wehub-resource-sync
92 lines
3.2 KiB
C++
92 lines
3.2 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/kernels/triangular_solve_kernel.h"
|
|
|
|
#include "paddle/common/ddim.h"
|
|
#include "paddle/phi/backends/cpu/cpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/empty_kernel.h"
|
|
#include "paddle/phi/kernels/expand_kernel.h"
|
|
#include "paddle/phi/kernels/funcs/blas/blas.h"
|
|
#include "paddle/phi/kernels/funcs/common_shape.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void TriangularSolveKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& y,
|
|
bool upper,
|
|
bool transpose,
|
|
bool unitriangular,
|
|
DenseTensor* out) {
|
|
if (x.numel() == 0 || y.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);
|
|
int x_bst_ndim = static_cast<int>(x_bst_dims_vec.size());
|
|
int y_bst_ndim = static_cast<int>(y_bst_dims_vec.size());
|
|
|
|
// Tensor broadcast to 'out' and temp 'x_bst'
|
|
IntArray x_bst_dims(x_bst_dims_vec);
|
|
DenseTensor x_bst = Empty<T, Context>(dev_ctx, x_bst_dims);
|
|
const T* x_bst_data = x_bst.data<T>();
|
|
ExpandKernel<T, Context>(dev_ctx, x, x_bst_dims, &x_bst);
|
|
|
|
out->Resize(y_bst_dims_vec);
|
|
T* out_data = dev_ctx.template Alloc<T>(out);
|
|
IntArray y_bst_dims(y_bst_dims_vec);
|
|
ExpandKernel<T, Context>(dev_ctx, y, y_bst_dims, out);
|
|
|
|
// Calculate use blas library
|
|
int M = static_cast<int>(y_bst_dims_vec[y_bst_ndim - 2]);
|
|
int N = static_cast<int>(y_bst_dims_vec[y_bst_ndim - 1]);
|
|
int batch_size = 1;
|
|
for (int i = 0; i < x_bst_ndim - 2; i++) {
|
|
batch_size *= static_cast<int>(x_bst_dims_vec[i]);
|
|
}
|
|
|
|
auto blas = funcs::GetBlas<CPUContext, T>(dev_ctx);
|
|
for (int i = 0; i < batch_size; i++) {
|
|
blas.TRSM(CblasLeft,
|
|
upper ? CblasUpper : CblasLower,
|
|
transpose ? CblasTrans : CblasNoTrans,
|
|
unitriangular ? CblasUnit : CblasNonUnit,
|
|
M,
|
|
N,
|
|
T(1),
|
|
x_bst_data + i * M * M,
|
|
std::max(1, M),
|
|
out_data + i * N * M,
|
|
std::max(1, N));
|
|
}
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(triangular_solve,
|
|
CPU,
|
|
ALL_LAYOUT,
|
|
phi::TriangularSolveKernel,
|
|
float,
|
|
double,
|
|
phi::complex64,
|
|
phi::complex128) {}
|