mirror of
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9f67176b82
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43208 This PR adds gradcheck for complex. The logic used for complex gradcheck is described in Section 3.5.3 here: https://arxiv.org/pdf/1701.00392.pdf More concretely, this PR introduces the following changes: 1. Updates get_numerical_jacobian to take as input a scalar value for vector (v). Adds gradcheck logic for C -> C, C-> R, R -> C. For R -> C functions, only the real value of gradient is propagated. 2. Adds backward definition for `torch.complex` and also adds a test to verify the definition added. 3. Updates backward for `mul`, `sin`, `cos`, `sinh`, `cosh`. 4. Adds tests for all `torch.real`, `torch.imag`, `torch.view_as_real`, `torch.view_as_complex`, `torch.conj`. Follow up tasks: 1. Add more thorough tests for R -> C cases. Specifically, add R->C test variants for functions. for e.g., `torch.mul(complex_tensor, real_tensor)` 2. Add back commented test in `common_methods_invocation.py`. 3. Add more special case checking for complex gradcheck to make debugging easier. 4. Update complex autograd note. 5. disable complex autograd for operators not tested for complex. Test Plan: Imported from OSS Reviewed By: zou3519 Differential Revision: D23655088 Pulled By: anjali411 fbshipit-source-id: caa75e09864b5f6ead0f988f6368dce64cf15deb
203 lines
14 KiB
C++
203 lines
14 KiB
C++
#pragma once
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// NB: Must be at the top of file to avoid including the deprecated "math.h".
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// https://stackoverflow.com/questions/6563810/m-pi-works-with-math-h-but-not-with-cmath-in-visual-studio
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#ifdef _MSC_VER
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#ifndef _USE_MATH_DEFINES
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#define _USE_MATH_DEFINES
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#endif
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#include <cmath>
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#endif
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#include <torch/csrc/autograd/generated/Functions.h>
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#include <ATen/ATen.h>
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namespace torch {
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namespace autograd {
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namespace generated {
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namespace details {
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// A simple way to imperatively compute index ranges for slots
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// that have been flattened
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struct IndexRangeGenerator {
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IndexRange range(size_t range_size) {
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i += range_size;
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return {i - range_size, i};
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}
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size_t size() { return i; }
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private:
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size_t i = 0;
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};
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bool any_variable_defined(variable_list& variables);
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void copy_range(variable_list& out, IndexRange range, const at::Tensor & t);
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void copy_range(variable_list& out, IndexRange range, at::ArrayRef<at::Tensor> t);
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at::Tensor not_implemented(const char* name);
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at::Tensor maybe_multiply(const at::Tensor & t, const at::Scalar & s);
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int64_t _safe_size(IntArrayRef sizes, IntArrayRef dim);
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Tensor restore_reduced_dims(const Tensor &output, IntArrayRef dims, bool keepdim);
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Tensor scale_grad_by_count(const Tensor &grad, const Tensor &mask, IntArrayRef dims);
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at::Tensor norm_backward(const at::Tensor & grad, const at::Tensor & self, const optional<at::Scalar> & p_, const at::Tensor & norm);
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at::Tensor norm_backward(at::Tensor grad, const at::Tensor & self, const optional<at::Scalar> & p_, at::Tensor norm, at::IntArrayRef dim, bool keepdim);
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at::Tensor pow_backward(at::Tensor grad, const at::Tensor & self, const at::Scalar & exponent_);
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at::Tensor pow_backward_self(at::Tensor grad, const at::Tensor & self, const at::Tensor & exponent);
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at::Tensor pow_backward_exponent(at::Tensor grad, const at::Tensor& self, const at::Tensor& exponent, at::Tensor result);
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at::Tensor pow_backward_exponent(at::Tensor grad, const at::Scalar & base, const at::Tensor& exponent, at::Tensor result);
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at::Tensor mul_tensor_backward(Tensor grad, Tensor other, ScalarType self_st);
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at::Tensor mvlgamma_backward(at::Tensor grad, const at::Tensor & self, int64_t p);
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at::Tensor permute_backwards(const at::Tensor & grad, at::IntArrayRef fwd_dims);
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at::Tensor rad2deg_backward(const at::Tensor& grad);
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at::Tensor deg2rad_backward(const at::Tensor& grad);
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at::Tensor unsqueeze_multiple(const at::Tensor & t, at::IntArrayRef dim, size_t n_dims);
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at::Tensor sum_backward(const at::Tensor & grad, at::IntArrayRef sizes, at::IntArrayRef dims, bool keepdim);
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at::Tensor nansum_backward(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dims, bool keepdim);
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std::vector<int64_t> reverse_list(const at::IntArrayRef list);
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at::Tensor reverse_dim(const at::Tensor& t, int64_t dim);
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at::Tensor prod_safe_zeros_backward(const at::Tensor &grad, const at::Tensor& inp, int64_t dim);
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at::Tensor prod_backward(const at::Tensor& grad, const at::Tensor& input, const at::Tensor& result);
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at::Tensor prod_backward(at::Tensor grad, const at::Tensor& input, at::Tensor result, int64_t dim, bool keepdim);
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at::Tensor solve_backward_self(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & A);
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at::Tensor solve_backward_A(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & A, const at::Tensor & solution);
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at::Tensor cumsum_backward(const at::Tensor & x, int64_t dim);
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at::Tensor logsumexp_backward(at::Tensor grad, const at::Tensor & self, at::Tensor result, at::IntArrayRef dim, bool keepdim);
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at::Tensor logcumsumexp_backward(at::Tensor grad, const at::Tensor & self, at::Tensor result, int64_t dim);
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at::Tensor unbind_backward(const variable_list& grads, int64_t dim);
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at::Tensor unsqueeze_to(const at::Tensor & self, at::IntArrayRef sizes);
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at::Tensor unsqueeze_to(const at::Tensor & self, int64_t dim, at::IntArrayRef sizes);
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std::vector<at::Tensor> cat_tensors_backward(const at::Tensor & grad, const std::vector<std::vector<int64_t>> &sizes, int64_t dim);
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at::Tensor clamp_backward(const at::Tensor & grad, const at::Tensor &self, const optional<at::Scalar> & min, const optional<at::Scalar> & max);
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at::Tensor mm_mat1_backward(const at::Tensor & grad, const at::Tensor & mat2, const at::Tensor & mat1, const at::Scalar & alpha);
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at::Tensor mm_mat2_backward(const at::Tensor & grad, const at::Tensor & mat1, at::IntArrayRef sizes, at::IntArrayRef strides, const at::Scalar & alpha);
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at::Tensor _sparse_addmm_sparse_backward(const at::Tensor& grad, const at::Tensor& sparse_, const at::Tensor& dense, const at::Scalar& alpha);
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at::Tensor renorm_backward(const at::Tensor & grad, const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm);
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at::Tensor sum_tensorlist(at::TensorList tl);
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at::Tensor repeat_backward(at::Tensor grad, int64_t input_dims, at::IntArrayRef repeats);
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at::Tensor _fused_dropout_backward(at::Tensor grad, at::Tensor mask, double p1m);
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at::Tensor evenly_distribute_backward(at::Tensor grad, const at::Tensor & input, const at::Tensor & value);
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at::Tensor var_backward(const at::Tensor & grad, const at::Tensor & self, bool unbiased);
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at::Tensor var_backward(at::Tensor grad, const at::Tensor & self, at::IntArrayRef dim, bool unbiased, bool keepdim);
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at::Tensor std_backward(const at::Tensor & result, const at::Tensor & grad, const at::Tensor & self, bool unbiased);
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at::Tensor std_backward(const at::Tensor & result, at::Tensor grad, const at::Tensor & self, at::IntArrayRef dim, bool unbiased, bool keepdim);
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at::Tensor mean_backward(at::Tensor grad, const at::IntArrayRef sizes, at::IntArrayRef dim, bool keepdim);
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at::Tensor mean_backward(at::Tensor grad, const at::IntArrayRef sizes, int numel);
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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);
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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);
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at::Tensor masked_scatter_backward(const at::Tensor & grad, const at::Tensor & mask, at::IntArrayRef sizes);
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at::Tensor cholesky_backward(at::Tensor grad, bool upper, at::Tensor L);
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at::Tensor cholesky_inverse_backward(at::Tensor grad, at::Tensor L, bool upper, at::Tensor inverse);
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at::Tensor split_with_sizes_backward(const std::vector<torch::autograd::Variable> &grads,
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IntArrayRef split_sizes, int64_t dim, IntArrayRef sizes, const at::TensorOptions &options);
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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);
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at::Tensor max_pool_double_backward(const at::Tensor & grad, const at::Tensor & indices, int dim);
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at::Tensor glu_double_backward(const at::Tensor & grad, const at::Tensor & grad_output, const at::Tensor & input, int64_t dim);
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at::Tensor glu_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & input, int64_t dim);
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at::Tensor infinitely_differentiable_silu_backward(const at::Tensor& grad_output, const at::Tensor& input);
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Tensor infinitely_differentiable_logit_backward(const Tensor& grad, const Tensor& self, c10::optional<double> eps);
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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);
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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);
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at::Tensor log_sigmoid_double_backward(const at::Tensor & grad, const at::Tensor & input);
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at::Tensor softmax_double_backward(const at::Tensor & grad, const at::Tensor & grad_output, int dim, const at::Tensor & output);
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at::Tensor log_softmax_double_backward(const at::Tensor & grad, const at::Tensor & grad_output, int dim, const at::Tensor & output);
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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);
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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);
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at::Tensor l1_loss_double_backward_grad_output(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, int64_t reduction);
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at::Tensor smooth_l1_loss_double_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, int64_t reduction);
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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);
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at::Tensor mse_loss_double_backward(const at::Tensor & grad, const at::Tensor & input, int64_t reduction);
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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);
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at::Tensor soft_margin_loss_double_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & target, int64_t reduction);
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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);
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at::Tensor softplus_double_backward(const at::Tensor & grad, const at::Tensor & input, at::Scalar beta, at::Scalar threshold);
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at::Tensor logdet_backward(const at::Tensor & grad, const at::Tensor& self, const at::Tensor& logdet);
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at::Tensor slogdet_backward(const at::Tensor& grad_logabsdet, const at::Tensor& self, const at::Tensor& signdet, const at::Tensor& logabsdet);
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at::Tensor log1p_backward(const at::Tensor& grad, const at::Tensor& self);
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at::Tensor sparse_constructor_values_backward(const at::Tensor& sparse_grad_out, const at::Tensor& indices, at::IntArrayRef values_shape);
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at::Tensor embedding_dense_double_backward(const at::Tensor & grad, const at::Tensor & indices);
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at::Tensor index_backward(at::Tensor zeros_like_self, at::TensorList indices, const at::Tensor& grad);
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at::Tensor _cudnn_ctc_loss_backward(const at::Tensor& grad_out, const at::Tensor& loss, const at::Tensor& raw_grad, bool zero_infinity);
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Tensor svd_backward(const std::vector<torch::autograd::Variable> &grads, const Tensor& self,
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bool some, bool compute_uv, const Tensor& raw_u, const Tensor& sigma, const Tensor& raw_v);
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Tensor symeig_backward(const std::vector<torch::autograd::Variable> &grads, const Tensor& self,
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bool eigenvectors, bool upper, const Tensor& lambda, const Tensor& v);
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std::tuple<Tensor, Tensor> triangular_solve_backward(
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const Tensor & grad_x, const Tensor & grad_m,
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const Tensor & b, const Tensor & a, const Tensor & x,
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const bool upper, const bool transpose, const bool unitriangular,
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std::array<bool, 2> output_mask);
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std::tuple<Tensor, Tensor, Tensor> _trilinear_backward(const Tensor& grad_out, const Tensor& i1, const Tensor& i2, const Tensor& i3,
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IntArrayRef expand1, IntArrayRef expand2, IntArrayRef expand3,
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IntArrayRef sumdim, int64_t unroll_dim, std::array<bool, 3> grad_mask);
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Tensor qr_backward(const std::vector<torch::autograd::Variable> &grads, const Tensor& self,
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bool some, const Tensor& Q, const Tensor& R);
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Tensor eig_backward(const std::vector<torch::autograd::Variable> &grads, const Tensor& self,
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bool eigenvectors, const Tensor& lambda, const Tensor& v);
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Tensor det_backward(const Tensor & grad, const Tensor& self, const Tensor& det);
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std::tuple<Tensor, Tensor, Tensor> batchnorm_double_backward(
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const Tensor & input,
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const c10::optional<Tensor> & gamma,
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const Tensor & ggI,
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const Tensor & ggG,
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const Tensor & ggB,
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const Tensor & gO,
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const c10::optional<Tensor> & running_mean,
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const c10::optional<Tensor> & running_var,
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bool training,
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double eps,
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const c10::optional<Tensor> & save_mean,
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const c10::optional<Tensor> & save_invstd,
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std::array<bool,3> output_mask);
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std::tuple<Tensor, Tensor> _euclidean_dist_backward(const Tensor & grad, const Tensor & x1, const Tensor & x2, const Tensor & res);
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Tensor kl_div_target_backward(Tensor grad_output, Tensor self, Tensor target, int64_t reduction, bool log_target);
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Tensor fft_backward(const Tensor& self, const Tensor& grad, int64_t signal_ndim,
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bool complex_input, bool complex_output,
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bool inverse, IntArrayRef checked_signal_sizes,
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int64_t normalization, bool onesided,
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IntArrayRef output_sizes);
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Tensor constant_pad_nd_backward(const Tensor& grad, IntArrayRef pad);
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std::tuple<Tensor, Tensor> cholesky_solve_backward(
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const Tensor& grad_x, const Tensor& self,
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const Tensor& input2, const Tensor& result, const bool upper);
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std::tuple<Tensor, Tensor, Tensor>
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infinitely_differentiable_native_group_norm_backward(
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const Tensor& dY,
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const Tensor& dmean,
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const Tensor& drstd,
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const Tensor& X,
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const Tensor& mean,
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const Tensor& rstd,
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const c10::optional<Tensor>& gamma,
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int64_t N,
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int64_t C,
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int64_t HxW,
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int64_t group,
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double eps,
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std::array<bool, 3> grad_input_mask);
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std::tuple<Tensor, Tensor, Tensor> prelu_double_backward(
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const Tensor & grad_grad_input,
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const Tensor & grad_grad_weight,
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const Tensor & grad_out,
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const Tensor & input_,
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const Tensor & weight_);
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Tensor as_strided_backward(Tensor grad, TensorGeometry input_geometry, IntArrayRef sizes, IntArrayRef strides, optional<int64_t> storage_offset_);
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std::tuple<Tensor, Tensor> atan2_backward(const Tensor& grad, const Tensor& self, const Tensor& other, std::array<bool, 2> output_mask);
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std::tuple<Tensor, Tensor, Tensor>
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infinitely_differentiable_native_layer_norm_backward(
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const Tensor& dY,
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const Tensor& dmean,
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const Tensor& drstd,
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const Tensor& X,
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const Tensor& mean,
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const Tensor& rstd,
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const c10::optional<Tensor>& gamma,
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int64_t M,
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int64_t N,
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double eps,
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std::array<bool, 3> grad_input_mask);
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} // namespace details
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} // namespace generated
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} // namespace autograd
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} // namespace torch
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