File size: 12,070 Bytes
6b14aab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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;
}