fix initializing data arguments

README.md

@@ -202,6 +202,33 @@ accelerate config # configure the environment
 accelerate launch src/train_XX.py # arguments (same as above)
 ```
 
+<details><summary>Example configuration for full-tuning with DeepSpeed ZeRO-2</summary>
+
+```yaml
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+  gradient_accumulation_steps: 4
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
+```
+
+</details>
+
 ### Evaluation (BLEU and ROUGE_CHINESE)
 
 ```bash

src/utils/common.py

@@ -103,11 +103,10 @@ def _init_adapter(
         lastest_checkpoint = None
 
         if model_args.checkpoint_dir is not None:
-            if not os.path.exists(os.path.join(model_args.checkpoint_dir[0], WEIGHTS_NAME)):
+            assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], WEIGHTS_NAME)), \
-                raise ValueError("Provided path ({}) does not contain a LoRA weight.".format(model_args.checkpoint_dir[0]))
+                "Provided path ({}) does not contain a LoRA weight.".format(model_args.checkpoint_dir[0])
-            if not os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)):
+            assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
-                raise ValueError("The given checkpoint may be not a LoRA checkpoint, \
+                "The given checkpoint may be not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."
-                                  please specify `--finetuning_type full/freeze` instead.")
 
             if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
                 checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
@@ -267,6 +266,8 @@ def prepare_args(
     transformers.utils.logging.enable_explicit_format()
 
     # Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
+    data_args.init_for_training()
+
     if stage != "sft" and training_args.predict_with_generate:
         raise ValueError("`predict_with_generate` cannot be set as True at PT, RM and PPO stages.")
 

src/utils/config.py

@@ -134,7 +134,7 @@ class DataTrainingArguments:
     )
     source_prefix: Optional[str] = field(
         default=None,
-        metadata={"help": "A prefix to add before every source text. Use `|` to separate multiple prefixes."}
+        metadata={"help": "A prefix to add before every source text. Use `|` to separate multiple prefixes in training."}
     )
     dev_ratio: Optional[float] = field(
         default=0,
@@ -145,7 +145,7 @@ class DataTrainingArguments:
         metadata={"help": "Which template to use for constructing prompts in training and inference."}
     )
 
-    def __post_init__(self): # support mixing multiple datasets
+    def init_for_training(self): # support mixing multiple datasets
         dataset_names = [ds.strip() for ds in self.dataset.split(",")]
         with open(os.path.join(self.dataset_dir, "dataset_info.json"), "r") as f:
             dataset_info = json.load(f)