hgemm_mma.cu

#include <stdio.h>
#include <stdlib.h>
#include <float.h>
#include <vector>
#include <algorithm>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cuda_bf16.h>
#include <cuda_fp8.h>
#include <mma.h>
#include <torch/types.h>
#include <torch/extension.h>
using namespace nvcuda;

#define WARP_SIZE 32
#define DEVICE_INLINE __device__ inline
#define HOST_DEVICE_INLINE __device__ __host__ inline
#define INT4(value) (reinterpret_cast<int4*>(&(value))[0])
#define FLOAT4(value) (reinterpret_cast<float4*>(&(value))[0])
#define HALF2(value) (reinterpret_cast<half2*>(&(value))[0])
#define BFLOAT2(value) (reinterpret_cast<__nv_bfloat162*>(&(value))[0])
#define LDST32BITS(value) (reinterpret_cast<half2*>(&(value))[0])
#define LDST64BITS(value) (reinterpret_cast<float2*>(&(value))[0])
#define LDST128BITS(value) (reinterpret_cast<float4*>(&(value))[0])
#define CP_ASYNC_COMMIT_GROUP() asm volatile("cp.async.commit_group;\n" ::)
#define CP_ASYNC_WAIT_ALL() asm volatile("cp.async.wait_all;\n" ::)
#define CP_ASYNC_WAIT_GROUP(n) asm volatile("cp.async.wait_group %0;\n" ::"n"(n))
// ca(cache all, L1 + L2): support 4, 8, 16 bytes, cg(cache global, L2): only support 16 bytes.
#define CP_ASYNC_CA(dst, src, bytes) asm volatile("cp.async.ca.shared.global.L2::128B [%0], [%1], %2;\n" ::"r"(dst), "l"(src), "n"(bytes))
#define CP_ASYNC_CG(dst, src, bytes) asm volatile("cp.async.cg.shared.global.L2::128B [%0], [%1], %2;\n" ::"r"(dst), "l"(src), "n"(bytes))
#define LDMATRIX_X1(R, addr) asm volatile("ldmatrix.sync.aligned.x1.m8n8.shared.b16 {%0}, [%1];\n" : "=r"(R) : "r"(addr))
#define LDMATRIX_X2(R0, R1, addr) asm volatile("ldmatrix.sync.aligned.x2.m8n8.shared.b16 {%0, %1}, [%2];\n" : "=r"(R0), "=r"(R1) : "r"(addr))
#define LDMATRIX_X4(R0, R1, R2, R3, addr) asm volatile("ldmatrix.sync.aligned.x4.m8n8.shared.b16 {%0, %1, %2, %3}, [%4];\n" : "=r"(R0), "=r"(R1), "=r"(R2), "=r"(R3) : "r"(addr))
#define LDMATRIX_X1_T(R, addr) asm volatile("ldmatrix.sync.aligned.x1.trans.m8n8.shared.b16 {%0}, [%1];\n" : "=r"(R) : "r"(addr))
#define LDMATRIX_X2_T(R0, R1, addr) asm volatile("ldmatrix.sync.aligned.x2.trans.m8n8.shared.b16 {%0, %1}, [%2];\n" : "=r"(R0), "=r"(R1) : "r"(addr))
#define LDMATRIX_X4_T(R0, R1, R2, R3, addr) asm volatile("ldmatrix.sync.aligned.x4.trans.m8n8.shared.b16 {%0, %1, %2, %3}, [%4];\n" : "=r"(R0), "=r"(R1), "=r"(R2), "=r"(R3) : "r"(addr))
#define HMMA16816(RD0, RD1, RA0, RA1, RA2, RA3, RB0, RB1, RC0, RC1) asm volatile("mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3, %4, %5}, {%6, %7}, {%8, %9};\n" : "=r"(RD0), "=r"(RD1) : "r"(RA0), "r"(RA1), "r"(RA2), "r"(RA3), "r"(RB0), "r"(RB1), "r"(RC0), "r"(RC1))

HOST_DEVICE_INLINE 
int div_ceil(int a, int b) { return (a % b != 0) ? (a / b + 1) : (a / b); }

// only 1 warp per block(32 threads), m16n8k16. A, B, C: all row_major.
template<const int MMA_M=16, const int MMA_N=8, const int MMA_K=16>
__global__ void hgemm_mma_m16n8k16_naive_kernel(half* A, half* B, half* C, 
                                                int M, int N, int K) {
  const int bx = blockIdx.x;
  const int by = blockIdx.y;
  const int NUM_K_TILES = div_ceil(K, MMA_K);
  constexpr int BM = MMA_M; // 16
  constexpr int BN = MMA_N; // 8
  constexpr int BK = MMA_K; // 16

  __shared__ half s_a[MMA_M][MMA_K]; // 16x16
  __shared__ half s_b[MMA_K][MMA_N]; // 16x8
  __shared__ half s_c[MMA_M][MMA_N]; // 16x8

  const int tid = threadIdx.y * blockDim.x + threadIdx.x; // within block
  const int lane_id = tid % WARP_SIZE; // 0~31

  // s_a[16][16], 每行16，每线程load 8，需要2线程，共16行，需2x16=32线程
  const int load_smem_a_m = tid / 2; // row 0~15
  const int load_smem_a_k = (tid % 2) * 8; // col 0,8
  // s_b[16][8], 每行8，每线程load 8，需要1线程，共16行，需16线程，只需一半线程加载
  const int load_smem_b_k = tid; // row 0~31, but only use 0~15
  const int load_smem_b_n = 0; // col 0
  const int load_gmem_a_m = by * BM + load_smem_a_m; // global m
  const int load_gmem_b_n = bx * BN + load_smem_b_n; // global n
  if (load_gmem_a_m >= M && load_gmem_b_n >= N) return;

  uint32_t RC[2] = {0, 0};

  #pragma unroll
  for (int k = 0; k < NUM_K_TILES; ++k) {
    // gmem_a -> smem_a
    int load_gmem_a_k = k * BK + load_smem_a_k; // global col of a
    int load_gmem_a_addr = load_gmem_a_m * K + load_gmem_a_k;
    LDST128BITS(s_a[load_smem_a_m][load_smem_a_k]) = (
      LDST128BITS(A[load_gmem_a_addr]));

    // gmem_b -> smem_b
    if (lane_id < MMA_K) {
      int load_gmem_b_k = k * MMA_K + load_smem_b_k; // global row of b
      int load_gmem_b_addr = load_gmem_b_k * N + load_gmem_b_n; 
      LDST128BITS(s_b[load_smem_b_k][load_smem_b_n]) = (
        LDST128BITS(B[load_gmem_b_addr]));
    }
    __syncthreads(); 

    uint32_t RA[4];
    uint32_t RB[2];
    
    // ldmatrix for s_a, ldmatrix.trans for s_b.
    // s_a: (0,1)*8 -> 0,8 -> [(0~15),(0,8)]
    uint32_t load_smem_a_ptr = __cvta_generic_to_shared(
      &s_a[lane_id % 16][(lane_id / 16) * 8]); 
    LDMATRIX_X4(RA[0], RA[1], RA[2], RA[3], load_smem_a_ptr);
    uint32_t load_smem_b_ptr = __cvta_generic_to_shared(
      &s_b[lane_id % 16][0]);
    LDMATRIX_X2_T(RB[0], RB[1], load_smem_b_ptr);

    HMMA16816(RC[0], RC[1], RA[0], RA[1], RA[2], RA[3], RB[0], RB[1], RC[0], RC[1]);

    __syncthreads();
  }
  
  // s_c[16][8], https://docs.nvidia.com/cuda/parallel-thread-execution/index.html
  // #matrix-fragments-for-mma-m16n8k16-with-floating-point-type
  // [0~7][0~3 u32 -> 0~7 f16], [8~15][0~3 u32 -> 0~7 f16]
  LDST32BITS(s_c[lane_id / 4    ][(lane_id % 4) * 2]) = LDST32BITS(RC[0]); 
  LDST32BITS(s_c[lane_id / 4 + 8][(lane_id % 4) * 2]) = LDST32BITS(RC[1]);

  __syncthreads();

  // store s_c[16][8]
  if (lane_id < MMA_M) {
    // store 128 bits per memory issue.
    int store_gmem_c_m = by * BM + lane_id;
    int store_gmem_c_n = bx * BN;
    int store_gmem_c_addr = store_gmem_c_m * N + store_gmem_c_n;
    LDST128BITS(C[store_gmem_c_addr]) = (LDST128BITS(s_c[lane_id][0]));
  }
}

// 128x128, mma2x4, warp4x4(64,32,16)
template<const int MMA_M=16, 
         const int MMA_N=8, 
         const int MMA_K=16,
         const int MMA_TILE_M=2,
         const int MMA_TILE_N=4,
         const int WARP_TILE_M=4,
         const int WARP_TILE_N=4,
         const int A_PAD=0, 
         const int B_PAD=0>
__global__ void  __launch_bounds__(256) 
hgemm_mma_m16n8k16_mma2x4_warp4x4_kernel(
  half* A, half* B, half* C, int M, int N, int K) {
  const int bx = blockIdx.x;
  const int by = blockIdx.y;
  const int NUM_K_TILES = div_ceil(K, MMA_K);
  constexpr int BM = MMA_M * MMA_TILE_M * WARP_TILE_M; // 16*2*4=128
  constexpr int BN = MMA_N * MMA_TILE_N * WARP_TILE_N; // 8*4*4=128
  constexpr int BK = MMA_K; // 16

  __shared__ half s_a[BM][BK+A_PAD]; // 128*16*2=4KB
  __shared__ half s_b[BK][BN+B_PAD]; // 16*128*2=4KB, 16*(128+16)*2=4.5KB

  const int tid = threadIdx.y * blockDim.x + threadIdx.x; // within block
  const int warp_id = tid / WARP_SIZE; // 0~7 warp_id within block
  const int lane_id = tid % WARP_SIZE; // 0~31
  const int warp_m = warp_id % 2; // 0,1
  const int warp_n = warp_id / 2; // 0,1,2,3

  // 先计算shared memory中的索引
  // tid和需要加载的smem s_a[BM][BK] 之间的索引关系 BM=128 BK=16 按行读取 A行主序
  // 对于s_a每行16个数据，每个线程读取8个，需要2个线程；总共128行，需要128x2刚好256线程
  int load_smem_a_m = tid / 2; // row 0~127
  int load_smem_a_k = (tid % 2 == 0) ? 0 : 8; // col 0,8
  // tid和需要加载的smem s_b[BK][BN] 之间的索引关系 BK=16 BN=128 按行读取 B行主序
  // 对于s_b每行128个数据，每个线程读8个数据，需要16个线程；总共16行，需要16x16=256个线程
  int load_smem_b_k = tid / 16; // row 0~15
  int load_smem_b_n = (tid % 16) * 8; // col 0,8,...,120
  // 再计算全局内存中的索引
  // 要加载到s_a中的元素对应到A全局内存中的行数 每个block负责出C中大小为BM*BN的块
  int load_gmem_a_m = by * BM + load_smem_a_m; // global row of a and c
  int load_gmem_b_n = bx * BN + load_smem_b_n; // global col of b and c

  uint32_t RC[WARP_TILE_M][WARP_TILE_N][2];
  #pragma unroll
  for (int i = 0; i < WARP_TILE_M; ++i) {
    #pragma unroll
    for (int j = 0; j < WARP_TILE_N; ++j) {
      RC[i][j][0] = 0;
      RC[i][j][1] = 0;
    }
  }
  
  #pragma unroll
  for (int k = 0; k < NUM_K_TILES; ++k) {
    // gmem -> smem
    int load_gmem_a_k = k * BK + load_smem_a_k; // global col of a
    int load_gmem_a_addr = load_gmem_a_m * K + load_gmem_a_k;
    int load_gmem_b_k = k * BK + load_smem_b_k; // global row of b
    int load_gmem_b_addr = load_gmem_b_k * N + load_gmem_b_n; 
    LDST128BITS(s_b[load_smem_b_k][load_smem_b_n]) = (
      LDST128BITS(B[load_gmem_b_addr]));
    LDST128BITS(s_a[load_smem_a_m][load_smem_a_k]) = (
      LDST128BITS(A[load_gmem_a_addr]));
    __syncthreads(); 

    // ldmatrix for s_a, ldmatrix.trans for s_b.
    uint32_t RA[WARP_TILE_M][4];
    uint32_t RB[WARP_TILE_N][2];

    // smem -> reg
    #pragma unroll
    for (int i = 0; i < WARP_TILE_M; ++i) {
      int warp_smem_a_m = warp_m * (MMA_M * WARP_TILE_M) + i * MMA_M;
      int lane_smem_a_m = warp_smem_a_m + lane_id % 16; // 0~15
      int lane_smem_a_k = (lane_id / 16) * 8; // 0,8
      uint32_t lane_smem_a_ptr = __cvta_generic_to_shared(
        &s_a[lane_smem_a_m][lane_smem_a_k]);
      LDMATRIX_X4(RA[i][0], RA[i][1], RA[i][2], RA[i][3], lane_smem_a_ptr);
    }

    #pragma unroll
    for (int j = 0; j < WARP_TILE_N; ++j) {
      int warp_smem_b_n = warp_n * (MMA_N * WARP_TILE_N) + j * MMA_N;
      int lane_smem_b_k = lane_id % 16;  // 0~15
      int lane_smem_b_n = warp_smem_b_n; // 0, MMA_N=8
      uint32_t lane_smem_b_ptr = __cvta_generic_to_shared(
        &s_b[lane_smem_b_k][lane_smem_b_n]);
      LDMATRIX_X2_T(RB[j][0], RB[j][1], lane_smem_b_ptr);
    }
    
    // MMA compute
    #pragma unroll
    for (int i = 0; i < WARP_TILE_M; ++i) {
      #pragma unroll
      for (int j = 0; j < WARP_TILE_N; ++j) {
        HMMA16816(RC[i][j][0], RC[i][j][1], 
                  RA[i][0], RA[i][1], RA[i][2], RA[i][3], 
                  RB[j][0], RB[j][1], 
                  RC[i][j][0], RC[i][j][1]);
      }
    }
    __syncthreads(); 
  }

  // reg -> gmem, MMA_MxMMA_N=16x8
  #pragma unroll
  for (int i = 0; i < WARP_TILE_M; ++i) {
    #pragma unroll
    for (int j = 0; j < WARP_TILE_N; ++j) {
      int store_warp_smem_c_m = warp_m * (MMA_M * WARP_TILE_M) + i * MMA_M;
      int store_warp_smem_c_n = warp_n * (MMA_N * WARP_TILE_N) + j * MMA_N;
      // mapping lane smem index -> global index.
      // [16][8], https://docs.nvidia.com/cuda/parallel-thread-execution/index.html
      // #matrix-fragments-for-mma-m16n8k16-with-floating-point-type
      // [0~7][0~3 u32 -> 0~7 f16], [8~15][0~3 u32 -> 0~7 f16]
      int store_lane_gmem_c_m = by * BM + store_warp_smem_c_m + lane_id / 4;
      int store_lane_gmem_c_n = bx * BN + store_warp_smem_c_n + (lane_id % 4) * 2;
      int store_gmem_c_addr_0 = store_lane_gmem_c_m * N + store_lane_gmem_c_n;
      int store_gmem_c_addr_1 = (store_lane_gmem_c_m + 8) * N + store_lane_gmem_c_n;
      // TODO: how to use LDST128BITS here ? reverse the loop order ?
      LDST32BITS(C[store_gmem_c_addr_0]) = LDST32BITS(RC[i][j][0]); 
      LDST32BITS(C[store_gmem_c_addr_1]) = LDST32BITS(RC[i][j][1]); 
    }
  }
}


// --------------------- PyTorch bindings for custom kernel -----------------------
#define STRINGFY(str) #str
#define TORCH_BINDING_COMMON_EXTENSION(func)   \
  m.def(STRINGFY(func), &func, STRINGFY(func));

#define CHECK_TORCH_TENSOR_DTYPE(T, th_type)                 \
if(((T).options().dtype() != (th_type))) {                   \
  std::cout << "Tensor Info:" << (T).options() << std::endl; \
  throw std::runtime_error("values must be "#th_type);       \
}

#define CHECK_TORCH_TENSOR_SHAPE(T, S0, S1)           \
if (((T).size(0) != (S0)) || ((T).size(1) != (S1))) { \
  throw std::runtime_error("Tensor size mismatch!");  \
}

// only 1 warp per block(32 threads), m16n8k16. A, B, C: all row_major.
void hgemm_mma_m16n8k16_naive(
  torch::Tensor a, torch::Tensor b, torch::Tensor c) {
  CHECK_TORCH_TENSOR_DTYPE(a, torch::kHalf)
  CHECK_TORCH_TENSOR_DTYPE(b, torch::kHalf)
  CHECK_TORCH_TENSOR_DTYPE(c, torch::kHalf)
  const int M = a.size(0);
  const int K = a.size(1);
  const int N = b.size(1); 
  CHECK_TORCH_TENSOR_SHAPE(a, M, K)
  CHECK_TORCH_TENSOR_SHAPE(b, K, N)
  CHECK_TORCH_TENSOR_SHAPE(c, M, N)
  constexpr int MMA_M = 16;
  constexpr int MMA_N = 8;
  constexpr int MMA_K = 16; 

  dim3 block(WARP_SIZE);
  dim3 grid(div_ceil(N, MMA_N), div_ceil(M, MMA_M));
 
  hgemm_mma_m16n8k16_naive_kernel<
    MMA_M, MMA_N, MMA_K><<<grid, block>>>(
    reinterpret_cast<half*>(a.data_ptr()),
    reinterpret_cast<half*>(b.data_ptr()),
    reinterpret_cast<half*>(c.data_ptr()),
    M, N, K
  );
}

// 128x128, mma2x4, warp4x4(64,32,16)
void hgemm_mma_m16n8k16_mma2x4_warp4x4(
  torch::Tensor a, torch::Tensor b, torch::Tensor c) {
  CHECK_TORCH_TENSOR_DTYPE(a, torch::kHalf)
  CHECK_TORCH_TENSOR_DTYPE(b, torch::kHalf)
  CHECK_TORCH_TENSOR_DTYPE(c, torch::kHalf)
  const int M = a.size(0);
  const int K = a.size(1);
  const int N = b.size(1); 
  CHECK_TORCH_TENSOR_SHAPE(a, M, K)
  CHECK_TORCH_TENSOR_SHAPE(b, K, N)
  CHECK_TORCH_TENSOR_SHAPE(c, M, N)
  constexpr int MMA_M = 16;
  constexpr int MMA_N = 8;
  constexpr int MMA_K = 16; 
  constexpr int MMA_TILE_M = 2;
  constexpr int MMA_TILE_N = 4; 
  constexpr int WARP_TILE_M = 4;
  constexpr int WARP_TILE_N = 4;
  constexpr int A_PAD = 0;
  constexpr int B_PAD = 16;
  constexpr int NUM_THREADS= (
    MMA_TILE_M * MMA_TILE_N * WARP_SIZE); // 2 * 4 * 32 = 256

  dim3 block(NUM_THREADS);
  dim3 grid(div_ceil(N, MMA_N * MMA_TILE_N * WARP_TILE_N), 
            div_ceil(M, MMA_M * MMA_TILE_M * WARP_TILE_M));

  hgemm_mma_m16n8k16_mma2x4_warp4x4_kernel<
    MMA_M, MMA_N, MMA_K, MMA_TILE_M, MMA_TILE_N, 
    WARP_TILE_M, WARP_TILE_N, A_PAD, B_PAD><<<grid, block>>>(
    reinterpret_cast<half*>(a.data_ptr()),
    reinterpret_cast<half*>(b.data_ptr()),
    reinterpret_cast<half*>(c.data_ptr()),
    M, N, K
  );
}