fix eval strategy compat (modelscope#1143)

swift/llm/dpo.py

@@ -10,8 +10,8 @@
 from transformers.utils import is_torch_npu_available
 
 from swift.trainers.dpo_trainers import DPOTrainer
-from swift.utils import (check_json_format, get_dist_setting, get_logger, get_main, get_model_info, is_ddp_plus_mp,
-                         is_dist, is_master, plot_images, seed_everything, show_layers)
+from swift.utils import (append_to_jsonl, check_json_format, get_dist_setting, get_logger, get_main, get_model_info,
+                         is_ddp_plus_mp, is_dist, is_local_master, is_master, plot_images, seed_everything, show_layers)
 from .tuner import prepare_model
 from .utils import (DPOArguments, Template, get_dataset, get_model_tokenizer, get_template, get_time_info,
                     set_generation_config)
@@ -156,6 +156,7 @@ def llm_dpo(args: DPOArguments) -> str:
 
     if val_dataset is None:
         training_args.evaluation_strategy = IntervalStrategy.NO
+        training_args.eval_strategy = IntervalStrategy.NO
         training_args.do_eval = False
     logger.info(f'train_dataset: {train_dataset}')
     logger.info(f'val_dataset: {val_dataset}')
@@ -227,9 +228,9 @@ def llm_dpo(args: DPOArguments) -> str:
         'model_info': model_info,
         'dataset_info': trainer.dataset_info,
     }
-    jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
-    with open(jsonl_path, 'a', encoding='utf-8') as f:
-        f.write(json.dumps(run_info) + '\n')
+    if is_local_master():
+        jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
+        append_to_jsonl(jsonl_path, run_info)
     return run_info
 
 

swift/llm/orpo.py

@@ -10,8 +10,8 @@
 from transformers.utils import is_torch_npu_available
 
 from swift.trainers.orpo_trainers import ORPOTrainer
-from swift.utils import (check_json_format, get_dist_setting, get_logger, get_main, get_model_info, is_ddp_plus_mp,
-                         is_dist, is_master, plot_images, seed_everything, show_layers)
+from swift.utils import (append_to_jsonl, check_json_format, get_dist_setting, get_logger, get_main, get_model_info,
+                         is_ddp_plus_mp, is_dist, is_local_master, is_master, plot_images, seed_everything, show_layers)
 from .tuner import prepare_model
 from .utils import (ORPOArguments, Template, get_dataset, get_model_tokenizer, get_template, get_time_info,
                     set_generation_config)
@@ -147,6 +147,7 @@ def llm_orpo(args: ORPOArguments) -> str:
 
     if val_dataset is None:
         training_args.evaluation_strategy = IntervalStrategy.NO
+        training_args.eval_strategy = IntervalStrategy.NO
         training_args.do_eval = False
     logger.info(f'train_dataset: {train_dataset}')
     logger.info(f'val_dataset: {val_dataset}')
@@ -230,9 +231,9 @@ def llm_orpo(args: ORPOArguments) -> str:
         'model_info': model_info,
         'dataset_info': trainer.dataset_info,
     }
-    jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
-    with open(jsonl_path, 'a', encoding='utf-8') as f:
-        f.write(json.dumps(run_info) + '\n')
+    if is_local_master():
+        jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
+        append_to_jsonl(jsonl_path, run_info)
     return run_info
 
 

swift/llm/sft.py

@@ -255,6 +255,7 @@ def llm_sft(args: SftArguments) -> Dict[str, Union[str, Any]]:
             val_dataset = LazyLLMDataset(val_dataset, template)
     if val_dataset is None:
         training_args.evaluation_strategy = IntervalStrategy.NO
+        training_args.eval_strategy = IntervalStrategy.NO
         training_args.do_eval = False
 
     padding_to = args.max_length if args.sft_type == 'longlora' else None

swift/llm/simpo.py

@@ -10,8 +10,8 @@
 from transformers.utils import is_torch_npu_available
 
 from swift.trainers.simpo_trainers import SimPOTrainer
-from swift.utils import (check_json_format, get_dist_setting, get_logger, get_main, get_model_info, is_ddp_plus_mp,
-                         is_dist, is_master, plot_images, seed_everything, show_layers)
+from swift.utils import (append_to_jsonl, check_json_format, get_dist_setting, get_logger, get_main, get_model_info,
+                         is_ddp_plus_mp, is_dist, is_local_master, is_master, plot_images, seed_everything, show_layers)
 from .tuner import prepare_model
 from .utils import (SimPOArguments, Template, get_dataset, get_model_tokenizer, get_template, get_time_info,
                     set_generation_config)
@@ -145,6 +145,7 @@ def llm_simpo(args: SimPOArguments) -> str:
 
     if val_dataset is None:
         training_args.evaluation_strategy = IntervalStrategy.NO
+        training_args.eval_strategy = IntervalStrategy.NO
         training_args.do_eval = False
     logger.info(f'train_dataset: {train_dataset}')
     logger.info(f'val_dataset: {val_dataset}')
@@ -215,9 +216,9 @@ def llm_simpo(args: SimPOArguments) -> str:
         'model_info': model_info,
         'dataset_info': trainer.dataset_info,
     }
-    jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
-    with open(jsonl_path, 'a', encoding='utf-8') as f:
-        f.write(json.dumps(run_info) + '\n')
+    if is_local_master():
+        jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
+        append_to_jsonl(jsonl_path, run_info)
     return run_info
 
 

swift/trainers/callback.py

@@ -8,7 +8,7 @@
                                            TrainerState)
 from transformers.trainer_utils import IntervalStrategy, has_length, speed_metrics
 
-from swift.utils import is_pai_training_job, use_torchacc
+from swift.utils import append_to_jsonl, is_pai_training_job, use_torchacc
 from .arguments import TrainingArguments
 
 
@@ -55,8 +55,7 @@ def on_log(self, args: TrainingArguments, state: TrainerState, control, logs=Non
                 logs[k] = round(logs[k], 8)
         if not is_pai_training_job() and state.is_local_process_zero:
             jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
-            with open(jsonl_path, 'a', encoding='utf-8') as f:
-                f.write(json.dumps(logs) + '\n')
+            append_to_jsonl(jsonl_path, logs)
         super().on_log(args, state, control, logs, **kwargs)
         if state.is_local_process_zero and self.training_bar is not None:
             self.training_bar.refresh()
@@ -67,8 +66,9 @@ class DefaultFlowCallbackNew(DefaultFlowCallback):
     def on_step_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
         control = super().on_step_end(args, state, control, **kwargs)
         # save the last ckpt
+        evaluation_strategy = args.eval_strategy if hasattr(args, 'eval_strategy') else args.evaluation_strategy
         if state.global_step == state.max_steps:
-            if args.evaluation_strategy != IntervalStrategy.NO:
+            if evaluation_strategy != IntervalStrategy.NO:
                 control.should_evaluate = True
             if args.save_strategy != IntervalStrategy.NO:
                 control.should_save = True
@@ -84,8 +84,7 @@ def on_log(self, args, state, control, logs=None, **kwargs):
                 logs[k] = round(logs[k], 8)
         if not is_pai_training_job() and state.is_local_process_zero:
             jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
-            with open(jsonl_path, 'a', encoding='utf-8') as f:
-                f.write(json.dumps(logs) + '\n')
+            append_to_jsonl(jsonl_path, logs)
 
         _ = logs.pop('total_flos', None)
         if state.is_local_process_zero: