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import os
import torch
import argparse
import warnings
import pytorch_lightning as pl
from pytorch_lightning import Trainer, strategies
import pytorch_lightning.callbacks as plc
from pytorch_lightning.loggers import CSVLogger
from pytorch_lightning.callbacks import TQDMProgressBar
from data_provider.pretrain_dm import PretrainDM
from data_provider.tune_dm import TuneDM
from model.opt_flash_attention import replace_opt_attn_with_flash_attn
from model.blip2_model import Blip2Model
from model.dist_funs import MyDeepSpeedStrategy
## for pyg bug
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
## for A5000 gpus
torch.set_float32_matmul_precision('medium') # can be medium (bfloat16), high (tensorfloat32), highest (float32)
try:
class MyDDPSpawnStrategy(strategies.DDPSpawnStrategy):
def load_model_state_dict(self, checkpoint):
assert self.lightning_module is not None
self.lightning_module.load_state_dict(checkpoint["state_dict"], strict=False)
except:
pass
def main(args):
pl.seed_everything(args.seed)
# model
if args.init_checkpoint:
model = Blip2Model(args)
ckpt = torch.load(args.init_checkpoint, map_location='cpu')
model.load_state_dict(ckpt['state_dict'], strict=False)
print(f"loaded model from {args.init_checkpoint}")
else:
model = Blip2Model(args)
print('total params:', sum(p.numel() for p in model.parameters()))
if args.opt_model.find('galactica') >= 0 or args.opt_model.find('t5') >= 0:
tokenizer = model.blip2opt.opt_tokenizer
elif args.opt_model.find('llama') >= 0 or args.opt_model.find('vicuna') >= 0:
tokenizer = model.blip2opt.llm_tokenizer
else:
raise NotImplementedError
# data
if args.mode in {'pretrain', 'pretrain_eval'}:
dm = PretrainDM(
num_workers=args.num_workers,
batch_size=args.batch_size,
root=args.root,
text_max_len=args.text_max_len,
rxn_max_len=args.rxn_max_len,
smi_max_len=args.smi_max_len,
tokenizer=tokenizer,
args=args
)
elif args.mode in {'ft', 'eval'}:
dm = TuneDM(
num_workers=args.num_workers,
batch_size=args.batch_size,
root=args.root,
text_max_len=args.text_max_len,
rxn_max_len=args.rxn_max_len,
smi_max_len=args.smi_max_len,
tokenizer=tokenizer,
downstream_task=args.downstream_task,
args=args
)
callbacks = [TQDMProgressBar(refresh_rate=args.tqdm_interval)]
## fixme save only used parameters
# callbacks.append(plc.ModelCheckpoint(dirpath="all_checkpoints/"+args.filename+"/", every_n_epochs=10, save_top_k=-1))
callbacks.append(plc.ModelCheckpoint(dirpath="all_checkpoints/"+args.filename+"/",
filename='{epoch:02d}',
every_n_epochs=args.save_every_n_epochs,
save_last=True,
save_top_k=-1,
save_on_train_epoch_end=True))
if len(args.devices.split(',')) > 1:
if args.strategy_name == 'fsdp':
strategy = strategies.DDPFullyShardedNativeStrategy()
elif args.strategy_name == 'deepspeed':
strategy = strategies.DeepSpeedStrategy(stage=3)
elif args.strategy_name == 'mydeepspeed':
strategy = MyDeepSpeedStrategy(stage=2)
else:
strategy = MyDDPSpawnStrategy(find_unused_parameters=True)
else:
strategy = None
args.devices = eval(args.devices)
logger = CSVLogger(save_dir=f'./all_checkpoints/{args.filename}/')
reload_freq = 1 if args.mode == 'pretrain' else 0
trainer = Trainer(
accelerator=args.accelerator,
devices=args.devices,
precision=args.precision,
max_epochs=args.max_epochs,
accumulate_grad_batches=args.accumulate_grad_batches,
check_val_every_n_epoch=args.check_val_every_n_epoch,
callbacks=callbacks,
strategy=strategy,
logger=logger,
reload_dataloaders_every_n_epochs=reload_freq
# limit_train_batches=100,
)
if args.mode in {'pretrain', 'ft'}:
trainer.fit(model, datamodule=dm, ckpt_path=args.ckpt_path)
elif args.mode in {'eval', 'pretrain_eval'}:
trainer.fit_loop.epoch_progress.current.completed = args.caption_eval_epoch - 1
trainer.validate(model, datamodule=dm)
# trainer.test(model, datamodule=dm)
else:
raise NotImplementedError()
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--filename', type=str, default="main")
parser.add_argument('--seed', type=int, default=42, help='random seed')
# MM settings
parser.add_argument('--mode', type=str, default='pretrain', choices=['pretrain', 'ft', 'eval', 'pretrain_eval'])
parser.add_argument('--strategy_name', type=str, default='mydeepspeed')
parser.add_argument('--iupac_prediction', action='store_true', default=False)
parser.add_argument('--ckpt_path', type=str, default=None)
# parser = Trainer.add_argparse_args(parser)
parser = Blip2Model.add_model_specific_args(parser) # add model args
parser = PretrainDM.add_model_specific_args(parser)
parser.add_argument('--accelerator', type=str, default='gpu')
parser.add_argument('--devices', type=str, default='0,1,2,3')
parser.add_argument('--precision', type=str, default='bf16-mixed')
parser.add_argument('--downstream_task', type=str, default='action', choices=['action', 'synthesis', 'caption', 'chebi'])
parser.add_argument('--max_epochs', type=int, default=10)
parser.add_argument('--enable_flash', action='store_true', default=False)
parser.add_argument('--disable_graph_cache', action='store_true', default=False)
parser.add_argument('--predict_rxn_condition', action='store_true', default=False)
parser.add_argument('--generate_restrict_tokens', action='store_true', default=False)
parser.add_argument('--train_restrict_tokens', action='store_true', default=False)
parser.add_argument('--smiles_type', type=str, default='default', choices=['default', 'canonical', 'restricted', 'unrestricted', 'r_smiles'])
parser.add_argument('--accumulate_grad_batches', type=int, default=1)
parser.add_argument('--tqdm_interval', type=int, default=50)
parser.add_argument('--check_val_every_n_epoch', type=int, default=1)
args = parser.parse_args()
if args.enable_flash:
replace_opt_attn_with_flash_attn()
print("=========================================")
for k, v in sorted(vars(args).items()):
print(k, '=', v)
print("=========================================")
return args
if __name__ == '__main__':
main(get_args())
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