/* Copyright (c) 2021 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 #include "paddle/fluid/framework/convert_utils.h" #include "paddle/fluid/framework/generator.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/fluid/operators/fill_constant_op.h" #ifdef PADDLE_WITH_MKLDNN #include "paddle/fluid/platform/mkldnn_helper.h" #endif namespace paddle { namespace operators { using Tensor = framework::Tensor; template class NPUGaussianRandomKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { float mean = context.Attr("mean"); float std = context.Attr("std"); auto* tensor = context.Output("Out"); tensor->mutable_data(context.GetPlace()); Tensor cpu_tensor(tensor->dtype()); cpu_tensor.Resize(tensor->dims()); T* cpu_data = cpu_tensor.mutable_data(platform::CPUPlace()); std::normal_distribution dist(mean, std); int64_t size = tensor->numel(); unsigned int seed = static_cast(context.Attr("seed")); auto engine = framework::GetCPURandomEngine(seed); for (int64_t i = 0; i < size; ++i) { cpu_data[i] = dist(*engine); } framework::TensorCopy( cpu_tensor, context.GetPlace(), context.template device_context(), tensor); context.template device_context() .Wait(); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OP_NPU_KERNEL(gaussian_random, ops::NPUGaussianRandomKernel);