callbacks.py

import os
import json
import time
from typing import TYPE_CHECKING
from datetime import timedelta

from transformers import TrainerCallback
from transformers.trainer_callback import TrainerControl, TrainerState
from transformers.trainer_utils import has_length, PREFIX_CHECKPOINT_DIR
from transformers.training_args import TrainingArguments

from llmtuner.extras.constants import LOG_FILE_NAME
from llmtuner.extras.logging import get_logger

if TYPE_CHECKING:
    from transformers import TrainingArguments, TrainerState, TrainerControl


logger = get_logger(__name__)


class SavePeftModelCallback(TrainerCallback):

    def on_save(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called after a checkpoint save.
        """
        output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
        getattr(kwargs.get("model"), "pretrained_model").save_pretrained(output_dir)
        return control

    def on_train_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
        r"""
        Event called at the end of training.
        """
        getattr(kwargs.get("model"), "pretrained_model").save_pretrained(args.output_dir)
        return control


class LogCallback(TrainerCallback):

    def __init__(self, runner=None):
        self.runner = runner
        self.in_training = False
        self.start_time = time.time()
        self.cur_steps = 0
        self.max_steps = 0
        self.elapsed_time = ""
        self.remaining_time = ""

    def timing(self):
        cur_time = time.time()
        elapsed_time = cur_time - self.start_time
        avg_time_per_step = elapsed_time / self.cur_steps if self.cur_steps != 0 else 0
        remaining_time = (self.max_steps - self.cur_steps) * avg_time_per_step
        self.elapsed_time = str(timedelta(seconds=int(elapsed_time)))
        self.remaining_time = str(timedelta(seconds=int(remaining_time)))

    def on_train_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the beginning of training.
        """
        if state.is_local_process_zero:
            self.in_training = True
            self.start_time = time.time()
            self.max_steps = state.max_steps
            if os.path.exists(os.path.join(args.output_dir, LOG_FILE_NAME)):
                logger.warning("Previous log file in this folder will be deleted.")
                os.remove(os.path.join(args.output_dir, LOG_FILE_NAME))

    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of training.
        """
        if state.is_local_process_zero:
            self.in_training = False
            self.cur_steps = 0
            self.max_steps = 0

    def on_substep_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of an substep during gradient accumulation.
        """
        if state.is_local_process_zero and self.runner is not None and self.runner.aborted:
            control.should_epoch_stop = True
            control.should_training_stop = True

    def on_step_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of a training step.
        """
        if state.is_local_process_zero:
            self.cur_steps = state.global_step
            self.timing()
            if self.runner is not None and self.runner.aborted:
                control.should_epoch_stop = True
                control.should_training_stop = True

    def on_evaluate(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called after an evaluation phase.
        """
        if state.is_local_process_zero and not self.in_training:
            self.cur_steps = 0
            self.max_steps = 0

    def on_predict(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", *other, **kwargs):
        r"""
        Event called after a successful prediction.
        """
        if state.is_local_process_zero and not self.in_training:
            self.cur_steps = 0
            self.max_steps = 0

    def on_log(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs) -> None:
        r"""
        Event called after logging the last logs.
        """
        if not state.is_local_process_zero:
            return

        logs = dict(
            current_steps=self.cur_steps,
            total_steps=self.max_steps,
            loss=state.log_history[-1].get("loss", None),
            eval_loss=state.log_history[-1].get("eval_loss", None),
            predict_loss=state.log_history[-1].get("predict_loss", None),
            reward=state.log_history[-1].get("reward", None),
            learning_rate=state.log_history[-1].get("learning_rate", None),
            epoch=state.log_history[-1].get("epoch", None),
            percentage=round(self.cur_steps / self.max_steps * 100, 2) if self.max_steps != 0 else 100,
            elapsed_time=self.elapsed_time,
            remaining_time=self.remaining_time
        )
        os.makedirs(args.output_dir, exist_ok=True)
        with open(os.path.join(args.output_dir, "trainer_log.jsonl"), "a", encoding="utf-8") as f:
            f.write(json.dumps(logs) + "\n")

    def on_prediction_step(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called after a prediction step.
        """
        eval_dataloader = kwargs.pop("eval_dataloader", None)
        if state.is_local_process_zero and has_length(eval_dataloader) and not self.in_training:
            if self.max_steps == 0:
                self.max_steps = len(eval_dataloader)
            self.cur_steps += 1
            self.timing()