update multi_modal_embedding example

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/12626062

examples/pytorch/multi_modal_embedding/finetune_multi_modal_embedding.py

@@ -1,15 +1,13 @@
 import os
 from dataclasses import dataclass, field
-from functools import partial
 
+from modelscope import MsDataset, TrainingArgs
 from modelscope.metainfo import Trainers
-from modelscope.msdatasets import MsDataset
 from modelscope.trainers import build_trainer
-from modelscope.trainers.args import (TrainingArgs, get_flatten_value,
-                                      set_flatten_value)
+from modelscope.trainers.training_args import set_flatten_value
 
 
-@dataclass
+@dataclass(init=False)
 class MultiModalEmbeddingArguments(TrainingArgs):
 
     trainer: str = field(
@@ -17,6 +15,12 @@ class MultiModalEmbeddingArguments(TrainingArgs):
             'help': 'The trainer used',
         })
 
+    work_dir: str = field(
+        default='./tmp',
+        metadata={
+            'help': 'The working path for saving checkpoint',
+        })
+
     use_fp16: bool = field(
         default=None,
         metadata={
@@ -35,7 +39,6 @@ class MultiModalEmbeddingArguments(TrainingArgs):
         default=None,
         metadata={
             'cfg_node': 'train.optimizer_hparams',
-            'cfg_getter': partial(get_flatten_value, exclusions=['lr']),
             'cfg_setter': set_flatten_value,
             'help': 'The optimizer init params except `lr`',
         })
@@ -51,7 +54,6 @@ class MultiModalEmbeddingArguments(TrainingArgs):
         default=None,
         metadata={
             'cfg_node': 'dataset.column_map',
-            'cfg_getter': get_flatten_value,
             'cfg_setter': set_flatten_value,
             'help': 'The column map for dataset',
         })
@@ -67,7 +69,6 @@ class MultiModalEmbeddingArguments(TrainingArgs):
         default=None,
         metadata={
             'cfg_node': 'train.lr_scheduler_hook',
-            'cfg_getter': get_flatten_value,
             'cfg_setter': set_flatten_value,
             'help': 'The parameters for lr scheduler hook',
         })
@@ -76,7 +77,6 @@ class MultiModalEmbeddingArguments(TrainingArgs):
         default=None,
         metadata={
             'cfg_node': 'train.optimizer_hook',
-            'cfg_getter': get_flatten_value,
             'cfg_setter': set_flatten_value,
             'help': 'The parameters for optimizer hook',
         })
@@ -92,23 +92,28 @@ class MultiModalEmbeddingArguments(TrainingArgs):
             'help': 'The data parallel world size',
         })
 
-    def __call__(self, config):
-        config = super().__call__(config)
-        config.merge_from_dict({'pretrained_model.model_name': self.model})
-        if self.clip_clamp:
-            config.train.hooks.append({'type': 'ClipClampLogitScaleHook'})
-        if self.world_size > 1:
-            config.train.launcher = 'pytorch'
-        return config
+
+config, args = MultiModalEmbeddingArguments().parse_cli().to_config()
+print(config, args)
 
 
-args = MultiModalEmbeddingArguments.from_cli(task='multi-modal-embedding')
-print(args)
+def cfg_modify_fn(cfg):
+    if args.use_model_config:
+        cfg.merge_from_dict(config)
+    else:
+        cfg = config
+    cfg.merge_from_dict({'pretrained_model.model_name': args.model})
+    if args.clip_clamp:
+        cfg.train.hooks.append({'type': 'ClipClampLogitScaleHook'})
+    if args.world_size > 1:
+        cfg.train.launcher = 'pytorch'
+    return cfg
+
 
 train_dataset = MsDataset.load(
-    args.dataset_name, namespace='modelscope', split='train')
+    args.train_dataset_name, namespace='modelscope', split='train')
 eval_dataset = MsDataset.load(
-    args.dataset_name, namespace='modelscope', split='validation')
+    args.train_dataset_name, namespace='modelscope', split='validation')
 
 os.makedirs(args.work_dir, exist_ok=True)
 kwargs = dict(
@@ -116,6 +121,6 @@ kwargs = dict(
     train_dataset=train_dataset,
     eval_dataset=eval_dataset,
     work_dir=args.work_dir,
-    cfg_modify_fn=args)
+    cfg_modify_fn=cfg_modify_fn)
 trainer = build_trainer(name=args.trainer, default_args=kwargs)
 trainer.train()

examples/pytorch/multi_modal_embedding/run_train.sh

@@ -6,14 +6,16 @@ PYTHONPATH=. torchrun --nproc_per_node $DATA_PARALLEL_SIZE \
     --trainer 'clip-multi-modal-embedding' \
     --work_dir './workspace/ckpts/clip' \
     --model 'damo/multi-modal_clip-vit-base-patch16_zh' \
-    --dataset_name 'muge' \
+    --train_dataset_name 'muge' \
     --dataset_column_map 'img=image,text=query' \
     --max_epochs 1 \
     --use_fp16 true \
     --per_device_train_batch_size 180 \
+    --train_data_worker 0 \
     --train_shuffle true \
     --train_drop_last true \
     --per_device_eval_batch_size 128 \
+    --eval_data_worker 0 \
     --eval_shuffle true \
     --eval_drop_last true \
     --save_ckpt_best true \
@@ -33,3 +35,4 @@ PYTHONPATH=. torchrun --nproc_per_node $DATA_PARALLEL_SIZE \
     --optimizer_hook 'type=TorchAMPOptimizerHook,cumulative_iters=1,loss_keys=loss' \
     --clip_clamp true \
     --world_size $DATA_PARALLEL_SIZE \
+    --use_model_config true \

examples/pytorch/text_classification/finetune_text_classification.py

@@ -2,7 +2,8 @@ import os
 from dataclasses import dataclass, field
 
 from modelscope import (EpochBasedTrainer, MsDataset, TrainingArgs,
-                        build_dataset_from_file, build_trainer)
+                        build_dataset_from_file)
+from modelscope.trainers import build_trainer
 
 
 def set_labels(labels):

examples/pytorch/text_generation/finetune_text_generation.py

@@ -1,8 +1,8 @@
 from dataclasses import dataclass, field
 
-from modelscope import (EpochBasedTrainer, MsDataset, TrainingArgs,
-                        build_trainer)
+from modelscope import EpochBasedTrainer, MsDataset, TrainingArgs
 from modelscope.metainfo import Trainers
+from modelscope.trainers import build_trainer
 
 
 @dataclass(init=False)

modelscope/trainers/multi_modal/clip/clip_trainer.py

@@ -176,11 +176,10 @@ class CLIPTrainer(EpochBasedTrainer):
         self.dataset_cfg = cfg.dataset
         if hasattr(self.dataset_cfg, 'column_map'):
             # cases where dataset key names are not "img" and "text"
-            img_key_name = getattr(self.dataset_cfg.column_map, 'img', 'img')
+            img_key_name = self.dataset_cfg['column_map'].get('img', 'img')
             preprocessor[ConfigKeys.train].set_input_img_key(img_key_name)
             preprocessor[ConfigKeys.val].set_input_img_key(img_key_name)
-            text_key_name = getattr(self.dataset_cfg.column_map, 'text',
-                                    'text')
+            text_key_name = self.dataset_cfg['column_map'].get('text', 'text')
             preprocessor[ConfigKeys.train].set_input_text_key(text_key_name)
             preprocessor[ConfigKeys.val].set_input_text_key(text_key_name)
         self.global_batch_size = cfg.train.dataloader.batch_size_per_gpu * world_size