diff --git a/vllm_ascend/ops/fused_moe.py b/vllm_ascend/ops/fused_moe.py
index 01da5bef35..7a3d29e6db 100644
--- a/vllm_ascend/ops/fused_moe.py
+++ b/vllm_ascend/ops/fused_moe.py
@@ -198,6 +198,7 @@ def fused_experts(
     num_experts = w1.shape[0]
     dtype = hidden_states.dtype
     device = hidden_states.device
+    topk_weights = topk_weights.to(dtype)
     # assert dtype in [torch.float32, torch.float16, torch.bfloat16
     #                  ], "Only float32, float16, and bfloat16 are supported"
 
@@ -615,32 +616,16 @@ def __init__(
         self.expert_map = None
         self.activation = activation
 
-        if self.ep_size > 1:
-            # Create a tensor of size num_experts filled with -1
-            self.local_num_experts, self.expert_map = determine_expert_map(
-                self.ep_size,
-                get_ep_group().rank_in_group, self.global_num_experts)
-            if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
-                self.tp_rank = get_etp_group().rank_in_group
-                self.ep_rank = get_ep_group().rank_in_group
-            else:
-                self.moe_parallel_config.tp_rank = get_etp_group(
-                ).rank_in_group
-                self.moe_parallel_config.ep_rank = get_ep_group().rank_in_group
-
+        # Create a tensor of size num_experts filled with -1
+        self.local_num_experts, self.expert_map = determine_expert_map(
+            self.ep_size,
+            get_ep_group().rank_in_group, self.global_num_experts)
+        if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
+            self.tp_rank = get_etp_group().rank_in_group
+            self.ep_rank = get_ep_group().rank_in_group
         else:
-            # Adjust TP size for DP attention
-            # haven't test its functionality yet, may remove in the future
-            if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
-                self.tp_rank = self.tp_size * self.dp_rank
-                self.ep_rank = 0
-                self.tp_size = self.tp_size * self.dp_size
-                self.ep_size = 1
-            else:
-                self.moe_parallel_config.tp_rank = self.tp_size * self.dp_rank
-                self.moe_parallel_config.ep_rank = 0
-                self.moe_parallel_config.tp_size = self.tp_size * self.dp_size
-                self.moe_parallel_config.ep_size = 1
+            self.moe_parallel_config.tp_rank = get_etp_group().rank_in_group
+            self.moe_parallel_config.ep_rank = get_ep_group().rank_in_group
 
             self.local_num_experts, self.expert_map = (self.global_num_experts,
                                                        None)
diff --git a/vllm_ascend/quantization/w8a8_dynamic.py b/vllm_ascend/quantization/w8a8_dynamic.py
index 5d2b442cf1..11d9b91270 100644
--- a/vllm_ascend/quantization/w8a8_dynamic.py
+++ b/vllm_ascend/quantization/w8a8_dynamic.py
@@ -342,6 +342,7 @@ def fused_experts(hidden_states: torch.Tensor,
     num_experts = w1.shape[0]
     dtype = hidden_states.dtype
     device = hidden_states.device
+    topk_weights = topk_weights.to(dtype)
 
     if expert_map is not None:
         # Generate token indices and flatten