| |
| import math |
| import os |
| import time |
|
|
| from tqdm import tqdm |
| from transformers import trainer |
| from transformers.trainer_callback import (DefaultFlowCallback, PrinterCallback, ProgressCallback, TrainerControl, |
| TrainerState) |
| from transformers.trainer_utils import IntervalStrategy, has_length, speed_metrics |
|
|
| from swift.utils import append_to_jsonl, is_pai_training_job, use_torchacc |
| from ..utils.utils import format_time |
| from .arguments import TrainingArguments |
|
|
|
|
| def add_train_message(logs, state, start_time) -> None: |
| logs['global_step/max_steps'] = f'{state.global_step}/{state.max_steps}' |
| train_percentage = state.global_step / state.max_steps if state.max_steps else 0. |
| logs['percentage'] = f'{train_percentage * 100:.2f}%' |
| elapsed = time.time() - start_time |
| logs['elapsed_time'] = format_time(elapsed) |
| if train_percentage != 0: |
| logs['remaining_time'] = format_time(elapsed / train_percentage - elapsed) |
| for k, v in logs.items(): |
| if isinstance(v, float): |
| logs[k] = round(logs[k], 8) |
|
|
|
|
| class ProgressCallbackNew(ProgressCallback): |
|
|
| def on_train_begin(self, args, state, control, **kwargs): |
| if state.is_world_process_zero: |
| self.training_bar = tqdm(desc='Train', total=state.max_steps, dynamic_ncols=True) |
| self.current_step = 0 |
| self.start_time = time.time() |
| if use_torchacc(): |
| self.warmup_start_time = 0 |
| self.warmup_metric = None |
| self.metric_warmup_step = int(args.metric_warmup_step |
| * state.max_steps) if args.metric_warmup_step < 1 else args.metric_warmup_step |
|
|
| def on_prediction_step(self, args, state: TrainerState, control, eval_dataloader=None, **kwargs): |
| if state.is_world_process_zero and has_length(eval_dataloader): |
| if self.prediction_bar is None: |
| if self.training_bar is not None: |
| self.training_bar.fp.write('\n') |
| self.prediction_bar = tqdm( |
| desc='Val', total=len(eval_dataloader), leave=True, dynamic_ncols=True, position=0) |
| self.prediction_bar.update() |
|
|
| def on_log(self, args: TrainingArguments, state: TrainerState, control, logs=None, **kwargs): |
|
|
| if use_torchacc(): |
| if state.global_step >= self.metric_warmup_step and self.warmup_start_time == 0: |
| self.warmup_start_time = time.time() |
| self.metric_warmup_step = state.global_step |
| if state.max_steps == state.global_step and self.warmup_metric is None: |
| num_steps = state.max_steps - self.metric_warmup_step |
| num_total_samples = args.train_dataset_sample |
| num_after_warmup_samples = int(num_total_samples / state.max_steps * num_steps) |
| self.warmup_metric = speed_metrics('warmup_train', self.warmup_start_time, num_after_warmup_samples, |
| num_steps) |
| self.warmup_metric['num_total_samples'] = num_total_samples |
| self.warmup_metric['num_after_warmup_samples'] = num_after_warmup_samples |
| if 'train_samples_per_second' in logs: |
| logs.update(self.warmup_metric) |
| state.log_history[-1] = logs |
|
|
| add_train_message(logs, state, self.start_time) |
| if not is_pai_training_job() and state.is_world_process_zero: |
| jsonl_path = os.path.join(args.output_dir, 'logging.jsonl') |
| append_to_jsonl(jsonl_path, logs) |
| super().on_log(args, state, control, logs, **kwargs) |
| if state.is_world_process_zero and self.training_bar is not None: |
| self.training_bar.refresh() |
|
|
|
|
| class DefaultFlowCallbackNew(DefaultFlowCallback): |
|
|
| def on_step_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs): |
| control = super().on_step_end(args, state, control, **kwargs) |
| |
| evaluation_strategy = args.eval_strategy if hasattr(args, 'eval_strategy') else args.evaluation_strategy |
| if state.global_step == state.max_steps: |
| if evaluation_strategy != IntervalStrategy.NO: |
| control.should_evaluate = True |
| if args.save_strategy != IntervalStrategy.NO: |
| control.should_save = True |
| return control |
|
|
| def on_epoch_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs): |
| control = super().on_epoch_end(args, state, control, **kwargs) |
| evaluation_strategy = args.eval_strategy if hasattr(args, 'eval_strategy') else args.evaluation_strategy |
| if args.max_epochs is not None and args.max_epochs <= math.ceil(state.epoch): |
| if evaluation_strategy != IntervalStrategy.NO: |
| control.should_evaluate = True |
| if args.save_strategy != IntervalStrategy.NO: |
| control.should_save = True |
| control.should_training_stop = True |
| return control |
|
|
|
|
| class PrinterCallbackNew(PrinterCallback): |
|
|
| def on_train_begin(self, args, state, control, **kwargs): |
| self.start_time = time.time() |
| return super().on_train_begin(args, state, control, **kwargs) |
|
|
| def on_log(self, args, state, control, logs=None, **kwargs): |
| add_train_message(logs, state, self.start_time) |
| if not is_pai_training_job() and state.is_world_process_zero: |
| jsonl_path = os.path.join(args.output_dir, 'logging.jsonl') |
| append_to_jsonl(jsonl_path, logs) |
|
|
| _ = logs.pop('total_flos', None) |
| if state.is_world_process_zero: |
| print(logs, flush=True) |
|
|
|
|
| |
| trainer.DEFAULT_PROGRESS_CALLBACK = ProgressCallbackNew |
| trainer.DEFAULT_CALLBACKS = [DefaultFlowCallbackNew] |
| trainer.PrinterCallback = PrinterCallbackNew |
|
|
