data: complete SGEMM upstream from 3 repos (siboehm+wangzyon+edtallison) + xllm fused_qknorm_rope + xattention kernels

SGEMM repos (upstream_ref/sgemm_cuda/, 41 files):
  siboehm/SGEMM_CUDA: kernel 1-12, runner, CMake, cuBLAS benchmark
  wangzyon/NVIDIA_SGEMM_PRACTICE: kernel 1-7 (Chinese comments), utils
  edtallison/sgemm-cuda: kernel 01-09 (learning notes), Makefile

xllm kernels (ex_engine/xllm_kernels/cuda/):
  fused_qknorm_rope.cu + bind — saves 128 kernel launches/fwd
  xattention/ — 6 files from upstream xllm
  headers: corex_compat_utils.h, topk_last_dim.cuh
  ilu/CMakeLists.txt

SO_BUILD_MANIFEST.md — complete .so inventory and call chain analysis

upstream_ref/sgemm_cuda/kernel_1.cuh

@@ -0,0 +1,19 @@
+#pragma once
+
+#include <cuda_runtime.h>
+#include <cublas_v2.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+__global__ __launch_bounds__(1024) void
+mysgemm_v1(int M, int N, int K, float alpha, float *A, float *B, float beta, float *C) {
+
+    int gx = blockIdx.x * blockDim.x + threadIdx.x; // 全局x
+    int gy = blockIdx.y * blockDim.y + threadIdx.y; // 全局y
+
+    float tmp = 0.;
+    for (int i = 0; i < K; i++) {
+        tmp += A[gy * K + i] * B[i * N + gx]; // 两次全局内存访问和一次FMA（累加乘）
+    }
+    C[gy * N + gx] = alpha * tmp + beta * C[gy * N + gx];
+}