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Genie_Finetune.py
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Genie_Finetune.py
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import argparse
import os
from transformers import set_seed
from diffusion_util.resample import create_named_schedule_sampler
from transformers import AutoTokenizer
import json
from util import logger
from train_util import dist_util
import torch
import torch.distributed as dist
from util.util import (
create_model_and_diffusion,
args_to_dict,
)
import collections
from data_util.s2s_data_util import load_s2s_data
from train_util.train_util import TrainLoop
from torch.serialization import default_restore_location
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
CheckpointState = collections.namedtuple("CheckpointState",
['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset'])
def get_arguments():
parser = argparse.ArgumentParser()
# out path
parser.add_argument('--checkpoint_path', type=str, default='', help='output path')
# load pretrain
parser.add_argument('--pretrain_model_path', type=str, default=None, help='using pretraining diffusion')
# load model
parser.add_argument('--model_arch', type=str, default='transformer', help='Core architecture of diffusion model')
parser.add_argument('--model_channels', type=int, default=768, help='Try to set it to the same size as the model hidden')
parser.add_argument('--in_channel', type=int, default=768, help='The input chanel size here must be the same as the word embedding size')
parser.add_argument('--out_channel', type=int, default=768, help='The dimension size of the output is recommended to be the same as that of word embedding for easy reasoning')
parser.add_argument('--dropout', type=float, default=0.1, help='')
parser.add_argument("--learn_sigma", default=False, action="store_true", help="Whether to learning variance")
parser.add_argument('--logits_mode', type=int, default=1, help='final logits mode of Diffusion model')
parser.add_argument('--vocab_size', type=int, default=30522, help='vocab size')
parser.add_argument('--config_name', type=str, default='bert-base-uncased', help='')
parser.add_argument('--token_emb_type', type=str, default='random', help='token embedding type')
parser.add_argument("--init_pretrained", default=False, action="store_true", help="Whether to using pretrain BERT encoder")
parser.add_argument("--fix_encoder", default=False, action="store_true",
help="Whether to training encoder")
# load diffusion
parser.add_argument('--diffusion_steps', type=int, default=2000, help='Diffusion model maximum T')
parser.add_argument('--use_kl', default=False, action="store_true", help="Whether to using kl loss in Diffsion loss")
parser.add_argument('--training_mode', type=str, default='e2e', help='using e2e simple loss or e2e loss or s2s loss')
parser.add_argument('--noise_schedule', type=str, default='sqrt', help='How to plan the noise change of Gaussian distribution')
parser.add_argument('--predict_xstart', default=False, action="store_true", help="Model prediction target, if True, predict xstart, if False, predict EPSILON")
parser.add_argument("--sigma_small", default=False, action="store_true", help="about learning variance")
parser.add_argument("--rescale_learned_sigmas", default=True, action="store_false", help="about learning variance")
parser.add_argument("--rescale_timesteps", default=True, action="store_false", help="about time rescale")
# sample t
parser.add_argument('--schedule_sampler', type=str, default='uniform', help='how to sample t per batch, uniform is Uniform sampling, loss-second-moment is Sampling according to loss')
# data args
parser.add_argument('--data_path', type=str, default='',help='data path')
parser.add_argument('--data_name', type=str, default='', help='data name')
# for seq2seq
parser.add_argument('--src_max_len', type=int, default=144, help='src max len')
parser.add_argument('--tgt_max_len', type=int, default=32, help='tgt max len')
parser.add_argument('--answer_max_len', type=int, default=10, help='tgt max len')
# for doc2query
parser.add_argument('--text_max_len', type=int, default=None, help='text max len')
parser.add_argument('--pas_max_len', type=int, default=None, help='pas max len')
# training args
parser.add_argument('--train_type', type=str, default='LM_Diffusion', help='LM_Diffusion or S2S_Diffusion')
parser.add_argument('--lr_anneal_steps', type=int, default=200000, help='total step')
parser.add_argument('--batch_size', type=int, default=64, help='')
parser.add_argument('--lr', type=float, default=1e-04, help='')
parser.add_argument('--warmup_steps', type=int, default=20000, help='')
parser.add_argument('--ema_rate', type=str, default='0.9999', help='ema training to stable model')
parser.add_argument('--resume_checkpoint', type=str, default=None, help='')
parser.add_argument('--eval_interval', type=int, default=2000, help='')
parser.add_argument('--log_interval', type=int, default=100, help='')
parser.add_argument('--save_interval', type=int, default=50000, help='')
parser.add_argument('--weight_decay', type=str, default=0.0, help='')
parser.add_argument('--gradient_clipping', type=float, default=-1., help='')
parser.add_argument("--use_fp16", default=False, action="store_true", help="about learning variance")
parser.add_argument('--fp16_scale_growth', type=float, default=1e-3, help='')
# seed
parser.add_argument('--seed', type=int, default=101, help='')
# muti-gpu
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
args = parser.parse_args()
return args
def setup_env(args):
if args.local_rank == -1:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# store args
if args.local_rank != -1:
args.world_size = torch.distributed.get_world_size()
args.rank = dist.get_rank()
def load_states_from_checkpoint(model_file: str) -> CheckpointState:
logger.info('Reading saved model from %s', model_file)
state_dict = torch.load(model_file, map_location=lambda s, l: default_restore_location(s, 'cpu'))
logger.info('model_state_dict keys %s', state_dict.keys())
return CheckpointState(**state_dict)
def main():
# args setting
args = get_arguments()
# out dir set
if dist.get_rank() == 0:
if not os.path.exists(args.checkpoint_path):
os.makedirs(args.checkpoint_path)
# dist.barrier()
logger.log(f'saving the hyperparameters to {args.checkpoint_path}/training_args.json')
with open(f'{args.checkpoint_path}/training_args.json', 'w') as f:
json.dump(args.__dict__, f, indent=2)
# seed setting
set_seed(args.seed)
# dpp setting
setup_env(args)
# dist_util.setup_dist()
# logger setting
log_path = os.path.join(args.checkpoint_path, 'log.txt')
logger.configure(dir=log_path)
model, diffusion = create_model_and_diffusion(
args
)
if args.pretrain_model_path is not None:
print("load model ckpt at :", args.pretrain_model_path)
saved_state = load_states_from_checkpoint(args.pretrain_model_path)
model.load_state_dict(saved_state.model_dict, strict=False)
model.to(args.device)
pytorch_total_params = sum(p.numel() for p in model.parameters())
logger.log(f'the parameter count is {pytorch_total_params}')
'''
time step schedule sampler
'''
schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
'''
tokenize
'''
logger.log("loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
'''
for s2s
'''
# load data (train)
train_data = load_s2s_data(
args,
split='train',
padding_mode='max_len',
tokenizer=tokenizer,
)
# load data (dev)
dev_data = load_s2s_data(
args,
split='dev',
padding_mode='max_len',
tokenizer=tokenizer,
)
'''
training
'''
logger.log("training Diffusion LM model...")
TrainLoop(
# training type
train_type=args.train_type,
# Training Core
model=model,
diffusion=diffusion,
data=train_data,
eval_data=dev_data,
schedule_sampler=schedule_sampler,
checkpoint_path=args.checkpoint_path,
# Training Parameters
batch_size=args.batch_size,
lr=args.lr,
ema_rate=args.ema_rate,
weight_decay=args.weight_decay,
lr_anneal_steps=args.lr_anneal_steps,
gradient_clipping=args.gradient_clipping,
# fp16
use_fp16=args.use_fp16,
fp16_scale_growth=args.fp16_scale_growth,
# Training Log
resume_checkpoint=args.resume_checkpoint,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
save_interval=args.save_interval,
# device
device=args.device,
# finetune data name
data_name=args.data_name
).run_loop()
if __name__ == "__main__":
main()