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

2 files changed, 22 insertions, 21 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h b/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
index 0efcd6253..a9027e97d 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
@@ -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
diff --git a/unsupported/test/cxx11_tensor_reduction.cpp b/unsupported/test/cxx11_tensor_reduction.cpp
index 4c8a95c34..ff8e18c07 100644
--- a/unsupported/test/cxx11_tensor_reduction.cpp
+++ b/unsupported/test/cxx11_tensor_reduction.cpp
@@ -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>());