diff --git a/llm/run_finetune.py b/llm/run_finetune.py
index 36e7b221729b..914f79ec939a 100644
--- a/llm/run_finetune.py
+++ b/llm/run_finetune.py
@@ -267,6 +267,7 @@ def main():
     model_config.using_fake_gate = model_args.using_fake_gate
     model_config.moe_subbatch_token_num = model_args.moe_subbatch_token_num
     model_config.aux_loss_alpha = model_args.aux_loss_alpha
+    model_config.gradient_accumulation_steps = training_args.gradient_accumulation_steps
     logger.info(f"Final model config: {model_config}")
 
     logger.info("Creating model")
diff --git a/paddlenlp/transformers/deepseek_v2/modeling.py b/paddlenlp/transformers/deepseek_v2/modeling.py
index b301ff5da008..375e4a3c8885 100644
--- a/paddlenlp/transformers/deepseek_v2/modeling.py
+++ b/paddlenlp/transformers/deepseek_v2/modeling.py
@@ -1002,6 +1002,26 @@ def linear_dtype_gaurd():
 
         # fmt: on
 
+        if self.config.tensor_parallel_degree > 1 and self.config.sequence_parallel:
+            def grad_allreduce_hook(param, accumulation_steps):
+                hcg = fleet.get_hybrid_communicate_group()
+                pg = hcg.get_model_parallel_group().process_group
+                step = [0]
+
+                @paddle.autograd.no_grad()
+                def __impl__():
+                    step[0] += 1
+                    if (step[0] % accumulation_steps) == 0:
+                        if hasattr(param, "main_grad"):
+                            pg.allreduce(param.main_grad).wait()
+                        else:
+                            pg.allreduce(param.grad).wait()
+
+                return __impl__
+            # kv_a_proj_with_mqa and q_a_proj grad need to be reduce between mp
+            self.kv_a_proj_with_mqa.weight._register_backward_hook(grad_allreduce_hook(self.kv_a_proj_with_mqa.weight, accumulation_steps=config.gradient_accumulation_steps))
+            self.q_a_proj.weight._register_backward_hook(grad_allreduce_hook(self.q_a_proj.weight, accumulation_steps=config.gradient_accumulation_steps))
+        
         self._init_rope()
 
         self.softmax_scale = self.q_head_dim ** (-0.5)