fix: 修复参数传递问题

khaosz/parallel/setup.py

@@ -81,6 +81,7 @@ def only_on_rank(rank, sync=False):
     def decorator(func):
         @wraps(func)
         def wrapper(*args, **kwargs):
+            ret_args = None
             if get_rank() == rank:
                 ret_args = func(*args, **kwargs)
 

khaosz/trainer/trainer.py

@@ -57,6 +57,8 @@ class Trainer:
             world_size=config.nprocs,
             master_addr=config.master_addr,
             master_port=config.master_port,
+            device_type=config.device_type,
+            device_ids=config.device_ids,
             checkpoint=checkpoint
         )
 

tests/conftest.py

@@ -96,7 +96,7 @@ def base_test_env(request: pytest.FixtureRequest):
     
     with open(config_path, 'w') as f:
         json.dump(config, f)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
     transformer_config = ModelConfig().load(config_path)
     model = Transformer(transformer_config).to(device=device)
     tokenizer = BpeTokenizer()

tests/trainer/test_callbacks.py

@@ -25,7 +25,8 @@ def test_callback_integration(base_test_env, random_dataset):
         checkpoint_interval=3,
         accumulation_steps=1,
         max_grad_norm=1.0,
-        random_seed=42
+        random_seed=42,
+        device_type=base_test_env["device"]
     )
     
 

tests/trainer/test_early_stopping.py

@@ -25,6 +25,7 @@ def test_early_stopping_simulation(base_test_env, early_stopping_dataset):
         checkpoint_interval=1,
         accumulation_steps=2,
         random_seed=np.random.randint(1e4),
+        device_type=base_test_env["device"]
     )
 
     trainer = Trainer(train_config)

tests/trainer/test_trainer.py

@@ -30,7 +30,8 @@ def test_different_batch_sizes(base_test_env, random_dataset):
             checkpoint_interval=5,
             accumulation_steps=1,
             max_grad_norm=1.0,
-            random_seed=np.random.randint(1000)
+            random_seed=np.random.randint(1000),
+            device_type=base_test_env["device"]
         )
         
         assert train_config.batch_size == batch_size
@@ -59,7 +60,8 @@ def test_gradient_accumulation(base_test_env, random_dataset):
             checkpoint_interval=10,
             accumulation_steps=accumulation_steps,
             max_grad_norm=1.0,
-            random_seed=42
+            random_seed=42,
+            device_type=base_test_env["device"]
         )
         
         trainer = Trainer(train_config)
@@ -96,7 +98,8 @@ def test_memory_efficient_training(base_test_env, random_dataset):
             checkpoint_interval=5,
             accumulation_steps=config["accumulation_steps"],
             max_grad_norm=1.0,
-            random_seed=42
+            random_seed=42,
+            device_type=base_test_env["device"]
         )
         
         assert train_config.accumulation_steps == config["accumulation_steps"]