Eigen, how to access the underlying array of a MatrixBase<Derived> - c++

Question

I need to access the array that contains the data of a MatrixBase Eigen matrix.
The Eigen library has the data() method which returns a pointer to an array, however it is only accessible from a Matrix type. The MatrixBase doesn't have a similar method, even though the MatrixBase class is supposed to act as a template and the actual type should be just a Matrix. If I try to access MatrixBase.data() I get a compile time error:
template <typename ScalarA, typename Index, typename DerivedB, typename DerivedC>
void uscgemv(float alpha,
const USCMatrix<ScalarA,Index> &a,
const MatrixBase<DerivedB> &b,
const MatrixBase<DerivedC> &c_const)
{
//...some code
float * bMat = b.data();
///more code
}
This code produces the following compile time error.
error: ‘const class Eigen::MatrixBase<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<float>, Eigen::Matrix<float, -1, 1> > >’ has no member named ‘data’
float * bMat = b.data();
So I have to resort to gimmicks such as...
float * bMat;
int bRows = b.rows();
int bCols = b.cols();
mallocPinnedMemory(&bMat, bRows*bCols*sizeof(float));
Eigen::Map<Matrix<float, Dynamic, Dynamic> > bmat_temp(bMat, bRows, bCols);
bmat_temp = b; //THis is SLOW, we should avoid it.
Then I can access the bMat array...
Those copies back-and-forth are the biggest cost in the gpu matrix multiplication, as I essentially I have to make an extra copy, before even coping to the device...
I can't use Eigen-magma, as this is sparse matrix-in-a-weird-format to a dense matrix (or sometimes vector) multiplication so I can't use any of the automatic gpu functions there. Also I would much rather not declare the matrices as something else, because that would require changing A LOT of lines of code across the whole program (which I didn't write).
EDIT: A static cast solution was proposed:
float * bMat = (static_cast<Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> >(b)).data();
However I get segfault the first time I try to access an element of the array bMat.
EDIT 2: I'm looking for a zero copy way to access the underlying arrays. I need to only be able to read b, but I also need to able to write to c. Currently c is unconst-d with the following macro:
#define UNCONST(t,c,uc) Eigen::MatrixBase<t> &uc = const_cast<Eigen::MatrixBase<t>&>(c);
EDIT 3: After cross posting to Eigen Forums it would seem I can't do better than the suggested answer.

Answer 1

MatrixBase is the base class of any dense expression. It does not necessarily correspond to an object with storage. For instance, can be the abstract representation of A+B, or in your case the abstract representation of a vector with constant values. You can make uscgemv accepts only expression having appropriate storage using the Ref<> class, e.g.:
template <typename ScalarA, typename Index>
void uscgemv(float alpha,
const USCMatrix<ScalarA,Index> &a,
Ref<const VectorXf> b,
Ref<VectorXf> c);
If the third argument does not match the storage of a VectorXf then it will be evaluated for you. Then you can safely call b.data(). To keep the scalar type of b generic, you can still declare it as MatrixBase<DerivedB>& and then copy it into a Ref<const Matrix<typename DerivedB::Scalar, DerivedB::RowsAtCompileTime, DerivedB::ColsAtCompileTime> >:
typedef Ref<const Matrix<typename DerivedB::Scalar, DerivedB::RowsAtCompileTime, DerivedB::ColsAtCompileTime> > RefB;
RefB actual_b(b);
actual_b.data();

Answer 2

I guess the issue is this: you are not allowed to get a pointer to data of a MatrixBase<Derived>, since the latter can be any kind of expression in Eigen, like a product of matrices for example. To get a pointer you probably have to first implicitly convert the MatrixBase<Derived> into a Matrix<Scalar, Dynamic, Dynamic>, then use the data() member of the latter.
So you can create a deep copy of the expression, i.e. use something like
Eigen::Matrix<typename Derived::Scalar, Eigen::Dynamic, Eigen::Dynamic tmp = b;
then use
tmp.data()
This code works now
#include <Eigen/Dense>
#include <iostream>
template<typename Derived>
void use_data\
(const Eigen::MatrixBase<Derived>& mat)
{
Eigen::Matrix<typename Derived::Scalar, Eigen::Dynamic, Eigen::Dynamic>tmp = mat();
typename Derived::Scalar* p = tmp.data();
std::cout << std::endl;
for(std::size_t i = 0; i < tmp.size(); i++)
std::cout << *(p+i) << " ";
}
int main()
{
Eigen::MatrixXd A = Eigen::MatrixXd::Random(2, 2);
Eigen::MatrixXd B = Eigen::MatrixXd::Random(2, 2);
// now A*B is an expression, of type MatrixBase<EigenSum....>
use_data(A + B);
}

Answer 3

There are an easy solution to solve your question, combine EigenMap, &a(0, 0) and const_cast you could resue the buffer of the MatrixBase.
Example :
template<typename Derived1,
typename Derived2>
void example(Eigen::MatrixBase<Derived1> const &input,
Eigen::MatrixBase<Derived2> const &output)
{
static_assert(std::is_same<Derived1::Scalar, Derived2::Scalar>::value,
"Data type of matrix input, weight, bias and output should be the same");
using Scalar = typename Derived3::Scalar;
using MatType = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
using Mapper = Eigen::Map<const MatType, Eigen::Aligned>;
//in the worst case, you can do const_cast<Scalar *> on
//&bias(0, 0).That is, if you cannot explicitly define the Map
//type as const
Mapper Map(&input(0, 0), input.size());
output.colwise() += Map;
}
}
I try it on windows 8, vc2013 32bits, Eigen version is 3.2.5, no segmentation fault occur(yet), every things looks perfectly fine. I also check the address of the Map, it is same as the original input. You can verify it with another example
#include <Eigen/Dense>
#include <iostream>
template<typename Derived>
void example_2(Eigen::MatrixBase<Derived> &input)
{
using Scalar = decltype(input[0]);
Eigen::Map<Derived> map(&input(0, 0),
input.rows(),
input.cols());
map(0, 0) = 300;
}
int main()
{
Eigen::MatrixXd mat(2, 2);
mat<<0, 1, 2, 3;
example_2(mat);
std::cout<<mat<<"\n\n";
return 0;
}
The first element of mat will be "300"