models/stylegan2/op_ori/upfirdn2d_kernel.cu

// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
//
// This work is made available under the Nvidia Source Code License-NC.
// To view a copy of this license, visit
// https://nvlabs.github.io/stylegan2/license.html

#include <torch/types.h>

#include <ATen/ATen.h>
#include <ATen/AccumulateType.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>

#include <cuda.h>
#include <cuda_runtime.h>


static __host__ __device__ __forceinline__ int floor_div(int a, int b) {
    int c = a / b;

    if (c * b > a) {
        c--;
    }

    return c;
}


struct UpFirDn2DKernelParams {
    int up_x;
    int up_y;
    int down_x;
    int down_y;
    int pad_x0;
    int pad_x1;
    int pad_y0;
    int pad_y1;

    int major_dim;
    int in_h;
    int in_w;
    int minor_dim;
    int kernel_h;
    int kernel_w;
    int out_h;
    int out_w;
    int loop_major;
    int loop_x;
};


template <typename scalar_t, int up_x, int up_y, int down_x, int down_y, int kernel_h, int kernel_w, int tile_out_h, int tile_out_w>
__global__ void upfirdn2d_kernel(scalar_t* out, const scalar_t* input, const scalar_t* kernel, const UpFirDn2DKernelParams p) {
    const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1;
    const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1;

    __shared__ volatile float sk[kernel_h][kernel_w];
    __shared__ volatile float sx[tile_in_h][tile_in_w];

    int minor_idx = blockIdx.x;
    int tile_out_y = minor_idx / p.minor_dim;
    minor_idx -= tile_out_y * p.minor_dim;
    tile_out_y *= tile_out_h;
    int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w;
    int major_idx_base = blockIdx.z * p.loop_major;

    if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | major_idx_base >= p.major_dim) {
        return;
    }

    for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; tap_idx += blockDim.x) {
        int ky = tap_idx / kernel_w;
        int kx = tap_idx - ky * kernel_w;
        scalar_t v = 0.0;

        if (kx < p.kernel_w & ky < p.kernel_h) {
            v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)];
        }

        sk[ky][kx] = v;
    }

    for (int loop_major = 0, major_idx = major_idx_base; loop_major < p.loop_major & major_idx < p.major_dim; loop_major++, major_idx++) {
        for (int loop_x = 0, tile_out_x = tile_out_x_base; loop_x < p.loop_x & tile_out_x < p.out_w; loop_x++, tile_out_x += tile_out_w) {
            int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0;
            int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0;
            int tile_in_x = floor_div(tile_mid_x, up_x);
            int tile_in_y = floor_div(tile_mid_y, up_y);

            __syncthreads();

            for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; in_idx += blockDim.x) {
                int rel_in_y = in_idx / tile_in_w;
                int rel_in_x = in_idx - rel_in_y * tile_in_w;
                int in_x = rel_in_x + tile_in_x;
                int in_y = rel_in_y + tile_in_y;

                scalar_t v = 0.0;

                if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) {
                    v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + minor_idx];
                }

                sx[rel_in_y][rel_in_x] = v;
            }

            __syncthreads();
            for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; out_idx += blockDim.x) {
                int rel_out_y = out_idx / tile_out_w;
                int rel_out_x = out_idx - rel_out_y * tile_out_w;
                int out_x = rel_out_x + tile_out_x;
                int out_y = rel_out_y + tile_out_y;

                int mid_x = tile_mid_x + rel_out_x * down_x;
                int mid_y = tile_mid_y + rel_out_y * down_y;
                int in_x = floor_div(mid_x, up_x);
                int in_y = floor_div(mid_y, up_y);
                int rel_in_x = in_x - tile_in_x;
                int rel_in_y = in_y - tile_in_y;
                int kernel_x = (in_x + 1) * up_x - mid_x - 1;
                int kernel_y = (in_y + 1) * up_y - mid_y - 1;

                scalar_t v = 0.0;

                #pragma unroll
                for (int y = 0; y < kernel_h / up_y; y++)
                    #pragma unroll
                    for (int x = 0; x < kernel_w / up_x; x++)
                        v += sx[rel_in_y + y][rel_in_x + x] * sk[kernel_y + y * up_y][kernel_x + x * up_x];

                if (out_x < p.out_w & out_y < p.out_h) {
                    out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + minor_idx] = v;
                }
            }
        }
    }
}


torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
    int up_x, int up_y, int down_x, int down_y,
    int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
    int curDevice = -1;
    cudaGetDevice(&curDevice);
    cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);

    UpFirDn2DKernelParams p;

    auto x = input.contiguous();
    auto k = kernel.contiguous();

    p.major_dim = x.size(0);
    p.in_h = x.size(1);
    p.in_w = x.size(2);
    p.minor_dim = x.size(3);
    p.kernel_h = k.size(0);
    p.kernel_w = k.size(1);
    p.up_x = up_x;
    p.up_y = up_y;
    p.down_x = down_x;
    p.down_y = down_y;
    p.pad_x0 = pad_x0;
    p.pad_x1 = pad_x1;
    p.pad_y0 = pad_y0;
    p.pad_y1 = pad_y1;

    p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / p.down_y;
    p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / p.down_x;

    auto out = at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options());

    int mode = -1;

    int tile_out_h;
    int tile_out_w;

    if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) {
        mode = 1;
        tile_out_h = 16;
        tile_out_w = 64;
    }

    if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 3 && p.kernel_w <= 3) {
        mode = 2;
        tile_out_h = 16;
        tile_out_w = 64;
    }

    if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) {
        mode = 3;
        tile_out_h = 16;
        tile_out_w = 64;
    }

    if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 2 && p.kernel_w <= 2) {
        mode = 4;
        tile_out_h = 16;
        tile_out_w = 64;
    }

    if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 4 && p.kernel_w <= 4) {
        mode = 5;
        tile_out_h = 8;
        tile_out_w = 32;
    }

    if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 2 && p.kernel_w <= 2) {
        mode = 6;
        tile_out_h = 8;
        tile_out_w = 32;
    }

    dim3 block_size;
    dim3 grid_size;

    if (tile_out_h > 0 && tile_out_w) {
        p.loop_major = (p.major_dim - 1) / 16384 + 1;
        p.loop_x = 1;
        block_size = dim3(32 * 8, 1, 1);
        grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim,
                         (p.out_w - 1) / (p.loop_x * tile_out_w) + 1,
                         (p.major_dim - 1) / p.loop_major + 1);
    }

    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] {
        switch (mode) {
        case 1:
            upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 4, 4, 16, 64><<<grid_size, block_size, 0, stream>>>(
                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
            );

            break;

        case 2:
            upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 3, 3, 16, 64><<<grid_size, block_size, 0, stream>>>(
                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
            );

            break;

        case 3:
            upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 4, 4, 16, 64><<<grid_size, block_size, 0, stream>>>(
                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
            );

            break;

        case 4:
            upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 2, 2, 16, 64><<<grid_size, block_size, 0, stream>>>(
                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
            );

            break;

        case 5:
            upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32><<<grid_size, block_size, 0, stream>>>(
                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
            );

            break;

        case 6:
            upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32><<<grid_size, block_size, 0, stream>>>(
                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
            );

            break;
        }
    });

    return out;
}