feat: import CUDA kernels from xllm/CCCL/FLA upstream repos

Sources cloned and tree'd (no --depth):
  - jd-opensource/xllm: ILU kernels, CUDA kernels, MoE kernels
  - NVIDIA/cccl: CUB tuning/dispatch headers (block-level primitives)
  - fla-org/flash-linear-attention: Triton GDN kernels
  - NVIDIA/cutlass: grouped GEMM reference (read, not copied)
  - Dao-AILab/flash-attention: attention kernel reference (SM80+, read only)

New CUDA kernels (from xllm, SM-agnostic, portable to BI-V100):
  ex_engine/xllm_kernels/cuda/activation.cu    (188 lines) — silu_and_mul, gelu
  ex_engine/xllm_kernels/cuda/norm.cu          (600 lines) — rms_norm, fused_add_rms_norm
  ex_engine/xllm_kernels/cuda/rope.cu          (258 lines) — rotary_embedding
  ex_engine/xllm_kernels/cuda/block_copy.cu    (209 lines) — copy_blocks, swap_blocks
  ex_engine/xllm_kernels/cuda/reshape_paged_cache.cu (101 lines) — KV cache ops
  ex_engine/xllm_kernels/cuda/headers/         (5 headers for compilation)

ILU bridge kernel sources (from xllm, verified SAME as upstream):
  ex_engine/xllm_kernels/ilu/    (10 files, 925 lines total)
  — activation.cpp, attention.cpp, fused_moe.cpp, group_gemm.cpp,
    matmul.cpp, norm.cpp, rope.cpp, ilu_ops_api.h, ixformer.h, utils.h

FLA Triton GDN kernels (for GatedDeltaNet without SM90+ FlashQLA):
  ex_engine/fla_kernels/gated_delta_rule/  (7 files, 2370 lines)
  — chunk_fwd.py (428), chunk.py (487), wy_fast.py (409),
    fused_recurrent.py (392), naive.py (161), gate.py (380)

CCCL sync (12 tuning + 14 dispatch headers updated from NVIDIA/cccl):
  cccl_upstream/cub/cub/device/dispatch/tuning/ — 12 changed files synced
  cccl_upstream/cub/cub/device/dispatch/ — 14 changed dispatch files synced

Compilation targets for real machine (ivcore10):
  1. CUDA kernels: --cuda-gpu-arch=ivcore10 via corex clang/16
  2. ILU bridges: torch.utils.cpp_extension linking ixformer .so
  3. FLA kernels: Triton JIT (if Triton works on BI-V100)

ex_engine/xllm_kernels/ilu/group_gemm.cpp

@@ -0,0 +1,39 @@
+/* Copyright 2025-2026 The xLLM Authors.
+
+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 "ilu_ops_api.h"
+
+namespace xllm::kernel::ilu {
+
+torch::Tensor group_gemm(torch::Tensor& input,
+                         torch::Tensor& weight,
+                         torch::Tensor& tokens_per_experts,
+                         const std::optional<torch::Tensor>& dst_to_src,
+                         torch::Tensor& output) {
+  infer::moe_w16a16_group_gemm(
+      output,
+      input,
+      weight,
+      tokens_per_experts,
+      dst_to_src,
+      /*bias=*/std::nullopt,
+      /*format=*/"TN",
+      /*persistent=*/0,
+      /*output_n=*/tokens_per_experts.sum().item<int64_t>());
+
+  return output;
+}
+
+}  // namespace xllm::kernel::ilu