// Copyright (c) 2023 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. #pragma once #include #include #include "paddle/phi/api/include/tensor.h" #include "paddle/phi/common/int_array.h" namespace paddle { namespace primitive { namespace backend { using Tensor = paddle::Tensor; using IntArray = paddle::experimental::IntArray; template Tensor tanh_grad(const Tensor& out, const Tensor& grad_out); template Tensor mean_grad(const Tensor& x, const Tensor& out_grad, const IntArray& axis = {}, bool keepdim = false, bool reduce_all = false); template std::tuple add_grad(const Tensor& x, const Tensor& y, const Tensor& out_grad, int axis); template Tensor divide(const Tensor& x, const Tensor& y); template Tensor add(const Tensor& x, const Tensor& y); template Tensor multiply(const Tensor& x, const Tensor& y); template Tensor elementwise_pow(const Tensor& x, const Tensor& y); template Tensor scale(const Tensor& x, const Scalar& scale = 1.0, float bias = 0.0, bool bias_after_scale = true); template Tensor sum(const Tensor& x, const IntArray& axis = {}, phi::DataType dtype = phi::DataType::UNDEFINED, bool keepdim = false); template Tensor full(const IntArray& shape, const Scalar& value, phi::DataType dtype = phi::DataType::FLOAT32, phi::Place place = phi::CPUPlace()); template std::tuple reshape(const Tensor& x, const IntArray& shape); template Tensor expand(const Tensor& x, const IntArray& shape); template Tensor tile(const Tensor& x, const IntArray& repeat_times = {}); template std::tuple divide_grad(const Tensor& x, const Tensor& y, const Tensor& out, const Tensor& out_grad, int axis); template Tensor sum_grad(const Tensor& x, const Tensor& out_grad, const IntArray& axis, bool keepdim, bool reduce_all); } // namespace backend } // namespace primitive } // namespace paddle