// 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/split_kernel.h" #include "paddle/fluid/operators/strided_memcpy.h" #include "paddle/phi/common/float16.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/infermeta/unary.h" #include "paddle/phi/kernels/funcs/concat_and_split_functor.h" namespace phi { template void SplitKernel(const Context& dev_ctx, const DenseTensor& x, const ScalarArray& num_or_sections, const Scalar& axis_scalar, std::vector outs) { // need to infershape output if (num_or_sections.FromTensor() || axis_scalar.FromTensor()) { std::vector out_metas; for (size_t i = 0; i < outs.size(); ++i) { out_metas.push_back(outs[i]); } phi::SplitInferMeta(x, num_or_sections, axis_scalar, &out_metas, true); for (size_t i = 0; i < out_metas.size(); ++i) { outs[i]->Resize(out_metas[i].dims()); } } std::vector shape_refer; for (size_t j = 0; j < outs.size(); ++j) { dev_ctx.template Alloc(outs[j]); shape_refer.emplace_back(outs[j]); } int axis = axis_scalar.to(); // Sometimes direct copies will be faster, this maybe need deeply analysis. if (axis == 0 && outs.size() < 10) { paddle::operators::StridedMemcpyWithAxis0( dev_ctx, x, shape_refer, &outs); } else { phi::funcs::SplitFunctor functor; functor(dev_ctx, x, shape_refer, axis, &outs); } } } // namespace phi PD_REGISTER_KERNEL(split, CPU, ALL_LAYOUT, phi::SplitKernel, float, double, int64_t, int, bool, phi::dtype::float16) {}