ref(upstream): add Deep-Spark xllm + vllm MoE/GDN reference code

Sources (Apache 2.0, cloned 2026-08-09):
- Deep-Spark/xllm: Iluvatar's official C++ inference engine
- Deep-Spark/vllm: Iluvatar's vllm fork

Key files for our EX Engine development:

MoE topk_softmax (fixes 2304 calls/token PyTorch fallback):
- xllm/kernels/cuda/moe/moe_topk_softmax_kernels.cuh
  CUB-based fused softmax+topk, power-of-2 expert count optimized
  For 64 experts: topk_gating_softmax<T,VPT=2,64,WARPS=4,BYTES=4>
- xllm/kernels/ilu/ixformer.h
  Official ixformer C++ API: topk_softmax(), paged_attention(), etc.
- xllm/kernels/ilu/fused_moe.cpp
  How xllm calls ixformer::infer::topk_softmax()
- ds_vllm/csrc/moe/topk_softmax_kernels.cu
  vllm-native topk_softmax (TensorRT-LLM derived, 874 lines)

GatedDeltaNet (fixes NaN in 4 GDN layers):
- xllm/layers/npu_torch/qwen3_gated_delta_net_base.cpp
  fp32 state accumulation, proper recurrent update

Complete FusedMoE pipeline reference:
- xllm/layers/ilu/fused_moe.cpp
  gate -> topk -> expand -> gemm1 -> act -> gemm2 -> combine

upstream_ref/xllm/kernels/ilu/fused_moe.cpp

@@ -0,0 +1,99 @@
+/* Copyright 2025 The xLLM Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://github.com/jd-opensource/xllm/blob/main/LICENSE
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <glog/logging.h>
+
+#include "ilu_ops_api.h"
+
+namespace xllm::kernel::ilu {
+
+std::tuple<torch::Tensor, torch::Tensor> moe_active_topk(
+    const torch::Tensor& input,
+    int64_t topk,
+    int64_t num_expert_group,
+    int64_t topk_group,
+    bool normalize,
+    const std::optional<torch::Tensor>& mask,
+    const std::string& normed_by,
+    const std::string& scoring_func,
+    double route_scale,
+    const std::optional<torch::Tensor>& e_score_correction_bias) {
+  torch::Tensor input_ = input.to(torch::kFloat32);
+  auto reduce_weight =
+      torch::empty({input.size(0), topk},
+                   torch::dtype(torch::kFloat).device(input.device()));
+  auto topk_indices =
+      torch::empty({input.size(0), topk},
+                   torch::dtype(torch::kInt32).device(input.device()));
+  auto token_expert_indices =
+      torch::empty({input.size(0), topk},
+                   torch::dtype(torch::kInt32).device(input.device()));
+
+  infer::topk_softmax(
+      reduce_weight, topk_indices, token_expert_indices, input_, false);
+
+  auto tt = reduce_weight.sum(-1);
+  if (normalize) {
+    reduce_weight = reduce_weight / reduce_weight.sum(-1).unsqueeze(-1);
+  }
+  return std::make_tuple(reduce_weight, topk_indices);
+}
+
+std::vector<torch::Tensor> moe_gen_idx(torch::Tensor& expert_id,
+                                       int64_t expert_num) {
+  auto src_dst = expert_id.new_empty({expert_id.numel()});
+  auto dst_src = torch::empty_like(src_dst);
+  auto expert_sizes_gpu = expert_id.new_empty({expert_num});
+  auto expert_sizes_gpu_cumsum = expert_id.new_zeros({expert_id.numel() + 1});
+  infer::moe_compute_token_index_api(expert_id,
+                                     src_dst,
+                                     dst_src,
+                                     expert_sizes_gpu,
+                                     /*expert_mask=*/std::nullopt,
+                                     /*expert_sizes_cpu*/ std::nullopt,
+                                     /*expert_sizes_gpu*/ std::nullopt,
+                                     0,
+                                     expert_num,
+                                     expert_num);
+
+  expert_sizes_gpu_cumsum = expert_sizes_gpu.cumsum(-1);
+  return {src_dst, dst_src, expert_sizes_gpu, expert_sizes_gpu_cumsum};
+}
+
+torch::Tensor moe_expand_input(const torch::Tensor& input,
+                               const torch::Tensor& gather_index,
+                               const torch::Tensor& combine_idx,
+                               int64_t topk) {
+  int64_t dst_tokens = input.size(0) * topk;
+  auto output = input.new_empty({dst_tokens, input.size(1)});
+  infer::moe_expand_input(
+      output, input, combine_idx, gather_index, dst_tokens, topk);
+
+  return output;
+}
+
+torch::Tensor moe_combine_result(torch::Tensor& input, torch::Tensor& weight) {
+  input = input.view({-1, weight.size(1), input.size(1)});
+  auto output = input.new_empty({input.size(0), input.size(2)});
+  infer::moe_output_reduce_sum(output,
+                               input,
+                               weight,
+                               /*mask=*/std::nullopt,
+                               /*extra_residual*/ std::nullopt,
+                               /*scaling_factor=*/1.0);
+  return output;
+}
+
+}  // namespace xllm::kernel::ilu