// 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/phi/kernels/reshape_kernel.h" #include "paddle/phi/backends/all_context.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/core/tensor_utils.h" #include "paddle/phi/infermeta/unary.h" #include "paddle/phi/kernels/funcs/common_shape.h" namespace phi { template void ReshapeKernel(const Context& dev_ctx, const DenseTensor& x, const IntArray& shape, DenseTensor* out) { MetaTensor meta_out(out); InferMetaFromVecValue(x, shape.GetData(), &meta_out); if (x.has_allocation() && x.Holder() == out->Holder()) { dev_ctx.Alloc(out, x.dtype()); return; } dev_ctx.Alloc(out, x.dtype()); if (out->numel() == 0) return; // TODO(chenweihang): the output dims are overwrite after copying, // here we need to use copy method that only copy data auto dims = out->dims(); phi::Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out); out->Resize(dims); out->ResetLoD(x.lod()); } template void ReshapeWithXShapeKernel(const Context& dev_ctx, const DenseTensor& x, const IntArray& shape, DenseTensor* out, DenseTensor* xshape UNUSED) { ReshapeKernel(dev_ctx, x, shape, out); } } // namespace phi PD_REGISTER_KERNEL_FOR_ALL_BACKEND_DTYPE(reshape, ALL_LAYOUT, phi::ReshapeKernel) {} // FIXME(dev): Need to deprecate it while // cleaning old IR system. PD_REGISTER_KERNEL_FOR_ALL_BACKEND_DTYPE(reshape_with_xshape, ALL_LAYOUT, phi::ReshapeWithXShapeKernel) { kernel->OutputAt(1).SetBackend(phi::Backend::UNDEFINED); }