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未验证 提交 ae8ca764 编写于 作者: W Weilong Wu 提交者： GitHub
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[Phi] Migrate matrix_solve to phi (#44298) 

* [Phi] Migrate matrix_solve to phi

* replace mutable_data with Alloc
上级 988abd6a
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Showing with 302 addition and 282 deletion
paddle/fluid/operators/eig_op.h
浏览文件 @ ae8ca764
......@@ -19,7 +19,6 @@
#include <algorithm>
#include <complex>
#include "paddle/fluid/operators/math/matrix_solve.h"
#include "paddle/fluid/operators/transpose_op.h"
#include "paddle/fluid/platform/for_range.h"
#include "paddle/phi/kernels/complex_kernel.h"
......@@ -30,6 +29,7 @@
#include "paddle/phi/kernels/funcs/diag_functor.h"
#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/matrix_solve.h"
#include "paddle/phi/kernels/funcs/slice.h"
#include "paddle/phi/kernels/funcs/unsqueeze.h"
#include "paddle/phi/kernels/matmul_kernel.h"
......@@ -366,7 +366,7 @@ void ComputeBackwardForComplexInput(
int k = rhs.dims()[rhs.dims().size() - 1];
auto* matrix_data = Vh.data<T>();
auto* rhs_data = rhs.data<T>();
math::SolveLinearSystem<T>(
phi::funcs::SolveLinearSystem<T>(
matrix_data, rhs_data, x_grad_data, m, k, batch_count);
}
......
paddle/fluid/operators/lstsq_op.h
浏览文件 @ ae8ca764
......@@ -21,13 +21,13 @@
#include "paddle/fluid/operators/eig_op.h"
#include "paddle/fluid/operators/math/eigen_values_vectors.h"
#include "paddle/fluid/operators/math/matrix_solve.h"
#include "paddle/fluid/operators/svd_helper.h"
#include "paddle/fluid/operators/transpose_op.h"
#include "paddle/fluid/platform/for_range.h"
#include "paddle/phi/kernels/funcs/complex_functors.h"
#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/matrix_solve.h"
#define EPSILON 1e-6
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ ae8ca764
......@@ -54,7 +54,6 @@ math_library(vol2col)
math_library(prelu)
math_library(bert_encoder_functor)
math_library(tree2col DEPS math_function)
math_library(matrix_solve)
cc_test(
selected_rows_functor_test
......
paddle/fluid/operators/math/matrix_solve.cu.cc 已删除 100644 → 0
浏览文件 @ 988abd6a
/* Copyright (c) 2021 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/fluid/operators/math/matrix_solve.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/solve_op.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace paddle {
namespace platform {
class CUDADeviceContext;
} // namespace platform
} // namespace paddle
namespace paddle {
namespace operators {
namespace math {
template <typename DeviceContext, typename T>
class MatrixSolveFunctor;
template <typename T>
class MatrixSolveFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& a,
const framework::Tensor& b,
framework::Tensor* out) {
#ifndef PADDLE_WITH_HIP
// solve the equation: Ax = B,
// use cuBlas cublas<S/D>getrfBatched funcion to performs the LU
// factorization of each matrix A,
// and then use cuBlas cublas<S/D>getriBatched function to solve the
// equation after LU factorization.
// ref:
// https://docs.nvidia.com/cuda/cublas/index.html#cublas-lt-t-gt-getrfbatched
const auto& a_dims = a.dims();
const int a_rank = a_dims.size();
int n = a_dims[a_rank - 1];
int lda = n;
int batch_size = a_rank > 2 ? a.numel() / (n * n) : 1;
const auto& b_dims = b.dims();
const int b_rank = b_dims.size();
int nrhs = b_dims[b_rank - 1];
int ldb = b_dims[b_rank - 2];
// make sure the out dims is right
out->Resize(b_dims);
out->mutable_data<T>(context.GetPlace());
// copy input A to a temporary tensor tmp_a,
// LU factorization, written back to original matrix A, so in the beginning,
// it's necessary to create a temporary tensor tmp_a.
Tensor tmp_a(a.dtype());
tmp_a.Resize(a.dims());
tmp_a.mutable_data<T>(context.GetPlace());
framework::TensorCopy(a, context.GetPlace(), &tmp_a);
// copy input B to a temporary tensor tmp_b, and transpose tmp_b,
// because cuBlas assumes column-major while Paddle uses row-majar.
Tensor tmp_b(b.type());
const auto& new_dims_vec = getNewDimsVec(b_dims);
tmp_b.Resize(phi::make_ddim(new_dims_vec));
tmp_b.mutable_data<T>(context.GetPlace());
phi::funcs::TransposeNormal<platform::CUDADeviceContext, T> trans;
std::vector<int> new_axis = getNewAxis(b_rank);
trans(context, b, &tmp_b, new_axis);
const T* a_data_in_gpu = tmp_a.data<T>();
const T* b_data_in_gpu = tmp_b.data<T>();
std::vector<const T*> cpu_ptrs(batch_size * 2);
for (int i = 0; i < batch_size; ++i) {
cpu_ptrs[i] = a_data_in_gpu + i * n * n;
cpu_ptrs[i + batch_size] = b_data_in_gpu + i * n * nrhs;
}
// Copy the addresses of A and tmp_b from host to device.
memory::allocation::AllocationPtr tmp_gpu_ptrs_data =
memory::Alloc(context, cpu_ptrs.size() * sizeof(T*));
memory::Copy(context.GetPlace(),
tmp_gpu_ptrs_data->ptr(),
platform::CPUPlace(),
static_cast<void*>(cpu_ptrs.data()),
cpu_ptrs.size() * sizeof(T*),
context.stream());
T** gpu_tmp_b_ptrs =
reinterpret_cast<T**>(tmp_gpu_ptrs_data->ptr()) + batch_size;
// Allocate device memory for BatchedGETRF's info and pivots.
int num_ints = n < 32 ? batch_size : batch_size * (n + 1);
memory::allocation::AllocationPtr tmp_gpu_info_data =
memory::Alloc(context, num_ints * sizeof(int));
int* gpu_info_ptr = reinterpret_cast<int*>(tmp_gpu_info_data->ptr());
auto blas = phi::funcs::GetBlas<platform::CUDADeviceContext, T>(context);
// only for singular checking
std::vector<int> info;
info.resize(batch_size);
int* gpu_pivot_ptr =
reinterpret_cast<int*>(tmp_gpu_info_data->ptr()) + batch_size;
// This function performs the LU factorization of each matrix A by the
// equation A = L * U. L and U are written back to original matrix A,
// and diagonal elements of L are discarded.
blas.BatchedGETRF(n,
reinterpret_cast<T**>(tmp_gpu_ptrs_data->ptr()),
gpu_pivot_ptr,
gpu_info_ptr,
batch_size);
// check whether BatchedGETRF is executed successfully or not
memory::Copy(platform::CPUPlace(),
info.data(),
context.GetPlace(),
gpu_info_ptr,
sizeof(int) * batch_size,
context.stream());
for (int i = 0; i < batch_size; ++i) {
PADDLE_ENFORCE_EQ(info[i],
0,
platform::errors::PreconditionNotMet(
"For batch [%d]: U(%d, %d) is zero, singular U. "
"Please check the matrix value and change it to a "
"non-singular matrix",
i,
info[i],
info[i]));
}
// hold the result code from BatchedGETRS
int host_info = 0;
// to solve the equation after LU factorization
CBLAS_TRANSPOSE transA = CblasTrans;
blas.BatchedGETRS(transA,
n,
nrhs,
reinterpret_cast<const T**>(tmp_gpu_ptrs_data->ptr()),
lda,
gpu_pivot_ptr,
gpu_tmp_b_ptrs,
ldb,
&host_info,
batch_size);
// check whether BatchedGETRS is executed successfully or not
PADDLE_ENFORCE_EQ(host_info,
0,
platform::errors::InvalidArgument(
"The [%d]'th argument to cublas*getrsBatched had "
"an illegal value.",
-host_info));
// transpose tmp_b to get the final result in row-major form.
phi::funcs::TransposeNormal<platform::CUDADeviceContext, T> trans2;
trans2(context, tmp_b, out, new_axis);
#else
compute_solve_eigen<platform::CUDADeviceContext, T>(context, a, b, out);
#endif
}
};
template class MatrixSolveFunctor<platform::CUDADeviceContext, float>;
template class MatrixSolveFunctor<platform::CUDADeviceContext, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/solve_op.h
浏览文件 @ ae8ca764
......@@ -20,11 +20,11 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/math/matrix_solve.h"
#include "paddle/fluid/operators/reduce_ops/reduce_sum_op.h"
#include "paddle/fluid/operators/squeeze_op.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/matrix_solve.h"
#if defined(__NVCC__) || defined(__HIPCC__)
#include "paddle/fluid/operators/reduce_ops/reduce_op.cu.h"
#endif
......@@ -351,7 +351,7 @@ static void linalg_solve(const framework::ExecutionContext& context,
out->mutable_data<T>(context.GetPlace());
auto& dev_ctx = context.template device_context<DeviceContext>();
math::MatrixSolveFunctor<DeviceContext, T> mat_solve;
phi::funcs::MatrixSolveFunctor<DeviceContext, T> mat_solve;
// input y can be vector or matrix
// but need to be unsqueezed if y is a vector
......@@ -425,67 +425,6 @@ static void linalg_solve(const framework::ExecutionContext& context,
}
}
// for TransposeNormal
static std::vector<int> getNewAxis(const int b_rank) {
std::vector<int> axis_1 = {0};
std::vector<int> axis_2 = {1, 0};
std::vector<int> axis_3 = {0, 2, 1};
std::vector<int> axis_4 = {0, 1, 3, 2};
std::vector<int> axis_5 = {0, 1, 2, 4, 3};
std::vector<int> axis_6 = {0, 1, 2, 3, 5, 4};
std::vector<int> axis_7 = {0, 1, 2, 3, 4, 6, 5};
std::vector<int> axis_8 = {0, 1, 2, 3, 4, 5, 7, 6};
std::vector<int> axis_9 = {0, 1, 2, 3, 4, 5, 6, 8, 7};
switch (b_rank) {
case 1:
return axis_1;
break;
case 2:
return axis_2;
break;
case 3:
return axis_3;
break;
case 4:
return axis_4;
break;
case 5:
return axis_5;
break;
case 6:
return axis_6;
break;
case 7:
return axis_7;
break;
case 8:
return axis_8;
break;
default:
return axis_9;
}
}
// for Resize
static std::vector<int64_t> getNewDimsVec(const DDim& b_dims) {
std::vector<int64_t> b_dims_vec = phi::vectorize(b_dims);
int size = b_dims_vec.size();
if (size >= 2) {
// swap the last 2 elements in b_dims_vec
int64_t temp = b_dims_vec[size - 1];
b_dims_vec[size - 1] = b_dims_vec[size - 2];
b_dims_vec[size - 2] = temp;
return b_dims_vec;
}
PADDLE_ENFORCE_NE(
b_dims_vec.empty(),
true,
platform::errors::PreconditionNotMet(
"The size of tensor b must not be %d after getting new dims", 0));
// if b_dims_vec.size() == 1, just retun original vec
return b_dims_vec;
}
template <typename DeviceContext, typename T>
class SolveKernel : public framework::OpKernel<T> {
public:
......@@ -553,11 +492,11 @@ class SolveGradKernel : public framework::OpKernel<T> {
tmp_dy.mutable_data<T>(ctx.GetPlace());
Tensor tmp_input(input->dtype());
const auto& new_dims_vec = getNewDimsVec(input->dims());
const auto& new_dims_vec = phi::funcs::getNewDimsVec(input->dims());
tmp_input.Resize(phi::make_ddim(new_dims_vec));
tmp_input.mutable_data<T>(ctx.GetPlace());
phi::funcs::TransposeNormal<DeviceContext, T> trans;
std::vector<int> new_axis = getNewAxis(input->dims().size());
std::vector<int> new_axis = phi::funcs::getNewAxis(input->dims().size());
auto& dev_ctx = ctx.template device_context<DeviceContext>();
trans(dev_ctx, *input, &tmp_input, new_axis);
......
paddle/phi/kernels/funcs/CMakeLists.txt
浏览文件 @ ae8ca764
......@@ -14,3 +14,4 @@ math_library(matrix_inverse DEPS dense_tensor eigen3 blas)
math_library(pooling DEPS dense_tensor)
math_library(segment_pooling)
math_library(sequence2batch)
math_library(matrix_solve DEPS dense_tensor eigen3 blas math_function)
paddle/fluid/operators/math/matrix_solve.cc → paddle/phi/kernels/funcs/matrix_solve.cc
浏览文件 @ ae8ca764
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
/* 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.
......@@ -12,30 +12,21 @@ 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/fluid/operators/math/matrix_solve.h"
#include "Eigen/Core"
#include "Eigen/LU"
#include "paddle/phi/kernels/funcs/blas/blas.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T>
class MatrixSolveFunctor<phi::CPUContext, T> {
public:
void operator()(const phi::CPUContext& dev_ctx,
const framework::Tensor& a,
const framework::Tensor& b,
framework::Tensor* out) {
compute_solve_eigen<phi::CPUContext, T>(dev_ctx, a, b, out);
}
};
template class MatrixSolveFunctor<phi::CPUContext, float>;
template class MatrixSolveFunctor<phi::CPUContext, double>;
} // namespace math
} // namespace operators
} // namespace paddle
#include "paddle/phi/kernels/funcs/matrix_solve.h"
namespace phi {
namespace funcs {
template <typename Context, typename T>
void MatrixSolveFunctor<Context, T>::operator()(const Context& dev_ctx,
const DenseTensor& a,
const DenseTensor& b,
DenseTensor* out) {
compute_solve_eigen<Context, T>(dev_ctx, a, b, out);
}
template class MatrixSolveFunctor<CPUContext, float>;
template class MatrixSolveFunctor<CPUContext, double>;
} // namespace funcs
} // namespace phi
paddle/phi/kernels/funcs/matrix_solve.cu 0 → 100644
浏览文件 @ ae8ca764
/* 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/funcs/matrix_solve.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
namespace funcs {
template <typename Context, typename T>
void MatrixSolveFunctor<Context, T>::operator()(const Context& context,
const DenseTensor& a,
const DenseTensor& b,
DenseTensor* out) {
#ifndef PADDLE_WITH_HIP
// solve the equation: Ax = B,
// use cuBlas cublas<S/D>getrfBatched funcion to performs the LU
// factorization of each matrix A,
// and then use cuBlas cublas<S/D>getriBatched function to solve the
// equation after LU factorization.
// ref:
// https://docs.nvidia.com/cuda/cublas/index.html#cublas-lt-t-gt-getrfbatched
const auto& a_dims = a.dims();
const int a_rank = a_dims.size();
int n = a_dims[a_rank - 1];
int lda = n;
int batch_size = a_rank > 2 ? a.numel() / (n * n) : 1;
const auto& b_dims = b.dims();
const int b_rank = b_dims.size();
int nrhs = b_dims[b_rank - 1];
int ldb = b_dims[b_rank - 2];
// make sure the out dims is right
out->Resize(b_dims);
context.template Alloc<T>(out);
// copy input A to a temporary tensor tmp_a,
// LU factorization, written back to original matrix A, so in the beginning,
// it's necessary to create a temporary tensor tmp_a.
DenseTensor tmp_a(a.dtype());
tmp_a.Resize(a.dims());
context.template Alloc<T>(&tmp_a);
paddle::framework::TensorCopy(a, context.GetPlace(), &tmp_a);
// copy input B to a temporary tensor tmp_b, and transpose tmp_b,
// because cuBlas assumes column-major while Paddle uses row-majar.
DenseTensor tmp_b(b.type());
const auto& new_dims_vec = getNewDimsVec(b_dims);
tmp_b.Resize(phi::make_ddim(new_dims_vec));
context.template Alloc<T>(&tmp_b);
phi::funcs::TransposeNormal<Context, T> trans;
std::vector<int> new_axis = getNewAxis(b_rank);
trans(context, b, &tmp_b, new_axis);
const T* a_data_in_gpu = tmp_a.data<T>();
const T* b_data_in_gpu = tmp_b.data<T>();
std::vector<const T*> cpu_ptrs(batch_size * 2);
for (int i = 0; i < batch_size; ++i) {
cpu_ptrs[i] = a_data_in_gpu + i * n * n;
cpu_ptrs[i + batch_size] = b_data_in_gpu + i * n * nrhs;
}
// Copy the addresses of A and tmp_b from host to device.
paddle::memory::allocation::AllocationPtr tmp_gpu_ptrs_data =
paddle::memory::Alloc(context, cpu_ptrs.size() * sizeof(T*));
paddle::memory::Copy(context.GetPlace(),
tmp_gpu_ptrs_data->ptr(),
phi::CPUPlace(),
static_cast<void*>(cpu_ptrs.data()),
cpu_ptrs.size() * sizeof(T*),
context.stream());
T** gpu_tmp_b_ptrs =
reinterpret_cast<T**>(tmp_gpu_ptrs_data->ptr()) + batch_size;
// Allocate device memory for BatchedGETRF's info and pivots.
int num_ints = n < 32 ? batch_size : batch_size * (n + 1);
paddle::memory::allocation::AllocationPtr tmp_gpu_info_data =
paddle::memory::Alloc(context, num_ints * sizeof(int));
int* gpu_info_ptr = reinterpret_cast<int*>(tmp_gpu_info_data->ptr());
auto blas = phi::funcs::GetBlas<Context, T>(context);
// only for singular checking
std::vector<int> info;
info.resize(batch_size);
int* gpu_pivot_ptr =
reinterpret_cast<int*>(tmp_gpu_info_data->ptr()) + batch_size;
// This function performs the LU factorization of each matrix A by the
// equation A = L * U. L and U are written back to original matrix A,
// and diagonal elements of L are discarded.
blas.BatchedGETRF(n,
reinterpret_cast<T**>(tmp_gpu_ptrs_data->ptr()),
gpu_pivot_ptr,
gpu_info_ptr,
batch_size);
// check whether BatchedGETRF is executed successfully or not
paddle::memory::Copy(phi::CPUPlace(),
info.data(),
context.GetPlace(),
gpu_info_ptr,
sizeof(int) * batch_size,
context.stream());
for (int i = 0; i < batch_size; ++i) {
PADDLE_ENFORCE_EQ(info[i],
0,
phi::errors::PreconditionNotMet(
"For batch [%d]: U(%d, %d) is zero, singular U. "
"Please check the matrix value and change it to a "
"non-singular matrix",
i,
info[i],
info[i]));
}
// hold the result code from BatchedGETRS
int host_info = 0;
// to solve the equation after LU factorization
CBLAS_TRANSPOSE transA = CblasTrans;
blas.BatchedGETRS(transA,
n,
nrhs,
reinterpret_cast<const T**>(tmp_gpu_ptrs_data->ptr()),
lda,
gpu_pivot_ptr,
gpu_tmp_b_ptrs,
ldb,
&host_info,
batch_size);
// check whether BatchedGETRS is executed successfully or not
PADDLE_ENFORCE_EQ(host_info,
0,
phi::errors::InvalidArgument(
"The [%d]'th argument to cublas*getrsBatched had "
"an illegal value.",
-host_info));
// transpose tmp_b to get the final result in row-major form.
phi::funcs::TransposeNormal<Context, T> trans2;
trans2(context, tmp_b, out, new_axis);
#else
compute_solve_eigen<Context, T>(context, a, b, out);
#endif
}
template class MatrixSolveFunctor<GPUContext, float>;
template class MatrixSolveFunctor<GPUContext, double>;
// TODO(wuweilong): remove these instantiations later
template class MatrixSolveFunctor<paddle::platform::CUDADeviceContext, float>;
template class MatrixSolveFunctor<paddle::platform::CUDADeviceContext, double>;
} // namespace funcs
} // namespace phi
paddle/fluid/operators/math/matrix_solve.h → paddle/phi/kernels/funcs/matrix_solve.h
浏览文件 @ ae8ca764
......@@ -18,18 +18,79 @@ limitations under the License. */
#include "Eigen/Core"
#include "Eigen/LU"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace operators {
namespace math {
template <typename DeviceContext, typename T>
void compute_solve_eigen(const DeviceContext& context,
const framework::Tensor& a,
const framework::Tensor& b,
framework::Tensor* out) {
#include "paddle/phi/backends/all_context.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/enforce.h"
namespace phi {
namespace funcs {
// for TransposeNormal
static std::vector<int> getNewAxis(const int b_rank) {
std::vector<int> axis_1 = {0};
std::vector<int> axis_2 = {1, 0};
std::vector<int> axis_3 = {0, 2, 1};
std::vector<int> axis_4 = {0, 1, 3, 2};
std::vector<int> axis_5 = {0, 1, 2, 4, 3};
std::vector<int> axis_6 = {0, 1, 2, 3, 5, 4};
std::vector<int> axis_7 = {0, 1, 2, 3, 4, 6, 5};
std::vector<int> axis_8 = {0, 1, 2, 3, 4, 5, 7, 6};
std::vector<int> axis_9 = {0, 1, 2, 3, 4, 5, 6, 8, 7};
switch (b_rank) {
case 1:
return axis_1;
break;
case 2:
return axis_2;
break;
case 3:
return axis_3;
break;
case 4:
return axis_4;
break;
case 5:
return axis_5;
break;
case 6:
return axis_6;
break;
case 7:
return axis_7;
break;
case 8:
return axis_8;
break;
default:
return axis_9;
}
}
// for Resize
static std::vector<int64_t> getNewDimsVec(const DDim& b_dims) {
std::vector<int64_t> b_dims_vec = phi::vectorize(b_dims);
int size = b_dims_vec.size();
if (size >= 2) {
// swap the last 2 elements in b_dims_vec
int64_t temp = b_dims_vec[size - 1];
b_dims_vec[size - 1] = b_dims_vec[size - 2];
b_dims_vec[size - 2] = temp;
return b_dims_vec;
}
PADDLE_ENFORCE_NE(
b_dims_vec.empty(),
true,
phi::errors::PreconditionNotMet(
"The size of tensor b must not be %d after getting new dims", 0));
// if b_dims_vec.size() == 1, just retun original vec
return b_dims_vec;
}
template <typename Context, typename T>
void compute_solve_eigen(const Context& context,
const DenseTensor& a,
const DenseTensor& b,
DenseTensor* out) {
using Matrix =
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
using EigenMatrixMap = Eigen::Map<Matrix>;
......@@ -51,7 +112,7 @@ void compute_solve_eigen(const DeviceContext& context,
const T* b_ptr = b.data<T>();
out->Resize(b_mat_dims); // make sure the out dims is right
T* out_ptr = out->mutable_data<T>(context.GetPlace());
T* out_ptr = context.template Alloc<T>(out);
if (a_batch_size == b_batch_size) {
for (int i = 0; i < a_batch_size; ++i) {
ConstEigenMatrixMap a_mat(a_ptr + i * n * n, n, n);
......@@ -63,13 +124,13 @@ void compute_solve_eigen(const DeviceContext& context,
PADDLE_ENFORCE_GT(
min_abs_pivot,
static_cast<T>(0),
platform::errors::InvalidArgument("Input is not invertible."));
phi::errors::InvalidArgument("Input is not invertible."));
out_mat.noalias() = lu.solve(b_mat);
}
} else {
PADDLE_ENFORCE_EQ(a_batch_size,
b_batch_size,
platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"All input tensors must have the same rank."));
}
}
......@@ -114,22 +175,21 @@ void SolveLinearSystem(T* matrix_data,
lu_decomposition.matrixLU().diagonal().cwiseAbs().minCoeff();
PADDLE_ENFORCE_GT(min_abs_piv,
Treal(0),
platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Something's wrong with SolveLinearSystem. "));
output = lu_decomposition.solve(input_rhs);
}
}
template <typename DeviceContext, typename T>
template <typename Context, typename T>
class MatrixSolveFunctor {
public:
void operator()(const DeviceContext& context,
const framework::Tensor& a,
const framework::Tensor& b,
framework::Tensor* out);
void operator()(const Context& context,
const DenseTensor& a,
const DenseTensor& b,
DenseTensor* out);
};
} // namespace math
} // namespace operators
} // namespace paddle
} // namespace funcs
} // namespace phi
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