/* 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 "paddle/pten/api/include/math.h" #include #include "glog/logging.h" #include "paddle/pten/api/lib/kernel_dispatch.h" #include "paddle/pten/api/lib/utils/allocator.h" #include "paddle/pten/include/core.h" #include "paddle/pten/include/infershape.h" #include "paddle/pten/infershape/unary.h" namespace paddle { namespace experimental { Tensor mean(const Tensor& x) { // 1. Get kernel signature and kernel auto kernel_key_set = ParseKernelKeyByInputArgs(x); auto kernel_key = kernel_key_set.GetHigestPriorityKernelKey(); auto kernel = pten::KernelFactory::Instance().SelectKernelOrThrowError( "mean", kernel_key); // 2. Get Device Context auto* dev_ctx = GetDeviceContextByBackend(kernel_key.backend()); auto kernel_context = pten::KernelContext(*dev_ctx); // 3. Auto data transform auto dense_x = std::dynamic_pointer_cast(x.impl()); kernel_context.EmplaceBackInput(dense_x); // 4. InferShape auto out_meta = ReductionInferShape(dense_x->meta()); // 5. Prepare outputs Tensor out; const auto allocator = std::make_shared( pten::TransToFluidPlace(kernel_key.backend())); auto dense_out = std::make_shared(allocator, out_meta); kernel_context.EmplaceBackOutput(dense_out); out.set_impl(dense_out); // 6. Call kernel kernel(&kernel_context); return out; } } // namespace experimental } // namespace paddle