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未验证 提交 effb70f4 编写于 作者: C crystal 提交者： GitHub
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[cherry-pick]CPU forward calculation replaces Eigen with Lapack (#35916) (#36091) 

cherry-pick #35916，CPU前向计算将Eigen替换为Lapack，修改linalg暴露规则
上级 14cdcde7
隐藏空白更改
内联 并排
Showing with 217 addition and 179 deletion
paddle/fluid/operators/eigh_op.cc
浏览文件 @ effb70f4
......@@ -147,18 +147,17 @@ REGISTER_OPERATOR(eigh, ops::EighOp, ops::EignOpMaker,
REGISTER_OPERATOR(eigh_grad, ops::EighGradOp);
REGISTER_OP_CPU_KERNEL(
eigh, ops::EighKernel<paddle::platform::CPUDeviceContext, float, float>,
ops::EighKernel<paddle::platform::CPUDeviceContext, double, double>,
ops::EighKernel<paddle::platform::CPUDeviceContext, float,
eigh, ops::EighKernel<paddle::platform::CPUDeviceContext, float>,
ops::EighKernel<paddle::platform::CPUDeviceContext, double>,
ops::EighKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::EighKernel<paddle::platform::CPUDeviceContext, double,
ops::EighKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
REGISTER_OP_CPU_KERNEL(
eigh_grad,
ops::EighGradKernel<paddle::platform::CPUDeviceContext, float, float>,
ops::EighGradKernel<paddle::platform::CPUDeviceContext, double, double>,
ops::EighGradKernel<paddle::platform::CPUDeviceContext, float,
eigh_grad, ops::EighGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::EighGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::EighGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::EighGradKernel<paddle::platform::CPUDeviceContext, double,
ops::EighGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
paddle/fluid/operators/eigh_op.cu
浏览文件 @ effb70f4
......@@ -16,18 +16,17 @@ limitations under the License. */
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
eigh, ops::EighKernel<paddle::platform::CUDADeviceContext, float, float>,
ops::EighKernel<paddle::platform::CUDADeviceContext, double, double>,
ops::EighKernel<paddle::platform::CUDADeviceContext, float,
eigh, ops::EighKernel<paddle::platform::CUDADeviceContext, float>,
ops::EighKernel<paddle::platform::CUDADeviceContext, double>,
ops::EighKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex<float>>,
ops::EighKernel<paddle::platform::CUDADeviceContext, double,
ops::EighKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex<double>>);
REGISTER_OP_CUDA_KERNEL(
eigh_grad,
ops::EighGradKernel<paddle::platform::CUDADeviceContext, float, float>,
ops::EighGradKernel<paddle::platform::CUDADeviceContext, double, double>,
ops::EighGradKernel<paddle::platform::CUDADeviceContext, float,
eigh_grad, ops::EighGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::EighGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::EighGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex<float>>,
ops::EighGradKernel<paddle::platform::CUDADeviceContext, double,
ops::EighGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex<double>>);
paddle/fluid/operators/eigh_op.h
浏览文件 @ effb70f4
......@@ -22,7 +22,7 @@ namespace operators {
using Tensor = framework::Tensor;
template <typename DeviceContext, typename ValueType, typename T>
template <typename DeviceContext, typename T>
class EighKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -31,15 +31,16 @@ class EighKernel : public framework::OpKernel<T> {
auto output_v = ctx.Output<Tensor>("Eigenvectors");
std::string lower = ctx.Attr<std::string>("UPLO");
bool is_lower = (lower == "L");
math::MatrixEighFunctor<DeviceContext, ValueType, T> functor;
math::MatrixEighFunctor<DeviceContext, T> functor;
functor(ctx, *input, output_w, output_v, is_lower, true);
}
};
template <typename DeviceContext, typename ValueType, typename T>
template <typename DeviceContext, typename T>
class EighGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
using ValueType = math::Real<T>;
auto& x_grad = *ctx.Output<framework::Tensor>(framework::GradVarName("X"));
x_grad.mutable_data<T>(ctx.GetPlace());
auto& output_w = *ctx.Input<Tensor>("Eigenvalues");
......
paddle/fluid/operators/math/eigen_values_vectors.h
浏览文件 @ effb70f4
......@@ -14,8 +14,8 @@
#pragma once
#include "Eigen/Core"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/math/lapack_function.h"
#include "paddle/fluid/operators/svd_helper.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/dynload/cusolver.h"
......@@ -25,84 +25,6 @@ namespace paddle {
namespace operators {
namespace math {
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using InputMatrixMap = Eigen::Map<
const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using OutputMatrixMap = Eigen::Map<
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
template <typename ValueType>
inline void ComputeFloatEigenvaluesAndVectors(ValueType *x_data,
ValueType *eigenvalues_data,
ValueType *eigenvectors_data,
int batches, int rows, int cols,
bool has_vectors) {
int stride = rows * cols;
for (int i = 0; i < batches; i++) {
auto m = InputMatrixMap<ValueType>(x_data + i * stride, rows, cols);
auto eigenvalues =
OutputMatrixMap<ValueType>(eigenvalues_data + i * rows, 1, rows);
auto eigenvectors =
OutputMatrixMap<ValueType>(eigenvectors_data + i * stride, rows, cols);
Eigen::SelfAdjointEigenSolver<Eigen::Matrix<
ValueType, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
eigen_solver(m, has_vectors ? Eigen::ComputeEigenvectors
: Eigen::EigenvaluesOnly);
PADDLE_ENFORCE_EQ(
eigen_solver.info(), Eigen::Success,
platform::errors::InvalidArgument(
"Self Adjoint Eigen decomposition is not successful. "
"The %d-th input matrice might not be not be positive definite.",
i));
eigenvalues = eigen_solver.eigenvalues().transpose();
if (has_vectors) {
eigenvectors = eigen_solver.eigenvectors();
}
}
}
template <typename T, typename ValueType>
inline void ComputeComplexEigenvaluesAndVectors(T *x_data,
ValueType *eigenvalues_data,
T *eigenvectors_data,
int batches, int rows, int cols,
bool has_vectors) {
using Complex = std::complex<ValueType>;
Complex *input = reinterpret_cast<Complex *>(x_data);
Complex *eigenvectors_data_ = reinterpret_cast<Complex *>(eigenvectors_data);
int stride = rows * cols;
for (int i = 0; i < batches; i++) {
auto m = InputMatrixMap<Complex>(input + i * stride, rows, cols);
auto eigenvalues =
OutputMatrixMap<ValueType>(eigenvalues_data + i * rows, 1, rows);
auto eigenvectors =
OutputMatrixMap<Complex>(eigenvectors_data_ + i * stride, rows, cols);
Eigen::SelfAdjointEigenSolver<
Eigen::Matrix<Complex, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
eigen_solver(m, has_vectors ? Eigen::ComputeEigenvectors
: Eigen::EigenvaluesOnly);
PADDLE_ENFORCE_EQ(
eigen_solver.info(), Eigen::Success,
platform::errors::InvalidArgument(
"Self Adjoint Eigen decomposition is not successful. "
"The %d-th input matrice might not be not be positive definite.",
i));
eigenvalues = eigen_solver.eigenvalues().transpose();
if (has_vectors) {
eigenvectors = eigen_solver.eigenvectors();
}
}
}
inline int64_t GetBatchSize(framework::DDim dims) {
int64_t batch_size = 1;
auto dim_size = dims.size();
......@@ -112,7 +34,20 @@ inline int64_t GetBatchSize(framework::DDim dims) {
return batch_size;
}
template <typename DeviceContext, typename ValueType, typename T>
static void CheckEighResult(const int batch, const int info) {
PADDLE_ENFORCE_LE(
info, 0,
platform::errors::PreconditionNotMet(
"For batch [%d]: the [%d] off-diagonal elements of an intermediate"
"tridiagonal form did not converge to zero",
batch, info));
PADDLE_ENFORCE_GE(
info, 0, platform::errors::PreconditionNotMet(
"For batch [%d]: the [%d] argument had an illegal value",
batch, info));
}
template <typename DeviceContext, typename T>
struct MatrixEighFunctor {
void operator()(const framework::ExecutionContext &ctx, const Tensor &input,
Tensor *eigen_values, Tensor *eigen_vectors, bool is_lower,
......@@ -122,43 +57,84 @@ struct MatrixEighFunctor {
// Calculates the eigenvalues ​​and eigenvectors of Hermitian or real
// symmetric matrices, and uses the variable has_vectors to
// control whether to return the eigenvectors.
template <typename ValueType, typename T>
struct MatrixEighFunctor<platform::CPUDeviceContext, ValueType, T> {
template <typename T>
struct MatrixEighFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const framework::ExecutionContext &ctx, const Tensor &input,
Tensor *eigen_values, Tensor *eigen_vectors, bool is_lower,
bool has_vectors) {
auto dims = input.dims();
auto output_value_dim = eigen_values->dims();
using ValueType = math::Real<T>;
auto *out_value = eigen_values->mutable_data<ValueType>(ctx.GetPlace());
int64_t batch_size = 1;
int dim_size = dims.size();
for (int64_t i = 0; i < dim_size - 2; i++) {
batch_size *= dims[i];
}
auto dito =
DeviceIndependenceTensorOperations<platform::CPUDeviceContext, T>(ctx);
Tensor input_tensor;
TensorCopy(input, ctx.GetPlace(), &input_tensor);
if (!is_lower) {
input_tensor = dito.Transpose(input);
}
int rows = dims[dims.size() - 2];
math::DeviceIndependenceTensorOperations<platform::CPUDeviceContext, T>(
ctx);
auto *value_data =
eigen_values->mutable_data<ValueType>(output_value_dim, ctx.GetPlace());
Tensor input_trans;
// lapack is a column-major storge, transpose make the input to
// have a continuous memory layout
input_trans = dito.Transpose(input);
auto *input_vector = input_trans.data<T>();
if (framework::IsComplexType(input_tensor.type())) {
auto *x_data = input_tensor.data<T>();
auto *vector_data = eigen_vectors->mutable_data<T>(dims, ctx.GetPlace());
ComputeComplexEigenvaluesAndVectors<T, ValueType>(
x_data, value_data, vector_data, batch_size, rows, rows, has_vectors);
} else {
auto *x_data = input_tensor.data<ValueType>();
auto *vector_data =
eigen_vectors->mutable_data<ValueType>(dims, ctx.GetPlace());
ComputeFloatEigenvaluesAndVectors<ValueType>(
x_data, value_data, vector_data, batch_size, rows, rows, has_vectors);
auto dims = input.dims();
int dim_size = dims.size();
int64_t batch_size = GetBatchSize(dims);
int vector_stride = dims[dim_size - 1] * dims[dim_size - 2];
int values_stride = dims[dim_size - 1];
char uplo = is_lower ? 'L' : 'U';
char jobz = has_vectors ? 'V' : 'N';
auto n = dims[dim_size - 1];
auto lda = std::max<int64_t>(1, n);
// if work = -1, it means that you need to use the lapack function to query
// the optimal value
int lwork = -1; // The length of the array work
int lrwork = -1; // The dimension of the array rwork,rwork is REAL array
int liwork = -1; // The dimension of the array iwork
int iwork_opt = -1; // The optimal length of the array liwork
T lwork_opt = static_cast<T>(-1); // The optimal length of the array work
ValueType rwork_opt =
static_cast<ValueType>(-1); // The optimal length of the array rwork
int info = 0;
// Call lapackEigh to get the optimal size of work data
math::lapackEigh<T, ValueType>(jobz, uplo, n, input_vector, lda, out_value,
&lwork_opt, lwork, &rwork_opt, lrwork,
&iwork_opt, liwork, &info);
lwork = std::max<int>(1, static_cast<int>(lwork_opt));
liwork = std::max<int>(1, iwork_opt);
Tensor rwork_tensor;
ValueType *rwork_data = nullptr;
// complex type
if (framework::IsComplexType(input.type())) {
lrwork = std::max<int>(1, static_cast<int>(rwork_opt));
rwork_data = rwork_tensor.mutable_data<ValueType>(
framework::make_ddim({lrwork}), ctx.GetPlace());
}
Tensor iwork_tensor, work_tensor;
auto *iwork_data = iwork_tensor.mutable_data<int>(
framework::make_ddim({liwork}), ctx.GetPlace());
auto *work_data = work_tensor.mutable_data<T>(framework::make_ddim({lwork}),
ctx.GetPlace());
for (auto i = 0; i < batch_size; i++) {
auto *value_data = out_value + i * values_stride;
auto *input_data = input_vector + i * vector_stride;
math::lapackEigh<T, Real<T>>(jobz, uplo, n, input_data, lda, value_data,
work_data, lwork, rwork_data, lrwork,
iwork_data, liwork, &info);
CheckEighResult(i, info);
}
if (has_vectors) {
PADDLE_ENFORCE_NOT_NULL(eigen_vectors,
platform::errors::InvalidArgument(
"When has_vectors is true,"
"the eigenvectors needs to be calculated, "
"so the eigenvectors must be provided."));
input_trans = dito.Transpose(input_trans);
eigen_vectors->ShareDataWith(input_trans);
}
}
};
......@@ -168,15 +144,22 @@ struct MatrixEighFunctor<platform::CPUDeviceContext, ValueType, T> {
// Calculates the eigenvalues ​​and eigenvectors of Hermitian or real
// symmetric matrices on GPU, and uses the variable has_vectors
// to control whether to return the eigenvectors.
template <typename ValueType, typename T>
struct MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T> {
template <typename T>
struct MatrixEighFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const framework::ExecutionContext &ctx, const Tensor &input,
Tensor *eigen_values, Tensor *eigen_vectors, bool is_lower,
bool has_vectors) {
using ValueType = math::Real<T>;
auto *out_value = eigen_values->mutable_data<ValueType>(ctx.GetPlace());
auto *out_vector = eigen_vectors->mutable_data<T>(ctx.GetPlace());
auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto dito =
math::DeviceIndependenceTensorOperations<platform::CUDADeviceContext,
T>(ctx);
Tensor input_trans;
input_trans = dito.Transpose(input);
auto *input_vector = input_trans.data<T>();
auto &dims = input.dims();
int dim_size = dims.size();
int64_t batch_size = GetBatchSize(dims);
......@@ -190,14 +173,6 @@ struct MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T> {
int lda = std::max<int>(1, n);
auto vector_stride = dims[dim_size - 1] * dims[dim_size - 2];
auto values_stride = dims[dim_size - 1];
auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto dito =
math::DeviceIndependenceTensorOperations<platform::CUDADeviceContext,
T>(ctx);
Tensor output_v_var_trans = dito.Transpose(input);
TensorCopy(output_v_var_trans, ctx.GetPlace(), eigen_vectors);
int lwork = 0;
auto info = memory::Alloc(dev_ctx, sizeof(int) * batch_size);
auto *info_ptr = reinterpret_cast<int *>(info->ptr());
......@@ -205,10 +180,8 @@ struct MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T> {
// When the input type is float32, and the feature value input dimension is
// greater than or equal to [*,32,32] and less than or equal to
// [*,512,512], Syevj has better performance.
bool use_syevj =
(eigen_vectors->type() == framework::proto::VarType::FP32 &&
values_stride >= 32 && values_stride <= 512);
bool use_syevj = (input.type() == framework::proto::VarType::FP32 &&
values_stride >= 32 && values_stride <= 512);
syevjInfo_t syevj_params;
if (use_syevj) {
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -216,52 +189,52 @@ struct MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T> {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cusolverDnSsyevj_bufferSize(
dev_ctx.cusolver_dn_handle(), jobz, uplo, n,
reinterpret_cast<const float *>(out_vector), lda,
reinterpret_cast<const float *>(input_vector), lda,
reinterpret_cast<const float *>(out_value), &lwork,
syevj_params));
} else {
EvdBuffer(dev_ctx.cusolver_dn_handle(), jobz, uplo, n, out_vector, lda,
EvdBuffer(dev_ctx.cusolver_dn_handle(), jobz, uplo, n, input_vector, lda,
out_value, &lwork);
}
auto work = memory::Alloc(dev_ctx, sizeof(T) * lwork);
auto *work_ptr = reinterpret_cast<T *>(work->ptr());
for (auto i = 0; i < batch_size; i++) {
auto vector_data = out_vector + i * vector_stride;
auto value_data = out_value + i * values_stride;
auto *input_data = input_vector + i * vector_stride;
auto *value_data = out_value + i * values_stride;
auto handle = dev_ctx.cusolver_dn_handle();
if (use_syevj) {
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cusolverDnSsyevj(
handle, jobz, uplo, n, reinterpret_cast<float *>(vector_data), lda,
handle, jobz, uplo, n, reinterpret_cast<float *>(input_data), lda,
reinterpret_cast<float *>(value_data),
reinterpret_cast<float *>(work_ptr), lwork, info_ptr,
syevj_params));
} else {
Evd(handle, jobz, uplo, n, vector_data, lda, value_data, work_ptr,
lwork, info_ptr);
Evd(handle, jobz, uplo, n, input_data, lda, value_data, work_ptr, lwork,
info_ptr);
}
int error_info;
int error_info = 0;
memory::Copy(platform::CPUPlace(), &error_info,
BOOST_GET_CONST(platform::CUDAPlace, dev_ctx.GetPlace()),
info_ptr, sizeof(int), dev_ctx.stream());
PADDLE_ENFORCE_EQ(
error_info, 0,
platform::errors::PreconditionNotMet(
"For batch [%d]: the [%d] argument had an illegal value", i,
error_info));
CheckEighResult(i, error_info);
}
if (use_syevj) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cusolverDnDestroySyevjInfo(syevj_params));
}
if (has_vectors) {
*eigen_vectors = dito.Transpose(*eigen_vectors);
PADDLE_ENFORCE_NOT_NULL(eigen_vectors,
platform::errors::InvalidArgument(
"When has_vectors is true,"
"the eigenvectors needs to be calculated,"
"so the eigenvectors must be provided."));
input_trans = dito.Transpose(input_trans);
eigen_vectors->ShareDataWith(input_trans);
}
}
using ValueType = math::Real<T>;
inline void EvdBuffer(cusolverDnHandle_t handle, cusolverEigMode_t jobz,
cublasFillMode_t uplo, int n, const T *A, int lda,
const ValueType *W, int *lwork) const;
......@@ -271,15 +244,14 @@ struct MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T> {
T *work, int lwork, int *devInfo) const;
};
#define FUNC_WITH_TYPES(m) \
m(float, float, Ssy, float) m(double, double, Dsy, double) \
m(float, paddle::platform::complex<float>, Che, cuComplex) \
m(double, paddle::platform::complex<double>, Zhe, cuDoubleComplex)
#define FUNC_WITH_TYPES(m) \
m(float, Ssy, float) m(double, Dsy, double) \
m(paddle::platform::complex<float>, Che, cuComplex) \
m(paddle::platform::complex<double>, Zhe, cuDoubleComplex)
#define EVDBUFFER_INSTANCE(ValueType, T, C, CastType) \
#define EVDBUFFER_INSTANCE(T, C, CastType) \
template <> \
inline void \
MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T>::EvdBuffer( \
inline void MatrixEighFunctor<platform::CUDADeviceContext, T>::EvdBuffer( \
cusolverDnHandle_t handle, cusolverEigMode_t jobz, \
cublasFillMode_t uplo, int n, const T *A, int lda, const ValueType *W, \
int *lwork) const { \
......@@ -291,10 +263,9 @@ struct MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T> {
FUNC_WITH_TYPES(EVDBUFFER_INSTANCE);
#define EVD_INSTANCE(ValueType, T, C, CastType) \
#define EVD_INSTANCE(T, C, CastType) \
template <> \
inline void \
MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T>::Evd( \
inline void MatrixEighFunctor<platform::CUDADeviceContext, T>::Evd( \
cusolverDnHandle_t handle, cusolverEigMode_t jobz, \
cublasFillMode_t uplo, int n, T *A, int lda, ValueType *W, T *work, \
int lwork, int *devInfo) const { \
......
paddle/fluid/operators/math/lapack_function.cc
浏览文件 @ effb70f4
......@@ -31,6 +31,49 @@ void lapackLu<float>(int m, int n, float *a, int lda, int *ipiv, int *info) {
platform::dynload::sgetrf_(&m, &n, a, &lda, ipiv, info);
}
// eigh
template <>
void lapackEigh<float>(char jobz, char uplo, int n, float *a, int lda, float *w,
float *work, int lwork, float *rwork, int lrwork,
int *iwork, int liwork, int *info) {
(void)rwork; // unused
(void)lrwork; // unused
platform::dynload::ssyevd_(&jobz, &uplo, &n, a, &lda, w, work, &lwork, iwork,
&liwork, info);
}
template <>
void lapackEigh<double>(char jobz, char uplo, int n, double *a, int lda,
double *w, double *work, int lwork, double *rwork,
int lrwork, int *iwork, int liwork, int *info) {
(void)rwork; // unused
(void)lrwork; // unused
platform::dynload::dsyevd_(&jobz, &uplo, &n, a, &lda, w, work, &lwork, iwork,
&liwork, info);
}
template <>
void lapackEigh<platform::complex<float>, float>(
char jobz, char uplo, int n, platform::complex<float> *a, int lda, float *w,
platform::complex<float> *work, int lwork, float *rwork, int lrwork,
int *iwork, int liwork, int *info) {
platform::dynload::cheevd_(&jobz, &uplo, &n,
reinterpret_cast<std::complex<float> *>(a), &lda,
w, reinterpret_cast<std::complex<float> *>(work),
&lwork, rwork, &lrwork, iwork, &liwork, info);
}
template <>
void lapackEigh<platform::complex<double>, double>(
char jobz, char uplo, int n, platform::complex<double> *a, int lda,
double *w, platform::complex<double> *work, int lwork, double *rwork,
int lrwork, int *iwork, int liwork, int *info) {
platform::dynload::zheevd_(&jobz, &uplo, &n,
reinterpret_cast<std::complex<double> *>(a), &lda,
w, reinterpret_cast<std::complex<double> *>(work),
&lwork, rwork, &lrwork, iwork, &liwork, info);
}
// Eig
template <>
void lapackEig<double>(char jobvl, char jobvr, int n, double *a, int lda,
......
paddle/fluid/operators/math/lapack_function.h
浏览文件 @ effb70f4
......@@ -20,12 +20,17 @@ namespace math {
// LU (for example)
template <typename T>
void lapackLu(int m, int n, T *a, int lda, int *ipiv, int *info);
void lapackLu(int m, int n, T* a, int lda, int* ipiv, int* info);
template <typename T, typename ValueType = T>
void lapackEigh(char jobz, char uplo, int n, T* a, int lda, ValueType* w,
T* work, int lwork, ValueType* rwork, int lrwork, int* iwork,
int liwork, int* info);
template <typename T1, typename T2 = T1>
void lapackEig(char jobvl, char jobvr, int n, T1 *a, int lda, T1 *w, T1 *vl,
int ldvl, T1 *vr, int ldvr, T1 *work, int lwork, T2 *rwork,
int *info);
void lapackEig(char jobvl, char jobvr, int n, T1* a, int lda, T1* w, T1* vl,
int ldvl, T1* vr, int ldvr, T1* work, int lwork, T2* rwork,
int* info);
} // namespace math
} // namespace operators
......
paddle/fluid/platform/dynload/lapack.h
浏览文件 @ effb70f4
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <complex>
#include <mutex>
#include "paddle/fluid/platform/complex.h"
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
#include "paddle/fluid/platform/port.h"
......@@ -28,6 +29,22 @@ extern "C" void dgetrf_(int *m, int *n, double *a, int *lda, int *ipiv,
extern "C" void sgetrf_(int *m, int *n, float *a, int *lda, int *ipiv,
int *info);
// evd
extern "C" void zheevd_(char *jobz, char *uplo, int *n, std::complex<double> *a,
int *lda, double *w, std::complex<double> *work,
int *lwork, double *rwork, int *lrwork, int *iwork,
int *liwork, int *info);
extern "C" void cheevd_(char *jobz, char *uplo, int *n, std::complex<float> *a,
int *lda, float *w, std::complex<float> *work,
int *lwork, float *rwork, int *lrwork, int *iwork,
int *liwork, int *info);
extern "C" void dsyevd_(char *jobz, char *uplo, int *n, double *a, int *lda,
double *w, double *work, int *lwork, int *iwork,
int *liwork, int *info);
extern "C" void ssyevd_(char *jobz, char *uplo, int *n, float *a, int *lda,
float *w, float *work, int *lwork, int *iwork,
int *liwork, int *info);
// geev
extern "C" void dgeev_(char *jobvl, char *jobvr, int *n, double *a, int *lda,
double *wr, double *wi, double *vl, int *ldvl,
......@@ -81,6 +98,10 @@ extern void *lapack_dso_handle;
#define LAPACK_ROUTINE_EACH(__macro) \
__macro(dgetrf_); \
__macro(sgetrf_); \
__macro(zheevd_); \
__macro(cheevd_); \
__macro(dsyevd_); \
__macro(ssyevd_); \
__macro(dgeev_); \
__macro(sgeev_); \
__macro(zgeev_); \
......
python/paddle/__init__.py
浏览文件 @ effb70f4
......@@ -106,7 +106,6 @@ from .tensor.linalg import slogdet # noqa: F401
from .tensor.linalg import multi_dot # noqa: F401
from .tensor.linalg import matrix_power # noqa: F401
from .tensor.linalg import svd # noqa: F401
from .tensor.linalg import eigh # noqa: F401
from .tensor.linalg import pinv # noqa: F401
from .tensor.linalg import solve # noqa: F401
from .tensor.logic import equal # noqa: F401
......
python/paddle/tensor/linalg.py
浏览文件 @ effb70f4
......@@ -1759,7 +1759,7 @@ def eigh(x, UPLO='L', name=None):
x_data = np.array([[1, -2j], [2j, 5]])
x = paddle.to_tensor(x_data)
out_value, out_vector = paddle.eigh(x, UPLO='L')
out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')
print(out_value)
#[0.17157288, 5.82842712]
print(out_vector)
......@@ -1780,7 +1780,7 @@ def eigh(x, UPLO='L', name=None):
raise ValueError(
"The input matrix must be batches of square matrices. But received x's dimention: {}".
format(x_shape))
if UPLO is not 'L' and UPLO is not 'U':
if UPLO != 'L' and UPLO != 'U':
raise ValueError(
"UPLO must be L or U. But received UPLO is: {}".format(UPLO))
......
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