diff --git a/trainer/train_agent.py b/trainer/train_agent.py
index 4619dbb..4bea13d 100644
--- a/trainer/train_agent.py
+++ b/trainer/train_agent.py
@@ -371,7 +371,7 @@ if __name__ == "__main__":
     parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
     parser.add_argument("--batch_size", type=int, default=2, help="批次大小")
     parser.add_argument("--learning_rate", type=float, default=3e-7, help="学习率")
-    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"), help="训练设备")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="数据类型 bfloat16/float16")
     parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
     parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
@@ -414,9 +414,9 @@ if __name__ == "__main__":
                                max_seq_len=args.max_seq_len + args.max_gen_len, use_moe=bool(args.use_moe))
     ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume == 1 else None
 
-    device_type = "cuda" if "cuda" in args.device else "cpu"
+    device_type = "cuda" if "cuda" in args.device else args.device
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.autocast(device_type=device_type, dtype=dtype)
 
     wandb = None
     if args.use_wandb and is_main_process():
diff --git a/trainer/train_distillation.py b/trainer/train_distillation.py
index dce5346..b471966 100644
--- a/trainer/train_distillation.py
+++ b/trainer/train_distillation.py
@@ -142,7 +142,7 @@ if __name__ == "__main__":
     parser.add_argument("--epochs", type=int, default=6, help="训练轮数")
     parser.add_argument("--batch_size", type=int, default=32, help="batch size")
     parser.add_argument("--learning_rate", type=float, default=5e-6, help="初始学习率")
-    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"), help="训练设备")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
     parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
     parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
@@ -179,9 +179,9 @@ if __name__ == "__main__":
     ckp_data = lm_checkpoint(lm_config_student, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
     
     # ========== 3. 设置混合精度 ==========
-    device_type = "cuda" if "cuda" in args.device else "cpu"
+    device_type = "cuda" if "cuda" in args.device else args.device
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.autocast(device_type=device_type, dtype=dtype)
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -201,7 +201,7 @@ if __name__ == "__main__":
     Logger(f'教师模型总参数量：{sum(p.numel() for p in teacher_model.parameters()) / 1e6:.3f} M')
     train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
     train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
-    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
+    scaler = torch.amp.GradScaler(device_type, enabled=(args.dtype == 'float16'))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
     # ========== 6. 从ckp恢复状态 ==========
diff --git a/trainer/train_dpo.py b/trainer/train_dpo.py
index 69fa773..44a073b 100644
--- a/trainer/train_dpo.py
+++ b/trainer/train_dpo.py
@@ -126,7 +126,7 @@ if __name__ == "__main__":
     parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
     parser.add_argument("--batch_size", type=int, default=4, help="batch size")
     parser.add_argument("--learning_rate", type=float, default=4e-8, help="初始学习率（建议<=5e-8避免遗忘）")
-    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"), help="训练设备")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
     parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
     parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
@@ -157,9 +157,9 @@ if __name__ == "__main__":
     ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
     
     # ========== 3. 设置混合精度 ==========
-    device_type = "cuda" if "cuda" in args.device else "cpu"
+    device_type = "cuda" if "cuda" in args.device else args.device
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.autocast(device_type=device_type, dtype=dtype)
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -181,7 +181,7 @@ if __name__ == "__main__":
     
     train_ds = DPODataset(args.data_path, tokenizer, max_length=args.max_seq_len)
     train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
-    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
+    scaler = torch.amp.GradScaler(device_type, enabled=(args.dtype == 'float16'))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
     # ========== 6. 从ckp恢复状态 ==========
diff --git a/trainer/train_full_sft.py b/trainer/train_full_sft.py
index 2610d1a..55b2599 100644
--- a/trainer/train_full_sft.py
+++ b/trainer/train_full_sft.py
@@ -79,7 +79,7 @@ if __name__ == "__main__":
     parser.add_argument("--epochs", type=int, default=2, help="训练轮数")
     parser.add_argument("--batch_size", type=int, default=16, help="batch size")
     parser.add_argument("--learning_rate", type=float, default=1e-5, help="初始学习率")
-    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"), help="训练设备")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
     parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
     parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
@@ -110,9 +110,9 @@ if __name__ == "__main__":
     ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
     
     # ========== 3. 设置混合精度 ==========
-    device_type = "cuda" if "cuda" in args.device else "cpu"
+    device_type = "cuda" if "cuda" in args.device else args.device
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.autocast(device_type=device_type, dtype=dtype)
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -127,7 +127,7 @@ if __name__ == "__main__":
     model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
     train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
     train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
-    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
+    scaler = torch.amp.GradScaler(device_type, enabled=(args.dtype == 'float16'))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
     # ========== 6. 从ckp恢复状态 ==========
diff --git a/trainer/train_grpo.py b/trainer/train_grpo.py
index 8a37dd6..cbae5f8 100755
--- a/trainer/train_grpo.py
+++ b/trainer/train_grpo.py
@@ -203,7 +203,7 @@ if __name__ == "__main__":
     parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
     parser.add_argument("--batch_size", type=int, default=2, help="batch size")
     parser.add_argument("--learning_rate", type=float, default=3e-7, help="初始学习率")
-    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"), help="训练设备")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
     parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
     parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
@@ -248,9 +248,9 @@ if __name__ == "__main__":
     ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
     
     # ========== 3. 设置混合精度 ==========
-    device_type = "cuda" if "cuda" in args.device else "cpu"
+    device_type = "cuda" if "cuda" in args.device else args.device
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.autocast(device_type=device_type, dtype=dtype)
     
     # ========== 4. 配wandb ==========
     wandb = None
diff --git a/trainer/train_lora.py b/trainer/train_lora.py
index 4717292..cd24565 100644
--- a/trainer/train_lora.py
+++ b/trainer/train_lora.py
@@ -73,7 +73,7 @@ if __name__ == "__main__":
     parser.add_argument("--epochs", type=int, default=10, help="训练轮数")
     parser.add_argument("--batch_size", type=int, default=32, help="batch size")
     parser.add_argument("--learning_rate", type=float, default=1e-4, help="初始学习率")
-    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"), help="训练设备")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
     parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
     parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
@@ -103,9 +103,9 @@ if __name__ == "__main__":
     ckp_data = lm_checkpoint(lm_config, weight=args.lora_name, save_dir='../checkpoints') if args.from_resume==1 else None
     
     # ========== 3. 设置混合精度 ==========
-    device_type = "cuda" if "cuda" in args.device else "cpu"
+    device_type = "cuda" if "cuda" in args.device else args.device
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.autocast(device_type=device_type, dtype=dtype)
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -139,7 +139,7 @@ if __name__ == "__main__":
     # ========== 6. 定义数据和优化器 ==========
     train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
     train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
-    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
+    scaler = torch.amp.GradScaler(device_type, enabled=(args.dtype == 'float16'))
     optimizer = optim.AdamW(lora_params, lr=args.learning_rate)
     
     # ========== 7. 从ckp恢复状态 ==========
diff --git a/trainer/train_ppo.py b/trainer/train_ppo.py
index 4290b63..77c5b98 100644
--- a/trainer/train_ppo.py
+++ b/trainer/train_ppo.py
@@ -311,7 +311,7 @@ if __name__ == "__main__":
     parser.add_argument("--batch_size", type=int, default=2, help="batch size")
     parser.add_argument("--learning_rate", type=float, default=3e-7, help="Actor学习率")
     parser.add_argument("--critic_learning_rate", type=float, default=5e-7, help="Critic学习率")
-    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"), help="训练设备")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
     parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
     parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
@@ -360,9 +360,9 @@ if __name__ == "__main__":
     ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
     
     # ========== 3. 设置混合精度 ==========
-    device_type = "cuda" if "cuda" in args.device else "cpu"
+    device_type = "cuda" if "cuda" in args.device else args.device
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.autocast(device_type=device_type, dtype=dtype)
     
     # ========== 4. 配wandb ==========
     wandb = None
diff --git a/trainer/train_pretrain.py b/trainer/train_pretrain.py
index a5d2fa2..91edbde 100644
--- a/trainer/train_pretrain.py
+++ b/trainer/train_pretrain.py
@@ -78,7 +78,7 @@ if __name__ == "__main__":
     parser.add_argument("--epochs", type=int, default=2, help="训练轮数")
     parser.add_argument("--batch_size", type=int, default=32, help="batch size")
     parser.add_argument("--learning_rate", type=float, default=5e-4, help="初始学习率")
-    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"), help="训练设备")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
     parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
     parser.add_argument("--accumulation_steps", type=int, default=8, help="梯度累积步数")
@@ -109,9 +109,9 @@ if __name__ == "__main__":
     ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
     
     # ========== 3. 设置混合精度 ==========
-    device_type = "cuda" if "cuda" in args.device else "cpu"
+    device_type = "cuda" if "cuda" in args.device else args.device
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.autocast(device_type=device_type, dtype=dtype)
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -126,7 +126,7 @@ if __name__ == "__main__":
     model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
     train_ds = PretrainDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
     train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
-    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
+    scaler = torch.amp.GradScaler(device_type, enabled=(args.dtype == 'float16'))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
     # ========== 6. 从ckp恢复状态 ==========