wehub-resource-sync
91 lines
3.1 KiB
C++
91 lines
3.1 KiB
C++
// 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/backends/cpu/cpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/full_kernel.h"
|
|
#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
|
|
|
|
#include "paddle/phi/kernels/impl/lu_kernel_impl.h"
|
|
#include "paddle/phi/kernels/lu_kernel.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void LUKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
bool pivot,
|
|
DenseTensor* out,
|
|
DenseTensor* pivots,
|
|
DenseTensor* infos) {
|
|
PADDLE_ENFORCE_EQ(pivot,
|
|
true,
|
|
errors::InvalidArgument(
|
|
"lu without pivoting is not implemented on the CPU, "
|
|
"but got pivots=False"));
|
|
|
|
if (x.numel() == 0) {
|
|
Full<int, Context>(dev_ctx, infos->dims(), static_cast<int>(0), infos);
|
|
Full<int, Context>(dev_ctx, pivots->dims(), static_cast<int>(0), pivots);
|
|
Full<T, Context>(dev_ctx, out->dims(), static_cast<T>(0), out);
|
|
return;
|
|
}
|
|
*out = Transpose2DTo6D<Context, T>(dev_ctx, x);
|
|
|
|
auto outdims = out->dims();
|
|
auto outrank = outdims.size();
|
|
|
|
int m = static_cast<int>(outdims[outrank - 1]);
|
|
int n = static_cast<int>(outdims[outrank - 2]);
|
|
int lda = std::max(1, m);
|
|
|
|
auto ipiv_dims = slice_ddim(outdims, 0, outrank - 1);
|
|
ipiv_dims[outrank - 2] = std::min(m, n);
|
|
pivots->Resize(ipiv_dims);
|
|
dev_ctx.template Alloc<int>(pivots);
|
|
auto ipiv_data = pivots->data<int>();
|
|
|
|
auto info_dims = slice_ddim(outdims, 0, outrank - 2);
|
|
infos->Resize(info_dims);
|
|
dev_ctx.template Alloc<int>(infos);
|
|
auto info_data = infos->data<int>();
|
|
|
|
auto batchsize = product(info_dims);
|
|
batchsize = std::max(static_cast<int>(batchsize), 1);
|
|
dev_ctx.template Alloc<T>(out);
|
|
auto out_data = out->data<T>();
|
|
for (int b = 0; b < batchsize; b++) {
|
|
auto out_data_item = &out_data[b * m * n];
|
|
int* info_data_item = &info_data[b];
|
|
int* ipiv_data_item = &ipiv_data[b * std::min(m, n)];
|
|
funcs::lapackLu<T>(
|
|
m, n, out_data_item, lda, ipiv_data_item, info_data_item);
|
|
}
|
|
*out = Transpose2DTo6D<Context, T>(dev_ctx, *out);
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(lu,
|
|
CPU,
|
|
ALL_LAYOUT,
|
|
phi::LUKernel,
|
|
float,
|
|
double,
|
|
phi::complex64,
|
|
phi::complex128) {
|
|
kernel->OutputAt(1).SetDataType(phi::DataType::INT32);
|
|
kernel->OutputAt(2).SetDataType(phi::DataType::INT32);
|
|
}
|