modify ofatrainer

modelscope/trainers/multi_modal/ofa/ofa_trainer.py

@@ -24,12 +24,13 @@ from .ofa_trainer_utils import (AdjustLabelSmoothedCrossEntropyCriterion,
 @TRAINERS.register_module(module_name=Trainers.ofa_tasks)
 class OFATrainer(EpochBasedTrainer):
 
-    def __init__(self, model: str, *args, **kwargs):
+    def __init__(self, model: str, cfg_file, work_dir, train_dataset,
+                 eval_dataset, *args, **kwargs):
         model = Model.from_pretrained(model)
         model_dir = model.model_dir
-        cfg_file = os.path.join(model_dir, ModelFile.CONFIGURATION)
+        # cfg_file = os.path.join(model_dir, ModelFile.CONFIGURATION)
         cfg = Config.from_file(cfg_file)
-        dataset = self._build_dataset_with_config(cfg)
+        # dataset = self._build_dataset_with_config(cfg)
         preprocessor = {
             ConfigKeys.train:
             OfaPreprocessor(
@@ -41,7 +42,7 @@ class OFATrainer(EpochBasedTrainer):
         # use torchrun launch
         world_size = int(os.environ.get('WORLD_SIZE', 1))
         epoch_steps = math.ceil(
-            len(dataset['train']) /  # noqa
+            len(train_dataset) /  # noqa
             (cfg.train.dataloader.batch_size_per_gpu * world_size))  # noqa
         cfg.train.lr_scheduler.num_train_steps = epoch_steps * cfg.train.max_epochs
         cfg.train.criterion.tokenizer = model.tokenizer
@@ -68,11 +69,11 @@ class OFATrainer(EpochBasedTrainer):
             cfg_file=cfg_file,
             model=model,
             data_collator=collator,
-            train_dataset=dataset['train'],
-            eval_dataset=dataset['valid'],
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
             preprocessor=preprocessor,
             optimizers=(optimizer, lr_scheduler),
-            work_dir=cfg.train.work_dir,
+            work_dir=work_dir,
             *args,
             **kwargs,
         )

tests/trainers/test_ofa_trainer.py

@@ -3,22 +3,51 @@ import glob
 import os
 import os.path as osp
 import shutil
+import tempfile
 import unittest
 
 from modelscope.metainfo import Trainers
+from modelscope.msdatasets import MsDataset
 from modelscope.trainers import build_trainer
+from modelscope.utils.constant import DownloadMode
 from modelscope.utils.test_utils import test_level
 
 
 class TestOfaTrainer(unittest.TestCase):
 
+    def setUp(self):
+        column_map = {'premise': 'text', 'hypothesis': 'text2'}
+        data_train = MsDataset.load(
+            dataset_name='glue',
+            subset_name='mnli',
+            namespace='modelscope',
+            split='train[:100]',
+            download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
+        self.train_dataset = MsDataset.from_hf_dataset(
+            data_train._hf_ds.rename_columns(column_map))
+        data_eval = MsDataset.load(
+            dataset_name='glue',
+            subset_name='mnli',
+            namespace='modelscope',
+            split='validation_matched[:8]',
+            download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
+        self.test_dataset = MsDataset.from_hf_dataset(
+            data_eval._hf_ds.rename_columns(column_map))
+
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_trainer(self):
         os.environ['LOCAL_RANK'] = '0'
         model_id = 'damo/ofa_text-classification_mnli_large_en'
-        default_args = {'model': model_id}
-        trainer = build_trainer(
-            name=Trainers.ofa_tasks, default_args=default_args)
+
+        kwargs = dict(
+            model=model_id,
+            cfg_file=
+            '/Users/running_you/.cache/modelscope/hub/damo/ofa_text-classification_mnli_large_en//configuration.json',
+            train_dataset=self.train_dataset,
+            eval_dataset=self.test_dataset,
+            work_dir='/Users/running_you/.cache/modelscope/hub/work/mnli')
+
+        trainer = build_trainer(name=Trainers.ofa_tasks, default_args=kwargs)
         os.makedirs(trainer.work_dir, exist_ok=True)
         trainer.train()
         assert len(