refactor(test): 更新训练配置参数名称并优化测试逻辑

tests/test_trainer.py

@@ -78,14 +78,13 @@ def test_dataset_loader(test_env):
 def test_training_config(test_env):
     optimizer = torch.optim.AdamW(test_env["model"].parameters())
     train_config = TrainConfig(
-        train_type="seq",
         dataset=test_env["dataset"],
         optimizer=optimizer,
-        ckpt_dir=test_env["test_dir"],
+        checkpoint_dir=test_env["test_dir"],
         n_epoch=1,
         batch_size=2,
-        n_iter_ckpt=5,
-        n_iter_step=1,
+        checkpoint_interval=5,
+        accumulation_steps=1,
         max_grad_norm=1.0,
         random_seed=42
     )
@@ -94,51 +93,56 @@ def test_training_config(test_env):
 def test_cosine_schedule(test_env):
     assert test_env is not None
     schedule_config = CosineScheduleConfig(
-        warning_step=100,
-        total_iters=1000
+        warmup_steps=100,
+        total_steps=1000
     )
     kwargs = schedule_config.get_kwargs()
-    assert kwargs["warning_step"] == 100
-    assert kwargs["lr_decay_iters"] == 900
+    assert kwargs["warmup_steps"] == 100
+    assert kwargs["lr_decay_steps"] == 900
     
 
 def test_sgdr_schedule(test_env):
     assert test_env is not None
     schedule_config = SgdrScheduleConfig(
-        warning_step=100,
+        warmup_steps=100,
         cycle_length=200,
-        T_mult=2
+        t_mult=2
     )
     kwargs = schedule_config.get_kwargs()
-    assert kwargs["warning_step"] == 100
+    assert kwargs["warmup_steps"] == 100
     assert kwargs["cycle_length"] == 200
-    assert kwargs["T_mult"] == 2
+    assert kwargs["t_mult"] == 2
     
 def test_trainer_train(test_env):
     optimizer = torch.optim.AdamW(test_env["model"].parameters())
     train_config = TrainConfig(
-        train_type="seq",
         dataset=test_env["dataset"],
         optimizer=optimizer,
-        ckpt_dir=test_env["test_dir"],
+        checkpoint_dir=test_env["test_dir"],
         n_epoch=1,
         batch_size=2,
-        n_iter_ckpt=5,
-        n_iter_step=1,
+        checkpoint_interval=5,
+        accumulation_steps=1,
         max_grad_norm=1.0,
         random_seed=42
     )
     schedule_config = CosineScheduleConfig(
-        warning_step=100,
-        total_iters=1000
+        warmup_steps=100,
+        total_steps=1000
+    )
+    
+    train_config.strategy = StrategyFactory.load(
+        test_env["model"], 
+        "seq",
+        pad_token_id=test_env["tokenizer"].pad_id
     )
     model_parameter = ModelParameter(
         test_env["model"], 
         test_env["tokenizer"], 
         test_env["transformer_config"]
     )
-    trainer = Trainer(model_parameter)
-    trainer.train(train_config, schedule_config)
+    trainer = Trainer(model_parameter, train_config, schedule_config)
+    trainer.train()
 
 def test_checkpoint(test_env):
     temp_dir = test_env["test_dir"]
@@ -195,36 +199,32 @@ def test_checkpoint_train(test_env):
 
     dataset = InterruptDataset(length=10, interrupt_idx=3)
     param = ModelParameter(model, tokenizer, config)
-    trainer = Trainer(param)
-    
     optimizer = torch.optim.AdamW(test_env["model"].parameters())
     train_config = TrainConfig(
-        train_type="seq",
         dataset=dataset,
         optimizer=optimizer,
-        ckpt_dir=test_env["test_dir"],
+        checkpoint_dir=test_env["test_dir"],
         n_epoch=2,
         batch_size=2,
-        n_iter_ckpt=5,
-        n_iter_step=1,
+        checkpoint_interval=5,
+        accumulation_steps=1,
         max_grad_norm=1.0,
         random_seed=42
     )
+    
+    train_config.strategy = StrategyFactory.load(
+        test_env["model"], 
+        "seq",
+        pad_token_id=test_env["tokenizer"].pad_id
+    )
     schedule_config = CosineScheduleConfig(
-        warning_step=100,
-        total_iters=1000
+        warmup_steps=100,
+        total_steps=1000
     )
+    trainer = Trainer(param, train_config, schedule_config)
+    
     try:
-        trainer.train(
-            train_config=train_config,
-            schedule_config=schedule_config,
-        )
+        trainer.train()
     except Exception:
         checkpoint = trainer.checkpoint
-        trainer.train(
-            train_config=train_config,
-            schedule_config=schedule_config,
-            train_checkpoint=checkpoint
-        )
-    
-    
+        trainer.train(train_checkpoint=checkpoint)