tocmo0nlord/axolotl
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

fix: lint

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

50
src/axolotl/core/trainer_builder.py
View File

@@ -253,9 +253,11 @@ class TrainerBuilderBase(abc.ABC):
logging_steps = ( logging_steps = (
self.cfg.logging_steps self.cfg.logging_steps
if self.cfg.logging_steps is not None if self.cfg.logging_steps is not None
else 500 # transformers defaults to 500 else (
if not total_num_steps 500 # transformers defaults to 500
else max(min(int(0.005 * total_num_steps), 10), 1) if not total_num_steps
else max(min(int(0.005 * total_num_steps), 10), 1)
)
) )
training_args_kwargs["warmup_ratio"] = warmup_ratio training_args_kwargs["warmup_ratio"] = warmup_ratio
@@ -301,13 +303,13 @@ class TrainerBuilderBase(abc.ABC):
training_args_kwargs["eval_strategy"] = self.cfg.eval_strategy training_args_kwargs["eval_strategy"] = self.cfg.eval_strategy
if self.cfg.gradient_checkpointing: if self.cfg.gradient_checkpointing:
training_args_kwargs[ training_args_kwargs["gradient_checkpointing"] = (
"gradient_checkpointing" self.cfg.gradient_checkpointing
] = self.cfg.gradient_checkpointing )
if self.cfg.gradient_checkpointing_kwargs is not None: if self.cfg.gradient_checkpointing_kwargs is not None:
training_args_kwargs[ training_args_kwargs["gradient_checkpointing_kwargs"] = (
"gradient_checkpointing_kwargs" self.cfg.gradient_checkpointing_kwargs
] = self.cfg.gradient_checkpointing_kwargs )
else: else:
training_args_kwargs["gradient_checkpointing_kwargs"] = { training_args_kwargs["gradient_checkpointing_kwargs"] = {
"use_reentrant": False "use_reentrant": False
@@ -336,9 +338,9 @@ class TrainerBuilderBase(abc.ABC):
training_args_kwargs["per_device_train_batch_size"] = self.cfg.micro_batch_size training_args_kwargs["per_device_train_batch_size"] = self.cfg.micro_batch_size
if self.cfg.eval_batch_size: if self.cfg.eval_batch_size:
training_args_kwargs[ training_args_kwargs["per_device_eval_batch_size"] = (
"per_device_eval_batch_size" self.cfg.eval_batch_size
] = self.cfg.eval_batch_size )
training_args_kwargs["save_total_limit"] = ( training_args_kwargs["save_total_limit"] = (
self.cfg.save_total_limit if self.cfg.save_total_limit else 4 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 {} 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_min_lr_ratio"] = self.cfg.cosine_min_lr_ratio
training_args_kwargs[ training_args_kwargs["cosine_constant_lr_ratio"] = (
"cosine_constant_lr_ratio" self.cfg.cosine_constant_lr_ratio
] = self.cfg.cosine_constant_lr_ratio )
return training_args_kwargs return training_args_kwargs
@@ -559,13 +561,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
if self.cfg.auto_find_batch_size is not None: if self.cfg.auto_find_batch_size is not None:
training_arguments_kwargs[ training_arguments_kwargs["auto_find_batch_size"] = (
"auto_find_batch_size" self.cfg.auto_find_batch_size
] = self.cfg.auto_find_batch_size )
training_arguments_kwargs[ training_arguments_kwargs["eval_accumulation_steps"] = (
"eval_accumulation_steps" self.cfg.gradient_accumulation_steps
] = self.cfg.gradient_accumulation_steps )
training_arguments_kwargs["num_train_epochs"] = self.cfg.num_epochs training_arguments_kwargs["num_train_epochs"] = self.cfg.num_epochs
training_arguments_kwargs["load_best_model_at_end"] = ( training_arguments_kwargs["load_best_model_at_end"] = (
@@ -605,9 +607,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
optim_args = self.cfg.optim_args optim_args = self.cfg.optim_args
training_arguments_kwargs["optim_args"] = optim_args training_arguments_kwargs["optim_args"] = optim_args
if self.cfg.optim_target_modules: if self.cfg.optim_target_modules:
training_arguments_kwargs[ training_arguments_kwargs["optim_target_modules"] = (
"optim_target_modules" self.cfg.optim_target_modules
] = self.cfg.optim_target_modules )
training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups