Merged eigen/eigen into default

This commit is contained in:
Nicolas Mellado 2015-07-15 10:15:54 +02:00
commit 7cecd39a84
9 changed files with 94 additions and 9 deletions

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@ -480,7 +480,7 @@ struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
dstAlignment = alignable ? Aligned : int(dstIsAligned)
};
const Scalar *dst_ptr = &kernel.dstEvaluator().coeffRef(0,0);
if((!bool(dstIsAligned)) && (Index(dst_ptr) % sizeof(Scalar))>0)
if((!bool(dstIsAligned)) && (size_t(dst_ptr) % sizeof(Scalar))>0)
{
// the pointer is not aligend-on scalar, so alignment is not possible
return dense_assignment_loop<Kernel,DefaultTraversal,NoUnrolling>::run(kernel);

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@ -50,12 +50,18 @@ int EIGEN_BLAS_FUNC(gemm)(char *opa, char *opb, int *m, int *n, int *k, RealScal
if(info)
return xerbla_(SCALAR_SUFFIX_UP"GEMM ",&info,6);
if (*m == 0 || *n == 0)
return 0;
if(beta!=Scalar(1))
{
if(beta==Scalar(0)) matrix(c, *m, *n, *ldc).setZero();
else matrix(c, *m, *n, *ldc) *= beta;
}
if(*k == 0)
return 0;
internal::gemm_blocking_space<ColMajor,Scalar,Scalar,Dynamic,Dynamic,Dynamic> blocking(*m,*n,*k,1,true);
int code = OP(*opa) | (OP(*opb) << 2);

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@ -193,6 +193,12 @@ template<typename _Scalar> class AlignedVector3
namespace internal {
template<typename _Scalar>
struct eval<AlignedVector3<_Scalar>, Dense>
{
typedef const AlignedVector3<_Scalar>& type;
};
template<typename Scalar>
struct evaluator<AlignedVector3<Scalar> >
: evaluator<Matrix<Scalar,4,1> >::type

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@ -492,6 +492,7 @@ class TensorBase<Derived, ReadOnlyAccessors>
protected:
template <typename Scalar, std::size_t NumIndices, int Options, typename IndexType> friend class Tensor;
template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
template <typename OtherDerived, int AccessLevel> friend class TensorBase;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
@ -508,6 +509,7 @@ class TensorBase<Derived, WriteAccessors> : public TensorBase<Derived, ReadOnlyA
static const int NumDimensions = DerivedTraits::NumDimensions;
template <typename Scalar, std::size_t NumIndices, int Options, typename IndexType> friend class Tensor;
template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
template <typename OtherDerived, int AccessLevel> friend class TensorBase;
EIGEN_DEVICE_FUNC

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@ -197,6 +197,23 @@ class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_,
}
#endif
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, ReadOnlyAccessors>& other)
{
typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
Assign assign(*this, other.derived());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, WriteAccessors>& other)
{
typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
Assign assign(*this, other.derived());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize& operator=(const TensorFixedSize& other)
{

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@ -62,8 +62,11 @@ struct TensorIntDivisor {
// fast ln2
const int leading_zeros = count_leading_zeros(divider);
const int log_div = N - (leading_zeros+1);
int log_div = N - leading_zeros;
// If divider is a power of two then log_div is 1 more than it should be.
if ((1ull << (log_div-1)) == divider) {
log_div--;
}
multiplier = (static_cast<uint64_t>(1) << (N+log_div)) / divider - (static_cast<uint64_t>(1) << N) + 1;
shift1 = log_div > 1 ? 1 : log_div;
shift2 = log_div > 1 ? log_div-1 : 0;

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@ -59,6 +59,11 @@ void alignedvector3()
f2.normalize();
r2.normalize();
VERIFY_IS_APPROX(f2,r2);
std::stringstream ss1, ss2;
ss1 << f1;
ss2 << r1;
VERIFY(ss1.str()==ss2.str());
}
void test_alignedvector3()

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@ -31,12 +31,20 @@ static void test_1d()
vec1(4) = 23.0; vec2(4) = 4.0;
vec1(5) = 42.0; vec2(5) = 5.0;
float data3[6];
TensorMap<TensorFixedSize<float, Sizes<6> > > vec3(data3, 6);
vec3 = vec1.sqrt();
float data4[6];
TensorMap<TensorFixedSize<float, Sizes<6>, RowMajor> > vec4(data4, 6);
vec4 = vec2.sqrt();
// Test against shallow copy.
TensorFixedSize<float, Sizes<6> > copy = vec1;
VERIFY_IS_NOT_EQUAL(vec1.data(), copy.data());
for (int i = 0; i < 6; ++i) {
VERIFY_IS_APPROX(vec1(i), copy(i));
}
copy = vec1;
VERIFY_IS_NOT_EQUAL(vec1.data(), copy.data());
for (int i = 0; i < 6; ++i) {
VERIFY_IS_APPROX(vec1(i), copy(i));
}
TensorFixedSize<float, Sizes<6> > vec3 = vec1.sqrt();
TensorFixedSize<float, Sizes<6>, RowMajor> vec4 = vec2.sqrt();
VERIFY_IS_EQUAL((vec3.size()), 6);
VERIFY_IS_EQUAL(vec3.rank(), 1);
@ -66,6 +74,30 @@ static void test_1d()
VERIFY_IS_APPROX(vec3(5), 42.0f + 5.0f);
}
static void test_tensor_map()
{
TensorFixedSize<float, Sizes<6> > vec1;
TensorFixedSize<float, Sizes<6>, RowMajor> vec2;
vec1(0) = 4.0; vec2(0) = 0.0;
vec1(1) = 8.0; vec2(1) = 1.0;
vec1(2) = 15.0; vec2(2) = 2.0;
vec1(3) = 16.0; vec2(3) = 3.0;
vec1(4) = 23.0; vec2(4) = 4.0;
vec1(5) = 42.0; vec2(5) = 5.0;
float data3[6];
TensorMap<TensorFixedSize<float, Sizes<6> > > vec3(data3, 6);
vec3 = vec1.sqrt() + vec2;
VERIFY_IS_APPROX(vec3(0), sqrtf(4.0));
VERIFY_IS_APPROX(vec3(1), sqrtf(8.0) + 1.0f);
VERIFY_IS_APPROX(vec3(2), sqrtf(15.0) + 2.0f);
VERIFY_IS_APPROX(vec3(3), sqrtf(16.0) + 3.0f);
VERIFY_IS_APPROX(vec3(4), sqrtf(23.0) + 4.0f);
VERIFY_IS_APPROX(vec3(5), sqrtf(42.0) + 5.0f);
}
static void test_2d()
{
float data1[6];
@ -192,6 +224,7 @@ static void test_array()
void test_cxx11_tensor_fixed_size()
{
CALL_SUBTEST(test_1d());
CALL_SUBTEST(test_tensor_map());
CALL_SUBTEST(test_2d());
CALL_SUBTEST(test_3d());
CALL_SUBTEST(test_array());

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@ -68,10 +68,23 @@ void test_unsigned_64bit()
}
void test_specific()
{
// A particular combination that exposed a bug in the past.
int64_t div = 209715200;
int64_t num = 3238002688;
Eigen::internal::TensorIntDivisor<int64_t> divider =
Eigen::internal::TensorIntDivisor<int64_t>(div);
int64_t result = num/div;
int64_t result_op = divider.divide(num);
VERIFY_IS_EQUAL(result, result_op);
}
void test_cxx11_tensor_intdiv()
{
CALL_SUBTEST_1(test_signed_32bit());
CALL_SUBTEST_2(test_unsigned_32bit());
CALL_SUBTEST_3(test_signed_64bit());
CALL_SUBTEST_4(test_unsigned_64bit());
CALL_SUBTEST_5(test_specific());
}