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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os.path as osp
import torch
from mmengine.dist import (collect_results, get_dist_info, get_rank, init_dist,
master_only)
from xtuner.registry import BUILDER
from xtuner.configs import cfgs_name_path
from xtuner.model.utils import guess_load_checkpoint
from mmengine.config import Config
from mmengine.fileio import PetrelBackend, get_file_backend
from mmengine.config import ConfigDict
from transformers import AutoConfig
def convert_dict2config_dict(input):
input = ConfigDict(**input)
for key in input.keys():
if isinstance(input[key], dict):
input[key] = convert_dict2config_dict(input[key])
return input
TORCH_DTYPE_MAP = dict(
fp16=torch.float16, bf16=torch.bfloat16, fp32=torch.float32, auto='auto')
def parse_args():
parser = argparse.ArgumentParser(description='toHF script')
parser.add_argument('config', help='config file name or path.')
parser.add_argument('--mllm-model-path', type=str, default='./OpenGVLab/InternVL2-4B', help='directory path to the base model.')
parser.add_argument("--radio-path", type=str, default='./nvidia/RADIO', help='directory path to the radio model.')
parser.add_argument(
'--save-path', type=str, default='./work_dirs/hf_model', help='save folder name')
parser.add_argument(
'--seed',
type=int,
default=0,
help='Random seed for reproducible text generation')
args = parser.parse_args()
return args
@master_only
def master_print(msg):
print(msg)
def main():
args = parse_args()
torch.manual_seed(args.seed)
rank = 0
world_size = 1
# build model
if not osp.isfile(args.config):
try:
args.config = cfgs_name_path[args.config]
except KeyError:
raise FileNotFoundError(f'Cannot find {args.config}')
# load config
cfg = Config.fromfile(args.config)
model = BUILDER.build(cfg.model)
model._merge_lora()
model.model.transfer_to_hf = True
all_state_dict = model.all_state_dict()
all_state_dict_new = {}
for key in all_state_dict.keys():
new_key = copy.deepcopy(key)
if new_key.startswith('model.'):
new_key = new_key[len('model.'):]
all_state_dict_new[new_key] = all_state_dict[key]
from projects.colva.colva_hf.internvl2_4b.configuration_internvl_chat import InternVLChatConfig
from projects.colva.colva_hf.internvl2_4b.modeling_internvl_chat import InternVLChatModel
mllm_config = AutoConfig.from_pretrained(args.mllm_model_path, trust_remote_code=True)
mllm_config_dict = mllm_config.to_dict()
radio_config = AutoConfig.from_pretrained(args.radio_path, trust_remote_code=True)
radio_config_dict = radio_config.to_dict()
radio_config_dict['auto_map'] = {
'AutoConfig': "configuraion_radio.RADIOConfig",
"AutoModel": "modeling_radio.RADIOModel"
}
mllm_config_dict.update({"radio_config": radio_config_dict})
colva_hf_config = InternVLChatConfig(**mllm_config_dict)
colva_hf_model = InternVLChatModel(colva_hf_config, vision_model=model.model.vision_model, language_model=model.model.language_model)
colva_hf_model.load_state_dict(all_state_dict_new)
colva_hf_model.save_pretrained(args.save_path)
print(f"Save the hf model into {args.save_path}")
if __name__ == '__main__':
main()
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