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# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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,
throughput="{:.2f}".format(state.num_input_tokens_seen / (time.time() - self.start_time)),
total_tokens=state.num_input_tokens_seen,
)
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}, 'throughput': {}}}".format(
logs["loss"], logs["learning_rate"], logs["epoch"], logs["throughput"]
)
)
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)
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