quant_cuda_kernel.cu

#include <torch/all.h>
#include <torch/python.h>
#include <cuda.h>
#include <cuda_runtime.h>

template <typename scalar_t>
__global__ void VecQuant2MatMulKernel(
    const  scalar_t* __restrict__ vec,
    const       int* __restrict__ mat,
           scalar_t* __restrict__ mul,
    const  scalar_t* __restrict__ scales,
    const  scalar_t* __restrict__ zeros,
    int batch,
    int vec_height, 	
    int height,
    int width
);

template <typename scalar_t>
__global__ void VecQuant3MatMulKernel(
    const  scalar_t* __restrict__ vec,
    const       int* __restrict__ mat,
           scalar_t* __restrict__ mul,
    const  scalar_t* __restrict__ scales,
    const  scalar_t* __restrict__ zeros,
    int batch,
    int vec_height, 	
    int height,
    int width
);

template <typename scalar_t>
__global__ void VecQuant4MatMulKernel(
    const  scalar_t* __restrict__ vec,
    const       int* __restrict__ mat,
           scalar_t* __restrict__ mul,
    const  scalar_t* __restrict__ scales,
    const  scalar_t* __restrict__ zeros,
    int batch,
    int vec_height, 	
    int height,
    int width
);

template <typename scalar_t>
__global__ void VecQuant8MatMulKernel(
    const  scalar_t* __restrict__ vec,
    const       int* __restrict__ mat,
           scalar_t* __restrict__ mul,
    const  scalar_t* __restrict__ scales,
    const  scalar_t* __restrict__ zeros,
    int batch,
    int vec_height, 	
    int height,
    int width
);

const int BLOCKWIDTH  = 256;
const int BLOCKHEIGHT2 =  16;
const int BLOCKHEIGHT3 =  24;
const int BLOCKHEIGHT4 =  32; 
const int BLOCKHEIGHT8 =  64;

__device__ inline unsigned int as_unsigned(int i) {
  return *reinterpret_cast<unsigned int*>(&i);
}

void vecquant2matmul_cuda(
  torch::Tensor vec,
  torch::Tensor mat,
  torch::Tensor mul,
  torch::Tensor scales,
  torch::Tensor zeros
) {
  int batch = vec.size(0);
  int vec_height = vec.size(1);
  int height = mat.size(0);
  int width = mat.size(1);

  dim3 blocks(
    (height + BLOCKHEIGHT2 - 1) / BLOCKHEIGHT2,
    (width + BLOCKWIDTH - 1) / BLOCKWIDTH,
    batch
  );
  dim3 threads(BLOCKWIDTH);

  AT_DISPATCH_FLOATING_TYPES(
    vec.type(), "vecquant2matmul_cuda", ([&] {
      VecQuant2MatMulKernel<<<blocks, threads>>>(
        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
        scales.data<scalar_t>(), zeros.data<scalar_t>(),
        batch, vec_height, height, width
      );
    })
  );
}

template <typename scalar_t>
__global__ void VecQuant2MatMulKernel(
    const  scalar_t* __restrict__ vec,
    const       int* __restrict__ mat,
           scalar_t* __restrict__ mul,
    const  scalar_t* __restrict__ scales,
    const  scalar_t* __restrict__ zeros,
    int batch,
    int vec_height,
    int height,
    int width
) {
  int b = blockIdx.z;
  int h = BLOCKHEIGHT2 * blockIdx.x;
  int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;

  __shared__ scalar_t blockvec[BLOCKWIDTH];
  blockvec[threadIdx.x] = vec[b * vec_height + (h / BLOCKHEIGHT2) * BLOCKWIDTH + threadIdx.x];
  __syncthreads();

  scalar_t scale = scales[w];
  scalar_t zero = zeros[w];

  scalar_t res = 0;
  int i = width * h + w;
  int k = 0;

  unsigned int tmp;

  while (k < BLOCKWIDTH) {
    tmp = as_unsigned(mat[i]);
    res += (scale * scalar_t((tmp >> 0) & 0x3) - zero) * blockvec[k + 0];
    res += (scale * scalar_t((tmp >> 2) & 0x3) - zero) * blockvec[k + 1];
    res += (scale * scalar_t((tmp >> 4) & 0x3) - zero) * blockvec[k + 2];
    res += (scale * scalar_t((tmp >> 6) & 0x3) - zero) * blockvec[k + 3];
    res += (scale * scalar_t((tmp >> 8) & 0x3) - zero) * blockvec[k + 4];
    res += (scale * scalar_t((tmp >> 10) & 0x3) - zero) * blockvec[k + 5];
    res += (scale * scalar_t((tmp >> 12) & 0x3) - zero) * blockvec[k + 6];
    res += (scale * scalar_t((tmp >> 14) & 0x3) - zero) * blockvec[k + 7];
    res += (scale * scalar_t((tmp >> 16) & 0x3) - zero) * blockvec[k + 8];
    res += (scale * scalar_t((tmp >> 18) & 0x3) - zero) * blockvec[k + 9];
    res += (scale * scalar_t((tmp >> 20) & 0x3) - zero) * blockvec[k + 10];
    res += (scale * scalar_t((tmp >> 22) & 0x3) - zero) * blockvec[k + 11];
    res += (scale * scalar_t((tmp >> 24) & 0x3) - zero) * blockvec[k + 12];
    res += (scale * scalar_t((tmp >> 26) & 0x3) - zero) * blockvec[k + 13];
    res += (scale * scalar_t((tmp >> 28) & 0x3) - zero) * blockvec[k + 14];
    res += (scale * scalar_t((tmp >> 30) & 0x3) - zero) * blockvec[k + 15];
    i += width;
    k += 16;
  }

  atomicAdd(&mul[b * width + w], res);
}

void vecquant3matmul_cuda(
  torch::Tensor vec,
  torch::Tensor mat,
  torch::Tensor mul,
  torch::Tensor scales,
  torch::Tensor zeros
) {
  int batch = vec.size(0);
  int vec_height = vec.size(1);
  int height = mat.size(0);
  int width = mat.size(1);

  dim3 blocks(
    (height + BLOCKHEIGHT3 - 1) / BLOCKHEIGHT3,
    (width + BLOCKWIDTH - 1) / BLOCKWIDTH,
    batch
  );
  dim3 threads(BLOCKWIDTH);

  AT_DISPATCH_FLOATING_TYPES(
    vec.type(), "vecquant3matmul_cuda", ([&] {
      VecQuant3MatMulKernel<<<blocks, threads>>>(
        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
        scales.data<scalar_t>(), zeros.data<scalar_t>(),
        batch, vec_height, height, width
      );
    })
  );
}

template <typename scalar_t>
__global__ void VecQuant3MatMulKernel(
    const  scalar_t* __restrict__ vec,
    const       int* __restrict__ mat,
           scalar_t* __restrict__ mul,
    const  scalar_t* __restrict__ scales,
    const  scalar_t* __restrict__ zeros,
    int batch,
    int vec_height,
    int height,
    int width
) {
  int b = blockIdx.z;
  int h = BLOCKHEIGHT3 * blockIdx.x;
  int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;

  __shared__ scalar_t blockvec[BLOCKWIDTH];
  blockvec[threadIdx.x] = vec[b * vec_height + (h / BLOCKHEIGHT3) * BLOCKWIDTH + threadIdx.x];
  __syncthreads();

  scalar_t scale = scales[w];
  scalar_t zero = zeros[w];

  scalar_t res = 0;
  int i = width * h + w;
  int k = 0;

  unsigned int tmp1;
  unsigned int tmp2;
  unsigned int tmp;

  while (k < BLOCKWIDTH) {
    tmp1 = as_unsigned(mat[i]);
    res += (scale * scalar_t((tmp1 >>  0) & 0x7) - zero) * blockvec[k + 0];
    res += (scale * scalar_t((tmp1 >>  3) & 0x7) - zero) * blockvec[k + 1];
    res += (scale * scalar_t((tmp1 >>  6) & 0x7) - zero) * blockvec[k + 2];
    res += (scale * scalar_t((tmp1 >>  9) & 0x7) - zero) * blockvec[k + 3];
    res += (scale * scalar_t((tmp1 >> 12) & 0x7) - zero) * blockvec[k + 4];
    res += (scale * scalar_t((tmp1 >> 15) & 0x7) - zero) * blockvec[k + 5];
    res += (scale * scalar_t((tmp1 >> 18) & 0x7) - zero) * blockvec[k + 6];
    res += (scale * scalar_t((tmp1 >> 21) & 0x7) - zero) * blockvec[k + 7];
    res += (scale * scalar_t((tmp1 >> 24) & 0x7) - zero) * blockvec[k + 8];
    res += (scale * scalar_t((tmp1 >> 27) & 0x7) - zero) * blockvec[k + 9];
    i += width;
    tmp2 = as_unsigned(mat[i]);
    tmp = (tmp1 >> 30) | ((tmp2 << 2) & 0x4);
    tmp2 >>= 1;
    res += (scale * scalar_t(tmp) - zero) * blockvec[k + 10];
    k += 11;
    res += (scale * scalar_t((tmp2 >>  0) & 0x7) - zero) * blockvec[k + 0];
    res += (scale * scalar_t((tmp2 >>  3) & 0x7) - zero) * blockvec[k + 1];
    res += (scale * scalar_t((tmp2 >>  6) & 0x7) - zero) * blockvec[k + 2];
    res += (scale * scalar_t((tmp2 >>  9) & 0x7) - zero) * blockvec[k + 3];
    res += (scale * scalar_t((tmp2 >> 12) & 0x7) - zero) * blockvec[k + 4];
    res += (scale * scalar_t((tmp2 >> 15) & 0x7) - zero) * blockvec[k + 5];
    res += (scale * scalar_t((tmp2 >> 18) & 0x7) - zero) * blockvec[k + 6];
    res += (scale * scalar_t((tmp2 >> 21) & 0x7) - zero) * blockvec[k + 7];
    res += (scale * scalar_t((tmp2 >> 24) & 0x7) - zero) * blockvec[k + 8];
    res += (scale * scalar_t((tmp2 >> 27) & 0x7) - zero) * blockvec[k + 9];
    i += width;
    tmp1 = as_unsigned(mat[i]);
    tmp = (tmp2 >> 30) | ((tmp1 << 1) & 0x6);
    tmp1 >>= 2;
    res += (scale * scalar_t(tmp) - zero) * blockvec[k + 10];
    k += 11;
    res += (scale * scalar_t((tmp1 >>  0) & 0x7) - zero) * blockvec[k + 0];
    res += (scale * scalar_t((tmp1 >>  3) & 0x7) - zero) * blockvec[k + 1];
    res += (scale * scalar_t((tmp1 >>  6) & 0x7) - zero) * blockvec[k + 2];
    res += (scale * scalar_t((tmp1 >>  9) & 0x7) - zero) * blockvec[k + 3];
    res += (scale * scalar_t((tmp1 >> 12) & 0x7) - zero) * blockvec[k + 4];
    res += (scale * scalar_t((tmp1 >> 15) & 0x7) - zero) * blockvec[k + 5];
    res += (scale * scalar_t((tmp1 >> 18) & 0x7) - zero) * blockvec[k + 6];
    res += (scale * scalar_t((tmp1 >> 21) & 0x7) - zero) * blockvec[k + 7];
    res += (scale * scalar_t((tmp1 >> 24) & 0x7) - zero) * blockvec[k + 8];
    res += (scale * scalar_t((tmp1 >> 27) & 0x7) - zero) * blockvec[k + 9];
    i += width;
    k += 10;
  }

  atomicAdd(&mul[b * width + w], res);
}

void vecquant4matmul_cuda(
  torch::Tensor vec,
  torch::Tensor mat,
  torch::Tensor mul,
  torch::Tensor scales,
  torch::Tensor zeros
) {
  int batch = vec.size(0);
  int vec_height = vec.size(1);
  int height = mat.size(0);
  int width = mat.size(1);

  dim3 blocks(
    (height + BLOCKHEIGHT4 - 1) / BLOCKHEIGHT4,
    (width + BLOCKWIDTH - 1) / BLOCKWIDTH,
    batch
  );
  dim3 threads(BLOCKWIDTH);

  AT_DISPATCH_FLOATING_TYPES(
    vec.type(), "vecquant4matmul_cuda", ([&] {
      VecQuant4MatMulKernel<<<blocks, threads>>>(
        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
        scales.data<scalar_t>(), zeros.data<scalar_t>(),
        batch, vec_height, height, width
      );
    })
  );
}

template <typename scalar_t>
__global__ void VecQuant4MatMulKernel(
    const  scalar_t* __restrict__ vec,
    const       int* __restrict__ mat,
           scalar_t* __restrict__ mul,
    const  scalar_t* __restrict__ scales,
    const  scalar_t* __restrict__ zeros,
    int batch,
    int vec_height,
    int height,
    int width
) {
  int b = blockIdx.z;
  int h = BLOCKHEIGHT4 * blockIdx.x;
  int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;

  __shared__ scalar_t blockvec[BLOCKWIDTH];
  blockvec[threadIdx.x] = vec[b * vec_height + (h / BLOCKHEIGHT4) * BLOCKWIDTH + threadIdx.x];
  __syncthreads();

  scalar_t scale = scales[w];
  scalar_t zero = zeros[w];

  scalar_t res = 0;
  int i = width * h + w;
  int k = 0;

  unsigned int tmp;

  while (k < BLOCKWIDTH) {
    tmp = as_unsigned(mat[i]);
    res += (scale * scalar_t((tmp >> 0) & 0xF) - zero) * blockvec[k + 0];
    res += (scale * scalar_t((tmp >> 4) & 0xF) - zero) * blockvec[k + 1];
    res += (scale * scalar_t((tmp >> 8) & 0xF) - zero) * blockvec[k + 2];
    res += (scale * scalar_t((tmp >> 12) & 0xF) - zero) * blockvec[k + 3];
    res += (scale * scalar_t((tmp >> 16) & 0xF) - zero) * blockvec[k + 4];
    res += (scale * scalar_t((tmp >> 20) & 0xF) - zero) * blockvec[k + 5];
    res += (scale * scalar_t((tmp >> 24) & 0xF) - zero) * blockvec[k + 6];
    res += (scale * scalar_t((tmp >> 28) & 0xF) - zero) * blockvec[k + 7];
    i += width;
    k += 8;
  }

  atomicAdd(&mul[b * width + w], res);
}

void vecquant8matmul_cuda(
  torch::Tensor vec,
  torch::Tensor mat,
  torch::Tensor mul,
  torch::Tensor scales,
  torch::Tensor zeros
) {
  int batch = vec.size(0);
  int vec_height = vec.size(1);
  int height = mat.size(0);
  int width = mat.size(1);

  dim3 blocks(
    (height + BLOCKHEIGHT8 - 1) / BLOCKHEIGHT8,
    (width + BLOCKWIDTH - 1) / BLOCKWIDTH,
    batch
  );
  dim3 threads(BLOCKWIDTH);

  AT_DISPATCH_FLOATING_TYPES(
    vec.type(), "vecquant8matmul_cuda", ([&] {
      VecQuant8MatMulKernel<<<blocks, threads>>>(
        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
        scales.data<scalar_t>(), zeros.data<scalar_t>(),
        batch, vec_height, height, width
      );
    })
  );
}

template <typename scalar_t>
__global__ void VecQuant8MatMulKernel(
    const  scalar_t* __restrict__ vec,
    const       int* __restrict__ mat,
           scalar_t* __restrict__ mul,
    const  scalar_t* __restrict__ scales,
    const  scalar_t* __restrict__ zeros,
    int batch,
    int vec_height,
    int height,
    int width
) {
  int b = blockIdx.z;
  int h = BLOCKHEIGHT8 * blockIdx.x;
  int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;

  __shared__ scalar_t blockvec[BLOCKWIDTH];
  blockvec[threadIdx.x] = vec[b * vec_height + (h / BLOCKHEIGHT8) * BLOCKWIDTH + threadIdx.x];
  __syncthreads();

  scalar_t scale = scales[w];
  scalar_t zero = zeros[w];

  scalar_t res = 0;
  int i = width * h + w;
  int k = 0;

  unsigned int tmp;

  while (k < BLOCKWIDTH) {
    tmp = as_unsigned(mat[i]);
    res += (scale * scalar_t((tmp >> 0) & 0xFF) - zero) * blockvec[k + 0];
    res += (scale * scalar_t((tmp >> 8) & 0xFF) - zero) * blockvec[k + 1];
    res += (scale * scalar_t((tmp >> 16) & 0xFF) - zero) * blockvec[k + 2];
    res += (scale * scalar_t((tmp >> 24) & 0xFF) - zero) * blockvec[k + 3];
    i += width;
    k += 4;
  }

  atomicAdd(&mul[b * width + w], res);
}