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| #pragma once
|
| // @generated from tools/autograd/templates/python_nn_functions_dispatch.h
|
| #include "torch/csrc/utils/auto_gil.h"
|
| #include <ATen/ATen.h>
|
| // Contains inline wrappers around ATen functions that release the GIL and
| // switch to the correct CUDA device.
|
| namespace torch { namespace autograd {
|
| using namespace at;
| using at::Generator;
|
| inline Tensor dispatch_adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::adaptive_avg_pool2d_out(out, self, output_size);
| }
| inline Tensor dispatch_adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size) {
|
| AutoNoGIL no_gil;
| return at::adaptive_avg_pool2d(self, output_size);
| }
| inline Tensor dispatch_adaptive_avg_pool3d(const Tensor & self, IntArrayRef output_size, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::adaptive_avg_pool3d_out(out, self, output_size);
| }
| inline Tensor dispatch_adaptive_avg_pool3d(const Tensor & self, IntArrayRef output_size) {
|
| AutoNoGIL no_gil;
| return at::adaptive_avg_pool3d(self, output_size);
| }
| inline std::tuple<Tensor,Tensor> dispatch_adaptive_max_pool2d(const Tensor & self, IntArrayRef output_size, Tensor & out, Tensor & indices) {
|
| AutoNoGIL no_gil;
| return at::adaptive_max_pool2d_out(out, indices, self, output_size);
| }
| inline std::tuple<Tensor,Tensor> dispatch_adaptive_max_pool2d(const Tensor & self, IntArrayRef output_size) {
|
| AutoNoGIL no_gil;
| return at::adaptive_max_pool2d(self, output_size);
| }
| inline std::tuple<Tensor,Tensor> dispatch_adaptive_max_pool3d(const Tensor & self, IntArrayRef output_size, Tensor & out, Tensor & indices) {
|
| AutoNoGIL no_gil;
| return at::adaptive_max_pool3d_out(out, indices, self, output_size);
| }
| inline std::tuple<Tensor,Tensor> dispatch_adaptive_max_pool3d(const Tensor & self, IntArrayRef output_size) {
|
| AutoNoGIL no_gil;
| return at::adaptive_max_pool3d(self, output_size);
| }
| inline Tensor dispatch_avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::avg_pool2d_out(out, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override);
| }
| inline Tensor dispatch_avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) {
|
| AutoNoGIL no_gil;
| return at::avg_pool2d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override);
| }
| inline Tensor dispatch_avg_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::avg_pool3d_out(out, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override);
| }
| inline Tensor dispatch_avg_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) {
|
| AutoNoGIL no_gil;
| return at::avg_pool3d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override);
| }
| inline Tensor dispatch_binary_cross_entropy(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::binary_cross_entropy_out(out, self, target, weight, reduction);
| }
| inline Tensor dispatch_binary_cross_entropy(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction) {
|
| AutoNoGIL no_gil;
| return at::binary_cross_entropy(self, target, weight, reduction);
| }
| inline Tensor dispatch_col2im(const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::col2im_out(out, self, output_size, kernel_size, dilation, padding, stride);
| }
| inline Tensor dispatch_col2im(const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) {
|
| AutoNoGIL no_gil;
| return at::col2im(self, output_size, kernel_size, dilation, padding, stride);
| }
| inline Tensor dispatch_elu(const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::elu_out(out, self, alpha, scale, input_scale);
| }
| inline Tensor dispatch_elu(const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale) {
|
| AutoNoGIL no_gil;
| return at::elu(self, alpha, scale, input_scale);
| }
| inline Tensor dispatch_elu_(Tensor self, Scalar alpha, Scalar scale, Scalar input_scale) {
|
| AutoNoGIL no_gil;
| return at::elu_(self, alpha, scale, input_scale);
| }
| inline std::tuple<Tensor,Tensor> dispatch_fractional_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples, Tensor & output, Tensor & indices) {
|
| AutoNoGIL no_gil;
| return at::fractional_max_pool2d_out(output, indices, self, kernel_size, output_size, random_samples);
| }
| inline std::tuple<Tensor,Tensor> dispatch_fractional_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples) {
|
| AutoNoGIL no_gil;
| return at::fractional_max_pool2d(self, kernel_size, output_size, random_samples);
| }
| inline std::tuple<Tensor,Tensor> dispatch_fractional_max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples, Tensor & output, Tensor & indices) {
|
| AutoNoGIL no_gil;
| return at::fractional_max_pool3d_out(output, indices, self, kernel_size, output_size, random_samples);
| }
| inline std::tuple<Tensor,Tensor> dispatch_fractional_max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples) {
|
| AutoNoGIL no_gil;
| return at::fractional_max_pool3d(self, kernel_size, output_size, random_samples);
| }
| inline Tensor dispatch_gelu(const Tensor & self) {
|
| AutoNoGIL no_gil;
| return at::gelu(self);
| }
| inline Tensor dispatch_glu(const Tensor & self, int64_t dim, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::glu_out(out, self, dim);
| }
| inline Tensor dispatch_glu(const Tensor & self, int64_t dim) {
|
| AutoNoGIL no_gil;
| return at::glu(self, dim);
| }
| inline Tensor dispatch_hardtanh(const Tensor & self, Scalar min_val, Scalar max_val, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::hardtanh_out(out, self, min_val, max_val);
| }
| inline Tensor dispatch_hardtanh(const Tensor & self, Scalar min_val, Scalar max_val) {
|
| AutoNoGIL no_gil;
| return at::hardtanh(self, min_val, max_val);
| }
| inline Tensor dispatch_hardtanh_(Tensor self, Scalar min_val, Scalar max_val) {
|
| AutoNoGIL no_gil;
| return at::hardtanh_(self, min_val, max_val);
| }
| inline Tensor dispatch_im2col(const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::im2col_out(out, self, kernel_size, dilation, padding, stride);
| }
| inline Tensor dispatch_im2col(const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) {
|
| AutoNoGIL no_gil;
| return at::im2col(self, kernel_size, dilation, padding, stride);
| }
| inline Tensor dispatch_l1_loss(const Tensor & self, const Tensor & target, int64_t reduction, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::l1_loss_out(out, self, target, reduction);
| }
| inline Tensor dispatch_l1_loss(const Tensor & self, const Tensor & target, int64_t reduction) {
|
| AutoNoGIL no_gil;
| return at::l1_loss(self, target, reduction);
| }
| inline Tensor dispatch_leaky_relu(const Tensor & self, Scalar negative_slope, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::leaky_relu_out(out, self, negative_slope);
| }
| inline Tensor dispatch_leaky_relu(const Tensor & self, Scalar negative_slope) {
|
| AutoNoGIL no_gil;
| return at::leaky_relu(self, negative_slope);
| }
| inline Tensor dispatch_leaky_relu_(Tensor self, Scalar negative_slope) {
|
| AutoNoGIL no_gil;
| return at::leaky_relu_(self, negative_slope);
| }
| inline Tensor dispatch_linear(const Tensor & input, const Tensor & weight, const Tensor & bias) {
|
| AutoNoGIL no_gil;
| return at::linear(input, weight, bias);
| }
| inline Tensor dispatch_log_sigmoid(const Tensor & self, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::log_sigmoid_out(out, self);
| }
| inline Tensor dispatch_log_sigmoid(const Tensor & self) {
|
| AutoNoGIL no_gil;
| return at::log_sigmoid(self);
| }
| inline std::tuple<Tensor,Tensor> dispatch_max_pool2d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, Tensor & out, Tensor & indices) {
|
| AutoNoGIL no_gil;
| return at::max_pool2d_with_indices_out(out, indices, self, kernel_size, stride, padding, dilation, ceil_mode);
| }
| inline std::tuple<Tensor,Tensor> dispatch_max_pool2d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
|
| AutoNoGIL no_gil;
| return at::max_pool2d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode);
| }
| inline std::tuple<Tensor,Tensor> dispatch_max_pool3d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, Tensor & out, Tensor & indices) {
|
| AutoNoGIL no_gil;
| return at::max_pool3d_with_indices_out(out, indices, self, kernel_size, stride, padding, dilation, ceil_mode);
| }
| inline std::tuple<Tensor,Tensor> dispatch_max_pool3d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
|
| AutoNoGIL no_gil;
| return at::max_pool3d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode);
| }
| inline Tensor dispatch_max_unpool2d(const Tensor & self, const Tensor & indices, IntArrayRef output_size, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::max_unpool2d_out(out, self, indices, output_size);
| }
| inline Tensor dispatch_max_unpool2d(const Tensor & self, const Tensor & indices, IntArrayRef output_size) {
|
| AutoNoGIL no_gil;
| return at::max_unpool2d(self, indices, output_size);
| }
| inline Tensor dispatch_max_unpool3d(const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::max_unpool3d_out(out, self, indices, output_size, stride, padding);
| }
| inline Tensor dispatch_max_unpool3d(const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding) {
|
| AutoNoGIL no_gil;
| return at::max_unpool3d(self, indices, output_size, stride, padding);
| }
| inline Tensor dispatch_mkldnn_linear(const Tensor & input, const Tensor & weight, const Tensor & bias) {
|
| AutoNoGIL no_gil;
| return at::mkldnn_linear(input, weight, bias);
| }
| inline Tensor dispatch_mkldnn_reorder_conv2d_weight(const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups) {
|
| AutoNoGIL no_gil;
| return at::mkldnn_reorder_conv2d_weight(self, padding, stride, dilation, groups);
| }
| inline Tensor dispatch_mse_loss(const Tensor & self, const Tensor & target, int64_t reduction, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::mse_loss_out(out, self, target, reduction);
| }
| inline Tensor dispatch_mse_loss(const Tensor & self, const Tensor & target, int64_t reduction) {
|
| AutoNoGIL no_gil;
| return at::mse_loss(self, target, reduction);
| }
| inline Tensor dispatch_multi_margin_loss(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::multi_margin_loss_out(out, self, target, p, margin, weight, reduction);
| }
| inline Tensor dispatch_multi_margin_loss(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction) {
|
| AutoNoGIL no_gil;
| return at::multi_margin_loss(self, target, p, margin, weight, reduction);
| }
| inline Tensor dispatch_multilabel_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::multilabel_margin_loss_out(out, self, target, reduction);
| }
| inline Tensor dispatch_multilabel_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction) {
|
| AutoNoGIL no_gil;
| return at::multilabel_margin_loss(self, target, reduction);
| }
| inline Tensor dispatch_nll_loss(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::nll_loss_out(out, self, target, weight, reduction, ignore_index);
| }
| inline Tensor dispatch_nll_loss(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) {
|
| AutoNoGIL no_gil;
| return at::nll_loss(self, target, weight, reduction, ignore_index);
| }
| inline Tensor dispatch_nll_loss2d(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::nll_loss2d_out(out, self, target, weight, reduction, ignore_index);
| }
| inline Tensor dispatch_nll_loss2d(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) {
|
| AutoNoGIL no_gil;
| return at::nll_loss2d(self, target, weight, reduction, ignore_index);
| }
| inline Tensor dispatch_one_hot(const Tensor & self, int64_t num_classes) {
|
| AutoNoGIL no_gil;
| return at::one_hot(self, num_classes);
| }
| inline Tensor dispatch_reflection_pad1d(const Tensor & self, IntArrayRef padding, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::reflection_pad1d_out(out, self, padding);
| }
| inline Tensor dispatch_reflection_pad1d(const Tensor & self, IntArrayRef padding) {
|
| AutoNoGIL no_gil;
| return at::reflection_pad1d(self, padding);
| }
| inline Tensor dispatch_reflection_pad2d(const Tensor & self, IntArrayRef padding, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::reflection_pad2d_out(out, self, padding);
| }
| inline Tensor dispatch_reflection_pad2d(const Tensor & self, IntArrayRef padding) {
|
| AutoNoGIL no_gil;
| return at::reflection_pad2d(self, padding);
| }
| inline Tensor dispatch_replication_pad1d(const Tensor & self, IntArrayRef padding, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::replication_pad1d_out(out, self, padding);
| }
| inline Tensor dispatch_replication_pad1d(const Tensor & self, IntArrayRef padding) {
|
| AutoNoGIL no_gil;
| return at::replication_pad1d(self, padding);
| }
| inline Tensor dispatch_replication_pad2d(const Tensor & self, IntArrayRef padding, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::replication_pad2d_out(out, self, padding);
| }
| inline Tensor dispatch_replication_pad2d(const Tensor & self, IntArrayRef padding) {
|
| AutoNoGIL no_gil;
| return at::replication_pad2d(self, padding);
| }
| inline Tensor dispatch_replication_pad3d(const Tensor & self, IntArrayRef padding, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::replication_pad3d_out(out, self, padding);
| }
| inline Tensor dispatch_replication_pad3d(const Tensor & self, IntArrayRef padding) {
|
| AutoNoGIL no_gil;
| return at::replication_pad3d(self, padding);
| }
| inline Tensor dispatch_rrelu_with_noise(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::rrelu_with_noise_out(out, self, noise, lower, upper, training, generator);
| }
| inline Tensor dispatch_rrelu_with_noise(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator) {
|
| AutoNoGIL no_gil;
| return at::rrelu_with_noise(self, noise, lower, upper, training, generator);
| }
| inline Tensor dispatch_rrelu_with_noise_(Tensor self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator) {
|
| AutoNoGIL no_gil;
| return at::rrelu_with_noise_(self, noise, lower, upper, training, generator);
| }
| inline Tensor dispatch_slow_conv_dilated2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) {
|
| AutoNoGIL no_gil;
| return at::slow_conv_dilated2d(self, weight, kernel_size, bias, stride, padding, dilation);
| }
| inline Tensor dispatch_slow_conv_dilated3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) {
|
| AutoNoGIL no_gil;
| return at::slow_conv_dilated3d(self, weight, kernel_size, bias, stride, padding, dilation);
| }
| inline Tensor dispatch_slow_conv_transpose2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::slow_conv_transpose2d_out(out, self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
| }
| inline Tensor dispatch_slow_conv_transpose2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation) {
|
| AutoNoGIL no_gil;
| return at::slow_conv_transpose2d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
| }
| inline Tensor dispatch_slow_conv_transpose3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::slow_conv_transpose3d_out(out, self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
| }
| inline Tensor dispatch_slow_conv_transpose3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation) {
|
| AutoNoGIL no_gil;
| return at::slow_conv_transpose3d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
| }
| inline Tensor dispatch_smooth_l1_loss(const Tensor & self, const Tensor & target, int64_t reduction, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::smooth_l1_loss_out(out, self, target, reduction);
| }
| inline Tensor dispatch_smooth_l1_loss(const Tensor & self, const Tensor & target, int64_t reduction) {
|
| AutoNoGIL no_gil;
| return at::smooth_l1_loss(self, target, reduction);
| }
| inline Tensor dispatch_soft_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::soft_margin_loss_out(out, self, target, reduction);
| }
| inline Tensor dispatch_soft_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction) {
|
| AutoNoGIL no_gil;
| return at::soft_margin_loss(self, target, reduction);
| }
| inline Tensor dispatch_softplus(const Tensor & self, Scalar beta, Scalar threshold, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::softplus_out(out, self, beta, threshold);
| }
| inline Tensor dispatch_softplus(const Tensor & self, Scalar beta, Scalar threshold) {
|
| AutoNoGIL no_gil;
| return at::softplus(self, beta, threshold);
| }
| inline Tensor dispatch_softshrink(const Tensor & self, Scalar lambd, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::softshrink_out(out, self, lambd);
| }
| inline Tensor dispatch_softshrink(const Tensor & self, Scalar lambd) {
|
| AutoNoGIL no_gil;
| return at::softshrink(self, lambd);
| }
| inline Tensor dispatch_thnn_conv2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::thnn_conv2d_out(out, self, weight, kernel_size, bias, stride, padding);
| }
| inline Tensor dispatch_thnn_conv2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) {
|
| AutoNoGIL no_gil;
| return at::thnn_conv2d(self, weight, kernel_size, bias, stride, padding);
| }
| inline Tensor dispatch_thnn_conv3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::thnn_conv3d_out(out, self, weight, kernel_size, bias, stride, padding);
| }
| inline Tensor dispatch_thnn_conv3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) {
|
| AutoNoGIL no_gil;
| return at::thnn_conv3d(self, weight, kernel_size, bias, stride, padding);
| }
| inline Tensor dispatch_thnn_conv_depthwise2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::thnn_conv_depthwise2d_out(out, self, weight, kernel_size, bias, stride, padding, dilation);
| }
| inline Tensor dispatch_thnn_conv_depthwise2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) {
|
| AutoNoGIL no_gil;
| return at::thnn_conv_depthwise2d(self, weight, kernel_size, bias, stride, padding, dilation);
| }
| inline Tensor dispatch_upsample_bicubic2d(const Tensor & self, IntArrayRef output_size, bool align_corners, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::upsample_bicubic2d_out(out, self, output_size, align_corners);
| }
| inline Tensor dispatch_upsample_bicubic2d(const Tensor & self, IntArrayRef output_size, bool align_corners) {
|
| AutoNoGIL no_gil;
| return at::upsample_bicubic2d(self, output_size, align_corners);
| }
| inline Tensor dispatch_upsample_bilinear2d(const Tensor & self, IntArrayRef output_size, bool align_corners, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::upsample_bilinear2d_out(out, self, output_size, align_corners);
| }
| inline Tensor dispatch_upsample_bilinear2d(const Tensor & self, IntArrayRef output_size, bool align_corners) {
|
| AutoNoGIL no_gil;
| return at::upsample_bilinear2d(self, output_size, align_corners);
| }
| inline Tensor dispatch_upsample_linear1d(const Tensor & self, IntArrayRef output_size, bool align_corners, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::upsample_linear1d_out(out, self, output_size, align_corners);
| }
| inline Tensor dispatch_upsample_linear1d(const Tensor & self, IntArrayRef output_size, bool align_corners) {
|
| AutoNoGIL no_gil;
| return at::upsample_linear1d(self, output_size, align_corners);
| }
| inline Tensor dispatch_upsample_nearest1d(const Tensor & self, IntArrayRef output_size, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::upsample_nearest1d_out(out, self, output_size);
| }
| inline Tensor dispatch_upsample_nearest1d(const Tensor & self, IntArrayRef output_size) {
|
| AutoNoGIL no_gil;
| return at::upsample_nearest1d(self, output_size);
| }
| inline Tensor dispatch_upsample_nearest2d(const Tensor & self, IntArrayRef output_size, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::upsample_nearest2d_out(out, self, output_size);
| }
| inline Tensor dispatch_upsample_nearest2d(const Tensor & self, IntArrayRef output_size) {
|
| AutoNoGIL no_gil;
| return at::upsample_nearest2d(self, output_size);
| }
| inline Tensor dispatch_upsample_nearest3d(const Tensor & self, IntArrayRef output_size, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::upsample_nearest3d_out(out, self, output_size);
| }
| inline Tensor dispatch_upsample_nearest3d(const Tensor & self, IntArrayRef output_size) {
|
| AutoNoGIL no_gil;
| return at::upsample_nearest3d(self, output_size);
| }
| inline Tensor dispatch_upsample_trilinear3d(const Tensor & self, IntArrayRef output_size, bool align_corners, Tensor out) {
|
| AutoNoGIL no_gil;
| return at::upsample_trilinear3d_out(out, self, output_size, align_corners);
| }
| inline Tensor dispatch_upsample_trilinear3d(const Tensor & self, IntArrayRef output_size, bool align_corners) {
|
| AutoNoGIL no_gil;
| return at::upsample_trilinear3d(self, output_size, align_corners);
| }
|
| }} // namespace torch::autograd
|
|
