// 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/prelu_kernel.h" #include "paddle/phi/backends/cpu/cpu_context.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void PReluKernel(const Context& dev_ctx, const DenseTensor& x, const DenseTensor& alpha, const std::string& mode, const std::string& data_format, DenseTensor* out) { const T* x_ptr = x.data(); const T* alpha_ptr = alpha.data(); T* o_ptr = dev_ctx.template Alloc(out); int numel = x.numel(); auto dim = x.dims(); int index = 0; int i = 0; if (mode == "channel") { if (data_format == "NCHW") { int temp = 1; for (int j = 2; j < dim.size(); j++) { temp *= dim[j]; } for (i = 0; i < numel; i++) { index = (i / temp) % dim[1]; o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i]; } } else { for (i = 0; i < numel; i++) { index = i % dim[dim.size() - 1]; o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i]; } } } else if (mode == "element") { int temp = 1; for (int j = 1; j < dim.size(); j++) { temp *= dim[j]; } for (i = 0; i < numel; i++) { index = i % temp; o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i]; } } else { for (i = 0; i < numel; i++) { o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[0] * x_ptr[i]; } } } } // namespace phi PD_REGISTER_KERNEL(prelu, CPU, ALL_LAYOUT, phi::PReluKernel, float, double) {}