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/layers/ilu/attention.h

@@ -0,0 +1,82 @@
+/* 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.
+==============================================================================*/
+
+#pragma once
+
+#include <torch/torch.h>
+
+#include <tuple>
+
+#include "framework/kv_cache/kv_cache.h"
+#include "framework/model/model_input_params.h"
+#include "layers/common/attention_metadata.h"
+
+namespace xllm {
+namespace layer {
+class AttentionImpl : public torch::nn::Module {
+ public:
+  AttentionImpl() = default;
+
+  AttentionImpl(int64_t num_heads,
+                int64_t head_size,
+                float scale,
+                int64_t num_kv_heads,
+                int64_t sliding_window);
+  AttentionImpl(int64_t num_heads,
+                int64_t head_size,
+                int64_t num_kv_heads,
+                int64_t v_head_dim,
+                int64_t sliding_window,
+                float scale,
+                bool use_fused_mla_qkv,
+                bool enable_lighting_indexer,
+                bool enable_mla);
+
+  std::tuple<torch::Tensor, std::optional<torch::Tensor>> forward(
+      const AttentionMetadata& attn_metadata,
+      torch::Tensor& query,
+      torch::Tensor& key,
+      torch::Tensor& value,
+      KVCache& kv_cache);
+
+  void prefill_forward(torch::Tensor& query,
+                       torch::Tensor& key,
+                       torch::Tensor& value,
+                       torch::Tensor& output,
+                       const torch::Tensor& k_cache,
+                       const std::optional<torch::Tensor>& v_cache,
+                       const AttentionMetadata& attn_metadata);
+
+  void decoder_forward(torch::Tensor& query,
+                       torch::Tensor& output,
+                       const torch::Tensor& k_cache,
+                       const std::optional<torch::Tensor>& v_cache,
+                       const AttentionMetadata& attn_metadata);
+
+ private:
+  int64_t num_heads_;
+  int64_t head_size_;
+  float scale_;
+  int64_t num_kv_heads_;
+  int64_t v_head_dim_;
+  bool use_fused_mla_qkv_;
+  bool enable_lighting_indexer_;
+  bool enable_mla_;
+  int64_t sliding_window_;
+};
+TORCH_MODULE(Attention);
+
+}  // namespace layer
+}  // namespace xllm