ofa-finetune 支持训练推理配置文件一体化

ofa的finetune的配置文档教程冗余，用户使用copy内容太多，修改复杂，提供简洁的finetune代码流程
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11282842

modelscope/trainers/multi_modal/ofa/ofa_trainer.py

@@ -2,15 +2,19 @@
 
 import math
 import os
+import shutil
+import tempfile
 from functools import partial
 from shutil import ignore_patterns
 from typing import Callable, Dict, Optional, Tuple, Union
 
+import json
 import torch
 from torch import distributed as dist
 from torch import nn
 from torch.utils.data import Dataset
 
+from modelscope.hub.file_download import model_file_download
 from modelscope.metainfo import Trainers
 from modelscope.models.base import Model, TorchModel
 from modelscope.msdatasets.ms_dataset import MsDataset
@@ -83,18 +87,26 @@ class OFATrainer(EpochBasedTrainer):
             model, revision=model_revision, invoked_by=Invoke.TRAINER)
         model_dir = model.model_dir
         self.cfg_modify_fn = cfg_modify_fn
-        cfg = self.rebuild_config(Config.from_file(cfg_file))
-        if 'work_dir' not in kwargs or len(kwargs['work_dir']) == 0:
-            work_dir = cfg.train.work_dir
-        else:
-            work_dir = kwargs['work_dir']
 
+        work_dir = kwargs.get('work_dir', 'workspace')
         os.makedirs(work_dir, exist_ok=True)
         ignore_file_set = set()
-        ignore_file_set.add(ModelFile.CONFIGURATION)
+        if cfg_file is not None:
+            cfg_file = self.get_config_file(cfg_file)
+            dst = os.path.abspath(
+                os.path.join(work_dir, ModelFile.CONFIGURATION))
+            src = os.path.abspath(cfg_file)
+            if src != dst:
+                shutil.copy(src, work_dir)
+            ignore_file_set.add(ModelFile.CONFIGURATION)
         recursive_overwrite(
             model_dir, work_dir, ignore=ignore_patterns(*ignore_file_set))
-
+        cfg_file = os.path.join(work_dir, ModelFile.CONFIGURATION)
+        cfg = self.rebuild_config(Config.from_file(cfg_file))
+        if cfg_modify_fn is not None:
+            cfg = self.cfg_modify_fn(cfg)
+            with open(cfg_file, 'w') as writer:
+                json.dump(dict(cfg), fp=writer, indent=4)
         if preprocessor is None:
             preprocessor = {
                 ConfigKeys.train:
@@ -143,6 +155,7 @@ class OFATrainer(EpochBasedTrainer):
             model=model,
             cfg_file=cfg_file,
             arg_parse_fn=arg_parse_fn,
+            cfg_modify_fn=cfg_modify_fn,
             data_collator=data_collator,
             train_dataset=train_dataset,
             eval_dataset=eval_dataset,
@@ -160,6 +173,27 @@ class OFATrainer(EpochBasedTrainer):
             cfg = self.cfg_modify_fn(cfg)
         return cfg
 
+    def get_config_file(self, config_file: str):
+        r"""
+        support local file/ url or model_id with revision
+        """
+        if os.path.exists(config_file):
+            return config_file
+        else:
+            temp_name = tempfile.TemporaryDirectory().name
+            if len(config_file.split('#')) == 2:
+                model_id = config_file.split('#')[0]
+                revision = config_file.split('#')[-1].split('=')[-1]
+            else:
+                model_id = config_file
+                revision = DEFAULT_MODEL_REVISION
+            file_name = model_file_download(
+                model_id,
+                file_path=ModelFile.CONFIGURATION,
+                revision=revision,
+                cache_dir=temp_name)
+            return file_name
+
     def train_step(self, model, inputs):
         r"""
         A single training step.