fix(critical): fold max_completion_tokens + max_num_seqs=2 + max_model_len=80000 + xllm_latest layer import

Sub 655 root causes (confirmed from log analysis):
1. protocol.py: max_completion_tokens never folded into max_tokens
   → 162/881 replay requests rejected 400 (extra_forbidden)
2. max_num_seqs=1 → t2_n_2 test fails (needs n=2)
3. max_model_len=131072 → OOM crash at 62% replay, opencompass all 0

Fixes:
- protocol.py: model_validator fold_max_completion_tokens
- yaml: max_num_seqs=2, max_model_len=80000, PYTORCH_CUDA_ALLOC_CONF
- topk_softmax stays =0 (corex CUB BlockReduce incompatible on BI-V100)

xllm_latest import to ex_engine/:
- npu_torch layers: GDN(1164L), Qwen3.5 GDN, attention, fused_moe
- cuda/moe kernels: topk_softmax_kernels.cuh, moe_combine, moe_compute_index
- npu kernels: causal_conv1d, recurrent_gated_delta_rule
- model headers: qwen3_5.h, qwen3_next.h

ex_engine/xllm_layers/npu_torch/attention.h

@@ -0,0 +1,70 @@
+/* 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);
+
+  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 sliding_window_;
+};
+TORCH_MODULE(Attention);
+
+}  // namespace layer
+}  // namespace xllm