src/axolotl/utils/callbacks.py

"""Callbacks for Trainer class"""

import os

from optimum.bettertransformer import BetterTransformer
from transformers import (
    TrainerCallback,
    TrainerControl,
    TrainerState,
    TrainingArguments,
)
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy


class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-methods
    """Callback to save the PEFT adapter"""

    def on_save(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        checkpoint_folder = os.path.join(
            args.output_dir,
            f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
        )

        peft_model_path = os.path.join(checkpoint_folder, "adapter_model")
        kwargs["model"].save_pretrained(peft_model_path)

        return control


class SaveBetterTransformerModelCallback(
    TrainerCallback
):  # pylint: disable=too-few-public-methods
    """Callback to save the BetterTransformer wrapped model"""

    def on_step_end(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        # Save
        if (
            args.save_strategy == IntervalStrategy.STEPS
            and args.save_steps > 0
            and state.global_step % args.save_steps == 0
        ):
            control.should_save = True

        if control.should_save:
            checkpoint_folder = os.path.join(
                args.output_dir,
                f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
            )

            model = BetterTransformer.reverse(kwargs["model"])
            model.save_pretrained(checkpoint_folder)

            # since we're saving here, we don't need the trainer loop to attempt to save too b/c
            # the trainer will raise an exception since it can't save a BetterTransformer wrapped model
            control.should_save = False
        return control