Hugging Face's logo

Spaces:
moyabill
/
OOTDiffusion
Sleeping

App Files Community
OOTDiffusion
File size: 9,828 Bytes 
3d49622
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
#include <ATen/ATen.h>

#include <thrust/device_ptr.h>
#include <thrust/transform.h>

#include <vector>

#include "utils/checks.h"
#include "utils/cuda.cuh"
#include "inplace_abn.h"

#include <ATen/cuda/CUDAContext.h>

// Operations for reduce
template<typename T>
struct SumOp {
  __device__ SumOp(const T *t, int c, int s)
      : tensor(t), chn(c), sp(s) {}
  __device__ __forceinline__ T operator()(int batch, int plane, int n) {
    return tensor[(batch * chn + plane) * sp + n];
  }
  const T *tensor;
  const int chn;
  const int sp;
};

template<typename T>
struct VarOp {
  __device__ VarOp(T m, const T *t, int c, int s)
      : mean(m), tensor(t), chn(c), sp(s) {}
  __device__ __forceinline__ T operator()(int batch, int plane, int n) {
    T val = tensor[(batch * chn + plane) * sp + n];
    return (val - mean) * (val - mean);
  }
  const T mean;
  const T *tensor;
  const int chn;
  const int sp;
};

template<typename T>
struct GradOp {
  __device__ GradOp(T _weight, T _bias, const T *_z, const T *_dz, int c, int s)
      : weight(_weight), bias(_bias), z(_z), dz(_dz), chn(c), sp(s) {}
  __device__ __forceinline__ Pair<T> operator()(int batch, int plane, int n) {
    T _y = (z[(batch * chn + plane) * sp + n] - bias) / weight;
    T _dz = dz[(batch * chn + plane) * sp + n];
    return Pair<T>(_dz, _y * _dz);
  }
  const T weight;
  const T bias;
  const T *z;
  const T *dz;
  const int chn;
  const int sp;
};

/***********
 * mean_var
 ***********/

template<typename T>
__global__ void mean_var_kernel(const T *x, T *mean, T *var, int num, int chn, int sp) {
  int plane = blockIdx.x;
  T norm = T(1) / T(num * sp);

  T _mean = reduce<T, SumOp<T>>(SumOp<T>(x, chn, sp), plane, num, sp) * norm;
  __syncthreads();
  T _var = reduce<T, VarOp<T>>(VarOp<T>(_mean, x, chn, sp), plane, num, sp) * norm;

  if (threadIdx.x == 0) {
    mean[plane] = _mean;
    var[plane] = _var;
  }
}

std::vector<at::Tensor> mean_var_cuda(at::Tensor x) {
  CHECK_CUDA_INPUT(x);

  // Extract dimensions
  int64_t num, chn, sp;
  get_dims(x, num, chn, sp);

  // Prepare output tensors
  auto mean = at::empty({chn}, x.options());
  auto var = at::empty({chn}, x.options());

  // Run kernel
  dim3 blocks(chn);
  dim3 threads(getNumThreads(sp));
  auto stream = at::cuda::getCurrentCUDAStream();
  AT_DISPATCH_FLOATING_TYPES(x.type(), "mean_var_cuda", ([&] {
    mean_var_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
        x.data<scalar_t>(),
        mean.data<scalar_t>(),
        var.data<scalar_t>(),
        num, chn, sp);
  }));

  return {mean, var};
}

/**********
 * forward
 **********/

template<typename T>
__global__ void forward_kernel(T *x, const T *mean, const T *var, const T *weight, const T *bias,
                               bool affine, float eps, int num, int chn, int sp) {
  int plane = blockIdx.x;

  T _mean = mean[plane];
  T _var = var[plane];
  T _weight = affine ? abs(weight[plane]) + eps : T(1);
  T _bias = affine ? bias[plane] : T(0);

  T mul = rsqrt(_var + eps) * _weight;

  for (int batch = 0; batch < num; ++batch) {
    for (int n = threadIdx.x; n < sp; n += blockDim.x) {
      T _x = x[(batch * chn + plane) * sp + n];
      T _y = (_x - _mean) * mul + _bias;

      x[(batch * chn + plane) * sp + n] = _y;
    }
  }
}

at::Tensor forward_cuda(at::Tensor x, at::Tensor mean, at::Tensor var, at::Tensor weight, at::Tensor bias,
                        bool affine, float eps) {
  CHECK_CUDA_INPUT(x);
  CHECK_CUDA_INPUT(mean);
  CHECK_CUDA_INPUT(var);
  CHECK_CUDA_INPUT(weight);
  CHECK_CUDA_INPUT(bias);

  // Extract dimensions
  int64_t num, chn, sp;
  get_dims(x, num, chn, sp);

  // Run kernel
  dim3 blocks(chn);
  dim3 threads(getNumThreads(sp));
  auto stream = at::cuda::getCurrentCUDAStream();
  AT_DISPATCH_FLOATING_TYPES(x.type(), "forward_cuda", ([&] {
    forward_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
        x.data<scalar_t>(),
        mean.data<scalar_t>(),
        var.data<scalar_t>(),
        weight.data<scalar_t>(),
        bias.data<scalar_t>(),
        affine, eps, num, chn, sp);
  }));

  return x;
}

/***********
 * edz_eydz
 ***********/

template<typename T>
__global__ void edz_eydz_kernel(const T *z, const T *dz, const T *weight, const T *bias,
                                T *edz, T *eydz, bool affine, float eps, int num, int chn, int sp) {
  int plane = blockIdx.x;

  T _weight = affine ? abs(weight[plane]) + eps : 1.f;
  T _bias = affine ? bias[plane] : 0.f;

  Pair<T> res = reduce<Pair<T>, GradOp<T>>(GradOp<T>(_weight, _bias, z, dz, chn, sp), plane, num, sp);
  __syncthreads();

  if (threadIdx.x == 0) {
    edz[plane] = res.v1;
    eydz[plane] = res.v2;
  }
}

std::vector<at::Tensor> edz_eydz_cuda(at::Tensor z, at::Tensor dz, at::Tensor weight, at::Tensor bias,
                                      bool affine, float eps) {
  CHECK_CUDA_INPUT(z);
  CHECK_CUDA_INPUT(dz);
  CHECK_CUDA_INPUT(weight);
  CHECK_CUDA_INPUT(bias);

  // Extract dimensions
  int64_t num, chn, sp;
  get_dims(z, num, chn, sp);

  auto edz = at::empty({chn}, z.options());
  auto eydz = at::empty({chn}, z.options());

  // Run kernel
  dim3 blocks(chn);
  dim3 threads(getNumThreads(sp));
  auto stream = at::cuda::getCurrentCUDAStream();
  AT_DISPATCH_FLOATING_TYPES(z.type(), "edz_eydz_cuda", ([&] {
    edz_eydz_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
        z.data<scalar_t>(),
        dz.data<scalar_t>(),
        weight.data<scalar_t>(),
        bias.data<scalar_t>(),
        edz.data<scalar_t>(),
        eydz.data<scalar_t>(),
        affine, eps, num, chn, sp);
  }));

  return {edz, eydz};
}

/***********
 * backward
 ***********/

template<typename T>
__global__ void backward_kernel(const T *z, const T *dz, const T *var, const T *weight, const T *bias, const T *edz,
	                        const T *eydz, T *dx, bool affine, float eps, int num, int chn, int sp) {
  int plane = blockIdx.x;

  T _weight = affine ? abs(weight[plane]) + eps : 1.f;
  T _bias = affine ? bias[plane] : 0.f;
  T _var = var[plane];
  T _edz = edz[plane];
  T _eydz = eydz[plane];

  T _mul = _weight * rsqrt(_var + eps);
  T count = T(num * sp);

  for (int batch = 0; batch < num; ++batch) {
    for (int n = threadIdx.x; n < sp; n += blockDim.x) {
      T _dz = dz[(batch * chn + plane) * sp + n];
      T _y = (z[(batch * chn + plane) * sp + n] - _bias) / _weight;

      dx[(batch * chn + plane) * sp + n] = (_dz - _edz / count - _y * _eydz / count) * _mul;
    }
  }
}

at::Tensor backward_cuda(at::Tensor z, at::Tensor dz, at::Tensor var, at::Tensor weight, at::Tensor bias,
                                      at::Tensor edz, at::Tensor eydz, bool affine, float eps) {
  CHECK_CUDA_INPUT(z);
  CHECK_CUDA_INPUT(dz);
  CHECK_CUDA_INPUT(var);
  CHECK_CUDA_INPUT(weight);
  CHECK_CUDA_INPUT(bias);
  CHECK_CUDA_INPUT(edz);
  CHECK_CUDA_INPUT(eydz);

  // Extract dimensions
  int64_t num, chn, sp;
  get_dims(z, num, chn, sp);

  auto dx = at::zeros_like(z);

  // Run kernel
  dim3 blocks(chn);
  dim3 threads(getNumThreads(sp));
  auto stream = at::cuda::getCurrentCUDAStream();
  AT_DISPATCH_FLOATING_TYPES(z.type(), "backward_cuda", ([&] {
    backward_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
        z.data<scalar_t>(),
        dz.data<scalar_t>(),
        var.data<scalar_t>(),
        weight.data<scalar_t>(),
        bias.data<scalar_t>(),
        edz.data<scalar_t>(),
        eydz.data<scalar_t>(),
        dx.data<scalar_t>(),
        affine, eps, num, chn, sp);
  }));

  return dx;
}

/**************
 * activations
 **************/

template<typename T>
inline void leaky_relu_backward_impl(T *z, T *dz, float slope, int64_t count) {
  // Create thrust pointers
  thrust::device_ptr<T> th_z = thrust::device_pointer_cast(z);
  thrust::device_ptr<T> th_dz = thrust::device_pointer_cast(dz);

  auto stream = at::cuda::getCurrentCUDAStream();
  thrust::transform_if(thrust::cuda::par.on(stream),
                       th_dz, th_dz + count, th_z, th_dz,
                       [slope] __device__ (const T& dz) { return dz * slope; },
                       [] __device__ (const T& z) { return z < 0; });
  thrust::transform_if(thrust::cuda::par.on(stream),
                       th_z, th_z + count, th_z,
                       [slope] __device__ (const T& z) { return z / slope; },
                       [] __device__ (const T& z) { return z < 0; });
}

void leaky_relu_backward_cuda(at::Tensor z, at::Tensor dz, float slope) {
  CHECK_CUDA_INPUT(z);
  CHECK_CUDA_INPUT(dz);

  int64_t count = z.numel();

  AT_DISPATCH_FLOATING_TYPES(z.type(), "leaky_relu_backward_cuda", ([&] {
    leaky_relu_backward_impl<scalar_t>(z.data<scalar_t>(), dz.data<scalar_t>(), slope, count);
  }));
}

template<typename T>
inline void elu_backward_impl(T *z, T *dz, int64_t count) {
  // Create thrust pointers
  thrust::device_ptr<T> th_z = thrust::device_pointer_cast(z);
  thrust::device_ptr<T> th_dz = thrust::device_pointer_cast(dz);

  auto stream = at::cuda::getCurrentCUDAStream();
  thrust::transform_if(thrust::cuda::par.on(stream),
                       th_dz, th_dz + count, th_z, th_z, th_dz,
                       [] __device__ (const T& dz, const T& z) { return dz * (z + 1.); },
                       [] __device__ (const T& z) { return z < 0; });
  thrust::transform_if(thrust::cuda::par.on(stream),
                       th_z, th_z + count, th_z,
                       [] __device__ (const T& z) { return log1p(z); },
                       [] __device__ (const T& z) { return z < 0; });
}

void elu_backward_cuda(at::Tensor z, at::Tensor dz) {
  CHECK_CUDA_INPUT(z);
  CHECK_CUDA_INPUT(dz);

  int64_t count = z.numel();

  AT_DISPATCH_FLOATING_TYPES(z.type(), "leaky_relu_backward_cuda", ([&] {
    elu_backward_impl<scalar_t>(z.data<scalar_t>(), dz.data<scalar_t>(), count);
  }));
}