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fix: lint

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This commit is contained in:
NanoCode012
2025-03-26 14:25:18 +07:00
parent 51c2adf3b1
commit b8025b34b9
1 changed files with 26 additions and 24 deletions
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50
src/axolotl/core/trainer_builder.py
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@@ -253,9 +253,11 @@ class TrainerBuilderBase(abc.ABC):
logging_steps = (
self.cfg.logging_steps
if self.cfg.logging_steps is not None
else 500 # transformers defaults to 500
if not total_num_steps
else max(min(int(0.005 * total_num_steps), 10), 1)
else (
500 # transformers defaults to 500
if not total_num_steps
else max(min(int(0.005 * total_num_steps), 10), 1)
)
)
training_args_kwargs["warmup_ratio"] = warmup_ratio
@@ -301,13 +303,13 @@ class TrainerBuilderBase(abc.ABC):
training_args_kwargs["eval_strategy"] = self.cfg.eval_strategy
if self.cfg.gradient_checkpointing:
training_args_kwargs[
"gradient_checkpointing"
] = self.cfg.gradient_checkpointing
training_args_kwargs["gradient_checkpointing"] = (
self.cfg.gradient_checkpointing
)
if self.cfg.gradient_checkpointing_kwargs is not None:
training_args_kwargs[
"gradient_checkpointing_kwargs"
] = self.cfg.gradient_checkpointing_kwargs
training_args_kwargs["gradient_checkpointing_kwargs"] = (
self.cfg.gradient_checkpointing_kwargs
)
else:
training_args_kwargs["gradient_checkpointing_kwargs"] = {
"use_reentrant": False
@@ -336,9 +338,9 @@ class TrainerBuilderBase(abc.ABC):
training_args_kwargs["per_device_train_batch_size"] = self.cfg.micro_batch_size
if self.cfg.eval_batch_size:
training_args_kwargs[
"per_device_eval_batch_size"
] = self.cfg.eval_batch_size
training_args_kwargs["per_device_eval_batch_size"] = (
self.cfg.eval_batch_size
)
training_args_kwargs["save_total_limit"] = (
self.cfg.save_total_limit if self.cfg.save_total_limit else 4
@@ -383,9 +385,9 @@ class TrainerBuilderBase(abc.ABC):
self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
)
training_args_kwargs["cosine_min_lr_ratio"] = self.cfg.cosine_min_lr_ratio
training_args_kwargs[
"cosine_constant_lr_ratio"
] = self.cfg.cosine_constant_lr_ratio
training_args_kwargs["cosine_constant_lr_ratio"] = (
self.cfg.cosine_constant_lr_ratio
)
return training_args_kwargs
@@ -559,13 +561,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
if self.cfg.auto_find_batch_size is not None:
training_arguments_kwargs[
"auto_find_batch_size"
] = self.cfg.auto_find_batch_size
training_arguments_kwargs["auto_find_batch_size"] = (
self.cfg.auto_find_batch_size
)
training_arguments_kwargs[
"eval_accumulation_steps"
] = self.cfg.gradient_accumulation_steps
training_arguments_kwargs["eval_accumulation_steps"] = (
self.cfg.gradient_accumulation_steps
)
training_arguments_kwargs["num_train_epochs"] = self.cfg.num_epochs
training_arguments_kwargs["load_best_model_at_end"] = (
@@ -605,9 +607,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
optim_args = self.cfg.optim_args
training_arguments_kwargs["optim_args"] = optim_args
if self.cfg.optim_target_modules:
training_arguments_kwargs[
"optim_target_modules"
] = self.cfg.optim_target_modules
training_arguments_kwargs["optim_target_modules"] = (
self.cfg.optim_target_modules
)
training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups