Spaces:
Runtime error
Runtime error
File size: 12,070 Bytes
9667e74 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 |
// 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/CUDAApplyUtils.cuh>
#include <ATen/cuda/CUDAContext.h>
#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>
__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input,
const scalar_t *kernel,
const UpFirDn2DKernelParams p) {
int minor_idx = blockIdx.x * blockDim.x + threadIdx.x;
int out_y = minor_idx / p.minor_dim;
minor_idx -= out_y * p.minor_dim;
int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y;
int major_idx_base = blockIdx.z * p.loop_major;
if (out_x_base >= p.out_w || out_y >= p.out_h ||
major_idx_base >= p.major_dim) {
return;
}
int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0;
int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h);
int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y;
int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y;
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, out_x = out_x_base;
loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) {
int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0;
int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w);
int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x;
int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x;
const scalar_t *x_p =
&input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim +
minor_idx];
const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x];
int x_px = p.minor_dim;
int k_px = -p.up_x;
int x_py = p.in_w * p.minor_dim;
int k_py = -p.up_y * p.kernel_w;
scalar_t v = 0.0f;
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
v += static_cast<scalar_t>(*x_p) * static_cast<scalar_t>(*k_p);
x_p += x_px;
k_p += k_px;
}
x_p += x_py - w * x_px;
k_p += k_py - w * k_px;
}
out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
minor_idx] = v;
}
}
}
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();
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 = -1;
int tile_out_w = -1;
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 > 0) {
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);
} else {
p.loop_major = (p.major_dim - 1) / 16384 + 1;
p.loop_x = 4;
block_size = dim3(4, 32, 1);
grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1,
(p.out_w - 1) / (p.loop_x * block_size.y) + 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;
default:
upfirdn2d_kernel_large<scalar_t><<<grid_size, block_size, 0, stream>>>(
out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(),
k.data_ptr<scalar_t>(), p);
}
});
return out;
} |