// 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. #include "paddle/phi/kernels/prelu_kernel.h" #include "paddle/phi/backends/xpu/enforce_xpu.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& data_format, const std::string& mode, DenseTensor* out) { using XPUType = typename XPUTypeTrait::Type; const T* x_ptr = x.data(); const T* alpha_ptr = alpha.data(); T* y_ptr = dev_ctx.template Alloc(out); auto x_dim = x.dims(); auto x_rank = x_dim.size(); std::vector x_shape(x_rank); for (int i = 0; i < x_rank; i++) { x_shape[i] = x_dim[i]; } auto alpha_dim = alpha.dims(); auto alpha_rank = alpha_dim.size(); std::vector alpha_shape(x_rank, 1); // same size with x_shape for (int i = 0; i < alpha_rank; i++) { alpha_shape[i] = alpha_dim[i]; } int r = xpu::prelu(dev_ctx.x_context(), reinterpret_cast(x_ptr), reinterpret_cast(alpha_ptr), reinterpret_cast(y_ptr), x_shape, alpha_shape); PADDLE_ENFORCE_XDNN_SUCCESS(r, "prelu"); } } // namespace phi PD_REGISTER_KERNEL(prelu, XPU, ALL_LAYOUT, phi::PReluKernel, float) {}