forked from p83651209/CPM-9G-8B
131 lines
4.6 KiB
Python
131 lines
4.6 KiB
Python
import functools
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import json
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import os
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import shutil
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import time
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from typing import List
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import bmtrain as bmt
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import torch
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from .log import logger
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def rename_if_exists(file_path):
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if not os.path.exists(file_path):
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return
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timestamp = time.strftime("%Y%m%d%H%M%S")
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file_dir, file_name = os.path.split(file_path)
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file_root, file_ext = os.path.splitext(file_name)
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new_file_name = f"{file_root}_bak_{timestamp}{file_ext}"
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new_file_path = os.path.join(file_dir, new_file_name)
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try:
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os.rename(file_path, new_file_path)
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logger.info(f"File '{file_name}' already exists. Renamed to '{new_file_name}'")
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except Exception as e:
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logger.warn(
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"rename file failed,file_path={file_path}, new_file_path={new_file_path},err={err}".format(
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file_path=file_path, new_file_path=new_file_path, err=str(e)
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)
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)
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def rename_if_exists_decorator(func):
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@functools.wraps(func)
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def wrapper(file_path, *args, **kwargs):
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rename_if_exists(file_path)
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return func(file_path, *args, **kwargs)
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return wrapper
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@rename_if_exists_decorator
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def bmt_save(file_path: str, model: torch.nn.Module, export_files: List[str] = None):
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bmt.save(model, file_path)
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if export_files is not None:
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export_files.append(file_path)
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@rename_if_exists_decorator
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def torch_save(file_path: str, obj: object, export_files: List[str] = None):
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torch.save(obj, file_path)
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if export_files is not None:
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export_files.append(file_path)
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@rename_if_exists_decorator
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def json_save(file_path: str, obj: object, export_files: List[str] = None):
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with open(file_path, "w") as data_f:
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json.dump(obj, data_f)
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if export_files is not None:
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export_files.append(file_path)
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def export(
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model: torch.nn.Module, dataloader, optimizer: bmt.optim.AdamOffloadOptimizer, global_step, args, final_save=False
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):
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"""
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一次 ckpt 保存：
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/{args.save}/
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├── {save_name}-{global_step}.rank-0.opt
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├── {save_name}-{global_step}.rank-n.opt
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├── job_{job_id}_ckpt_{global_step}/ # checkpoint 导出为模型版本时，job_{job_id}_ckpt_{global_step}/ 路径下文件会一起导出，创建一个模型组版本
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├── config.json
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├── vocabs.txt
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├── {args.save_name}-{global_step}.pt
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├── {args.save_name}-{global_step}.data
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├── {args.save_name}-{global_step}.data.json
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└── {args.save_name}-{global_step}.success
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"""
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export_model_dir = os.path.join(args.save, f"l_{global_step}")
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os.makedirs(export_model_dir, exist_ok=True)
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base_file_name = f"{args.save_name}-{global_step}" if global_step > -1 else args.save_name
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logger.info(f"start to export ckpt, save_dir={export_model_dir}, file prefix={base_file_name}")
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export_files = []
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# model checkpoint
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bmt_save(
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file_path=os.path.join(export_model_dir, base_file_name + ".pt"),
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model=model,
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export_files=export_files,
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)
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# opt is only used for continual pre-training, not the final opt
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if not final_save:
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grad_path = os.path.join(
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args.save,
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args.save_name + ("-%d.rank-%d.opt" % (global_step % (args.save_iters * 5), bmt.rank())),
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)
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torch.save(optimizer.state_dict(), grad_path)
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logger.info(f"Successfully save grad file: {grad_path}")
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all_states = dataloader.state_dict()
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if bmt.rank() == 0:
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# data checkpoint
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# rank 0 writes the dataloader state
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torch_save(
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file_path=os.path.join(export_model_dir, base_file_name + ".data"),
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obj=all_states,
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export_files=export_files,
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)
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# data checkpoint json
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# rank 0 writes the dataloader state into the json file
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data_p_json = {k: v for k, v in all_states.items()}
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for k in data_p_json:
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data_p_json[k] = {k_of_v: data_p_json[k][k_of_v].tolist() for k_of_v in data_p_json[k]}
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json_save(
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file_path=os.path.join(export_model_dir, base_file_name + ".data.json"),
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obj=data_p_json,
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export_files=export_files,
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)
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# config 和 vocabs 和模型文件一起存储
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model_cfg_path = os.path.join(export_model_dir, "config.json")
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model_vocab_path = os.path.join(export_model_dir, "vocabs.txt")
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export_files.extend([model_cfg_path, model_vocab_path])
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shutil.copy(args.model_config, model_cfg_path)
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shutil.copy(args.vocab, model_vocab_path)
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logger.info(f"Successfully save model files! {export_files}")
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del all_states
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return export_model_dir
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