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refactor pissa, improve llamaboard

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Former-commit-id: 619556e46c19718f702c97df5d570a2a4c5fb13a
This commit is contained in:
hiyouga
2024-06-28 01:04:24 +08:00
parent edc7498111
commit 46f0189e88
16 changed files with 219 additions and 216 deletions
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23
src/llamafactory/train/sft/trainer.py
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@@ -26,7 +26,8 @@ from transformers import Seq2SeqTrainer
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from ..trainer_utils import convert_pissa_adapter, create_custom_optimzer, create_custom_scheduler
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimzer, create_custom_scheduler
if TYPE_CHECKING:
@@ -50,19 +51,18 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
) -> None:
super().__init__(**kwargs)
self.finetuning_args = finetuning_args
self.processor = processor
if processor is not None:
self.add_callback(SaveProcessorCallback(processor))
if finetuning_args.pissa_convert:
if self.is_deepspeed_enabled:
self.accelerator.deepspeed_config = self.accelerator.state.deepspeed_plugin.deepspeed_config
self.deepspeed = self._wrap_model(self.model_wrapped)
self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
self.add_callback(PissaConvertCallback)
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.callback_handler.add_callback(BAdamCallback)
self.add_callback(BAdamCallback)
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
@@ -75,15 +75,6 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
output_dir = output_dir if output_dir is not None else self.args.output_dir
if self.finetuning_args.pissa_convert:
convert_pissa_adapter(output_dir, state_dict, self.accelerator, self.model, self.args)
if self.processor is not None:
getattr(self.processor, "image_processor").save_pretrained(output_dir)
def prediction_step(
self,
model: "torch.nn.Module",