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bug #1144: fix regression in x=y+A*x (aliasing), and move evaluator_traits::AssumeAliasing to evaluator_assume_aliasing.

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This commit is contained in:
Gael Guennebaud
2016-01-09 08:30:38 +01:00
parent f9d71a1729
commit 8b9dc9f0df
11 changed files with 77 additions and 44 deletions
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35
test/product.h
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@@ -145,14 +145,31 @@ template<typename MatrixType> void product(const MatrixType& m)
VERIFY_IS_APPROX(res.col(r).noalias() = square * square.col(r), (square * square.col(r)).eval());
// inner product
Scalar x = square2.row(c) * square2.col(c2);
VERIFY_IS_APPROX(x, square2.row(c).transpose().cwiseProduct(square2.col(c2)).sum());
{
Scalar x = square2.row(c) * square2.col(c2);
VERIFY_IS_APPROX(x, square2.row(c).transpose().cwiseProduct(square2.col(c2)).sum());
}
// outer product
VERIFY_IS_APPROX(m1.col(c) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.row(r).transpose() * m1.col(c).transpose(), m1.block(r,0,1,cols).transpose() * m1.block(0,c,rows,1).transpose());
VERIFY_IS_APPROX(m1.block(0,c,rows,1) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.col(c) * m1.block(r,0,1,cols), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.leftCols(1) * m1.row(r), m1.block(0,0,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.col(c) * m1.topRows(1), m1.block(0,c,rows,1) * m1.block(0,0,1,cols));
{
VERIFY_IS_APPROX(m1.col(c) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.row(r).transpose() * m1.col(c).transpose(), m1.block(r,0,1,cols).transpose() * m1.block(0,c,rows,1).transpose());
VERIFY_IS_APPROX(m1.block(0,c,rows,1) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.col(c) * m1.block(r,0,1,cols), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.leftCols(1) * m1.row(r), m1.block(0,0,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.col(c) * m1.topRows(1), m1.block(0,c,rows,1) * m1.block(0,0,1,cols));
}
// Aliasing
{
ColVectorType x(cols); x.setRandom();
ColVectorType z(x);
ColVectorType y(cols); y.setZero();
ColSquareMatrixType A(cols,cols); A.setRandom();
// CwiseBinaryOp
VERIFY_IS_APPROX(x = y + A*x, A*z);
x = z;
// CwiseUnaryOp
VERIFY_IS_APPROX(x = Scalar(1.)*(A*x), A*z);
}
}

23
test/product_large.cpp
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@@ -9,6 +9,27 @@
#include "product.h"
template<typename T>
void test_aliasing()
{
int rows = internal::random<int>(1,12);
int cols = internal::random<int>(1,12);
typedef Matrix<T,Dynamic,Dynamic> MatrixType;
typedef Matrix<T,Dynamic,1> VectorType;
VectorType x(cols); x.setRandom();
VectorType z(x);
VectorType y(rows); y.setZero();
MatrixType A(rows,cols); A.setRandom();
// CwiseBinaryOp
VERIFY_IS_APPROX(x = y + A*x, A*z); // OK because "y + A*x" is marked as "assume-aliasing"
x = z;
// CwiseUnaryOp
VERIFY_IS_APPROX(x = T(1.)*(A*x), A*z); // OK because 1*(A*x) is replaced by (1*A*x) which is a Product<> expression
x = z;
// VERIFY_IS_APPROX(x = y-A*x, -A*z); // Not OK in 3.3 because x is resized before A*x gets evaluated
x = z;
}
void test_product_large()
{
for(int i = 0; i < g_repeat; i++) {
@@ -17,6 +38,8 @@ void test_product_large()
CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_1( test_aliasing<float>() );
}
#if defined EIGEN_TEST_PART_6

6
test/product_notemporary.cpp
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@@ -43,10 +43,16 @@ template<typename MatrixType> void product_notemporary(const MatrixType& m)
r1 = internal::random<Index>(8,rows-r0);
VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()), 1);
VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()).transpose(), 1);
VERIFY_EVALUATION_COUNT( m3.noalias() = m1 * m2.adjoint(), 0);
VERIFY_EVALUATION_COUNT( m3 = s1 * (m1 * m2.transpose()), 1);
// VERIFY_EVALUATION_COUNT( m3 = m3 + s1 * (m1 * m2.transpose()), 1);
VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * (m1 * m2.transpose()), 0);
VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()), 1);
VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()).transpose(), 1);
VERIFY_EVALUATION_COUNT( m3.noalias() = m3 + m1 * m2.transpose(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() += m3 + m1 * m2.transpose(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() -= m3 + m1 * m2.transpose(), 0);