refactor(trainer): 统一参数命名以提升可读性

khaosz/trainer/strategy.py

@@ -190,7 +190,7 @@ class TrainConfig:
         default=None,
         metadata={"help": "Optimizer for training."}
     )
-    ckpt_dir: str = field(
+    checkpoint_dir: str = field(
         default="./checkpoint",
         metadata={"help": "Checkpoint directory."}
     )
@@ -202,11 +202,11 @@ class TrainConfig:
         default=4,
         metadata={"help": "Batch size for training."}
     )
-    n_iter_ckpt: int = field(
+    checkpoint_interval: int = field(
         default=5000,
         metadata={"help": "Number of iterations between checkpoints."}
     )
-    n_iter_step: int = field(
+    accumulation_steps: int = field(
         default=1,
         metadata={"help": "Number of iterations between steps."}
     )
@@ -256,7 +256,7 @@ class ScheduleConfig(ABC):
 
 @dataclass
 class CosineScheduleConfig(ScheduleConfig):
-    total_steps: int = field(  # 更准确的命名
+    total_steps: int = field(
         default=None,
         metadata={"help": "Total training steps for cosine schedule."}
     )

khaosz/trainer/trainer.py

@@ -32,7 +32,7 @@ class Trainer:
         train_config: TrainConfig
     ):
         current_iter = len(loss_list)
-        save_path = os.path.join(train_config.ckpt_dir, f"iter_{current_iter}")
+        save_path = os.path.join(train_config.checkpoint_dir, f"iter_{current_iter}")
         self.checkpoint.loss_list = loss_list
         self.checkpoint.optim_state = train_config.optimizer.state_dict()
         self.checkpoint.save(save_path)
@@ -93,7 +93,7 @@ class Trainer:
                 #backward
                 loss.backward()
                 #step
-                if current_iter % train_config.n_iter_step == 0:
+                if current_iter % train_config.accumulation_steps == 0:
                     clip_grad_norm_(
                         self.checkpoint.model.parameters(),
                         train_config.max_grad_norm
@@ -108,7 +108,7 @@ class Trainer:
                     "lr": f"{train_config.optimizer.param_groups[0]['lr']:.2e}"
                 })
                 #save checkpotint
-                if current_iter - last_ckpt_iter >= train_config.n_iter_ckpt:
+                if current_iter - last_ckpt_iter >= train_config.checkpoint_interval:
                     self.save_checkpoint(loss_list, train_config)
                     last_ckpt_iter = current_iter