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train.py
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train.py
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import os
import argparse
import datetime
import json
import time
import numpy as np
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import timm.optim.optim_factory as optim_factory
import util.misc as misc
from util.misc import NativeScalerWithGradNormCount as NativeScaler
from engine import train_one_epoch
from util.datasets import ScienceQADataSet,InstrcutDataSet
from lavin.mm_adaptation import LaVIN
import random
import bitsandbytes as bnb
def get_args_parser():
parser = argparse.ArgumentParser('MAE pre-training', add_help=False)
parser.add_argument('--batch_size', default=64, type=int,
help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')
parser.add_argument('--epochs', default=400, type=int)
parser.add_argument('--bits', default='16bit', type=str,choices=['4bit','8bit','16bit'],
help='Quantization bits for training, fp16 by default')
parser.add_argument('--accum_iter', default=1, type=int,
help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')
# Model parameters
parser.add_argument('--llama_model_path', default='./llama', type=str,
help='path of llama model')
parser.add_argument('--llm_model', default='7B', type=str, metavar='MODEL',
help='Name of llm model to train')
parser.add_argument('--use_vicuna', action='store_true', help='use vicuna weights')
parser.add_argument('--cpu_load', action='store_true', help='load the model on cpu and avoid OOM on gpu')
#block is not supported now.
parser.add_argument('--adapter_type', type=str, default='attn', metavar='LENGTH',choices=['block','attn'],
help='the insert position of adapter layer')
parser.add_argument('--visual_adapter_type', type=str, default='normal', metavar='LENGTH',choices=['normal','router','router_block'],
help='the type of adapter layer')
parser.add_argument('--adapter_dim', type=int, default=8, metavar='LENGTH', help='the dims of adapter layer')
parser.add_argument('--hidden_proj', type=int, default=128, metavar='LENGTH',
help='the visual adapter dim')
parser.add_argument('--temperature', type=float, default=10., metavar='LENGTH',
help='the temperature of router')
parser.add_argument('--n_prompt', type=int, default=10, metavar='LENGTH',
help='the length of visual features')
parser.add_argument('--adapter_scale', type=float, default=1., metavar='LENGTH', help='the scales of adapter layer')
parser.add_argument('--drop_path', type=float, default=0., metavar='LENGTH', help='drop path')
parser.add_argument('--max_seq_len', type=int, default=512, metavar='LENGTH',
help='the maximum sequence length')
# Optimizer parameters
parser.add_argument('--weight_decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--lr', type=float, default=None, metavar='LR',
help='learning rate (absolute lr)')
parser.add_argument('--clip_grad', type=float, default=None, metavar='clip gradient',
help='clips gradient norm of an iterable of parameters')
parser.add_argument('--blr', type=float, default=1e-3, metavar='LR',
help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
parser.add_argument('--min_lr', type=float, default=0., metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0')
parser.add_argument('--gradient_checkpointing', action='store_true',
help='saving memory costs via gradient_checkpointing')
parser.add_argument('--warmup_epochs', type=float, default=40, metavar='N',
help='epochs to warmup LR')
# Dataset parameters
parser.add_argument('--data_path', default='/instruction_dataset/', type=str,
help='dataset path')
parser.add_argument('--output_dir', default='./output_dir',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default='./output_dir',
help='path where to tensorboard log')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--resume', default='',
help='resume from checkpoint')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin_mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
parser.set_defaults(pin_mem=True)
# distributed training parameters
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--dist_on_itp', action='store_true')
parser.add_argument('--dist_url', default='env://',
help='url used to set up distributed training')
#datasets
parser.add_argument('--prompt_format',
type=str,
default='CQM-A',
choices=[
'CQM-A', 'CQM-LA', 'CQM-EA', 'CQM-LEA', 'CQM-ELA', 'CQM-AL', 'CQM-AE', 'CQM-ALE', 'QCM-A',
'QCM-LA', 'QCM-EA', 'QCM-LEA', 'QCM-ELA', 'QCM-AL', 'QCM-AE', 'QCM-ALE', 'QCML-A', 'QCME-A',
'QCMLE-A', 'QCLM-A', 'QCEM-A', 'QCLEM-A', 'QCML-AE'
],
help='prompt format template')
parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
parser.add_argument('--caption_file', type=str, default='../data/captions.json')
parser.add_argument('--data_root', type=str, default='../data')
parser.add_argument('--use_caption', action='store_true', help='use image captions or not')
parser.add_argument('--do_pretrain', action='store_true', help='pre-train on large scale vl instruction')
return parser
def main(args):
misc.init_distributed_mode(args)
print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
print("{}".format(args).replace(', ', ',\n'))
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + misc.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
g = torch.Generator()
g.manual_seed(seed)
random.seed(seed)
cudnn.benchmark = False
cudnn.deterministic = True
if args.do_pretrain:
dataset_train = InstrcutDataSet(args, 'all', args.llama_model_path, args.max_seq_len)
else:
dataset_train = ScienceQADataSet(args, 'train', args.llama_model_path, args.max_seq_len)
print(dataset_train)
num_tasks = misc.get_world_size()
global_rank = misc.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
)
print("Sampler_train = %s" % str(sampler_train))
if global_rank == 0 and args.log_dir is not None:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = SummaryWriter(log_dir=args.log_dir)
else:
log_writer = None
data_loader_train = torch.utils.data.DataLoader(
dataset_train, sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
generator=g,
)
# define the model
model = LaVIN(args)
model.to(device)
#for debug. print the data type.
for name, param in model.named_parameters():
print(name,param.dtype)
model_without_ddp = model
#for debug. print the model.
# print("Model = %s" % str(model_without_ddp))
eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size()
if args.lr is None: # only base_lr is specified
args.lr = args.blr * eff_batch_size / 256
print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
print("actual lr: %.2e" % args.lr)
print("accumulate grad iterations: %d" % args.accum_iter)
print("effective batch size: %d" % eff_batch_size)
if args.distributed:
print(args.gpu)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu],find_unused_parameters=True)
model_without_ddp = model.module
# following timm: set wd as 0 for bias and norm layers
param_groups = optim_factory.param_groups_weight_decay(model_without_ddp, args.weight_decay)
#following qlora: apply paged optimizer
optimizer = bnb.optim.AdamW32bit(param_groups, lr=args.lr, betas=(0.9, 0.95),is_paged=True) #torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))
print(optimizer)
#mixed precision scaler
loss_scaler = NativeScaler()
misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
train_stats = train_one_epoch(
model, data_loader_train,
optimizer, device, epoch, loss_scaler,
log_writer=log_writer,
args=args
)
if args.output_dir:
misc.save_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch)
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch,}
if args.output_dir and misc.is_main_process():
if log_writer is not None:
log_writer.flush()
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if __name__ == '__main__':
args = get_args_parser()
args = args.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
main(args)