disentangle model from tuner and rename modules

src/llmtuner/train/tuner.py

@@ -0,0 +1,51 @@
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from llmtuner.extras.callbacks import LogCallback
+from llmtuner.extras.logging import get_logger
+from llmtuner.model import get_train_args, get_infer_args, load_model_and_tokenizer
+from llmtuner.train.pt import run_pt
+from llmtuner.train.sft import run_sft
+from llmtuner.train.rm import run_rm
+from llmtuner.train.ppo import run_ppo
+from llmtuner.train.dpo import run_dpo
+
+if TYPE_CHECKING:
+    from transformers import TrainerCallback
+
+
+logger = get_logger(__name__)
+
+
+def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None):
+    model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
+    callbacks = [LogCallback()] if callbacks is None else callbacks
+
+    if finetuning_args.stage == "pt":
+        run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif finetuning_args.stage == "sft":
+        run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif finetuning_args.stage == "rm":
+        run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif finetuning_args.stage == "ppo":
+        run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif finetuning_args.stage == "dpo":
+        run_dpo(model_args, data_args, training_args, finetuning_args, callbacks)
+    else:
+        raise ValueError("Unknown task.")
+
+
+def export_model(args: Optional[Dict[str, Any]] = None, max_shard_size: Optional[str] = "10GB"):
+    model_args, _, finetuning_args, _ = get_infer_args(args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
+    model.config.use_cache = True
+    model.save_pretrained(model_args.export_dir, max_shard_size=max_shard_size)
+    try:
+        tokenizer.padding_side = "left" # restore padding side
+        tokenizer.init_kwargs["padding_side"] = "left"
+        tokenizer.save_pretrained(model_args.export_dir)
+    except:
+        logger.warning("Cannot save tokenizer, please copy the files manually.")
+
+
+if __name__ == "__main__":
+    run_exp()