wehub-resource-sync
108 lines
3.8 KiB
C++
108 lines
3.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/eig_kernel.h"
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#include "paddle/phi/kernels/cpu/eig.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void EigKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out_w,
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DenseTensor* out_v) {
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dev_ctx.template Alloc<dtype::Complex<T>>(out_w);
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dev_ctx.template Alloc<dtype::Complex<T>>(out_v);
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if (x.numel() == 0) {
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return;
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}
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if (!IsComplexType(x.dtype())) {
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int batch_count = BatchCount(x);
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int order = static_cast<int>(x.dims(-1));
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PADDLE_ENFORCE_LT(0,
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order,
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errors::InvalidArgument(
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"The order of Input(X) should be greater than 0."));
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DenseTensor out_w_real;
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DenseTensor out_v_real;
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// double the size of out_w_real, the first half stores the real part,
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// the next half stores the imag part
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std::vector<int64_t> real_w_dims = vectorize<int64_t>(out_w->dims());
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real_w_dims.back() *= 2;
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out_w_real.Resize(real_w_dims);
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dev_ctx.template Alloc<dtype::Real<T>>(&out_w_real);
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out_v_real.Resize(x.dims());
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dev_ctx.template Alloc<dtype::Real<T>>(&out_v_real);
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ApplyEigKernel<dtype::Real<T>, Context>(
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x, &out_w_real, &out_v_real, dev_ctx);
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// 1. extract real part & imag part from out_w_real
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DenseTensor out_w_real_part =
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funcs::Slice<T>(dev_ctx, out_w_real, {-1}, {0}, {order});
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DenseTensor out_w_imag_part =
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funcs::Slice<T>(dev_ctx, out_w_real, {-1}, {order}, {order * 2});
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// 2. construct complex values
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auto* out_w_real_part_ptr = out_w_real_part.data<dtype::Real<T>>();
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auto* out_w_imag_part_ptr = out_w_imag_part.data<dtype::Real<T>>();
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int out_w_numel = static_cast<int>(out_w->numel());
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funcs::ForRange<Context> for_range(dev_ctx, out_w_numel);
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funcs::RealImagToComplexFunctor<dtype::Complex<T>> functor(
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out_w_real_part_ptr,
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out_w_imag_part_ptr,
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dev_ctx.template Alloc<dtype::Complex<T>>(out_w),
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out_w_numel);
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for_range(functor);
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// 3. construct complex vectors
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DenseTensor out_v_real_trans = TransposeLast2Dim<T>(dev_ctx, out_v_real);
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DenseTensor out_v_trans;
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out_v_trans.Resize(x.dims());
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dev_ctx.template Alloc<dtype::Complex<T>>(&out_v_trans);
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ConstructComplexVectors<dtype::Real<T>, dtype::Complex<T>, Context>(
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&out_v_trans, *out_w, out_v_real_trans, dev_ctx, batch_count, order);
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TransposeTwoAxis<dtype::Complex<T>, Context>(
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out_v_trans, out_v, x.dims().size() - 1, x.dims().size() - 2, dev_ctx);
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} else {
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ApplyEigKernel<T, Context>(x, out_w, out_v, dev_ctx);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(eig,
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CPU,
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ALL_LAYOUT,
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phi::EigKernel,
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float,
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double,
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phi::complex64,
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phi::complex128) {
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if (kernel_key.dtype() == phi::DataType::FLOAT32 ||
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kernel_key.dtype() == phi::DataType::FLOAT64) {
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kernel->OutputAt(0).SetDataType(phi::dtype::ToComplex(kernel_key.dtype()));
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kernel->OutputAt(1).SetDataType(phi::dtype::ToComplex(kernel_key.dtype()));
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}
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}
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