// 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/shape_kernel.h" #include "paddle/phi/backends/onednn/onednn_reuse.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void ShapeKernel(const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) { DDim x_dims = x.dims(); // Output of shape op is often fed as x to fill_constant ops // and we need to rotate a shape otherwise Tensors of wrong shape may be // allocated if (OneDNNContext::tls().get_cur_paddle_data_layout() == DataLayout::kNHWC && x_dims.size() >= 3) { auto rdims = vectorize(x_dims); std::rotate(rdims.begin() + 1, rdims.begin() + 2, rdims.end()); x_dims = make_ddim(rdims); } out->Resize({x_dims.size()}); auto out_data = dev_ctx.template Alloc(out); for (int i = 0; i < x_dims.size(); ++i) { out_data[i] = x_dims[i]; } dnnl::memory::desc out_mem_desc( vectorize(out->dims()), funcs::ToOneDNNDataType(out->dtype()), funcs::GetPlainOneDNNFormat(out->dims().size())); out->set_mem_desc(out_mem_desc); } } // namespace phi PD_REGISTER_KERNEL(shape, OneDNN, ONEDNN, phi::ShapeKernel, float, phi::dtype::bfloat16, int8_t, uint8_t) { kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); }