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fix: rename optimzer to optimizer

This commit is contained in:
moontidef 2024-08-07 10:05:01 +08:00
parent 55f32dfbf9
commit 40908a36fa
7 changed files with 13 additions and 13 deletions
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4
src/llamafactory/train/dpo/trainer.py
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@ -29,7 +29,7 @@ from trl.trainer import disable_dropout_in_model
from ...extras.constants import IGNORE_INDEX
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimzer, create_custom_scheduler, get_batch_logps
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
if TYPE_CHECKING:
@ -106,7 +106,7 @@ class CustomDPOTrainer(DPOTrainer):
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
return super().create_optimizer()
def create_scheduler(

4
src/llamafactory/train/kto/trainer.py
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@ -28,7 +28,7 @@ from trl.trainer import disable_dropout_in_model
from ...extras.constants import IGNORE_INDEX
from ..callbacks import SaveProcessorCallback
from ..trainer_utils import create_custom_optimzer, create_custom_scheduler, get_batch_logps
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
if TYPE_CHECKING:
@ -101,7 +101,7 @@ class CustomKTOTrainer(KTOTrainer):
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
return super().create_optimizer()
def create_scheduler(

4
src/llamafactory/train/ppo/trainer.py
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@ -39,7 +39,7 @@ from trl.models.utils import unwrap_model_for_generation
from ...extras.logging import get_logger
from ...extras.misc import AverageMeter, count_parameters, get_current_device, get_logits_processor
from ..callbacks import FixValueHeadModelCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimzer, create_custom_scheduler
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
from .ppo_utils import dump_layernorm, get_rewards_from_server, replace_model, restore_layernorm
@ -303,7 +303,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",
) -> "torch.optim.Optimizer":
optimizer = create_custom_optimzer(model, training_args, finetuning_args)
optimizer = create_custom_optimizer(model, training_args, finetuning_args)
if optimizer is None:
decay_params, nodecay_params = [], []
decay_param_names = self.get_decay_parameter_names(model)

4
src/llamafactory/train/pt/trainer.py
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@ -19,7 +19,7 @@ from transformers import Trainer
from ...extras.logging import get_logger
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimzer, create_custom_scheduler
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
if TYPE_CHECKING:
@ -57,7 +57,7 @@ class CustomTrainer(Trainer):
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
return super().create_optimizer()
def create_scheduler(

4
src/llamafactory/train/rm/trainer.py
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@ -25,7 +25,7 @@ from transformers import Trainer
from ...extras.logging import get_logger
from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimzer, create_custom_scheduler
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
if TYPE_CHECKING:
@ -65,7 +65,7 @@ class PairwiseTrainer(Trainer):
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
return super().create_optimizer()
def create_scheduler(

4
src/llamafactory/train/sft/trainer.py
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@ -27,7 +27,7 @@ from transformers import Seq2SeqTrainer
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimzer, create_custom_scheduler
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
if TYPE_CHECKING:
@ -66,7 +66,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
return super().create_optimizer()
def create_scheduler(

2
src/llamafactory/train/trainer_utils.py
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@ -366,7 +366,7 @@ def _create_badam_optimizer(
return optimizer
def create_custom_optimzer(
def create_custom_optimizer(
model: "PreTrainedModel",
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",