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
|
#include "caffe2/core/context_gpu.h"
#include "caffe2/operators/reduction_ops.h"
#include "caffe2/utils/conversions.h"
#include "caffe2/utils/cub_namespace.cuh"
namespace caffe2 {
REGISTER_CUDA_OPERATOR(SumElements, SumElementsOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(SumElementsInt, SumElementsIntOp<int, CUDAContext>);
REGISTER_CUDA_OPERATOR(SumSqrElements, SumSqrElementsOp<CUDAContext>);
REGISTER_CUDA_OPERATOR(RowwiseMax, MaxReductionOp<float, CUDAContext, true>);
REGISTER_CUDA_OPERATOR(ColwiseMax, MaxReductionOp<float, CUDAContext, false>);
REGISTER_CUDA_OPERATOR(
RowwiseMaxGradient,
MaxReductionGradientOp<float, CUDAContext, true>)
REGISTER_CUDA_OPERATOR(
ColwiseMaxGradient,
MaxReductionGradientOp<float, CUDAContext, false>)
REGISTER_CUDA_OPERATOR(
SumElementsGradient,
SumElementsGradientOp<float, CUDAContext>);
template <typename T>
__global__ void
SumElementsGradientKernel(bool average, const int N, const T* dY, T* dX) {
const T value = average ? (*dY) / N : *dY;
CUDA_1D_KERNEL_LOOP(i, N) {
dX[i] = value;
}
}
__global__ void rowwise_max_gradient_kernel(
const int batch_size,
const int M,
const int N,
const float* X,
const float* Y,
const float* dY,
float* dX) {
const int input_size = M * N;
CUDA_1D_KERNEL_LOOP(i, batch_size * M * N) {
const int b_i = i / input_size;
const int b_n = i / input_size / N;
const int y_index = b_i * M + b_n;
if (X[i] == Y[y_index]) {
dX[i] = dY[y_index];
} else {
dX[i] = 0.0;
}
}
}
template <>
bool SumSqrElementsOp<CUDAContext>::RunOnDevice() {
return DispatchHelper<TensorTypes<float, at::Half>>::call(this, Input(0));
}
__global__ void colwise_max_gradient_kernel(
const int batch_size,
const int M,
const int N,
const float* X,
const float* Y,
const float* dY,
float* dX) {
const int input_size = M * N;
CUDA_1D_KERNEL_LOOP(i, batch_size * M * N) {
const int b_i = i / input_size;
const int b_n = i % input_size % N;
const int y_index = b_i * N + b_n;
if (X[i] == Y[y_index]) {
dX[i] = dY[y_index];
} else {
dX[i] = 0.0;
}
}
}
template <>
bool SumElementsGradientOp<float, CUDAContext>::RunOnDevice() {
auto& X = Input(0);
auto& dY = Input(1);
TORCH_DCHECK_EQ(dY.numel(), 1);
auto* dX = Output(0, X.sizes(), at::dtype<float>());
SumElementsGradientKernel<float>
<<<CAFFE_GET_BLOCKS(X.numel()),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
average_,
X.numel(),
dY.data<float>(),
dX->template mutable_data<float>());
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
template <typename T, class Context, bool ROWWISE>
bool MaxReductionGradientOp<T, Context, ROWWISE>::RunOnDevice() {
auto& X = Input(0);
auto& Y = Input(1);
auto& dY = Input(2);
auto* dX = Output(0, X.sizes(), at::dtype<T>());
CAFFE_ENFORCE_EQ(X.dim(), 3);
const int batch_size = X.dim32(0);
const int M = X.dim32(1);
const int N = X.dim32(2);
const T* Xdata = X.template data<T>();
const T* Ydata = Y.template data<T>();
const T* dYdata = dY.template data<T>();
T* dXdata = dX->template mutable_data<T>();
const int input_size = M * N;
if (ROWWISE) {
rowwise_max_gradient_kernel<<<
CAFFE_GET_BLOCKS(batch_size * input_size),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
batch_size, M, N, Xdata, Ydata, dYdata, dXdata);
C10_CUDA_KERNEL_LAUNCH_CHECK();
} else {
colwise_max_gradient_kernel<<<
CAFFE_GET_BLOCKS(batch_size * input_size),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
batch_size, M, N, Xdata, Ydata, dYdata, dXdata);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
return true;
}
} // namespace caffe2
|
