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