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train.py
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train.py
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import argparse
import copy
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0, "
import os.path as osp
import time
import re
import loralib as lora
import torch
import torch.nn as nn
import mmcv
from mmcv.runner import init_dist
from mmcv.utils import Config, DictAction, get_git_hash
from mmseg import __version__
from mmseg.apis import set_random_seed
from mmcv_custom import train_segmentor
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
from mmseg.utils import collect_env, get_root_logger
from mmseg.models.backbones import EVA2
def parse_args():
parser = argparse.ArgumentParser(description='Train a segmentor')
parser.add_argument('--work-dir', help='the dir to save logs and models')
parser.add_argument(
'--load-from', help='the checkpoint file to load weights from')
parser.add_argument(
'--resume-from', help='the checkpoint file to resume from')
parser.add_argument(
'--no-validate',
action='store_true',
help='whether not to evaluate the checkpoint during training')
group_gpus = parser.add_mutually_exclusive_group()
group_gpus.add_argument(
'--gpus',
type=int,
help='number of gpus to use '
'(only applicable to non-distributed training)')
group_gpus.add_argument(
'--gpu-ids',
type=int,
nargs='+',
help='ids of gpus to use '
'(only applicable to non-distributed training)')
parser.add_argument('--seed', type=int, default=None, help='random seed')
parser.add_argument(
'--deterministic',
action='store_true',
help='whether to set deterministic options for CUDNN backend.')
parser.add_argument(
'--options', nargs='+', action=DictAction, help='custom options')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
'''
def find_eva_linear_names(model):
lora_module_names = set()
for name, module in model.backbone.named_modules():
if isinstance(module, bnb.nn.Linear4bit):
names = name.split('.')
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
if 'lm_head' in lora_module_names:
lora_module_names.remove('lm_head')
return list(lora_module_names)
def get_accelerate_model(args, model, checkpoint_dir=None):
from transformers import (
PreTrainedModel,
PretrainedConfig,
AutoModelForCausalLM,
BitsAndBytesConfig,
)
from peft import (
prepare_model_for_kbit_training,
LoraConfig,
get_peft_model,
PeftModel
)
from peft.tuners.lora import LoraLayer
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
import bitsandbytes as bnb
pconfig=PretrainedConfig(is_encoder_decoder=True,torch_dtype=torch.float32)
prtr=PreTrainedModel(pconfig)
# prtr.save_pretrained('workbench/pretrained/')
model = AutoModelForCausalLM.from_pretrained(
None,
state_dict=model.state_dict(),
config=prtr,
load_in_4bit=True,
device_map='auto',
max_memory={0: '5120MB'},
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4',
),
torch_dtype=torch.bfloat16,
)
setattr(model, 'model_parallel', True)
setattr(model, 'is_parallelizable', True)
model.config.torch_dtype=torch.bfloat16
model=prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
model.gradient_checkpointing_enable()
if checkpoint_dir is not None:
print("Loading adapters from checkpoint.")
model = PeftModel.from_pretrained(model, osp.join(checkpoint_dir, 'adapter_model'), is_trainable=True)
else:
print(f'Adding LoRA modules...')
modules = find_eva_linear_names(model)
config = LoraConfig(
r=args.lora_rank,
lora_alpha=args.lora_alpha,
target_modules=modules,
lora_dropout=0.1,
bias="none",
)
model=get_peft_model(model, config)
for name, module in model.named_modules():
if isinstance(module, LoraLayer):
module = module.to(torch.bfloat16)
if 'norm' in name:
module = module.to(torch.float32)
if 'lm_head' in name or 'embed_tokens' in name:
if hasattr(module, 'weight') and module.weight.dtype == torch.float32:
module = module.to(torch.bfloat16)
return model
'''
def get_finetune_model(model, code, verbose=False):
def freeze_match(name, f_list):
ret = False
for n in f_list:
ret = ret or (re.search(n, name) is not None)
return ret
freeze_list = []
if code == 1:
checkpoint = torch.load(model.backbone.pretrained, map_location='cpu')["model"]
freeze_list.extend([f"backbone.{key}" for key in checkpoint.keys()])
if verbose:
print("**frozen parameters**")
print(f"List: {freeze_list}")
for key, value in model.named_parameters():
value.requires_grad = not freeze_match(key, freeze_list)
if verbose:
print(key, value.requires_grad)
return model
def main(args, info, verbose=False):
finetune_code, neck_name = info["finetune_code"], info["config_neck"]
config = f"configs/{neck_name}_neck.py"
cfg = Config.fromfile(config)
if args.options is not None:
cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(config))[0])
cfg.work_dir = osp.join(cfg.work_dir, f"neck_{neck_name}_finetune_{finetune_code}")
if args.load_from is not None:
cfg.load_from = args.load_from
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir.replace("/hy-tmp/", "")))
# dump config
cfg.dump(osp.join(cfg.work_dir.replace("/hy-tmp/", ""), osp.basename(config)))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir.replace("/hy-tmp/", ""), f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
logger.info(f"Neck: {neck_name}; Finetune_code: {finetune_code}")
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
if verbose:
logger.info('Environment info:\n' + dash_line + env_info + '\n' + dash_line)
logger.info(f'Config:\n{cfg.pretty_text}')
meta['env_info'] = env_info
# set random seeds
if args.seed is not None:
if verbose:
logger.info(f'Distributed training: {distributed}')
logger.info(f'Set random seed to {args.seed}, deterministic: '
f'{args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
meta['exp_name'] = osp.basename(config)
model = build_segmentor(
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg')
)
# finetune_code: {0: non_freeze; 1: freeze_loaded_eva2}
if finetune_code > 0:
model = get_finetune_model(model, finetune_code, verbose=verbose)
if verbose:
logger.info(model)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
PALETTE=datasets[0].PALETTE)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_segmentor(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)
if __name__ == '__main__':
args = parse_args()
neck_choices = ["fpn", "sfp", "linear"]
# finetune_code = {0: no-freeze, 1: freeze EVA}
for n in neck_choices:
hyper_info = {
"finetune_code": 0,
"config_neck": n,
}
main(args, hyper_info)