llama-cpp-python/vendor/llama.cpp/ggml-cuda/pool2d.cu

#include "pool2d.cuh"

template <typename Ti, typename To>
static  __global__ void pool2d_nchw_kernel(
        const int ih, const int iw, const int oh, const int ow,
        const int kh, const int kw, const int sh, const int sw,
        const int ph, const int pw, const int parallel_elements,
        const Ti* src, To* dst, const enum ggml_op_pool op) {
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    if (idx >= parallel_elements) {
        return;
    }

    const int I_HW = ih * iw;
    const int O_HW = oh * ow;
    const int nc = idx / O_HW;
    const int cur_oh = idx % O_HW / ow;
    const int cur_ow = idx % O_HW % ow;
    const Ti* i_ptr = src + nc * I_HW;
    To* o_ptr = dst + nc * O_HW;
    const int start_h = cur_oh * sh - ph;
    const int bh = max(0, start_h);
    const int eh = min(ih, start_h + kh);
    const int start_w = cur_ow * sw - pw;
    const int bw = max(0, start_w);
    const int ew = min(iw, start_w + kw);
    const To scale = 1. / (kh * kw);
    To res = 0;

    switch (op) {
        case GGML_OP_POOL_AVG: res = 0; break;
        case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
        default: assert(false);
    }

    for (int i = bh; i < eh; i += 1) {
        for (int j = bw; j < ew; j += 1) {
#if __CUDA_ARCH__ >= 350
            Ti cur = __ldg(i_ptr + i * iw + j);
#else
            Ti cur = i_ptr[i * iw + j];
#endif
            switch (op) {
                case GGML_OP_POOL_AVG: res += cur * scale; break;
                case GGML_OP_POOL_MAX: res = max(res, (To)cur); break;
                default: assert(false);
            }
        }
    }
    o_ptr[cur_oh * ow + cur_ow] = res;
}

static void pool2d_nchw_kernel_f32_f32_cuda(
        const int ih, const int iw, const int oh, const int ow,
        const int kh, const int kw, const int sh, const int sw,
        const int ph, const int pw, const int parallel_elements,
        const float * src, float * dst, const enum ggml_op_pool op,
        cudaStream_t stream) {

    const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE;
    dim3 block_nums(num_blocks);
    pool2d_nchw_kernel<<<block_nums, CUDA_POOL2D_BLOCK_SIZE, 0, stream>>>(ih, iw, oh, ow, kh, kw, sh, sw, ph, pw, parallel_elements, src, dst, op);
}

void ggml_cuda_op_pool2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const ggml_tensor * src0 = dst->src[0];
    const float * src0_d = (const float *)src0->data;
    float * dst_d = (float *)dst->data;
    cudaStream_t stream = ctx.stream();

    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);

    const int32_t * opts = (const int32_t *)dst->op_params;
    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
    const int k0 = opts[1];
    const int k1 = opts[2];
    const int s0 = opts[3];
    const int s1 = opts[4];
    const int p0 = opts[5];
    const int p1 = opts[6];

    const int64_t IH = src0->ne[1];
    const int64_t IW = src0->ne[0];

    const int64_t N = dst->ne[3];
    const int64_t OC = dst->ne[2];
    const int64_t OH = dst->ne[1];
    const int64_t OW = dst->ne[0];

    const int parallel_elements = N * OC * OH * OW;

    pool2d_nchw_kernel_f32_f32_cuda(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_d, dst_d, op, stream);
}