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bug #1569: fix Tensor<half>::mean() on AVX with respective unit test.

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This commit is contained in:
Gael Guennebaud 2018-07-19 13:15:40 +02:00
parent 1f523e7304
commit 7ccb623746
2 changed files with 22 additions and 21 deletions
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2
unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
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@ -140,7 +140,7 @@ struct reducer_traits<SumReducer<T>, Device> {
template <typename T> struct MeanReducer
{
static const bool PacketAccess = packet_traits<T>::HasAdd && !NumTraits<T>::IsInteger;
static const bool PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasDiv && !NumTraits<T>::IsInteger;
static const bool IsStateful = true;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE

41
unsupported/test/cxx11_tensor_reduction.cpp
View File

@ -53,20 +53,20 @@ static void test_trivial_reductions() {
}
}
template <int DataLayout>
template <typename Scalar,int DataLayout>
static void test_simple_reductions() {
Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
Tensor<Scalar, 4, DataLayout> tensor(2, 3, 5, 7);
tensor.setRandom();
array<ptrdiff_t, 2> reduction_axis2;
reduction_axis2[0] = 1;
reduction_axis2[1] = 3;
Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis2);
Tensor<Scalar, 2, DataLayout> result = tensor.sum(reduction_axis2);
VERIFY_IS_EQUAL(result.dimension(0), 2);
VERIFY_IS_EQUAL(result.dimension(1), 5);
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 5; ++j) {
float sum = 0.0f;
Scalar sum = Scalar(0.0f);
for (int k = 0; k < 3; ++k) {
for (int l = 0; l < 7; ++l) {
sum += tensor(i, k, j, l);
@ -77,7 +77,7 @@ static void test_simple_reductions() {
}
{
Tensor<float, 0, DataLayout> sum1 = tensor.sum();
Tensor<Scalar, 0, DataLayout> sum1 = tensor.sum();
VERIFY_IS_EQUAL(sum1.rank(), 0);
array<ptrdiff_t, 4> reduction_axis4;
@ -85,7 +85,7 @@ static void test_simple_reductions() {
reduction_axis4[1] = 1;
reduction_axis4[2] = 2;
reduction_axis4[3] = 3;
Tensor<float, 0, DataLayout> sum2 = tensor.sum(reduction_axis4);
Tensor<Scalar, 0, DataLayout> sum2 = tensor.sum(reduction_axis4);
VERIFY_IS_EQUAL(sum2.rank(), 0);
VERIFY_IS_APPROX(sum1(), sum2());
@ -98,7 +98,7 @@ static void test_simple_reductions() {
VERIFY_IS_EQUAL(result.dimension(1), 7);
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 7; ++j) {
float prod = 1.0f;
Scalar prod = Scalar(1.0f);
for (int k = 0; k < 2; ++k) {
for (int l = 0; l < 5; ++l) {
prod *= tensor(k, i, l, j);
@ -109,7 +109,7 @@ static void test_simple_reductions() {
}
{
Tensor<float, 0, DataLayout> prod1 = tensor.prod();
Tensor<Scalar, 0, DataLayout> prod1 = tensor.prod();
VERIFY_IS_EQUAL(prod1.rank(), 0);
array<ptrdiff_t, 4> reduction_axis4;
@ -117,7 +117,7 @@ static void test_simple_reductions() {
reduction_axis4[1] = 1;
reduction_axis4[2] = 2;
reduction_axis4[3] = 3;
Tensor<float, 0, DataLayout> prod2 = tensor.prod(reduction_axis4);
Tensor<Scalar, 0, DataLayout> prod2 = tensor.prod(reduction_axis4);
VERIFY_IS_EQUAL(prod2.rank(), 0);
VERIFY_IS_APPROX(prod1(), prod2());
@ -130,7 +130,7 @@ static void test_simple_reductions() {
VERIFY_IS_EQUAL(result.dimension(1), 7);
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 7; ++j) {
float max_val = std::numeric_limits<float>::lowest();
Scalar max_val = std::numeric_limits<Scalar>::lowest();
for (int k = 0; k < 2; ++k) {
for (int l = 0; l < 5; ++l) {
max_val = (std::max)(max_val, tensor(k, i, l, j));
@ -141,7 +141,7 @@ static void test_simple_reductions() {
}
{
Tensor<float, 0, DataLayout> max1 = tensor.maximum();
Tensor<Scalar, 0, DataLayout> max1 = tensor.maximum();
VERIFY_IS_EQUAL(max1.rank(), 0);
array<ptrdiff_t, 4> reduction_axis4;
@ -149,7 +149,7 @@ static void test_simple_reductions() {
reduction_axis4[1] = 1;
reduction_axis4[2] = 2;
reduction_axis4[3] = 3;
Tensor<float, 0, DataLayout> max2 = tensor.maximum(reduction_axis4);
Tensor<Scalar, 0, DataLayout> max2 = tensor.maximum(reduction_axis4);
VERIFY_IS_EQUAL(max2.rank(), 0);
VERIFY_IS_APPROX(max1(), max2());
@ -162,7 +162,7 @@ static void test_simple_reductions() {
VERIFY_IS_EQUAL(result.dimension(1), 7);
for (int i = 0; i < 5; ++i) {
for (int j = 0; j < 7; ++j) {
float min_val = (std::numeric_limits<float>::max)();
Scalar min_val = (std::numeric_limits<Scalar>::max)();
for (int k = 0; k < 2; ++k) {
for (int l = 0; l < 3; ++l) {
min_val = (std::min)(min_val, tensor(k, l, i, j));
@ -173,7 +173,7 @@ static void test_simple_reductions() {
}
{
Tensor<float, 0, DataLayout> min1 = tensor.minimum();
Tensor<Scalar, 0, DataLayout> min1 = tensor.minimum();
VERIFY_IS_EQUAL(min1.rank(), 0);
array<ptrdiff_t, 4> reduction_axis4;
@ -181,7 +181,7 @@ static void test_simple_reductions() {
reduction_axis4[1] = 1;
reduction_axis4[2] = 2;
reduction_axis4[3] = 3;
Tensor<float, 0, DataLayout> min2 = tensor.minimum(reduction_axis4);
Tensor<Scalar, 0, DataLayout> min2 = tensor.minimum(reduction_axis4);
VERIFY_IS_EQUAL(min2.rank(), 0);
VERIFY_IS_APPROX(min1(), min2());
@ -194,7 +194,7 @@ static void test_simple_reductions() {
VERIFY_IS_EQUAL(result.dimension(1), 7);
for (int i = 0; i < 5; ++i) {
for (int j = 0; j < 7; ++j) {
float sum = 0.0f;
Scalar sum = Scalar(0.0f);
int count = 0;
for (int k = 0; k < 2; ++k) {
for (int l = 0; l < 3; ++l) {
@ -207,7 +207,7 @@ static void test_simple_reductions() {
}
{
Tensor<float, 0, DataLayout> mean1 = tensor.mean();
Tensor<Scalar, 0, DataLayout> mean1 = tensor.mean();
VERIFY_IS_EQUAL(mean1.rank(), 0);
array<ptrdiff_t, 4> reduction_axis4;
@ -215,7 +215,7 @@ static void test_simple_reductions() {
reduction_axis4[1] = 1;
reduction_axis4[2] = 2;
reduction_axis4[3] = 3;
Tensor<float, 0, DataLayout> mean2 = tensor.mean(reduction_axis4);
Tensor<Scalar, 0, DataLayout> mean2 = tensor.mean(reduction_axis4);
VERIFY_IS_EQUAL(mean2.rank(), 0);
VERIFY_IS_APPROX(mean1(), mean2());
@ -487,8 +487,9 @@ static void test_reduce_middle_dims() {
EIGEN_DECLARE_TEST(cxx11_tensor_reduction) {
CALL_SUBTEST(test_trivial_reductions<ColMajor>());
CALL_SUBTEST(test_trivial_reductions<RowMajor>());
CALL_SUBTEST(test_simple_reductions<ColMajor>());
CALL_SUBTEST(test_simple_reductions<RowMajor>());
CALL_SUBTEST(( test_simple_reductions<float,ColMajor>() ));
CALL_SUBTEST(( test_simple_reductions<float,RowMajor>() ));
CALL_SUBTEST(( test_simple_reductions<Eigen::half,ColMajor>() ));
CALL_SUBTEST(test_reductions_in_expr<ColMajor>());
CALL_SUBTEST(test_reductions_in_expr<RowMajor>());
CALL_SUBTEST(test_full_reductions<ColMajor>());