Merge f6f56f0a7d into 4a68da72d5

feat: 添加 macOS Metal (MPS) 设备支持

eval_llm.py

@@ -6,7 +6,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
 from model.model_lora import *
-from trainer.trainer_utils import setup_seed, get_model_params
+from trainer.trainer_utils import setup_seed, get_model_params, get_default_device
 warnings.filterwarnings('ignore')
 
 def init_model(args):
@@ -27,7 +27,9 @@ def init_model(args):
     else:
         model = AutoModelForCausalLM.from_pretrained(args.load_from, trust_remote_code=True)
     get_model_params(model, model.config)
-    return model.half().eval().to(args.device), tokenizer
+    if 'cuda' in args.device:
+        model = model.half()
+    return model.eval().to(args.device), tokenizer
 
 def main():
     parser = argparse.ArgumentParser(description="MiniMind模型推理与对话")
@@ -45,7 +47,7 @@ def main():
     parser.add_argument('--open_thinking', default=0, type=int, help="是否开启自适应思考（0=否，1=是）")
     parser.add_argument('--historys', default=0, type=int, help="携带历史对话轮数（需为偶数，0表示不携带历史）")
     parser.add_argument('--show_speed', default=1, type=int, help="显示decode速度（tokens/s）")
-    parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help="运行设备")
+    parser.add_argument('--device', default=get_default_device(), type=str, help="运行设备")
     args = parser.parse_args()
     
     prompts = [

trainer/train_agent.py

@@ -24,7 +24,7 @@ from torch.optim.lr_scheduler import CosineAnnealingLR
 from transformers import AutoTokenizer
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
 from dataset.lm_dataset import AgentRLDataset
-from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model, LMForRewardModel
+from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model, LMForRewardModel, get_default_device
 from trainer.rollout_engine import create_rollout_engine, compute_per_token_logps
 
 warnings.filterwarnings('ignore')
@@ -378,7 +378,7 @@ 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=get_default_device(), 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")
@@ -421,9 +421,14 @@ 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 ("mps" if "mps" in args.device else "cpu")
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    if device_type == "cuda":
+        autocast_ctx = torch.cuda.amp.autocast(dtype=dtype)
+    elif device_type == "mps":
+        autocast_ctx = torch.amp.autocast("mps", dtype=dtype)
+    else:
+        autocast_ctx = nullcontext()
 
     wandb = None
     if args.use_wandb and is_main_process():
@@ -480,7 +485,7 @@ if __name__ == "__main__":
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
-        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True, collate_fn=collate_fn)
+        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=(device_type == "cuda"), collate_fn=collate_fn)
         if skip > 0:
             Logger(f'Epoch [{epoch+1}/{args.epochs}]: skip {start_step} steps')
             rl_train_epoch(epoch, loader, len(loader) + skip, rollout_engine, ref_model, reward_model, start_step, wandb, use_sglang = (args.rollout_engine == "sglang"))

trainer/train_distillation.py

@@ -17,7 +17,7 @@ from torch.nn.parallel import DistributedDataParallel
 from torch.utils.data import DataLoader, DistributedSampler
 from model.model_minimind import MiniMindConfig
 from dataset.lm_dataset import SFTDataset
-from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler
+from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler, get_default_device
 
 warnings.filterwarnings('ignore')
 
@@ -151,7 +151,7 @@ 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=get_default_device(), 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")
@@ -188,9 +188,14 @@ 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 ("mps" if "mps" in args.device else "cpu")
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    if device_type == "cuda":
+        autocast_ctx = torch.cuda.amp.autocast(dtype=dtype)
+    elif device_type == "mps":
+        autocast_ctx = torch.amp.autocast("mps", dtype=dtype)
+    else:
+        autocast_ctx = nullcontext()
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -210,7 +215,7 @@ 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.cuda.amp.GradScaler(enabled=(device_type == "cuda" and args.dtype == "float16"))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
     # ========== 6. 从ckp恢复状态 ==========
@@ -235,7 +240,7 @@ if __name__ == "__main__":
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
-        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=(device_type == "cuda"))
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
             train_epoch(epoch, loader, len(loader) + skip, teacher_model, lm_config_student, start_step, wandb, args.alpha, args.temperature)

trainer/train_dpo.py

@@ -17,7 +17,7 @@ from torch.nn.parallel import DistributedDataParallel
 from torch.utils.data import DataLoader, DistributedSampler
 from model.model_minimind import MiniMindConfig
 from dataset.lm_dataset import DPODataset
-from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler
+from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler, get_default_device
 
 warnings.filterwarnings('ignore')
 
@@ -135,7 +135,7 @@ 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=get_default_device(), 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")
@@ -166,9 +166,14 @@ 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 ("mps" if "mps" in args.device else "cpu")
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    if device_type == "cuda":
+        autocast_ctx = torch.cuda.amp.autocast(dtype=dtype)
+    elif device_type == "mps":
+        autocast_ctx = torch.amp.autocast("mps", dtype=dtype)
+    else:
+        autocast_ctx = nullcontext()
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -190,7 +195,7 @@ 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.cuda.amp.GradScaler(enabled=(device_type == "cuda" and args.dtype == "float16"))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
     # ========== 6. 从ckp恢复状态 ==========
@@ -215,7 +220,7 @@ if __name__ == "__main__":
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
-        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=(device_type == "cuda"))
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
             train_epoch(epoch, loader, len(loader) + skip, ref_model, lm_config, start_step, wandb, args.beta)

trainer/train_full_sft.py

@@ -16,7 +16,7 @@ from torch.nn.parallel import DistributedDataParallel
 from torch.utils.data import DataLoader, DistributedSampler
 from model.model_minimind import MiniMindConfig
 from dataset.lm_dataset import SFTDataset
-from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler
+from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler, get_default_device
 
 warnings.filterwarnings('ignore')
 
@@ -88,7 +88,7 @@ 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=get_default_device(), 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")
@@ -118,9 +118,14 @@ 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 ("mps" if "mps" in args.device else "cpu")
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    if device_type == "cuda":
+        autocast_ctx = torch.cuda.amp.autocast(dtype=dtype)
+    elif device_type == "mps":
+        autocast_ctx = torch.amp.autocast("mps", dtype=dtype)
+    else:
+        autocast_ctx = nullcontext()
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -135,7 +140,7 @@ 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.cuda.amp.GradScaler(enabled=(device_type == "cuda" and args.dtype == "float16"))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
     # ========== 6. 从ckp恢复状态 ==========
@@ -160,7 +165,7 @@ if __name__ == "__main__":
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
-        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=(device_type == "cuda"))
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
             train_epoch(epoch, loader, len(loader) + skip, start_step, wandb)

trainer/train_grpo.py

@@ -22,7 +22,7 @@ from torch.optim.lr_scheduler import CosineAnnealingLR
 from transformers import AutoModel
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
 from dataset.lm_dataset import RLAIFDataset
-from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model, LMForRewardModel
+from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model, LMForRewardModel, get_default_device
 from trainer.rollout_engine import create_rollout_engine
 
 warnings.filterwarnings('ignore')
@@ -210,7 +210,7 @@ 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=get_default_device(), 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")
@@ -255,9 +255,14 @@ 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 ("mps" if "mps" in args.device else "cpu")
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    if device_type == "cuda":
+        autocast_ctx = torch.cuda.amp.autocast(dtype=dtype)
+    elif device_type == "mps":
+        autocast_ctx = torch.amp.autocast("mps", dtype=dtype)
+    else:
+        autocast_ctx = nullcontext()
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -321,7 +326,7 @@ if __name__ == "__main__":
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
-        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=(device_type == "cuda"))
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
             grpo_train_epoch(epoch, loader, len(loader) + skip, rollout_engine, ref_model, reward_model, start_step, wandb, use_sglang = (args.rollout_engine == "sglang"))

trainer/train_lora.py

@@ -17,7 +17,7 @@ from torch.utils.data import DataLoader, DistributedSampler
 from model.model_minimind import MiniMindConfig
 from dataset.lm_dataset import SFTDataset
 from model.model_lora import save_lora, apply_lora
-from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler
+from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler, get_default_device
 
 warnings.filterwarnings('ignore')
 
@@ -82,7 +82,7 @@ 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=get_default_device(), 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")
@@ -112,9 +112,14 @@ 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 ("mps" if "mps" in args.device else "cpu")
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    if device_type == "cuda":
+        autocast_ctx = torch.cuda.amp.autocast(dtype=dtype)
+    elif device_type == "mps":
+        autocast_ctx = torch.amp.autocast("mps", dtype=dtype)
+    else:
+        autocast_ctx = nullcontext()
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -148,7 +153,7 @@ 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.cuda.amp.GradScaler(enabled=(device_type == "cuda" and args.dtype == "float16"))
     optimizer = optim.AdamW(lora_params, lr=args.learning_rate)
     
     # ========== 7. 从ckp恢复状态 ==========
@@ -173,7 +178,7 @@ if __name__ == "__main__":
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
-        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=(device_type == "cuda"))
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
             train_epoch(epoch, loader, len(loader) + skip, lora_params, start_step, wandb)

trainer/train_ppo.py

@@ -21,7 +21,7 @@ from torch.nn.utils import clip_grad_norm_
 from torch.optim.lr_scheduler import CosineAnnealingLR
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
 from dataset.lm_dataset import RLAIFDataset
-from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model, LMForRewardModel
+from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model, LMForRewardModel, get_default_device
 from trainer.rollout_engine import create_rollout_engine
 
 warnings.filterwarnings('ignore')
@@ -301,7 +301,7 @@ 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=get_default_device(), 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")
@@ -349,9 +349,14 @@ 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 ("mps" if "mps" in args.device else "cpu")
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    if device_type == "cuda":
+        autocast_ctx = torch.cuda.amp.autocast(dtype=dtype)
+    elif device_type == "mps":
+        autocast_ctx = torch.amp.autocast("mps", dtype=dtype)
+    else:
+        autocast_ctx = nullcontext()
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -424,7 +429,7 @@ if __name__ == "__main__":
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
-        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=(device_type == "cuda"))
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
             ppo_train_epoch(epoch, loader, len(loader) + skip, rollout_engine, ref_model, actor_scheduler, critic_scheduler, reward_model, start_step, wandb, use_sglang = (args.rollout_engine == "sglang"))

trainer/train_pretrain.py

@@ -16,7 +16,7 @@ from torch.nn.parallel import DistributedDataParallel
 from torch.utils.data import DataLoader, DistributedSampler
 from model.model_minimind import MiniMindConfig
 from dataset.lm_dataset import PretrainDataset
-from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler
+from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler, get_default_device
 
 warnings.filterwarnings('ignore')
 
@@ -87,7 +87,7 @@ 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=get_default_device(), 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")
@@ -117,9 +117,14 @@ 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 ("mps" if "mps" in args.device else "cpu")
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
-    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    if device_type == "cuda":
+        autocast_ctx = torch.cuda.amp.autocast(dtype=dtype)
+    elif device_type == "mps":
+        autocast_ctx = torch.amp.autocast("mps", dtype=dtype)
+    else:
+        autocast_ctx = nullcontext()
     
     # ========== 4. 配wandb ==========
     wandb = None
@@ -134,7 +139,7 @@ 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.cuda.amp.GradScaler(enabled=(device_type == "cuda" and args.dtype == "float16"))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
     # ========== 6. 从ckp恢复状态 ==========
@@ -159,7 +164,7 @@ if __name__ == "__main__":
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
-        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=(device_type == "cuda"))
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
             train_epoch(epoch, loader, len(loader) + skip, start_step, wandb)

trainer/trainer_utils.py

@@ -15,6 +15,15 @@ from torch.utils.data import Sampler
 from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
 from model.model_minimind import MiniMindForCausalLM
 
+def get_default_device():
+    """运行时自动选择最优设备，CUDA > MPS > CPU"""
+    if torch.cuda.is_available():
+        return "cuda"
+    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+        return "mps"
+    return "cpu"
+
+
 def get_model_params(model, config):
     total = sum(p.numel() for p in model.parameters()) / 1e6
     n_routed = getattr(config, 'n_routed_experts', getattr(config, 'num_experts', 0))
@@ -45,6 +54,9 @@ def init_distributed_mode():
     if int(os.environ.get("RANK", -1)) == -1:
         return 0  # 非DDP模式
 
+    if not torch.cuda.is_available():
+        return 0  # MPS/CPU 不支持 NCCL DDP，静默跳过
+
     dist.init_process_group(backend="nccl")
     local_rank = int(os.environ["LOCAL_RANK"])
     torch.cuda.set_device(local_rank)
@@ -55,6 +67,7 @@ def setup_seed(seed: int):
     random.seed(seed)
     np.random.seed(seed)
     torch.manual_seed(seed)
+    if torch.cuda.is_available():
         torch.cuda.manual_seed(seed)
         torch.cuda.manual_seed_all(seed)
         torch.backends.cudnn.deterministic = True
@@ -103,7 +116,10 @@ def lm_checkpoint(lm_config, weight='full_sft', model=None, optimizer=None, epoc
         torch.save(resume_data, resume_tmp)
         os.replace(resume_tmp, resume_path)
         del state_dict, resume_data
+        if torch.cuda.is_available():
             torch.cuda.empty_cache()
+        elif hasattr(torch, "mps") and torch.backends.mps.is_available():
+            torch.mps.empty_cache()
     else:  # 加载模式
         if os.path.exists(resume_path):
             ckp_data = torch.load(resume_path, map_location='cpu')
@@ -116,7 +132,9 @@ def lm_checkpoint(lm_config, weight='full_sft', model=None, optimizer=None, epoc
         return None
 
 
-def init_model(lm_config, from_weight='pretrain', tokenizer_path='../model', save_dir='../out', device='cuda'):
+def init_model(lm_config, from_weight='pretrain', tokenizer_path='../model', save_dir='../out', device=None):
+    if device is None:
+        device = get_default_device()
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
     model = MiniMindForCausalLM(lm_config)
 
@@ -158,7 +176,9 @@ class SkipBatchSampler(Sampler):
 
 
 class LMForRewardModel:
-    def __init__(self, model_path, device="cuda", dtype=torch.float16):
+    def __init__(self, model_path, device=None, dtype=torch.float16):
+        if device is None:
+            device = get_default_device()
         self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
         self.model = AutoModel.from_pretrained(model_path, torch_dtype=dtype, trust_remote_code=True)
         self.model = self.model.to(device).eval()