矢量化矩阵乘法 - 2022.1 简体中文

以下提供了 (64 * 64) x (64 * 64) int8 x int8 矩阵乘法内核代码的示例。矩阵乘法形状为 4*16*8。对于矩阵乘法，输入数据形状经过重塑。

const int SHIFT=10;
//For element mmul
const int M=4;
const int K=16;
const int N=8;

//Total matrix sizes
const int rowA=64;
const int colA=64;
const int colB=64;

//mmul numbers
const int num_rowA=rowA/M; 
const int num_colA=colA/K;
const int num_colB=colB/N;

void matrix_mul(input_window<int8> * __restrict matA, input_window<int8> * __restrict matB, output_window<int8> * __restrict matC){
  using MMUL = aie::mmul<M, K, N, int8, int8>;
  const int8* __restrict pA=(int8*)matA->ptr;
  const int8* __restrict pB=(int8*)matB->ptr;
  int8* __restrict pC=(int8*)matC->ptr;

  //For profiling only 
  unsigned cycle_num[2];
  aie::tile tile=aie::tile::current();
  cycle_num[0]=tile.cycles();//cycle counter of the AI Engine tile

  int8 * __restrict pC1 = pC;
  for (unsigned i = 0; i < num_rowA; i++) {//for output row number of element matrix 
    for (unsigned j = 0; j < num_colB; j++) {//for output col number of element matrix
      const int8 * __restrict pA1 = pA + ( i * num_colA + 0) * MMUL::size_A;
      const int8 * __restrict pB1 = pB + ( 0 * num_colB + j) * MMUL::size_B;

      aie::vector<int8, MMUL::size_A> A0 = aie::load_v<MMUL::size_A>(pA1); pA1 += MMUL::size_A;
      aie::vector<int8, MMUL::size_B> B0 = aie::load_v<MMUL::size_B>(pB1); pB1 += MMUL::size_B * num_colB;

      MMUL C00; 
      C00.mul(A0, B0);

      for (unsigned k = 0; k < num_colA-1; k++) chess_prepare_for_pipelining {
        A0 = aie::load_v<MMUL::size_A>(pA1); pA1 += MMUL::size_A;
        B0 = aie::load_v<MMUL::size_B>(pB1); pB1 += MMUL::size_B * num_colB;
        C00.mac(A0, B0);
      }

      aie::store_v(pC1, C00.template to_vector<int8>(SHIFT)); pC1 += MMUL::size_C;
    }
  }

  //For profiling only 
  cycle_num[1]=tile.cycles();//cycle counter of the AI Engine tile
  printf("start=%d,end=%d,total=%d\n",cycle_num[0],cycle_num[1],cycle_num[1]-cycle_num[0]);
}

将 -v 添加到 aiecompiler 中，并在 Work/aie/<COL_ROW>/<COL_ROW>.log 中查看内核编译 log 日志：

HW do-loop #765 in ".../matrix_mul.cc", line 43: (loop #13) :
critical cycle of length 4 : b97 -> b99 -> b101 -> b102 -> b103 -> b104 -> b97
minimum length due to resources: 4
scheduling HW do-loop #765
(algo 1a) -> # cycles: 13 
(modulo) -> # cycles: 4i 5i 6 ok (required budget ratio: 1) 
(resume algo) -> after folding: 6 (folded over 2 iterations)
-> HW do-loop #765 in ".../Vitis/2022.1/aietools/include/aie_api/detail/aie1/mmul_8_8.hpp", line 278: (loop #13) : 6 cycles

资源限制为 4，但对于最内层循环，每次循环迭代需耗时 6 个周期。此外，内层循环的循环计数仅为 num_colA-1=3。因此，有必要观察通过循环平铺并由工具在外层循环中对更大量的指令进行流水打拍是否能有所帮助。此循环调整后的指令如下所示：

for (unsigned i = 0; i < num_rowA; i++) {
  for (unsigned j = 0; j < num_colB; j++) chess_prepare_for_pipelining {
    const int8 * __restrict pA1 = pA + ( i * num_colA + 0) * MMUL::size_A;
    const int8 * __restrict pB1 = pB + ( 0 * num_colB + j) * MMUL::size_B;
    aie::vector<int8, MMUL::size_A> A0 = aie::load_v<MMUL::size_A>(pA1); pA1 += MMUL::size_A;
    aie::vector<int8, MMUL::size_B> B0 = aie::load_v<MMUL::size_B>(pB1); pB1 += MMUL::size_B * num_colB;
    
    MMUL C00; C00.mul(A0, B0);
    for (unsigned k = 0; k < num_colA-1; k++) chess_flatten_loop {
      A0 = aie::load_v<MMUL::size_A>(pA1); pA1 += MMUL::size_A;
      B0 = aie::load_v<MMUL::size_B>(pB1); pB1 += MMUL::size_B * num_colB;
      C00.mac(A0, B0);
    }
    aie::store_v(pC1, C00.template to_vector<int8>(SHIFT)); pC1 += MMUL::size_C;
  }
}