test(sampler): 删除冗余的训练恢复测试用例

tests/test_sampler.py

@@ -1,5 +1,3 @@
-import os
-import torch
 from khaosz.core import *
 from khaosz.trainer import *
 from khaosz.trainer.data_util import *
@@ -57,55 +55,7 @@ def test_sampler_state_persistence(random_dataset):
     
     assert indices2 == indices3
 
-def test_training_resume_with_sampler(base_test_env, random_dataset):
-    """Test that training can resume correctly with sampler state"""
-    test_dir = base_test_env["test_dir"]
-    dataset = random_dataset
-    
-    # Initial training config
-    optimizer = torch.optim.AdamW(base_test_env["model"].parameters())
-    train_config = TrainConfig(
-        dataset=dataset,
-        optimizer=optimizer,
-        checkpoint_dir=test_dir,
-        n_epoch=1,
-        batch_size=2,
-        checkpoint_interval=5,
-        accumulation_steps=1,
-        max_grad_norm=1.0,
-        random_seed=42
-    )
-    
-    train_config.strategy = StrategyFactory.load(base_test_env["model"], "seq")
-    model_parameter = ModelParameter(
-        base_test_env["model"], 
-        base_test_env["tokenizer"], 
-        base_test_env["transformer_config"]
-    )
-    schedule_config = CosineScheduleConfig(warmup_steps=10, total_steps=20)
-    
-    # First training run - stop after a few steps
-    trainer = Trainer(model_parameter, train_config, schedule_config)
-    try:
-        # Run for a few steps then interrupt
-        for i, _ in enumerate(trainer.train()):
-            if i >= 3:  # Run for 3 steps then stop
-                break
-    except Exception:
-        pass
-    
-    # Load checkpoint
-    checkpoint_path = os.path.join(test_dir, "iter_3")
-    checkpoint = Checkpoint().load(checkpoint_path)
-    
-    # Resume training
-    trainer = Trainer(model_parameter, train_config, schedule_config)
-    resumed_checkpoint = trainer.train(checkpoint)
-    
-    # Check that training resumed from correct point
-    assert resumed_checkpoint.sampler_state['current_iter'] > 3
-
-def test_sampler_across_epochs(base_test_env, random_dataset):
+def test_sampler_across_epochs(random_dataset):
     """Test sampler behavior across multiple epochs"""
     dataset = random_dataset
     n = len(dataset)