diff --git a/paddlenlp/transformers/deepseek_v2/modeling.py b/paddlenlp/transformers/deepseek_v2/modeling.py
index d96e1120b331..90dd25b8fa36 100644
--- a/paddlenlp/transformers/deepseek_v2/modeling.py
+++ b/paddlenlp/transformers/deepseek_v2/modeling.py
@@ -3163,6 +3163,28 @@ def forward(
         )
 
 
+class FastCrossEntropyFunction(paddle.autograd.PyLayer):
+    @staticmethod
+    def forward(ctx, preds, labels):
+
+        softmax_val, loss = paddle._C_ops.cross_entropy_with_softmax(preds, labels, False, True, False, -100, -1)
+
+        # print("softmax val", softmax_val.dtype)
+
+        ctx.save_for_backward(labels, softmax_val)
+        return loss
+
+    @staticmethod
+    def backward(ctx, dout):
+        labels, softmax_val = ctx.saved_tensor()
+
+        preds_grad = paddle.incubate.nn.functional.cross_entropy_with_softmax_bwd_w_downcast(
+            labels, softmax_val.cast(paddle.float32), dout.cast(paddle.float32)
+        )
+
+        return preds_grad, None
+
+
 class DeepseekV2PretrainingCriterion(nn.Layer):
     """
     Criterion for Mixtral.
@@ -3190,7 +3212,7 @@ def forward(self, prediction_scores, masked_lm_labels, router_loss=None, mtp_log
 
         def compute_loss(preds, labels):
             with paddle.amp.auto_cast(False):
-                masked_lm_loss = self.loss_func(preds.astype("float32"), labels.unsqueeze(2))
+                masked_lm_loss = FastCrossEntropyFunction.apply(preds, labels.unsqueeze(2))
                 binary_sequence = paddle.where(
                     masked_lm_loss > 0, paddle.ones_like(masked_lm_loss), paddle.zeros_like(masked_lm_loss)
                 )
diff --git a/paddlenlp/transformers/deepseek_v2/modeling_pp.py b/paddlenlp/transformers/deepseek_v2/modeling_pp.py
index 99b817ebe32b..f2cd941af934 100644
--- a/paddlenlp/transformers/deepseek_v2/modeling_pp.py
+++ b/paddlenlp/transformers/deepseek_v2/modeling_pp.py
@@ -1191,7 +1191,11 @@ def forward_backward(self, inputs, output_grad, combine_bw_event_to_wait=None, p
             paddle.base.core.nvprof_nvtx_pop()  # dense_attn_moe_combine
 
             paddle.base.core.nvprof_nvtx_push("moe_mlp")
+            assert WeightGradStore.funcs_queue.empty()
+            WeightGradStore.enabled = True
             output_grad = self.backward_node.mlp_backward(output_grad)
+            WeightGradStore.enabled = False
+            WeightGradStore.flush()
             paddle.base.core.nvprof_nvtx_pop()  # moe_mlp
 
             paddle.base.core.nvprof_nvtx_push("dense_mlp_moe_dispatch")
@@ -1199,6 +1203,12 @@ def forward_backward(self, inputs, output_grad, combine_bw_event_to_wait=None, p
                 output_grad, async_finish=True, allocate_on_comm_stream=True
             )
             dispatch_bw_event = deep_ep.get_event_from_comm_stream(self.backward_node.moe_group.id)
+
+            paddle.base.core.nvprof_nvtx_push("dispatch_backward_dw")
+            WeightGradStore.pop()
+            assert WeightGradStore.funcs_queue.empty()
+            paddle.base.core.nvprof_nvtx_pop()
+
             inputs = self.forward_node.mlp_node.forward(inputs)
             dispatch_bw_event.calc_stream_wait(self.backward_node.moe_group.id)
             paddle.base.core.nvprof_nvtx_pop()  # dense_mlp_moe_dispatch
@@ -1307,17 +1317,13 @@ def build_overlapped_nodes(forward_chunk, backward_chunk):
     overlap_node = OverlapedScheduleChunk(forward_overlap_layers, backward_overlap_layers, use_fuion=DSV3_USE_FP8_GEMM)
     return forward_pre_node, backward_pre_node, overlap_node, forward_post_node, backward_post_node
 
+
 class EmbeddingFunction(paddle.autograd.PyLayer):
     @staticmethod
-    def forward(ctx, x, weight):        
-        out =  paddle.nn.functional.embedding(
-            x,
-            weight=weight,
-            padding_idx=None,
-            max_norm=None,
-            norm_type=2.0,
-            sparse=False,
-            scale_grad_by_freq=False )
+    def forward(ctx, x, weight):
+        out = paddle.nn.functional.embedding(
+            x, weight=weight, padding_idx=None, max_norm=None, norm_type=2.0, sparse=False, scale_grad_by_freq=False
+        )
 
         ctx.save_for_backward(x, weight)
         return out
@@ -1325,15 +1331,15 @@ def forward(ctx, x, weight):
     @staticmethod
     def backward(ctx, dout):
         x, weight = ctx.saved_tensor()
-        
-        if hasattr( weight, "main_grad" ):
-            paddle.incubate.nn.functional.embedding_grad_add_to_(x, weight.main_grad, dout)            
+
+        if hasattr(weight, "main_grad"):
+            paddle.incubate.nn.functional.embedding_grad_add_to_(x, weight.main_grad, dout)
         else:
             paddle.incubate.nn.functional.embedding_grad_add_to_(x, weight.grad, dout)
 
-        
         return None, None
 
+
 class DeepseekV2EmbeddingPipe(nn.Layer):
     def __init__(self, config: DeepseekV2Config):
         super(DeepseekV2EmbeddingPipe, self).__init__()
@@ -1363,7 +1369,7 @@ def forward(self, args):
             _type_: _description_
         """
         input_ids, attention_mask, attn_mask_startend_row_indices, position_ids = parse_args(args)
-        inputs_embeds = EmbeddingFunction.apply( input_ids, self.embed_tokens.weight )
+        inputs_embeds = EmbeddingFunction.apply(input_ids, self.embed_tokens.weight)
 
         batch_size, seq_length = input_ids.shape
         if self.config.num_nextn_predict_layers > 0: