// 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/tril_triu_kernel.h" #include "paddle/phi/backends/xpu/enforce_xpu.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void TrilTriuKernel(const Context& ctx, const DenseTensor& x, int diagonal, bool lower, DenseTensor* out) { using XPUType = typename XPUTypeTrait::Type; ctx.template Alloc(out); auto xshape = vectorize(x.dims()); int r = 0; if (lower) { r = xpu::tril(ctx.x_context(), reinterpret_cast(x.data()), reinterpret_cast(out->data()), xshape, diagonal); PADDLE_ENFORCE_XDNN_SUCCESS(r, "tril_op"); } else { r = xpu::triu(ctx.x_context(), reinterpret_cast(x.data()), reinterpret_cast(out->data()), xshape, diagonal); PADDLE_ENFORCE_XDNN_SUCCESS(r, "triu_op"); } } } // namespace phi PD_REGISTER_KERNEL( tril_triu, XPU, ALL_LAYOUT, phi::TrilTriuKernel, int, float) {}