// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "paddle/phi/kernels/hierarchical_sigmoid_grad_kernel.h" #include "paddle/phi/backends/cpu/cpu_context.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/cpu/hierarchical_sigmoid_grad.h" namespace phi { template void HierarchicalSigmoidGradKernel(const Context& ctx, const DenseTensor& x, const DenseTensor& w, const DenseTensor& label, paddle::optional path, paddle::optional code, paddle::optional bias, const DenseTensor& pre_out, const DenseTensor& out_grad, int num_classes, bool remote_prefetch, int trainer_id, const std::vector& height_sections, const std::vector& epmap, const std::vector& table_names, bool is_sparse, DenseTensor* x_grad, DenseTensor* w_grad, DenseTensor* bias_grad) { HierarchicalSigmoidGradKernelImpl(ctx, x, w, label, path, code, bias, pre_out, out_grad, num_classes, remote_prefetch, trainer_id, height_sections, epmap, table_names, is_sparse, x_grad, w_grad, bias_grad); } } // namespace phi PD_REGISTER_KERNEL(hierarchical_sigmoid_grad, CPU, ALL_LAYOUT, phi::HierarchicalSigmoidGradKernel, float, double) {}