rename package

src/llamafactory/train/ppo/workflow.py

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+# Inspired by: https://github.com/lvwerra/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py
+
+from typing import TYPE_CHECKING, List, Optional
+
+from transformers import DataCollatorWithPadding
+
+from ...data import get_dataset
+from ...extras.callbacks import FixValueHeadModelCallback
+from ...extras.misc import fix_valuehead_checkpoint
+from ...extras.ploting import plot_loss
+from ...model import load_model, load_tokenizer
+from ..utils import create_ref_model, create_reward_model
+from .trainer import CustomPPOTrainer
+
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+
+    from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
+
+
+def run_ppo(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None,
+):
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="ppo", **tokenizer_module)
+    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
+
+    tokenizer.padding_side = "left"  # use left-padding in generation while using right-padding in training
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    # Create reference model and reward model
+    ref_model = create_ref_model(model_args, finetuning_args, add_valuehead=True)
+    reward_model = create_reward_model(model, model_args, finetuning_args)
+
+    # Initialize our Trainer
+    ppo_trainer = CustomPPOTrainer(
+        model_args=model_args,
+        training_args=training_args,
+        finetuning_args=finetuning_args,
+        generating_args=generating_args,
+        callbacks=callbacks + [FixValueHeadModelCallback()],
+        model=model,
+        reward_model=reward_model,
+        ref_model=ref_model,
+        dataset=dataset,
+        data_collator=data_collator,
+        **tokenizer_module,
+    )
+
+    # Training
+    if training_args.do_train:
+        ppo_trainer.ppo_train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        ppo_trainer.save_model()
+        if training_args.should_save:
+            fix_valuehead_checkpoint(model, training_args.output_dir, training_args.save_safetensors)
+        ppo_trainer.save_state()  # must be called after save_model to have a folder
+        if ppo_trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "reward"])