// 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/randperm_kernel.h" #include "paddle/phi/core/kernel_registry.h" // See Note [ Why still include the fluid headers? ] #include "paddle/fluid/memory/memcpy.h" namespace phi { template void RandpermRawKernel( const Context& dev_ctx, int n, DataType dtype, int seed, DenseTensor* out) { DenseTensor tmp; tmp.Resize(phi::make_ddim({n})); T* tmp_data = dev_ctx.template HostAlloc(&tmp); std::shared_ptr engine; if (seed) { engine = std::make_shared(); engine->seed(seed); } else { engine = dev_ctx.GetHostGenerator()->GetCPUEngine(); } for (int i = 0; i < n; ++i) { tmp_data[i] = static_cast(i); } std::shuffle(tmp_data, tmp_data + n, *engine); T* out_data = dev_ctx.template Alloc(out); auto size = out->numel() * paddle::experimental::SizeOf(out->dtype()); paddle::memory::Copy( out->place(), out_data, tmp.place(), tmp_data, size, 0); } 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, GPU, ALL_LAYOUT, phi::RandpermRawKernel, float, double, int, int64_t) {} PD_REGISTER_KERNEL(randperm, GPU, ALL_LAYOUT, phi::RandpermKernel, float, double, int, int64_t) {}