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src/llmfactory/extras/callbacks.py

+import json
+import logging
+import os
+import signal
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor
+from datetime import timedelta
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+import transformers
+from transformers import TrainerCallback
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length
+
+from .constants import TRAINER_LOG
+from .logging import LoggerHandler, get_logger
+from .misc import fix_valuehead_checkpoint
+
+
+if TYPE_CHECKING:
+    from transformers import TrainerControl, TrainerState, TrainingArguments
+
+
+logger = get_logger(__name__)
+
+
+class FixValueHeadModelCallback(TrainerCallback):
+    def on_save(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called after a checkpoint save.
+        """
+        if args.should_save:
+            fix_valuehead_checkpoint(
+                model=kwargs.pop("model"),
+                output_dir=os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step)),
+                safe_serialization=args.save_safetensors,
+            )
+
+
+class LogCallback(TrainerCallback):
+    def __init__(self, output_dir: str) -> None:
+        r"""
+        Initializes a callback for logging training and evaluation status.
+        """
+        """ Progress """
+        self.start_time = 0
+        self.cur_steps = 0
+        self.max_steps = 0
+        self.elapsed_time = ""
+        self.remaining_time = ""
+        self.thread_pool: Optional["ThreadPoolExecutor"] = None
+        """ Status """
+        self.aborted = False
+        self.do_train = False
+        """ Web UI """
+        self.webui_mode = os.environ.get("LLAMABOARD_ENABLED", "0").lower() in ["true", "1"]
+        if self.webui_mode:
+            signal.signal(signal.SIGABRT, self._set_abort)
+            self.logger_handler = LoggerHandler(output_dir)
+            logging.root.addHandler(self.logger_handler)
+            transformers.logging.add_handler(self.logger_handler)
+
+    def _set_abort(self, signum, frame) -> None:
+        self.aborted = True
+
+    def _reset(self, max_steps: int = 0) -> None:
+        self.start_time = time.time()
+        self.cur_steps = 0
+        self.max_steps = max_steps
+        self.elapsed_time = ""
+        self.remaining_time = ""
+
+    def _timing(self, cur_steps: int) -> None:
+        cur_time = time.time()
+        elapsed_time = cur_time - self.start_time
+        avg_time_per_step = elapsed_time / cur_steps if cur_steps != 0 else 0
+        remaining_time = (self.max_steps - cur_steps) * avg_time_per_step
+        self.cur_steps = cur_steps
+        self.elapsed_time = str(timedelta(seconds=int(elapsed_time)))
+        self.remaining_time = str(timedelta(seconds=int(remaining_time)))
+
+    def _write_log(self, output_dir: str, logs: Dict[str, Any]) -> None:
+        with open(os.path.join(output_dir, TRAINER_LOG), "a", encoding="utf-8") as f:
+            f.write(json.dumps(logs) + "\n")
+
+    def _create_thread_pool(self, output_dir: str) -> None:
+        os.makedirs(output_dir, exist_ok=True)
+        self.thread_pool = ThreadPoolExecutor(max_workers=1)
+
+    def _close_thread_pool(self) -> None:
+        if self.thread_pool is not None:
+            self.thread_pool.shutdown(wait=True)
+            self.thread_pool = None
+
+    def on_init_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called at the end of the initialization of the `Trainer`.
+        """
+        if (
+            args.should_save
+            and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
+            and args.overwrite_output_dir
+        ):
+            logger.warning("Previous trainer log in this folder will be deleted.")
+            os.remove(os.path.join(args.output_dir, TRAINER_LOG))
+
+    def on_train_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called at the beginning of training.
+        """
+        if args.should_save:
+            self.do_train = True
+            self._reset(max_steps=state.max_steps)
+            self._create_thread_pool(output_dir=args.output_dir)
+
+    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called at the end of training.
+        """
+        self._close_thread_pool()
+
+    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 self.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 self.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 not self.do_train:
+            self._close_thread_pool()
+
+    def on_predict(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called after a successful prediction.
+        """
+        if not self.do_train:
+            self._close_thread_pool()
+
+    def on_log(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called after logging the last logs.
+        """
+        if not args.should_save:
+            return
+
+        self._timing(cur_steps=state.global_step)
+        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),
+            accuracy=state.log_history[-1].get("rewards/accuracies", 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,
+        )
+        logs = {k: v for k, v in logs.items() if v is not None}
+        if self.webui_mode and all(key in logs for key in ["loss", "learning_rate", "epoch"]):
+            logger.info(
+                "{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}}}".format(
+                    logs["loss"], logs["learning_rate"], logs["epoch"]
+                )
+            )
+
+        if self.thread_pool is not None:
+            self.thread_pool.submit(self._write_log, args.output_dir, logs)
+
+    def on_prediction_step(
+        self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs
+    ):
+        r"""
+        Event called after a prediction step.
+        """
+        if self.do_train:
+            return
+
+        if self.aborted:
+            sys.exit(0)
+
+        if not args.should_save:
+            return
+
+        eval_dataloader = kwargs.pop("eval_dataloader", None)
+        if has_length(eval_dataloader):
+            if self.max_steps == 0:
+                self._reset(max_steps=len(eval_dataloader))
+                self._create_thread_pool(output_dir=args.output_dir)
+
+            self._timing(cur_steps=self.cur_steps + 1)
+            if self.cur_steps % 5 == 0 and self.thread_pool is not None:
+                logs = dict(
+                    current_steps=self.cur_steps,
+                    total_steps=self.max_steps,
+                    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,
+                )
+                self.thread_pool.submit(self._write_log, args.output_dir, logs)