-
Notifications
You must be signed in to change notification settings - Fork 21.4k
/
FunctionsManual.h
207 lines (199 loc) · 14.6 KB
/
FunctionsManual.h
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
#pragma once
// NB: Must be at the top of file to avoid including the deprecated "math.h".
// https://stackoverflow.com/questions/6563810/m-pi-works-with-math-h-but-not-with-cmath-in-visual-studio
#ifdef _MSC_VER
#ifndef _USE_MATH_DEFINES
#define _USE_MATH_DEFINES
#endif
#include <cmath>
#endif
#include <torch/csrc/autograd/generated/Functions.h>
#include <ATen/ATen.h>
namespace torch {
namespace autograd {
namespace generated {
namespace details {
// A simple way to imperatively compute index ranges for slots
// that have been flattened
struct IndexRangeGenerator {
IndexRange range(size_t range_size) {
i += range_size;
return {i - range_size, i};
}
size_t size() { return i; }
private:
size_t i = 0;
};
bool any_variable_defined(variable_list& variables);
void copy_range(variable_list& out, IndexRange range, const at::Tensor & t);
void copy_range(variable_list& out, IndexRange range, at::ArrayRef<at::Tensor> t);
at::Tensor copysign_tensor_backward(Tensor grad, Tensor self, Tensor other);
at::Tensor not_implemented(const char* name);
at::Tensor handle_r_to_c(ScalarType self_st, Tensor gradient_result);
at::Tensor maybe_multiply(const at::Tensor & t, const at::Scalar & s);
int64_t _safe_size(IntArrayRef sizes, IntArrayRef dim);
Tensor restore_reduced_dims(const Tensor &output, IntArrayRef dims, bool keepdim);
Tensor scale_grad_by_count(const Tensor &grad, const Tensor &mask, IntArrayRef dims);
at::Tensor norm_backward(const at::Tensor & grad, const at::Tensor & self, const optional<at::Scalar> & p_, const at::Tensor & norm);
at::Tensor norm_backward(at::Tensor grad, const at::Tensor & self, const optional<at::Scalar> & p_, at::Tensor norm, at::IntArrayRef dim, bool keepdim);
at::Tensor pow_backward(at::Tensor grad, const at::Tensor & self, const at::Scalar & exponent_);
at::Tensor pow_backward_self(at::Tensor grad, const at::Tensor & self, const at::Tensor & exponent);
at::Tensor pow_backward_exponent(at::Tensor grad, const at::Tensor& self, const at::Tensor& exponent, at::Tensor result);
at::Tensor pow_backward_exponent(at::Tensor grad, const at::Scalar & base, const at::Tensor& exponent, at::Tensor result);
at::Tensor mul_tensor_backward(Tensor grad, Tensor other, ScalarType self_st);
at::Tensor div_tensor_self_backward(Tensor grad, Tensor other, ScalarType self_st);
at::Tensor div_tensor_other_backward(Tensor grad, Tensor self, Tensor other);
at::Tensor mvlgamma_backward(at::Tensor grad, const at::Tensor & self, int64_t p);
at::Tensor permute_backwards(const at::Tensor & grad, at::IntArrayRef fwd_dims);
at::Tensor rad2deg_backward(const at::Tensor& grad);
at::Tensor deg2rad_backward(const at::Tensor& grad);
at::Tensor unsqueeze_multiple(const at::Tensor & t, at::IntArrayRef dim, size_t n_dims);
at::Tensor sum_backward(const at::Tensor & grad, at::IntArrayRef sizes, at::IntArrayRef dims, bool keepdim);
at::Tensor nansum_backward(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dims, bool keepdim);
std::vector<int64_t> reverse_list(const at::IntArrayRef list);
at::Tensor reverse_dim(const at::Tensor& t, int64_t dim);
at::Tensor prod_safe_zeros_backward(const at::Tensor &grad, const at::Tensor& inp, int64_t dim);
at::Tensor prod_backward(const at::Tensor& grad, const at::Tensor& input, const at::Tensor& result);
at::Tensor prod_backward(at::Tensor grad, const at::Tensor& input, at::Tensor result, int64_t dim, bool keepdim);
at::Tensor solve_backward_self(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & A);
at::Tensor solve_backward_A(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & A, const at::Tensor & solution);
at::Tensor cumsum_backward(const at::Tensor & x, int64_t dim);
at::Tensor logsumexp_backward(at::Tensor grad, const at::Tensor & self, at::Tensor result, at::IntArrayRef dim, bool keepdim);
at::Tensor logcumsumexp_backward(at::Tensor grad, const at::Tensor & self, at::Tensor result, int64_t dim);
at::Tensor unbind_backward(const variable_list& grads, int64_t dim);
at::Tensor unsqueeze_to(const at::Tensor & self, at::IntArrayRef sizes);
at::Tensor unsqueeze_to(const at::Tensor & self, int64_t dim, at::IntArrayRef sizes);
std::vector<at::Tensor> cat_tensors_backward(const at::Tensor & grad, const std::vector<std::vector<int64_t>> &sizes, int64_t dim);
at::Tensor clamp_backward(const at::Tensor & grad, const at::Tensor &self, const optional<at::Scalar> & min, const optional<at::Scalar> & max);
at::Tensor mm_mat1_backward(const at::Tensor & grad, const at::Tensor & mat2, const at::Tensor & mat1, const at::Scalar & alpha);
at::Tensor mm_mat2_backward(const at::Tensor & grad, const at::Tensor & mat1, at::IntArrayRef sizes, at::IntArrayRef strides, const at::Scalar & alpha);
at::Tensor _sparse_addmm_sparse_backward(const at::Tensor& grad, const at::Tensor& sparse_, const at::Tensor& dense, const at::Scalar& alpha);
at::Tensor renorm_backward(const at::Tensor & grad, const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm);
at::Tensor sum_tensorlist(at::TensorList tl);
at::Tensor repeat_backward(at::Tensor grad, at::IntArrayRef repeats, at::IntArrayRef input_shape);
at::Tensor _fused_dropout_backward(at::Tensor grad, at::Tensor mask, double p1m);
at::Tensor evenly_distribute_backward(at::Tensor grad, const at::Tensor & input, const at::Tensor & value);
at::Tensor sgn_backward(Tensor result, Tensor grad, Tensor self);
at::Tensor var_backward(const at::Tensor & grad, const at::Tensor & self, bool unbiased);
at::Tensor var_backward(at::Tensor grad, const at::Tensor & self, at::IntArrayRef dim, bool unbiased, bool keepdim);
at::Tensor std_backward(const at::Tensor & result, const at::Tensor & grad, const at::Tensor & self, bool unbiased);
at::Tensor std_backward(const at::Tensor & result, at::Tensor grad, const at::Tensor & self, at::IntArrayRef dim, bool unbiased, bool keepdim);
at::Tensor mean_backward(at::Tensor grad, const at::IntArrayRef sizes, at::IntArrayRef dim, bool keepdim);
at::Tensor mean_backward(at::Tensor grad, const at::IntArrayRef sizes, int numel);
at::Tensor var_std_mean_backward(const variable_list& grads, const at::Tensor & self, const at::Tensor & r1, const at::Tensor & r2, at::IntArrayRef dim, bool unbiased, bool keepdim, bool is_std);
at::Tensor var_std_mean_backward(const variable_list& grads, const at::Tensor & self, const at::Tensor & r1, const at::Tensor & r2, bool unbiased, bool is_std);
at::Tensor masked_scatter_backward(const at::Tensor & grad, const at::Tensor & mask, at::IntArrayRef sizes);
at::Tensor cholesky_backward(at::Tensor grad, bool upper, at::Tensor L);
at::Tensor cholesky_inverse_backward(at::Tensor grad, at::Tensor L, bool upper, at::Tensor inverse);
at::Tensor split_with_sizes_backward(const std::vector<torch::autograd::Variable> &grads,
IntArrayRef split_sizes, int64_t dim, IntArrayRef sizes, const at::TensorOptions &options);
at::Tensor split_backward(const std::vector<torch::autograd::Variable> &grads, int64_t split_size, int64_t dim, at::IntArrayRef sizes, const at::TensorOptions &options);
at::Tensor max_pool_double_backward(const at::Tensor & grad, const at::Tensor & indices, int dim);
at::Tensor glu_double_backward(const at::Tensor & grad, const at::Tensor & grad_output, const at::Tensor & input, int64_t dim);
at::Tensor glu_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & input, int64_t dim);
at::Tensor infinitely_differentiable_silu_backward(const at::Tensor& grad_output, const at::Tensor& input);
Tensor infinitely_differentiable_logit_backward(const Tensor& grad, const Tensor& self, c10::optional<double> eps);
at::Tensor kl_div_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, int64_t reduction, bool log_target);
at::Tensor binary_cross_entropy_with_logits_target_backward(const at::Tensor& grad_output, const at::Tensor& self, const at::Tensor& target, const c10::optional<at::Tensor>& weight, const c10::optional<at::Tensor>& pos_weight, int64_t reduction);
at::Tensor log_sigmoid_double_backward(const at::Tensor & grad, const at::Tensor & input);
at::Tensor softmax_double_backward(const at::Tensor & grad, const at::Tensor & grad_output, int dim, const at::Tensor & output);
at::Tensor log_softmax_double_backward(const at::Tensor & grad, const at::Tensor & grad_output, int dim, const at::Tensor & output);
at::Tensor binary_cross_entropy_double_backward(const at::Tensor & grad_output, const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, const c10::optional<at::Tensor>& weight, int64_t reduction);
at::Tensor binary_cross_entropy_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, const c10::optional<at::Tensor>& weight, int64_t reduction);
at::Tensor l1_loss_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, int64_t reduction);
at::Tensor smooth_l1_loss_double_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, int64_t reduction, double beta);
at::Tensor smooth_l1_loss_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & target, int64_t reduction, double beta);
at::Tensor mse_loss_double_backward(const at::Tensor & grad, const at::Tensor & input, int64_t reduction);
at::Tensor mse_loss_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & target, int64_t reduction);
at::Tensor soft_margin_loss_double_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, int64_t reduction);
at::Tensor soft_margin_loss_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & target, int64_t reduction);
at::Tensor softplus_double_backward(const at::Tensor & grad, const at::Tensor & input, at::Scalar beta, at::Scalar threshold);
at::Tensor logdet_backward(const at::Tensor & grad, const at::Tensor& self, const at::Tensor& logdet);
at::Tensor slogdet_backward(const at::Tensor& grad_logabsdet, const at::Tensor& self, const at::Tensor& signdet, const at::Tensor& logabsdet);
at::Tensor log1p_backward(const at::Tensor& grad, const at::Tensor& self);
at::Tensor sparse_constructor_values_backward(const at::Tensor& sparse_grad_out, const at::Tensor& indices, at::IntArrayRef values_shape);
at::Tensor embedding_dense_double_backward(const at::Tensor & grad, const at::Tensor & indices);
at::Tensor index_backward(at::Tensor zeros_like_self, at::TensorList indices, const at::Tensor& grad);
at::Tensor _cudnn_ctc_loss_backward(const at::Tensor& grad_out, const at::Tensor& loss, const at::Tensor& raw_grad, bool zero_infinity);
Tensor svd_backward(const std::vector<torch::autograd::Variable> &grads, const Tensor& self,
bool some, bool compute_uv, const Tensor& raw_u, const Tensor& sigma, const Tensor& raw_v);
Tensor symeig_backward(const std::vector<torch::autograd::Variable> &grads, const Tensor& self,
bool eigenvectors, bool upper, const Tensor& lambda, const Tensor& v);
std::tuple<Tensor, Tensor> triangular_solve_backward(
const Tensor & grad_x, const Tensor & grad_m,
const Tensor & b, const Tensor & a, const Tensor & x,
const bool upper, const bool transpose, const bool unitriangular,
std::array<bool, 2> output_mask);
std::tuple<Tensor, Tensor, Tensor> _trilinear_backward(const Tensor& grad_out, const Tensor& i1, const Tensor& i2, const Tensor& i3,
IntArrayRef expand1, IntArrayRef expand2, IntArrayRef expand3,
IntArrayRef sumdim, int64_t unroll_dim, std::array<bool, 3> grad_mask);
Tensor qr_backward(const std::vector<torch::autograd::Variable> &grads, const Tensor& self,
bool some, const Tensor& Q, const Tensor& R);
Tensor eig_backward(const std::vector<torch::autograd::Variable> &grads, const Tensor& self,
bool eigenvectors, const Tensor& lambda, const Tensor& v);
Tensor det_backward(const Tensor & grad, const Tensor& self, const Tensor& det);
std::tuple<Tensor, Tensor, Tensor> batchnorm_double_backward(
const Tensor & input,
const c10::optional<Tensor> & gamma,
const Tensor & ggI,
const Tensor & ggG,
const Tensor & ggB,
const Tensor & gO,
const c10::optional<Tensor> & running_mean,
const c10::optional<Tensor> & running_var,
bool training,
double eps,
const c10::optional<Tensor> & save_mean,
const c10::optional<Tensor> & save_invstd,
std::array<bool,3> output_mask);
std::tuple<Tensor, Tensor> _euclidean_dist_backward(const Tensor & grad, const Tensor & x1, const Tensor & x2, const Tensor & res);
Tensor kl_div_target_backward(Tensor grad_output, Tensor self, Tensor target, int64_t reduction, bool log_target);
Tensor fft_backward(const Tensor& self, const Tensor& grad, int64_t signal_ndim,
bool complex_input, bool complex_output,
bool inverse, IntArrayRef checked_signal_sizes,
int64_t normalization, bool onesided,
IntArrayRef output_sizes);
Tensor constant_pad_nd_backward(const Tensor& grad, IntArrayRef pad);
std::tuple<Tensor, Tensor> cholesky_solve_backward(
const Tensor& grad_x, const Tensor& self,
const Tensor& input2, const Tensor& result, const bool upper);
std::tuple<Tensor, Tensor, Tensor>
infinitely_differentiable_native_group_norm_backward(
const Tensor& dY,
const Tensor& dmean,
const Tensor& drstd,
const Tensor& X,
const Tensor& mean,
const Tensor& rstd,
const c10::optional<Tensor>& gamma,
int64_t N,
int64_t C,
int64_t HxW,
int64_t group,
double eps,
std::array<bool, 3> grad_input_mask);
std::tuple<Tensor, Tensor, Tensor> prelu_double_backward(
const Tensor & grad_grad_input,
const Tensor & grad_grad_weight,
const Tensor & grad_out,
const Tensor & input_,
const Tensor & weight_);
Tensor as_strided_backward(Tensor grad, TensorGeometry input_geometry, IntArrayRef sizes, IntArrayRef strides, optional<int64_t> storage_offset_);
std::tuple<Tensor, Tensor> atan2_backward(const Tensor& grad, const Tensor& self, const Tensor& other, std::array<bool, 2> output_mask);
std::tuple<Tensor, Tensor, Tensor>
infinitely_differentiable_native_layer_norm_backward(
const Tensor& dY,
const Tensor& dmean,
const Tensor& drstd,
const Tensor& X,
const Tensor& mean,
const Tensor& rstd,
const c10::optional<Tensor>& gamma,
int64_t M,
int64_t N,
double eps,
std::array<bool, 3> grad_input_mask);
} // namespace details
} // namespace generated
} // namespace autograd
} // namespace torch