[fix] experts unused

model/model_minimind.py

@@ -315,7 +315,9 @@ class MOEFeedForward(nn.Module):
             x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
             y = torch.empty_like(x, dtype=x.dtype)
             for i, expert in enumerate(self.experts):
-                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype)  # 确保类型一致
+                expert_out = expert(x[flat_topk_idx == i])
+                if expert_out.shape[0] > 0: y[flat_topk_idx == i] = expert_out.to(y.dtype)
+                else: y[flat_topk_idx == i] = expert_out.to(y.dtype) + 0 * sum(p.sum() for p in expert.parameters())
             y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
             y = y.view(*orig_shape)
         else:

trainer/train_distill_reason.py

@@ -161,7 +161,7 @@ if __name__ == "__main__":
     # ========== 7. DDP包模型 ==========
     if dist.is_initialized():
         model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        model = DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):

trainer/train_distillation.py

@@ -210,7 +210,7 @@ if __name__ == "__main__":
     # ========== 7. DDP包模型 ==========
     if dist.is_initialized():
         model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        model = DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config_student, 'use_moe', False))
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):

trainer/train_dpo.py

@@ -195,7 +195,7 @@ if __name__ == "__main__":
     # ========== 7. DDP包模型 ==========
     if dist.is_initialized():
         model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        model = DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):

trainer/train_full_sft.py

@@ -149,7 +149,7 @@ if __name__ == "__main__":
     # ========== 7. DDP包模型 ==========
     if dist.is_initialized():
         model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        model = DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):

trainer/train_grpo.py

@@ -272,7 +272,7 @@ if __name__ == "__main__":
     # ========== 7. DDP包模型 ==========
     if dist.is_initialized():
         model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        model = DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):

trainer/train_lora.py

@@ -162,7 +162,7 @@ if __name__ == "__main__":
     # ========== 8. DDP包模型 ==========
     if dist.is_initialized():
         model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        model = DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     
     # ========== 9. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):

trainer/train_ppo.py

@@ -344,8 +344,8 @@ if __name__ == "__main__":
     if dist.is_initialized():
         actor_model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
         critic_model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        actor_model = DistributedDataParallel(actor_model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        actor_model = DistributedDataParallel(actor_model, device_ids=[local_rank])
-        critic_model = DistributedDataParallel(critic_model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        critic_model = DistributedDataParallel(critic_model, device_ids=[local_rank])
         old_actor_model.to(args.device)
     
     # ========== 8. 开始训练 ==========

trainer/train_pretrain.py

@@ -148,7 +148,7 @@ if __name__ == "__main__":
     # ========== 7. DDP包模型 ==========
     if dist.is_initialized():
         model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        model = DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):

trainer/train_spo.py

@@ -322,7 +322,7 @@ if __name__ == "__main__":
     # ========== 7. DDP包模型 ==========
     if dist.is_initialized():
         model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
-        model = DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):