// 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/randperm_kernel.h" #include "paddle/phi/backends/xpu/enforce_xpu.h" #include "paddle/phi/backends/xpu/xpu_context.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void RandpermRawKernel( const Context& dev_ctx, int n, DataType dtype, int seed, DenseTensor* out) { std::shared_ptr engine; if (seed) { engine = std::make_shared(); engine->seed(seed); } else { engine = dev_ctx.GetGenerator()->GetCPUEngine(); } if (dev_ctx.GetPlace().GetType() == phi::AllocationType::CPU) { T* out_data = dev_ctx.template HostAlloc(out); for (int i = 0; i < n; ++i) { out_data[i] = static_cast(i); } std::shuffle(out_data, out_data + n, *engine); } else { dev_ctx.template Alloc(out); phi::DenseTensor tmp_tensor; tmp_tensor.Resize(phi::make_ddim({n})); T* tmp_data = dev_ctx.template HostAlloc(&tmp_tensor); for (int i = 0; i < n; ++i) { tmp_data[i] = static_cast(i); } std::shuffle(tmp_data, tmp_data + n, *engine); Copy(dev_ctx, tmp_tensor, dev_ctx.GetPlace(), true, out); } } template void RandpermKernel(const Context& dev_ctx, int n, DataType dtype, DenseTensor* out) { RandpermRawKernel(dev_ctx, n, dtype, 0, out); } } // namespace phi PD_REGISTER_KERNEL(randperm_raw, XPU, ALL_LAYOUT, phi::RandpermRawKernel, int, int64_t, float, double) {} PD_REGISTER_KERNEL(randperm, XPU, ALL_LAYOUT, phi::RandpermKernel, int, int64_t, float, double) {}