test(checkpoint): 添加多进程检查点测试功能

tests/data/test_checkpoint.py

@@ -1,9 +1,12 @@
-from pathlib import Path
+import os
-import tempfile
 import torch
+import tempfile
+
+from pathlib import Path
 from torch.optim import AdamW
 from torch.optim.lr_scheduler import CosineAnnealingLR
 from khaosz.data.checkpoint import Checkpoint
+from khaosz.parallel.setup import spawn_parallel_fn
 
 def test_single_process():
     model = torch.nn.Linear(10, 5)
@@ -48,5 +51,42 @@ def test_single_process():
         png_files = list(Path(tmpdir).glob("*.png"))
         assert png_files
 
+def simple_training():
+    rank = int(os.environ.get('LOCAL_RANK', 0))
+    
+    # 简单的训练逻辑
+    model = torch.nn.Linear(10, 5)
+    optimizer = AdamW(model.parameters(), lr=1e-3)
+    scheduler = CosineAnnealingLR(optimizer, T_max=10)
+    
+    # 训练步骤
+    for epoch in range(2):
+        for iteration in range(5):
+            x = torch.randn(16, 10)
+            y = torch.randn(16, 5)
+            loss = model(x).mean()
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+        scheduler.step()
+
+    checkpoint = Checkpoint(
+        optimizer_state_dict=optimizer.state_dict(),
+        scheduler_state_dict=scheduler.state_dict(),
+        epoch=2,
+        iteration=10,
+        metrics={"loss": [0.3, 0.2, 0.1]}
+    )
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        checkpoint.save(tmpdir)
+        loaded = Checkpoint.load(tmpdir)
+        assert loaded.epoch == 2
+        print(f"Rank {rank}: Checkpoint test passed")
+
 def test_multi_process():
-    pass
+    spawn_parallel_fn(
+        simple_training,
+        world_size=2,
+        backend="gloo"
+    )