Fix sparse_extra_3, disable counting temporaries for testing DynamicSparseMatrix.

Multiplication of column-major `DynamicSparseMatrix`es involves three
temporaries:
- two for transposing twice to sort the coefficients
(`ConservativeSparseSparseProduct.h`, L160-161)
- one for a final copy assignment (`SparseAssign.h`, L108)
The latter is avoided in an optimization for `SparseMatrix`.

Since `DynamicSparseMatrix` is deprecated in favor of `SparseMatrix`, it's not
worth the effort to optimize further, so I simply disabled counting
temporaries via a macro.

Note that due to the inclusion of `sparse_product.cpp`, the `sparse_extra`
tests actually re-run all the original `sparse_product` tests as well.

We may want to simply drop the `DynamicSparseMatrix` tests altogether, which
would eliminate the test duplication.

Related to #2048

3 files changed, 25 insertions, 20 deletions

diff --git a/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
index 9db119b67..948650253 100644
--- a/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
+++ b/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
@@ -10,7 +10,7 @@
 #ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
 #define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
 
-namespace Eigen { 
+namespace Eigen {
 
 namespace internal {
 
@@ -25,16 +25,16 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
   Index rows = lhs.innerSize();
   Index cols = rhs.outerSize();
   eigen_assert(lhs.outerSize() == rhs.innerSize());
-  
+
   ei_declare_aligned_stack_constructed_variable(bool,   mask,     rows, 0);
   ei_declare_aligned_stack_constructed_variable(ResScalar, values,   rows, 0);
   ei_declare_aligned_stack_constructed_variable(Index,  indices,  rows, 0);
-  
+
   std::memset(mask,0,sizeof(bool)*rows);
 
   evaluator<Lhs> lhsEval(lhs);
   evaluator<Rhs> rhsEval(rhs);
-  
+
   // estimate the number of non zero entries
   // given a rhs column containing Y non zeros, we assume that the respective Y columns
   // of the lhs differs in average of one non zeros, thus the number of non zeros for
@@ -141,7 +141,7 @@ struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,C
     typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
     typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrixAux;
     typedef typename sparse_eval<ColMajorMatrixAux,ResultType::RowsAtCompileTime,ResultType::ColsAtCompileTime,ColMajorMatrixAux::Flags>::type ColMajorMatrix;
-    
+
     // If the result is tall and thin (in the extreme case a column vector)
     // then it is faster to sort the coefficients inplace instead of transposing twice.
     // FIXME, the following heuristic is probably not very good.
@@ -155,7 +155,7 @@ struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,C
     else
     {
       ColMajorMatrixAux resCol(lhs.rows(),rhs.cols());
-      // ressort to transpose to sort the entries
+      // resort to transpose to sort the entries
       internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrixAux>(lhs, rhs, resCol, false);
       RowMajorMatrix resRow(resCol);
       res = resRow.markAsRValue();
diff --git a/test/sparse_product.cpp b/test/sparse_product.cpp
index c8caebef7..6e85f6914 100644
--- a/test/sparse_product.cpp
+++ b/test/sparse_product.cpp
@@ -100,6 +100,7 @@ template<typename SparseMatrixType> void sparse_product()
     VERIFY_IS_APPROX(m4=(m2t.transpose()*m3t.transpose()).pruned(0), refMat4=refMat2t.transpose()*refMat3t.transpose());
     VERIFY_IS_APPROX(m4=(m2*m3t.transpose()).pruned(0), refMat4=refMat2*refMat3t.transpose());
 
+#ifndef EIGEN_SPARSE_PRODUCT_IGNORE_TEMPORARY_COUNT
     // make sure the right product implementation is called:
     if((!SparseMatrixType::IsRowMajor) && m2.rows()<=m3.cols())
     {
@@ -107,6 +108,7 @@ template<typename SparseMatrixType> void sparse_product()
       VERIFY_EVALUATION_COUNT(m4 = (m2*m3).pruned(0), 1);
       VERIFY_EVALUATION_COUNT(m4 = (m2*m3).eval().pruned(0), 4);
     }
+#endif
 
     // and that pruning is effective:
     {
@@ -151,7 +153,7 @@ template<typename SparseMatrixType> void sparse_product()
     VERIFY_IS_APPROX(dm4.noalias()-=m2*refMat3, refMat4-=refMat2*refMat3);
     VERIFY_IS_APPROX(dm4=m2*(refMat3+refMat3), refMat4=refMat2*(refMat3+refMat3));
     VERIFY_IS_APPROX(dm4=m2t.transpose()*(refMat3+refMat5)*0.5, refMat4=refMat2t.transpose()*(refMat3+refMat5)*0.5);
-    
+
     // sparse * dense vector
     VERIFY_IS_APPROX(dm4.col(0)=m2*refMat3.col(0), refMat4.col(0)=refMat2*refMat3.col(0));
     VERIFY_IS_APPROX(dm4.col(0)=m2*refMat3t.transpose().col(0), refMat4.col(0)=refMat2*refMat3t.transpose().col(0));
@@ -182,7 +184,7 @@ template<typename SparseMatrixType> void sparse_product()
       VERIFY_IS_APPROX( m4=m2.middleCols(c,1)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose());
       VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
       VERIFY_IS_APPROX(dm4=m2.col(c)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose());
-      
+
       VERIFY_IS_APPROX(m4=dm5.col(c1)*m2.col(c).transpose(), refMat4=dm5.col(c1)*refMat2.col(c).transpose());
       VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
       VERIFY_IS_APPROX(m4=dm5.col(c1)*m2.middleCols(c,1).transpose(), refMat4=dm5.col(c1)*refMat2.col(c).transpose());
@@ -211,23 +213,23 @@ template<typename SparseMatrixType> void sparse_product()
     }
 
     VERIFY_IS_APPROX(m6=m6*m6, refMat6=refMat6*refMat6);
-    
+
     // sparse matrix * sparse vector
     ColSpVector cv0(cols), cv1;
     DenseVector dcv0(cols), dcv1;
     initSparse(2*density,dcv0, cv0);
-    
+
     RowSpVector rv0(depth), rv1;
     RowDenseVector drv0(depth), drv1(rv1);
     initSparse(2*density,drv0, rv0);
 
-    VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0);    
+    VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0);
     VERIFY_IS_APPROX(rv1=rv0*m3, drv1=drv0*refMat3);
     VERIFY_IS_APPROX(cv1=m3t.adjoint()*cv0, dcv1=refMat3t.adjoint()*dcv0);
     VERIFY_IS_APPROX(cv1=rv0*m3, dcv1=drv0*refMat3);
     VERIFY_IS_APPROX(rv1=m3*cv0, drv1=refMat3*dcv0);
   }
-  
+
   // test matrix - diagonal product
   {
     DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
@@ -243,7 +245,7 @@ template<typename SparseMatrixType> void sparse_product()
     VERIFY_IS_APPROX(m3=m2.transpose()*d2, refM3=refM2.transpose()*d2);
     VERIFY_IS_APPROX(m3=d2*m2, refM3=d2*refM2);
     VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1*refM2.transpose());
-    
+
     // also check with a SparseWrapper:
     DenseVector v1 = DenseVector::Random(cols);
     DenseVector v2 = DenseVector::Random(rows);
@@ -252,12 +254,12 @@ template<typename SparseMatrixType> void sparse_product()
     VERIFY_IS_APPROX(m3=m2.transpose()*v2.asDiagonal(), refM3=refM2.transpose()*v2.asDiagonal());
     VERIFY_IS_APPROX(m3=v2.asDiagonal()*m2, refM3=v2.asDiagonal()*refM2);
     VERIFY_IS_APPROX(m3=v1.asDiagonal()*m2.transpose(), refM3=v1.asDiagonal()*refM2.transpose());
-    
+
     VERIFY_IS_APPROX(m3=v2.asDiagonal()*m2*v1.asDiagonal(), refM3=v2.asDiagonal()*refM2*v1.asDiagonal());
 
     VERIFY_IS_APPROX(v2=m2*v1.asDiagonal()*v1, refM2*v1.asDiagonal()*v1);
     VERIFY_IS_APPROX(v3=v2.asDiagonal()*m2*v1, v2.asDiagonal()*refM2*v1);
-    
+
     // evaluate to a dense matrix to check the .row() and .col() iterator functions
     VERIFY_IS_APPROX(d3=m2*d1, refM3=refM2*d1);
     VERIFY_IS_APPROX(d3=m2.transpose()*d2, refM3=refM2.transpose()*d2);
@@ -310,20 +312,20 @@ template<typename SparseMatrixType> void sparse_product()
     VERIFY_IS_APPROX(x.noalias()+=mUp.template selfadjointView<Upper>()*b, refX+=refS*b);
     VERIFY_IS_APPROX(x.noalias()-=mLo.template selfadjointView<Lower>()*b, refX-=refS*b);
     VERIFY_IS_APPROX(x.noalias()+=mS.template selfadjointView<Upper|Lower>()*b, refX+=refS*b);
-    
+
     // sparse selfadjointView with sparse matrices
     SparseMatrixType mSres(rows,rows);
     VERIFY_IS_APPROX(mSres = mLo.template selfadjointView<Lower>()*mS,
                      refX = refLo.template selfadjointView<Lower>()*refS);
     VERIFY_IS_APPROX(mSres = mS * mLo.template selfadjointView<Lower>(),
                      refX = refS * refLo.template selfadjointView<Lower>());
-    
+
     // sparse triangularView with dense matrices
     VERIFY_IS_APPROX(x=mA.template triangularView<Upper>()*b, refX=refA.template triangularView<Upper>()*b);
     VERIFY_IS_APPROX(x=mA.template triangularView<Lower>()*b, refX=refA.template triangularView<Lower>()*b);
     VERIFY_IS_APPROX(x=b*mA.template triangularView<Upper>(), refX=b*refA.template triangularView<Upper>());
     VERIFY_IS_APPROX(x=b*mA.template triangularView<Lower>(), refX=b*refA.template triangularView<Lower>());
-    
+
     // sparse triangularView with sparse matrices
     VERIFY_IS_APPROX(mSres = mA.template triangularView<Lower>()*mS,   refX = refA.template triangularView<Lower>()*refS);
     VERIFY_IS_APPROX(mSres = mS * mA.template triangularView<Lower>(), refX = refS * refA.template triangularView<Lower>());
@@ -368,9 +370,9 @@ void bug_942()
 
   Vector d(1);
   d[0] = 2;
-  
+
   double res = 2;
-  
+
   VERIFY_IS_APPROX( ( cmA*d.asDiagonal() ).eval().coeff(0,0), res );
   VERIFY_IS_APPROX( ( d.asDiagonal()*rmA ).eval().coeff(0,0), res );
   VERIFY_IS_APPROX( ( rmA*d.asDiagonal() ).eval().coeff(0,0), res );
diff --git a/unsupported/test/sparse_extra.cpp b/unsupported/test/sparse_extra.cpp
index b5d656fdc..cbb799acc 100644
--- a/unsupported/test/sparse_extra.cpp
+++ b/unsupported/test/sparse_extra.cpp
@@ -22,6 +22,9 @@ static long g_dense_op_sparse_count = 0;
 #endif
 
 #define EIGEN_NO_DEPRECATED_WARNING
+// Disable counting of temporaries, since sparse_product(DynamicSparseMatrix)
+// has an extra copy-assignment.
+#define EIGEN_SPARSE_PRODUCT_IGNORE_TEMPORARY_COUNT
 #include "sparse_product.cpp"
 
 #if 0 // sparse_basic(DynamicSparseMatrix) does not compile at all -> disabled