fix(trainer): 更新检查点保存和加载逻辑

khaosz/data/checkpoint.py

@@ -1,15 +1,12 @@
 import os
 import json
+import torch
+import torch.distributed as dist
 import matplotlib.pyplot as plt
+
 from pathlib import Path
 from typing import Dict, Optional, Any
-
+from khaosz.parallel.setup import get_rank
-import torch.distributed as dist
-from torch.distributed.checkpoint import save, load
-
-
-def get_rank() -> int:
-    return dist.get_rank() if dist.is_initialized() else 0
 
 
 class Checkpoint:
@@ -53,8 +50,8 @@ class Checkpoint:
             "optimizer": self.optimizer_state_dict,
             "scheduler": self.scheduler_state_dict
         }
-
+        with open(save_path / f"state_dict_rank_{get_rank()}.pt", "wb") as f:
-        save(state_dict, checkpoint_id=str(save_path))
+            torch.save(state_dict, f)
 
     @classmethod
     def load(
@@ -62,9 +59,9 @@ class Checkpoint:
         save_dir: str,
     ) -> "Checkpoint":
 
-        save_path = str(Path(save_dir))
         rank = get_rank()
-
+        save_path = Path(save_dir)
+        
         meta = {}
         if rank == 0:
             with open(Path(save_dir) / "meta.json", "r") as f:
@@ -75,11 +72,8 @@ class Checkpoint:
             dist.broadcast_object_list(meta_list, src=0)
             meta = meta_list[0]
 
-        state_dict = {
+        with open(save_path / f"state_dict_rank_{get_rank()}.pt", "rb") as f:
-            "optimizer": {},
+            state_dict = torch.load(f)
-            "scheduler": {}
-        }
-        load(state_dict, checkpoint_id=save_path, no_dist=True)
 
         return cls(
             optimizer_state_dict=state_dict["optimizer"],

khaosz/trainer/__init__.py

@@ -1,5 +1,4 @@
 from khaosz.trainer.trainer import Trainer
-from khaosz.trainer.checkpoint import Checkpoint
 from khaosz.trainer.strategy import StrategyFactory
 from khaosz.trainer.schedule import SchedulerFactory
 
@@ -16,9 +15,6 @@ __all__ = [
     # trainer
     "Trainer",
     
-    # checkpoint
-    "Checkpoint",
-    
     # factory
     "StrategyFactory",
     "SchedulerFactory",

khaosz/trainer/train_callback.py

@@ -17,7 +17,7 @@ from khaosz.trainer.metric_util import (
     grad_std,
     grad_nan_num
 )
-from khaosz.trainer.checkpoint import Checkpoint
+from khaosz.data.checkpoint import Checkpoint
 
 if TYPE_CHECKING:
     from khaosz.trainer.train_context import TrainContext

khaosz/trainer/train_context.py

@@ -4,7 +4,7 @@ from torch.optim.lr_scheduler import LRScheduler
 from torch.utils.data import DataLoader
 
 from khaosz.data import ResumableDistributedSampler
-from khaosz.trainer.checkpoint import Checkpoint
+from khaosz.data.checkpoint import Checkpoint
 from khaosz.trainer.strategy import StrategyFactory, BaseStrategy
 from khaosz.config.train_config import TrainConfig
 from khaosz.parallel.setup import get_current_device, get_world_size, get_rank

khaosz/trainer/trainer.py

@@ -9,7 +9,7 @@ from khaosz.trainer.train_callback import (
     SchedulerCallback
 )
 from khaosz.trainer.train_context import TrainContext, TrainContextBuilder
-from khaosz.trainer.checkpoint import Checkpoint
+from khaosz.data.checkpoint import Checkpoint
 from khaosz.parallel.setup import spawn_parallel_fn
 
 logger = logging.getLogger(__name__)

tests/test_checkpoint.py

@@ -0,0 +1,52 @@
+from pathlib import Path
+import tempfile
+import torch
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from khaosz.data.checkpoint import Checkpoint
+
+def test_single_process():
+    model = torch.nn.Linear(10, 5)
+    optimizer = AdamW(model.parameters(), lr=1e-3)
+    scheduler = CosineAnnealingLR(optimizer, T_max=10)
+
+    for epoch in range(3):
+        for iteration in range(10):
+
+            x = torch.randn(32, 10)
+            y = torch.randn(32, 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=3,
+        iteration=30,
+        metrics={
+            "loss": [0.5, 0.4, 0.3, 0.2, 0.1],
+            "accuracy": [0.6, 0.7, 0.8, 0.85, 0.9]
+        }
+    )
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        checkpoint.save(tmpdir, save_metric_plot=True)
+        
+        loaded_checkpoint = Checkpoint.load(tmpdir)
+        
+        assert loaded_checkpoint.epoch == 3
+        assert loaded_checkpoint.iteration == 30
+        assert loaded_checkpoint.metrics["loss"] == [0.5, 0.4, 0.3, 0.2, 0.1]
+        
+        assert 'param_groups' in loaded_checkpoint.optimizer_state_dict
+        assert 'state' in loaded_checkpoint.optimizer_state_dict
+        
+        png_files = list(Path(tmpdir).glob("*.png"))
+        assert png_files
+
+def test_multi_process():
+    pass

tests/test_early_stopping.py

@@ -3,7 +3,7 @@ import torch
 import numpy as np
 from khaosz.config import *
 from khaosz.trainer import *
-
+from khaosz.data.checkpoint import Checkpoint
 
 def test_early_stopping_simulation(base_test_env, early_stopping_dataset):
     """Simulate early stopping behavior"""