wehub-resource-sync
106 lines
3.5 KiB
C++
106 lines
3.5 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.
|
|
|
|
#pragma once
|
|
|
|
#include "paddle/phi/kernels/cholesky_solve_kernel.h"
|
|
#include "paddle/phi/kernels/complex_kernel.h"
|
|
#include "paddle/phi/kernels/empty_kernel.h"
|
|
#include "paddle/phi/kernels/expand_kernel.h"
|
|
#include "paddle/phi/kernels/funcs/common_shape.h"
|
|
#include "paddle/phi/kernels/transpose_kernel.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
class CholeskySolveFunctor {
|
|
public:
|
|
void operator()(const Context& dev_ctx,
|
|
bool upper,
|
|
int M,
|
|
int N,
|
|
T* Adata,
|
|
int lda,
|
|
T* Bdata,
|
|
int* devInfo);
|
|
};
|
|
|
|
template <typename T, typename Context>
|
|
void CholeskySolveKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& y,
|
|
bool upper,
|
|
DenseTensor* out) {
|
|
if (out && out->numel() == 0) {
|
|
dev_ctx.template Alloc<T>(out);
|
|
return;
|
|
}
|
|
// get broadcast dim
|
|
std::vector<int64_t> x_bst_dims_vec;
|
|
std::vector<int64_t> y_bst_dims_vec;
|
|
std::tie(x_bst_dims_vec, y_bst_dims_vec) =
|
|
funcs::MatrixGetBroadcastDims(x, y);
|
|
IntArray x_bst_dims(x_bst_dims_vec);
|
|
IntArray y_bst_dims(y_bst_dims_vec);
|
|
|
|
DenseTensor y_bst = Empty<T, Context>(dev_ctx, y_bst_dims);
|
|
ExpandKernel<T, Context>(dev_ctx, y, y_bst_dims, &y_bst);
|
|
|
|
// Tensor broadcast to temp 'x_bst' and 'y_bst'
|
|
DenseTensor x_bst = Empty<T, Context>(dev_ctx, x_bst_dims);
|
|
ExpandKernel<T, Context>(dev_ctx, x, x_bst_dims, &x_bst);
|
|
|
|
// calculate y_bst's conjugate for complex
|
|
DenseTensor y_bst_conj = Conj<T, Context>(dev_ctx, y_bst);
|
|
y_bst_conj = TransposeLast2Dim<T>(dev_ctx, y_bst_conj);
|
|
T* y_bst_conj_data = y_bst_conj.data<T>();
|
|
|
|
// calculate x_bst's conjugate for complex
|
|
DenseTensor x_bst_conj = Conj<T, Context>(dev_ctx, x_bst);
|
|
x_bst_conj = TransposeLast2Dim<T>(dev_ctx, x_bst_conj);
|
|
|
|
// copy x_bst's conjugate to 'result'
|
|
DenseTensor result;
|
|
Copy<Context>(dev_ctx, x_bst_conj, dev_ctx.GetPlace(), false, &result);
|
|
T* res_data = result.data<T>();
|
|
|
|
// CPU use lapack, GPU use cusolver
|
|
int x_bst_ndim = x_bst_dims_vec.size();
|
|
int M = static_cast<int>(x_bst_dims_vec[x_bst_ndim - 2]);
|
|
int N = static_cast<int>(x_bst_dims_vec[x_bst_ndim - 1]);
|
|
int batchsize = product(slice_ddim(x_bst.dims(), 0, x_bst_ndim - 2));
|
|
|
|
DenseTensor info = Empty<int, Context>(dev_ctx, IntArray({batchsize}));
|
|
int* info_data = info.data<int>();
|
|
|
|
CholeskySolveFunctor<T, Context> functor;
|
|
for (int i = 0; i < batchsize; ++i) {
|
|
functor(dev_ctx,
|
|
upper,
|
|
M,
|
|
N,
|
|
y_bst_conj_data + i * M * M,
|
|
std::max(1, M),
|
|
res_data + i * M * N,
|
|
info_data + i);
|
|
}
|
|
|
|
// calculate out's conjugate for complex
|
|
result = TransposeLast2Dim<T>(dev_ctx, result);
|
|
out->Resize(x_bst_dims_vec);
|
|
ConjKernel<T, Context>(dev_ctx, result, out);
|
|
}
|
|
|
|
} // namespace phi
|