap_config(v)
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else:
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config_dict[k] = v
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return config_dict
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def _get_ranks_name():
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# tensor parallel
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tp_local_rank = 0
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if gpc.is_initialized(ParallelMode.TENSOR):
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tp_local_rank = gpc.get_local_rank(ParallelMode.TENSOR)
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# pipeline parallel
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pp_local_rank = 0
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if gpc.is_initialized(ParallelMode.PIPELINE):
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pp_local_rank = gpc.get_local_rank(ParallelMode.PIPELINE)
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ranks_name = f'tp{tp_local_rank}-pp{pp_local_rank}'
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return ranks_name
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def _get_standard_checkpoint_filename(epoch: int, suffix: str = ''):
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ranks_name = _get_ranks_name()
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return f'epoch{epoch}-{ranks_name}{suffix}.pt'
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def get_checkpoint_path(checkpoint_dir: str, epoch: int, suffix: str = ''):
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'''This is a function to generate the checkpoint path from the (checkpoint_dir, epoch, suffix, gpu_parallel_rank) tuple.
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This is useful during generation and recuperation of the checkpoint.
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:param checkpoint_dir: set up a directory for saving checkpoints
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:type checkpoint_dir: str
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:param epoch: epoch number (indicate how many epochs have you trained this model)
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:type epoch: int
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:param suffix: additional notation to specify the model or checkpoint, defaults to ''
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:type suffix: str, optional
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:return: checkpoint path to be generated
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:rtype: path
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'''
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ckpt_filename = _get_standard_checkpoint_filename(epoch, suffix)
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return os.path.join(checkpoint_dir, ckpt_filename)
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def _ensure_directory_exists(filename: str):
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# ensure the directory exists
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dir = os.path.dirname(filename)
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if not os.path.exists(dir):
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os.makedirs(dir)
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def get_latest_checkpoint_pattern(suffix: str = ''):
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'''Generate Regular expression of latest checkpoint's pattern
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:param suffix: additional notation to specify the model or checkpoint, defaults to ''
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:type suffix: str, optional
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:return: checkpoint pattern
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:rtype: regular expression
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'''
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ranks_name = _get_ranks_name()
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ckpt_pattern = re.compile(f'epoch(\d+)-{ranks_name}{suffix}\.pt')
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return ckpt_pattern
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def get_latest_checkpoint_path(checkpoint_dir: str, suffix: str = ''):
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'''This is a function to retrieve the latest checkpoint path from the (checkpoint_dir, suffix, gpu_parallel_rank) tuple.
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This is useful during recuperation of the checkpoint, especially when you do not know the epoch number.
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:param checkpoint_dir: directory for saving checkpoints
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:type checkpoint_dir: str
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:param suffix: additional notation to specify the model or checkpoint, defaults to ''
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:type suffix: str, optional
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:raises FileNotFoundError: raise error when we cannot find the latest checkpoint file with inputs given
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:return: the latest checkpoint path to be retrieved
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:rtype: path
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'''
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CKPT_NAME_PAT = get_latest_checkpoint_pattern(suffix=suffix)
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last_epoch = -1
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assert osp.isdir(checkpoint_dir), f'{checkpoint_dir} is not a directory'
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for filename in os.listdir(checkpoint_dir):
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ret = CKPT_NAME_PAT.match(filename)
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if ret:
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epoch = int(ret[0].split('-')[0].lstrip('epoch'))
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if epoch > last_epoch:
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last_epoch = epoch
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if last_epoch == -1:
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ranks_name = _get_ranks_name()
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raise FileNotFoundError(f"Cannot find the latest checkpoint file for {ranks_name} in {checkpoint_dir}")
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else:
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target_file = _get_standard_checkpoint_filename(last_epoch, suffix=suffix)
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path = osp.join(checkpoint_dir, target_file)
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return path
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def save_checkpoint(checkpoint_path: str,
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epoch: int,
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model: torch.nn.Module,
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optimizer: torch.optim.Optimizer,
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lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
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**kwargs):
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'''Given a directory to store the checkpoints, saves all the training components' parameters or buffers, such as model, optimizer, lr_scheduler and etc. into a checkpoint dictionary.
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This method can be used for both colosalai nn.BaseModel and normal pytorch nn.Module.
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:param checkpoint_path: set up a directory for saving checkpoints
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:type checkpoint_path: str
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:param epoch: epoch number (indicate how many epochs have you trained this model)
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:type epoch: int
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:param model: model to be registered
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:type model: torch.nn.Module
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:param optimizer: optimizer to be registered
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:type optimizer: torch.optim.Optimizer
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:param lr_scheduler: lr_scheduler to be registered, defaults to None
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:type lr_scheduler: torch.optim.lr_scheduler._LRScheduler, optional
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'''
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# for compatibility with normal pytorch nn.Module
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if hasattr(model, 'state_dict_for_save_checkpoint'):
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model_sd = model.state_dict_for_save_checkpoint()
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else:
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model_sd = model.state_dict()
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# ckpt container
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checkpoint = {
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'epoch': epoch,
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'model': model_sd,
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'optimizer': optimizer.state_dict(),
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**kwargs
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}
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if lr_scheduler is not None:
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checkpoint['lr_scheduler'] = lr_scheduler.state_dict()
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_ensure_directory_exists(checkpoint_path)
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torch.save(checkpoint, checkpoint_path)
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def load_checkpoint(checkpoint_path: str,
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model: torch.nn.Module,
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optimizer: torch.optim.Optimizer,
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lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
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finetune: bool = False,
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strict: bool = True) -> Tuple:
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'''Loads the checkpoint file.
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If finetune is False, then we intend to continue/resume the training process from the checkpoint given.
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So we copy parameters and buffers from state_dict into these modules(model, optimizer,lr_scheduler) and its descendants.
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If finetune is True, then only the weights and buffers of model should be reload.
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If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.
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:param checkpoint_path: the exact and matched checkpoint_path directory to retrieve appropriate state_dict
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:type checkpoint_path: str
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:param model: model to reload parameters and buffers
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:type model: torch.nn.Module
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:param optimizer: optimizer to recuperate
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:type optimizer: torch.optim.Optimizer
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:param lr_scheduler: lr_scheduler to recuperate, defaults to None
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:type lr_scheduler: torch.optim.lr_scheduler._LRScheduler, optional
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:param finetune: whether to finetune the model with new dataset or continue the pre-training, defaults to False
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:type finetune: bool, optional
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:param strict: whether to strictly enforce that the keys in
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:attr:`state_dict` of the checkpoint match the names of
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parameters and buffers in model., defaults to True
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:type strict: bool, optional
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:raises ValueError: raise error if the model/optimizer cannot successfully be recuperated
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:return: (the epoch number of the checkpoint retrieved, the checkpoint retrieved)
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:rtype: Tuple
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'''
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# Load the checkpoint.
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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try:
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last_epoch = checkpoint.pop('epoch') if not finetune else 0
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model.load_state_dict(checkpoint.pop('model'), strict=strict)
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except KeyError:
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raise ValueError('Checkpoint is corrupted')
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if not finetune:
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try:
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optimizer.load_state_dict(checkpoint.pop('optimizer'))
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except KeyError:
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raise ValueError('Checkpoint is corrupted')
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if lr_scheduler is not None and 'lr_scheduler' in checkpoint:
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lr_scheduler.load_state_dict(checkpoint.pop('lr_scheduler'))
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return last_epoch, checkpoint
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