[Feature]EPLB:Adapt DispatchGmmCombineDecode operator to eplb tensor list and expert token numbers (#5552)

#### What this PR does / why we need it?
This PR adapt DispatchGmmCombineDecode operator to eplb tensor list and
expert token numbers.

This operator support gmm1, gmm2, gmm1Scale and gmm2Scale in format of
list.
This operator support couting how many token each local expert recieves
by expertTokensNum .


- vLLM version: v0.13.0
- vLLM main:
7157596103

More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476

csrc/torch_binding_meta.cpp

@@ -157,10 +157,10 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> grouped_matmul_swiglu_quant_weigh
 std::tuple<at::Tensor, at::Tensor> dispatch_gmm_combine_decode_meta(
     const at::Tensor &x,
     const at::Tensor &expert_ids,
-    const at::Tensor &gmm1_permuted_weight,
-    const at::Tensor &gmm1_permuted_weight_scale,
-    const at::Tensor &gmm2_weight,
-    const at::Tensor &gmm2_weight_scale,
+    const at::TensorList &gmm1_permuted_weight,
+    const at::TensorList &gmm1_permuted_weight_scale,
+    const at::TensorList &gmm2_weight,
+    const at::TensorList &gmm2_weight_scale,
     const at::Tensor &expert_scales,
     const c10::optional<at::Tensor> &expert_smooth_scales,
     const c10::optional<at::Tensor> &x_active_mask,
@@ -181,9 +181,10 @@ std::tuple<at::Tensor, at::Tensor> dispatch_gmm_combine_decode_meta(
 
     bool is_shared_expert = (ep_rank_id < shared_expert_rank_num);
     int64_t num_local_experts = is_shared_expert ? 1 : moe_expert_num / (ep_rank_size - shared_expert_rank_num);
-    at::Tensor ep_recv_count = at::empty({num_local_experts * ep_rank_size}, expert_ids.options().device(at::kMeta));
-
-    return {output, ep_recv_count};
+    auto opts = expert_ids.options().dtype(at::kLong); 
+    at::Tensor expert_token_nums = at::empty({num_local_experts}, opts.device(at::kMeta)); 
+    
+    return {output, expert_token_nums};
 }
 
 void batch_matmul_transpose(const at::Tensor &tensor_a, const at::Tensor &tensor_b, at::Tensor &tensor_c,