/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/autograd
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/autograd/FunctionsManual.h (19904B)
#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
#endif
#include
#include
namespace torch {
namespace autograd {
namespace generated {
namespace details {
extern const char* kCudnnDoubleBackwardMsg;
// 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;
};
Tensor toNonOptFwGrad(const c10::optional& t);
Tensor toNonOptPrimal(const c10::optional& t);
Tensor toNonOptTensor(const c10::optional& t);
bool any_variable_defined(const 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 t);
at::Tensor copysign_tensor_self_backward(const Tensor & grad, const Tensor & self, const Tensor & result);
at::Tensor not_implemented(const char* name, const char* reason="");
std::vector not_implemented_list(const char* name, const char* reason="");
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 & p_, const at::Tensor & norm);
at::Tensor norm_backward(at::Tensor grad, const at::Tensor & self, const optional & p_, at::Tensor norm, at::IntArrayRef dim, bool keepdim);
at::Tensor linalg_vector_norm_backward(at::Tensor grad, const at::Tensor & self, const at::Scalar & ord, at::Tensor norm, const c10::optional & opt_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 angle_backward(at::Tensor grad, const at::Tensor& self);
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 div_tensor_self_backward(Tensor grad, Tensor other, ScalarType self_st, const c10::optional& rounding_mode);
at::Tensor div_tensor_other_backward(Tensor grad, Tensor self, Tensor other, const c10::optional& rounding_mode);
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 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_jvp(const at::Tensor& input_primal, const at::Tensor& input_tangent, const at::Tensor& other_tangent, const at::Tensor& result);
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 & grad, 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 cat_tensors_backward(const at::Tensor & grad, const std::vector> &sizes, const std::vector &dtypes, int64_t dim);
at::Tensor clamp_backward(const at::Tensor & grad, const at::Tensor &self, const optional& min, const optional& max);
at::Tensor clamp_backward(const at::Tensor & grad, const at::Tensor &self, const at::Tensor& min, const at::Tensor& max);
std::tuple clamp_backward_min_max(const at::Tensor& grad, const at::Tensor& self, const at::Tensor& min, const at::Tensor& max, const std::array&);
at::IntArrayRef strides_or_error(const Tensor & input, c10::string_view const & input_name);
at::Tensor mm_mat1_backward(const Tensor & grad, const Tensor & mat2, at::IntArrayRef mat1_sizes, at::IntArrayRef mat1_strides, const 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 sparse_sparse_matmul_backward(const at::Tensor& grad, const at::Tensor& mat1, const at::Tensor& mat2,int64_t grad_order);
at::Tensor renorm_backward(const at::Tensor & grad, const at::Tensor & self, const at::Scalar& p, int64_t dim, const at::Scalar& maxnorm);
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(at::Tensor grad, const at::Tensor& self, c10::optional dim, c10::optional correction, bool keepdim);
at::Tensor std_backward(const at::Tensor& result, const at::Tensor& grad, const at::Tensor& self, c10::optional dim, c10::optional correction, 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, int64_t numel);
at::Tensor var_std_mean_backward(const variable_list& grads, const at::Tensor& self, const at::Tensor& r1, const at::Tensor& r2, c10::optional dim, c10::optional correction, bool keepdim, 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_jvp(const at::Tensor& input_tangent, const at::Tensor& L, bool upper);
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 &grads,
IntArrayRef split_sizes, int64_t dim, IntArrayRef sizes, const at::TensorOptions &options);
at::Tensor split_backward(const std::vector &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);
at::Tensor infinitely_differentiable_mish_backward(const at::Tensor& grad_output, const at::Tensor& input);
Tensor infinitely_differentiable_logit_backward(const Tensor& grad, const Tensor& self, c10::optional 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);
Tensor binary_cross_entropy_target_backward(
const Tensor& grad,
const Tensor& self,
const Tensor& target,
const c10::optional& weight,
int64_t reduction);
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& weight, const c10::optional& 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& 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& weight, int64_t reduction);
at::Tensor l1_loss_double_backward(const at::Tensor & grad, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & target, int64_t reduction);
at::Tensor 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);
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 huber_loss_double_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, int64_t reduction, double delta);
at::Tensor huber_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 delta);
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, const at::Scalar& beta, const 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 sinc_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::Tensor embedding_dense_double_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t padding_idx);
at::Tensor index_backward(at::Tensor zeros_like_self, const torch::List>& 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);
at::Tensor elu_double_backward(const Tensor& grad, const Tensor& grad_output, const Scalar& alpha, const Scalar& scale, const Scalar& input_scale, bool is_result, const Tensor& self_or_result);
Tensor svd_backward(const std::vector &grads, const Tensor& self,
bool some, bool compute_uv, const Tensor& raw_u, const Tensor& sigma, const Tensor& raw_v);
Tensor slice_backward_wrapper(
const at::Tensor& grad,
const c10::IntArrayRef& input_sizes,
int64_t dim,
c10::optional start,
c10::optional end,
int64_t step);
Tensor linalg_eig_backward(const std::vector &grads, const Tensor& self,
const Tensor& L, const Tensor& V);
Tensor eigh_jvp_eigenvectors(const Tensor& input_tangent, const Tensor& eigenvalues, const Tensor& eigenvectors);
Tensor eigh_jvp_eigenvalues(const Tensor& input_tangent, const Tensor& eigenvalues, const Tensor& eigenvectors);
Tensor eigh_backward(const std::vector &grads, const Tensor& self,
bool eigenvectors, const Tensor& L, const Tensor& V);
std::tuple 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 output_mask);
std::tuple _trilinear_backward(const Tensor& grad_out, const Tensor& i1, const Tensor& i2, const Tensor& i3,
IntArrayRef expand1, IntArrayRef expand2, IntArrayRef expand3,
IntArrayRef sumdim, std::array grad_mask);
Tensor linalg_qr_backward(const std::vector &grads, const Tensor& self,
c10::string_view mode, const Tensor& Q, const Tensor& R);
Tensor eig_backward(const std::vector &grads, const Tensor& self,
bool eigenvectors, const Tensor& lambda, const Tensor& v);
Tensor linalg_det_backward(const Tensor & grad, const Tensor& self, const Tensor& det);
std::tuple batchnorm_double_backward(
const Tensor & input,
const c10::optional & gamma,
const Tensor & ggI,
const Tensor & ggG,
const Tensor & ggB,
const Tensor & gO,
const c10::optional & running_mean,
const c10::optional & running_var,
bool training,
double eps,
const c10::optional & save_mean,
const c10::optional & save_invstd,
std::array output_mask);
std::tuple _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 fft_r2c_backward(const Tensor& grad, IntArrayRef dim, int64_t normalization,
bool onesided, int64_t last_dim_size);
Tensor fft_c2r_backward(const Tensor& grad, IntArrayRef dim, int64_t normalization);
Tensor constant_pad_nd_backward(const Tensor& grad, IntArrayRef pad);
std::tuple cholesky_solve_backward(
const Tensor& grad_x, const Tensor& self,
const Tensor& input2, const Tensor& result, const bool upper);
std::tuple
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& gamma,
int64_t N,
int64_t C,
int64_t HxW,
int64_t group,
double eps,
std::array grad_input_mask);
std::tuple 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 storage_offset_);
std::tuple atan2_backward(const Tensor& grad, const Tensor& self, const Tensor& other, std::array output_mask);
std::tuple layer_norm_double_backward(
const Tensor & input,
const c10::optional & gamma,
const Tensor & ggI,
const Tensor & ggG,
const Tensor & ggB,
const Tensor & gO,
const Tensor & save_mean,
const Tensor & save_invstd,
IntArrayRef normalized_shape,
std::array output_mask);
std::tuple householder_product_backward(const Tensor& grad, const Tensor& input, const Tensor& tau);
std::tuple polar_backward(
const Tensor& grad,
const Tensor& result);
Tensor i1_backward(
const Tensor& grad,
const Tensor& self,
const Tensor& result);
Tensor i1e_backward(
const Tensor& grad,
const Tensor& self,
const Tensor& result);
std::tuple lu_solve_backward(
const Tensor& grad,
const Tensor& self,
const Tensor& LU_data,
const Tensor& LU_pivots
);
Tensor lu_solve_forward_AD(
const Tensor& B_t,
const Tensor& LU_data_t,
const Tensor& LU_data,
const Tensor& LU_pivots,
const Tensor& X
);
Tensor lu_unpack_backward(
const variable_list& grads,
const Tensor& LU_data,
bool unpack_data
);
Tensor _det_lu_based_helper_backward(
const Tensor& det_grad,
const Tensor& det,
const Tensor& self,
const Tensor& lu,
const Tensor& pivs
);
Tensor lu_backward_base(
const variable_list& grads,
const Tensor& self,
const Tensor& P,
const Tensor& L,
const Tensor& U
);
Tensor _lu_with_info_backward(
const Tensor& grad,
const Tensor& self,
const Tensor& LU,
const Tensor& pivs
);
Tensor _lu_with_info_jvp(
const Tensor& dX,
const Tensor& LU,
const Tensor& pivs
);
Tensor cat_jvp(at::TensorList tensors, int64_t dim);
Tensor cumprod_jvp(Tensor self_t, Tensor self_p, Tensor result, int dim);
Tensor gather_with_keepdimed_indices(const Tensor& input, int64_t dim, const Tensor& indices, bool keepdim);
Tensor evenly_read_jvp(const Tensor& fw_grad, const Tensor & input, const Tensor & value);
} // namespace details
} // namespace generated
} // namespace autograd
} // namespace torch