support DPO training (2305.18290)

Former-commit-id: 6d98de148e4af63a7028dfaeb6cf86eb56a4488f

src/llmtuner/tuner/core/parser.py

@@ -19,7 +19,7 @@ from llmtuner.hparams import (
 logger = get_logger(__name__)
 
 
-def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None):
+def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
     if args is not None:
         return parser.parse_dict(args)
     elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
@@ -32,26 +32,53 @@ def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None)
 
 def parse_train_args(
     args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments]:
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    Seq2SeqTrainingArguments,
+    FinetuningArguments,
+    GeneratingArguments,
+    GeneralArguments
+]:
     parser = HfArgumentParser((
-        ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments
+        ModelArguments,
+        DataArguments,
+        Seq2SeqTrainingArguments,
+        FinetuningArguments,
+        GeneratingArguments,
+        GeneralArguments
     ))
     return _parse_args(parser, args)
 
 
 def parse_infer_args(
     args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]:
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    FinetuningArguments,
+    GeneratingArguments
+]:
     parser = HfArgumentParser((
-        ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+        ModelArguments,
+        DataArguments,
+        FinetuningArguments,
+        GeneratingArguments
     ))
     return _parse_args(parser, args)
 
 
 def get_train_args(
     args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments]:
-    model_args, data_args, training_args, finetuning_args, general_args = parse_train_args(args)
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    Seq2SeqTrainingArguments,
+    FinetuningArguments,
+    GeneratingArguments,
+    GeneralArguments
+]:
+    model_args, data_args, training_args, finetuning_args, generating_args, general_args = parse_train_args(args)
 
     # Setup logging
     if training_args.should_log:
@@ -68,7 +95,7 @@ def get_train_args(
     data_args.init_for_training()
 
     if general_args.stage != "sft" and training_args.predict_with_generate:
-        raise ValueError("`predict_with_generate` cannot be set as True at PT, RM and PPO stages.")
+        raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
 
     if training_args.do_train and training_args.predict_with_generate:
         raise ValueError("`predict_with_generate` cannot be set as True while training.")
@@ -76,6 +103,15 @@ def get_train_args(
     if general_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
         raise ValueError("Please enable `predict_with_generate` to save model predictions.")
 
+    if general_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type != "lora":
+        raise ValueError("RM and PPO training can only be performed with the LoRA method.")
+
+    if general_args.stage in ["ppo", "dpo"] and not training_args.do_train:
+        raise ValueError("PPO and DPO stage can only be performed at training.")
+
+    if general_args.stage == "ppo" and model_args.reward_model is None:
+        raise ValueError("Reward model is necessary for PPO training.")
+
     if training_args.max_steps == -1 and data_args.streaming:
         raise ValueError("Please specify `max_steps` in streaming mode.")
 
@@ -133,12 +169,17 @@ def get_train_args(
     # Set seed before initializing model.
     transformers.set_seed(training_args.seed)
 
-    return model_args, data_args, training_args, finetuning_args, general_args
+    return model_args, data_args, training_args, finetuning_args, generating_args, general_args
 
 
 def get_infer_args(
     args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]:
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    FinetuningArguments,
+    GeneratingArguments
+]:
     model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)
 
     if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":