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// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d_kernel.cu |
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// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. |
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// |
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// This work is made available under the Nvidia Source Code License-NC. |
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// To view a copy of this license, visit |
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// https://nvlabs.github.io/stylegan2/license.html |
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#include <torch/types.h> |
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#include <ATen/ATen.h> |
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#include <ATen/AccumulateType.h> |
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#include <ATen/cuda/CUDAApplyUtils.cuh> |
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#include <ATen/cuda/CUDAContext.h> |
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#include <cuda.h> |
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#include <cuda_runtime.h> |
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static __host__ __device__ __forceinline__ int floor_div(int a, int b) { |
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int c = a / b; |
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if (c * b > a) { |
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c |
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} |
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return c; |
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} |
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struct UpFirDn2DKernelParams { |
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int up_x; |
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int up_y; |
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int down_x; |
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int down_y; |
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int pad_x0; |
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int pad_x1; |
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int pad_y0; |
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int pad_y1; |
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int major_dim; |
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int in_h; |
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int in_w; |
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int minor_dim; |
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int kernel_h; |
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int kernel_w; |
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int out_h; |
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int out_w; |
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int loop_major; |
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int loop_x; |
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}; |
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template <typename scalar_t> |
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__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input, |
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const scalar_t *kernel, |
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const UpFirDn2DKernelParams p) { |
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int minor_idx = blockIdx.x * blockDim.x + threadIdx.x; |
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int out_y = minor_idx / p.minor_dim; |
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minor_idx -= out_y * p.minor_dim; |
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int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y; |
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int major_idx_base = blockIdx.z * p.loop_major; |
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if (out_x_base >= p.out_w || out_y >= p.out_h || |
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major_idx_base >= p.major_dim) { |
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return; |
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} |
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int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0; |
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int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h); |
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int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y; |
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int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y; |
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for (int loop_major = 0, major_idx = major_idx_base; |
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loop_major < p.loop_major && major_idx < p.major_dim; |
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loop_major++, major_idx++) { |
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for (int loop_x = 0, out_x = out_x_base; |
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loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) { |
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int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0; |
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int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w); |
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int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x; |
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int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x; |
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const scalar_t *x_p = |
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&input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + |
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minor_idx]; |
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const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x]; |
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int x_px = p.minor_dim; |
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int k_px = -p.up_x; |
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int x_py = p.in_w * p.minor_dim; |
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int k_py = -p.up_y * p.kernel_w; |
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scalar_t v = 0.0f; |
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for (int y = 0; y < h; y++) { |
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for (int x = 0; x < w; x++) { |
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v += static_cast<scalar_t>(*x_p) * static_cast<scalar_t>(*k_p); |
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x_p += x_px; |
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k_p += k_px; |
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} |
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x_p += x_py - w * x_px; |
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k_p += k_py - w * k_px; |
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} |
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out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + |
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minor_idx] = v; |
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} |
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} |
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} |
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template <typename scalar_t, int up_x, int up_y, int down_x, int down_y, |
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int kernel_h, int kernel_w, int tile_out_h, int tile_out_w> |
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__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input, |
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const scalar_t *kernel, |
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const UpFirDn2DKernelParams p) { |
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const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; |
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const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; |
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__shared__ volatile float sk[kernel_h][kernel_w]; |
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__shared__ volatile float sx[tile_in_h][tile_in_w]; |
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int minor_idx = blockIdx.x; |
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int tile_out_y = minor_idx / p.minor_dim; |
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minor_idx -= tile_out_y * p.minor_dim; |
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tile_out_y *= tile_out_h; |
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int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; |
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int major_idx_base = blockIdx.z * p.loop_major; |
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if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | |
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major_idx_base >= p.major_dim) { |
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return; |
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} |
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for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; |
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tap_idx += blockDim.x) { |
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int ky = tap_idx / kernel_w; |
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int kx = tap_idx - ky * kernel_w; |
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scalar_t v = 0.0; |
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if (kx < p.kernel_w & ky < p.kernel_h) { |
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v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; |
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} |
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sk[ky][kx] = v; |
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} |
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for (int loop_major = 0, major_idx = major_idx_base; |
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loop_major < p.loop_major & major_idx < p.major_dim; |
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loop_major++, major_idx++) { |
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for (int loop_x = 0, tile_out_x = tile_out_x_base; |
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loop_x < p.loop_x & tile_out_x < p.out_w; |
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loop_x++, tile_out_x += tile_out_w) { |
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int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; |
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int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; |
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int tile_in_x = floor_div(tile_mid_x, up_x); |
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int tile_in_y = floor_div(tile_mid_y, up_y); |
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__syncthreads(); |
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for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; |
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in_idx += blockDim.x) { |
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int rel_in_y = in_idx / tile_in_w; |
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int rel_in_x = in_idx - rel_in_y * tile_in_w; |
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int in_x = rel_in_x + tile_in_x; |
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int in_y = rel_in_y + tile_in_y; |
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scalar_t v = 0.0; |
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if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { |
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v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * |
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p.minor_dim + |
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minor_idx]; |
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} |
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sx[rel_in_y][rel_in_x] = v; |
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} |
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__syncthreads(); |
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for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; |
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out_idx += blockDim.x) { |
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int rel_out_y = out_idx / tile_out_w; |
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int rel_out_x = out_idx - rel_out_y * tile_out_w; |
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int out_x = rel_out_x + tile_out_x; |
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int out_y = rel_out_y + tile_out_y; |
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int mid_x = tile_mid_x + rel_out_x * down_x; |
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int mid_y = tile_mid_y + rel_out_y * down_y; |
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int in_x = floor_div(mid_x, up_x); |
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int in_y = floor_div(mid_y, up_y); |
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int rel_in_x = in_x - tile_in_x; |
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int rel_in_y = in_y - tile_in_y; |
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int kernel_x = (in_x + 1) * up_x - mid_x - 1; |
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int kernel_y = (in_y + 1) * up_y - mid_y - 1; |
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scalar_t v = 0.0; |
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#pragma unroll |
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for (int y = 0; y < kernel_h / up_y; y++) |
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#pragma unroll |
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for (int x = 0; x < kernel_w / up_x; x++) |
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v += sx[rel_in_y + y][rel_in_x + x] * |
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sk[kernel_y + y * up_y][kernel_x + x * up_x]; |
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if (out_x < p.out_w & out_y < p.out_h) { |
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out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + |
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minor_idx] = v; |
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} |
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} |
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} |
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} |
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} |
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torch::Tensor upfirdn2d_op(const torch::Tensor &input, |
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const torch::Tensor &kernel, int up_x, int up_y, |
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int down_x, int down_y, int pad_x0, int pad_x1, |
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int pad_y0, int pad_y1) { |
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int curDevice = -1; |
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cudaGetDevice(&curDevice); |
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cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); |
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UpFirDn2DKernelParams p; |
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auto x = input.contiguous(); |
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auto k = kernel.contiguous(); |
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p.major_dim = x.size(0); |
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p.in_h = x.size(1); |
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p.in_w = x.size(2); |
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p.minor_dim = x.size(3); |
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p.kernel_h = k.size(0); |
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p.kernel_w = k.size(1); |
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p.up_x = up_x; |
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p.up_y = up_y; |
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p.down_x = down_x; |
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p.down_y = down_y; |
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p.pad_x0 = pad_x0; |
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p.pad_x1 = pad_x1; |
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p.pad_y0 = pad_y0; |
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p.pad_y1 = pad_y1; |
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p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / |
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p.down_y; |
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p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / |
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p.down_x; |
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auto out = |
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at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); |
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int mode = -1; |
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int tile_out_h = -1; |
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int tile_out_w = -1; |
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if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && |
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p.kernel_h <= 4 && p.kernel_w <= 4) { |
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mode = 1; |
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tile_out_h = 16; |
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tile_out_w = 64; |
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} |
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if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && |
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p.kernel_h <= 3 && p.kernel_w <= 3) { |
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mode = 2; |
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tile_out_h = 16; |
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tile_out_w = 64; |
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} |
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if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && |
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p.kernel_h <= 4 && p.kernel_w <= 4) { |
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mode = 3; |
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tile_out_h = 16; |
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tile_out_w = 64; |
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} |
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if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && |
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p.kernel_h <= 2 && p.kernel_w <= 2) { |
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mode = 4; |
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tile_out_h = 16; |
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tile_out_w = 64; |
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} |
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if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && |
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p.kernel_h <= 4 && p.kernel_w <= 4) { |
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mode = 5; |
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tile_out_h = 8; |
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tile_out_w = 32; |
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} |
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if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && |
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p.kernel_h <= 2 && p.kernel_w <= 2) { |
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mode = 6; |
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tile_out_h = 8; |
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tile_out_w = 32; |
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} |
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dim3 block_size; |
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dim3 grid_size; |
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if (tile_out_h > 0 && tile_out_w > 0) { |
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p.loop_major = (p.major_dim - 1) / 16384 + 1; |
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p.loop_x = 1; |
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block_size = dim3(32 * 8, 1, 1); |
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grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, |
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(p.out_w - 1) / (p.loop_x * tile_out_w) + 1, |
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(p.major_dim - 1) / p.loop_major + 1); |
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} else { |
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p.loop_major = (p.major_dim - 1) / 16384 + 1; |
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p.loop_x = 4; |
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block_size = dim3(4, 32, 1); |
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grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1, |
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(p.out_w - 1) / (p.loop_x * block_size.y) + 1, |
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(p.major_dim - 1) / p.loop_major + 1); |
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} |
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AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { |
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switch (mode) { |
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case 1: |
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upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 4, 4, 16, 64> |
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<<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(), |
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x.data_ptr<scalar_t>(), |
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k.data_ptr<scalar_t>(), p); |
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break; |
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case 2: |
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upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 3, 3, 16, 64> |
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<<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(), |
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x.data_ptr<scalar_t>(), |
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k.data_ptr<scalar_t>(), p); |
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break; |
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case 3: |
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upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 4, 4, 16, 64> |
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<<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(), |
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x.data_ptr<scalar_t>(), |
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k.data_ptr<scalar_t>(), p); |
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break; |
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case 4: |
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upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 2, 2, 16, 64> |
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<<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(), |
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x.data_ptr<scalar_t>(), |
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k.data_ptr<scalar_t>(), p); |
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break; |
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case 5: |
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upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32> |
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<<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(), |
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x.data_ptr<scalar_t>(), |
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k.data_ptr<scalar_t>(), p); |
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break; |
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case 6: |
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upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32> |
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<<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(), |
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x.data_ptr<scalar_t>(), |
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k.data_ptr<scalar_t>(), p); |
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break; |
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default: |
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upfirdn2d_kernel_large<scalar_t><<<grid_size, block_size, 0, stream>>>( |
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out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), |
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k.data_ptr<scalar_t>(), p); |
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} |
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}); |
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return out; |
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} |
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