disentangle model from tuner and rename modules

Former-commit-id: 02cbf91e7e424f8379c1fed01b82a5f7a83b6947

src/llmtuner/train/dpo/workflow.py

@@ -0,0 +1,102 @@
+# Inspired by: https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py
+
+from peft import PeftModel
+from typing import TYPE_CHECKING, Optional, List
+from transformers import Seq2SeqTrainingArguments
+
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.extras.logging import get_logger
+from llmtuner.extras.ploting import plot_loss
+from llmtuner.hparams import ModelArguments
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
+from llmtuner.train.dpo.collator import DPODataCollatorWithPadding
+from llmtuner.train.dpo.trainer import CustomDPOTrainer
+
+if TYPE_CHECKING:
+    from transformers import TrainerCallback
+    from llmtuner.hparams import DataArguments, FinetuningArguments
+
+
+logger = get_logger(__name__)
+
+
+def run_dpo(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
+):
+    dataset = get_dataset(model_args, data_args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft")
+    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
+    data_collator = DPODataCollatorWithPadding(
+        tokenizer=tokenizer,
+        pad_to_multiple_of=4,
+        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
+    )
+
+    # Create reference model
+    if finetuning_args.dpo_ref_model is not None:
+        ref_model_args_dict = model_args.to_dict()
+        ref_model_args_dict.update(dict(
+            model_name_or_path=finetuning_args.dpo_ref_model,
+            checkpoint_dir=finetuning_args.dpo_ref_model_checkpoint
+        ))
+        ref_model_args = ModelArguments(**ref_model_args_dict)
+        ref_model, _ = load_model_and_tokenizer(ref_model_args, finetuning_args, is_trainable=False, stage="sft")
+        logger.info("Created reference model from {}".format(finetuning_args.dpo_ref_model))
+    elif training_args.do_train:
+        if isinstance(model, PeftModel):
+            ref_model = None
+        else:
+            ref_model, _ = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, stage="sft")
+            logger.info("Created reference model from the model itself.")
+    else:
+        ref_model = model
+
+    # Update arguments
+    training_args_dict = training_args.to_dict()
+    training_args_dict.update(dict(remove_unused_columns=False)) # important for pairwise dataset
+    training_args = Seq2SeqTrainingArguments(**training_args_dict)
+
+    # Initialize our Trainer
+    trainer = CustomDPOTrainer(
+        beta=finetuning_args.dpo_beta,
+        model=model,
+        ref_model=ref_model,
+        args=training_args,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        **split_dataset(dataset, data_args, training_args)
+    )
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and model_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval")
+        if id(model) == id(ref_model): # unable to compute rewards without a reference model
+            logger.warning("Pass `dpo_ref_model` for computing rewards at evaluation.")
+            remove_keys = [key for key in metrics.keys() if "rewards" in key]
+            for key in remove_keys:
+                metrics.pop(key)
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Create model card
+    if training_args.do_train:
+        if training_args.push_to_hub:
+            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
+        else:
+            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))