diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
index 21a6b66e8..24a6343e8 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
@@ -73,7 +73,7 @@ struct TensorOpResourceRequirements {
   // expression tree (like reductions) to communicate resources
   // requirements based on local state (like the total number of reductions
   // to be computed).
-  TensorOpResourceRequirements(internal::TensorBlockShapeType shape,
+  TensorOpResourceRequirements(TensorBlockShapeType shape,
                                const Index size)
       : block_shape(shape), block_total_size(size) {}
 };
@@ -90,9 +90,9 @@ EIGEN_STRONG_INLINE void MergeResourceRequirements(
   *block_shape = resources[0].block_shape;
   *block_total_size = resources[0].block_total_size;
   for (std::vector<TensorOpResourceRequirements>::size_type i = 1; i < resources.size(); ++i) {
-    if (resources[i].block_shape == TensorBlockShapeType::kSkewedInnerDims &&
-        *block_shape != TensorBlockShapeType::kSkewedInnerDims) {
-      *block_shape = TensorBlockShapeType::kSkewedInnerDims;
+    if (resources[i].block_shape == kSkewedInnerDims &&
+        *block_shape != kSkewedInnerDims) {
+      *block_shape = kSkewedInnerDims;
     }
     *block_total_size =
         numext::maxi(*block_total_size, resources[i].block_total_size);
@@ -152,11 +152,11 @@ struct TensorBlockCopyOp {
     const Scalar* src_base = &src_data[src_index];
     Scalar* dst_base = &dst_data[dst_index];
 
-    typedef const Eigen::Array<Scalar, Dynamic, 1> Src;
-    typedef Eigen::Array<Scalar, Dynamic, 1> Dst;
+    typedef const Array<Scalar, Dynamic, 1> Src;
+    typedef Array<Scalar, Dynamic, 1> Dst;
 
-    typedef Eigen::Map<Src, 0, InnerStride<> > SrcMap;
-    typedef Eigen::Map<Dst, 0, InnerStride<> > DstMap;
+    typedef Map<Src, 0, InnerStride<> > SrcMap;
+    typedef Map<Dst, 0, InnerStride<> > DstMap;
 
     const SrcMap src(src_base, num_coeff_to_copy, InnerStride<>(src_stride));
     DstMap dst(dst_base, num_coeff_to_copy, InnerStride<>(dst_stride));
@@ -178,10 +178,8 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout,
           bool BlockRead>
 class TensorBlockIO {
  public:
-  typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
-      TensorBlock;
-  typedef typename internal::TensorBlockCopyOp<Scalar, StorageIndex>
-      TensorBlockCopyOp;
+  typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
+  typedef TensorBlockCopyOp<Scalar, StorageIndex> BlockCopyOp;
 
  protected:
   struct BlockIteratorState {
@@ -194,7 +192,7 @@ class TensorBlockIO {
   };
 
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Copy(
-      const TensorBlock& block, StorageIndex first_coeff_index,
+      const Block& block, StorageIndex first_coeff_index,
       const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
       const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data,
       Scalar* dst_data) {
@@ -290,8 +288,8 @@ class TensorBlockIO {
     const StorageIndex block_total_size =
         NumDims == 0 ? 1 : block.block_sizes().TotalSize();
     for (StorageIndex i = 0; i < block_total_size; i += block_inner_dim_size) {
-      TensorBlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride,
-                             dst_data, inputIndex, input_stride, src_data);
+      BlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride,
+                       dst_data, inputIndex, input_stride, src_data);
       // Update index.
       for (int j = 0; j < num_squeezed_dims; ++j) {
         if (++block_iter_state[j].count < block_iter_state[j].size) {
@@ -320,13 +318,11 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
 class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims,
                                                Layout, /*BlockRead=*/true> {
  public:
-  typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
-      TensorBlock;
-  typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true>
-      Base;
+  typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
+  typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true> Base;
 
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      TensorBlock* block, const Scalar* src_data) {
+      Block* block, const Scalar* src_data) {
     array<StorageIndex, NumDims> tensor_to_block_dim_map;
     for (int i = 0; i < NumDims; ++i) {
       tensor_to_block_dim_map[i] = i;
@@ -336,7 +332,7 @@ class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims,
   }
 
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      TensorBlock* block, StorageIndex first_coeff_index,
+      Block* block, StorageIndex first_coeff_index,
       const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
       const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data) {
     Base::Copy(*block, first_coeff_index, tensor_to_block_dim_map,
@@ -357,13 +353,11 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
 class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims,
                                                Layout, /*BlockRead=*/false> {
  public:
-  typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
-      TensorBlock;
-  typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false>
-      Base;
+  typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
+  typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false> Base;
 
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      const TensorBlock& block, Scalar* dst_data) {
+      const Block& block, Scalar* dst_data) {
     array<StorageIndex, NumDims> tensor_to_block_dim_map;
     for (int i = 0; i < NumDims; ++i) {
       tensor_to_block_dim_map[i] = i;
@@ -373,7 +367,7 @@ class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims,
   }
 
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      const TensorBlock& block, StorageIndex first_coeff_index,
+      const Block& block, StorageIndex first_coeff_index,
       const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
       const array<StorageIndex, NumDims>& tensor_strides, Scalar* dst_data) {
     Base::Copy(block, first_coeff_index, tensor_to_block_dim_map,
@@ -401,13 +395,13 @@ struct TensorBlockCwiseBinaryOp {
       const StorageIndex left_stride, const LeftScalar* left_data,
       const StorageIndex right_index, const StorageIndex right_stride,
       const RightScalar* right_data) {
-    typedef const Eigen::Array<LeftScalar, Dynamic, 1> Lhs;
-    typedef const Eigen::Array<RightScalar, Dynamic, 1> Rhs;
-    typedef Eigen::Array<OutputScalar, Dynamic, 1> Out;
+    typedef const Array<LeftScalar, Dynamic, 1> Lhs;
+    typedef const Array<RightScalar, Dynamic, 1> Rhs;
+    typedef Array<OutputScalar, Dynamic, 1> Out;
 
-    typedef Eigen::Map<Lhs, 0, InnerStride<> > LhsMap;
-    typedef Eigen::Map<Rhs, 0, InnerStride<> > RhsMap;
-    typedef Eigen::Map<Out, 0, InnerStride<> > OutMap;
+    typedef Map<Lhs, 0, InnerStride<> > LhsMap;
+    typedef Map<Rhs, 0, InnerStride<> > RhsMap;
+    typedef Map<Out, 0, InnerStride<> > OutMap;
 
     const LeftScalar* lhs_base = &left_data[left_index];
     const RightScalar* rhs_base = &right_data[right_index];
@@ -417,8 +411,7 @@ struct TensorBlockCwiseBinaryOp {
     const RhsMap rhs(rhs_base, num_coeff, InnerStride<>(right_stride));
     OutMap out(out_base, num_coeff, InnerStride<>(output_stride));
 
-    out =
-        Eigen::CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor);
+    out = CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor);
   }
 };
 
@@ -434,8 +427,7 @@ struct TensorBlockCwiseBinaryOp {
 template <typename BinaryFunctor, typename StorageIndex, typename OutputScalar,
           int NumDims, int Layout>
 struct TensorBlockCwiseBinaryIO {
-  typedef typename internal::TensorBlock<OutputScalar, StorageIndex, NumDims,
-                                         Layout>::Dimensions Dimensions;
+  typedef typename TensorBlock<OutputScalar, StorageIndex, NumDims, Layout>::Dimensions Dimensions;
 
   struct BlockIteratorState {
     StorageIndex output_stride, output_span;
@@ -627,8 +619,7 @@ struct TensorBlockView {
 template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
 class TensorBlockMapper {
  public:
-  typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
-      TensorBlock;
+  typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
   typedef DSizes<StorageIndex, NumDims> Dimensions;
 
   TensorBlockMapper(const Dimensions& dims,
@@ -663,7 +654,7 @@ class TensorBlockMapper {
     }
   }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block
   GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
     StorageIndex first_coeff_index = 0;
     DSizes<StorageIndex, NumDims> coords;
@@ -711,8 +702,7 @@ class TensorBlockMapper {
       }
     }
 
-    return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides,
-                       data);
+    return Block(first_coeff_index, sizes, strides, m_tensor_strides, data);
   }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {
@@ -742,7 +732,7 @@ class TensorBlockMapper {
         block_dim_sizes[i] = 1;
       }
     } else if (block_dim_sizes.TotalSize() > min_target_size) {
-      if (block_shape == TensorBlockShapeType::kUniformAllDims) {
+      if (block_shape == kUniformAllDims) {
         // Tensor will not fit within 'min_target_size' budget: calculate tensor
         // block dimension sizes based on "square" dimension size target.
         const size_t dim_size_target = static_cast<const size_t>(
@@ -773,7 +763,7 @@ class TensorBlockMapper {
             total_size = total_size_other_dims * block_dim_sizes[dim];
           }
         }
-      } else if (block_shape == TensorBlockShapeType::kSkewedInnerDims) {
+      } else if (block_shape == kSkewedInnerDims) {
         StorageIndex coeff_to_allocate = min_target_size;
         for (int i = 0; i < NumDims; ++i) {
           const int dim = cond<Layout>()(i, NumDims - i - 1);
@@ -818,8 +808,7 @@ class TensorBlockMapper {
 template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
 class TensorSliceBlockMapper {
  public:
-  typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
-      TensorBlock;
+  typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
   typedef DSizes<StorageIndex, NumDims> Dimensions;
 
   TensorSliceBlockMapper(const Dimensions& tensor_dims,
@@ -860,7 +849,7 @@ class TensorSliceBlockMapper {
     }
   }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block
   GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
     StorageIndex first_coeff_index = 0;
     DSizes<StorageIndex, NumDims> coords;
@@ -917,8 +906,7 @@ class TensorSliceBlockMapper {
       }
     }
 
-    return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides,
-                       data);
+    return Block(first_coeff_index, sizes, strides, m_tensor_strides, data);
   }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h b/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
index e604456e8..5d619efd8 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
@@ -152,13 +152,7 @@ struct TensorContractionParams {
 //   1. Elementwise Relu transformation following Conv2D.
 //   2. AddBias to the Conv2D output channels dimension.
 //
-// See expected implementation in NoOpOutputKernel.
-struct OutputKernel {
-  template <typename Index, typename Scalar>
-  using OutputMapper = internal::blas_data_mapper<Scalar, Index, ColMajor>;
-};
-
-// Output kernel that does absolutely nothing.
+// The NoOpOutputKernel implements an output kernel that does absolutely nothing.
 struct NoOpOutputKernel {
   /**
    * Tensor contraction evaluator calls this kernel after finishing each block
@@ -177,7 +171,7 @@ struct NoOpOutputKernel {
    */
   template <typename Index, typename Scalar>
   EIGEN_ALWAYS_INLINE void operator()(
-      const OutputKernel::OutputMapper<Index, Scalar>& /*output_mapper*/,
+      const internal::blas_data_mapper<Scalar, Index, ColMajor>& /*output_mapper*/,
       const TensorContractionParams& /*params*/, Index /*i*/,
       Index /*j*/, Index /*num_rows*/, Index /*num_cols*/) const {}
 };
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h b/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
index 39410e63d..ab5990c14 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
@@ -20,8 +20,8 @@ namespace Eigen {
   *
   */
 namespace internal {
-template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_>
-struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> >
+template<typename CustomUnaryFunc, typename XprType>
+struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
 {
   typedef typename XprType::Scalar Scalar;
   typedef typename XprType::StorageKind StorageKind;
@@ -31,34 +31,26 @@ struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> >
   static const int NumDimensions = traits<XprType>::NumDimensions;
   static const int Layout = traits<XprType>::Layout;
 
-   template <class T> struct MakePointer {
-    // Intermediate typedef to workaround MSVC issue.
-    typedef MakePointer_<T> MakePointerT;
-    typedef typename MakePointerT::Type Type;
-    typedef typename MakePointerT::RefType RefType;
-    typedef typename MakePointerT::ScalarType ScalarType;
-  };
-  typedef typename MakePointer<typename internal::remove_const<typename XprType::CoeffReturnType>::type>::Type PointerType;
 };
 
-template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_>
-struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, Eigen::Dense>
+template<typename CustomUnaryFunc, typename XprType>
+struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense>
 {
-  typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>& type;
+  typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType>& type;
 };
 
-template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_>
-struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> >
+template<typename CustomUnaryFunc, typename XprType>
+struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
 {
-  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> type;
+  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type;
 };
 
 }  // end namespace internal
 
 
 
-template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_>
-class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, ReadOnlyAccessors>
+template<typename CustomUnaryFunc, typename XprType>
+class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors>
 {
   public:
   typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar;
@@ -85,10 +77,10 @@ class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFun
 
 
 // Eval as rvalue
-template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_, typename Device>
-struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, Device>
+template<typename CustomUnaryFunc, typename XprType, typename Device>
+struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device>
 {
-  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> ArgType;
+  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType;
   typedef typename internal::traits<ArgType>::Index Index;
   static const int NumDims = internal::traits<ArgType>::NumDimensions;
   typedef DSizes<Index, NumDims> Dimensions;
@@ -96,7 +88,7 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
   typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
   typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
   static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename Eigen::internal::traits<ArgType>::PointerType PointerType;
+  typedef typename PointerType<CoeffReturnType, Device>::Type PointerT;
 
   enum {
     IsAligned = false,
@@ -115,12 +107,12 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerType data) {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerT data) {
     if (data) {
       evalTo(data);
       return false;
     } else {
-      m_result = static_cast<PointerType>(
+      m_result = static_cast<PointerT>(
           m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar)));
       evalTo(m_result);
       return true;
@@ -148,14 +140,14 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
     return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
   }
 
-  EIGEN_DEVICE_FUNC PointerType data() const { return m_result; }
+  EIGEN_DEVICE_FUNC PointerT data() const { return m_result; }
 
 #ifdef EIGEN_USE_SYCL
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { return m_device; }
 #endif
 
  protected:
-  EIGEN_DEVICE_FUNC void evalTo(PointerType data) {
+  EIGEN_DEVICE_FUNC void evalTo(PointerT data) {
     TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(data, m_dimensions);
     m_op.func().eval(m_op.expression(), result, m_device);
   }
@@ -163,7 +155,7 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
   Dimensions m_dimensions;
   const ArgType m_op;
   const Device& m_device;
-  PointerType m_result;
+  PointerT m_result;
 };
 
 
@@ -176,8 +168,8 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
   *
   */
 namespace internal {
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_>
-struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_> >
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> >
 {
   typedef typename internal::promote_storage_type<typename LhsXprType::Scalar,
                                                   typename RhsXprType::Scalar>::ret Scalar;
@@ -194,34 +186,26 @@ struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, Mak
   static const int NumDimensions = traits<LhsXprType>::NumDimensions;
   static const int Layout = traits<LhsXprType>::Layout;
 
- template <class T> struct MakePointer {
-    // Intermediate typedef to workaround MSVC issue.
-    typedef MakePointer_<T> MakePointerT;
-    typedef typename MakePointerT::Type Type;
-    typedef typename MakePointerT::RefType RefType;
-    typedef typename MakePointerT::ScalarType ScalarType;
-  };
-  typedef typename MakePointer<CoeffReturnType>::Type PointerType;
 };
 
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_>
-struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_>, Eigen::Dense>
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Eigen::Dense>
 {
   typedef const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>& type;
 };
 
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_>
-struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_> >
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> >
 {
-  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_> type;
+  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> type;
 };
 
 }  // end namespace internal
 
 
 
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType,template <class> class MakePointer_>
-class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_>, ReadOnlyAccessors>
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, ReadOnlyAccessors>
 {
   public:
   typedef typename internal::traits<TensorCustomBinaryOp>::Scalar Scalar;
@@ -254,10 +238,10 @@ class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinary
 
 
 // Eval as rvalue
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_, typename Device>
-struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_>, Device>
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, typename Device>
+struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Device>
 {
-  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_> XprType;
+  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> XprType;
   typedef typename internal::traits<XprType>::Index Index;
   static const int NumDims = internal::traits<XprType>::NumDimensions;
   typedef DSizes<Index, NumDims> Dimensions;
@@ -265,7 +249,7 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType,
   typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
   typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
   static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename Eigen::internal::traits<XprType>::PointerType PointerType;
+  typedef typename PointerType<CoeffReturnType, Device>::Type PointerT;
 
   enum {
     IsAligned = false,
@@ -284,12 +268,12 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType,
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerType data) {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerT data) {
     if (data) {
       evalTo(data);
       return false;
     } else {
-      m_result = static_cast<PointerType>(m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType)));
+      m_result = static_cast<PointerT>(m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType)));
       evalTo(m_result);
       return true;
     }
@@ -316,14 +300,14 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType,
     return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
   }
 
-  EIGEN_DEVICE_FUNC PointerType data() const { return m_result; }
+  EIGEN_DEVICE_FUNC PointerT data() const { return m_result; }
 
 #ifdef EIGEN_USE_SYCL
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { return m_device; }
 #endif
 
  protected:
-  EIGEN_DEVICE_FUNC void evalTo(PointerType data) {
+  EIGEN_DEVICE_FUNC void evalTo(PointerT data) {
     TensorMap<Tensor<CoeffReturnType, NumDims, Layout> > result(data, m_dimensions);
     m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device);
   }
@@ -331,7 +315,7 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType,
   Dimensions m_dimensions;
   const XprType m_op;
   const Device& m_device;
-  PointerType m_result;
+  PointerT m_result;
 };
 
 
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
index 0cefe42dd..9b9587de5 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
@@ -132,7 +132,7 @@ class TensorExecutor<Expression, DefaultDevice, Vectorizable,
     if (needs_assign) {
       // Size tensor blocks to fit in cache (or requested target block size).
       Index block_total_size = numext::mini(cache_size, total_size);
-      TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims;
+      TensorBlockShapeType block_shape = kSkewedInnerDims;
       // Query expression tree for desired block size/shape.
       std::vector<TensorOpResourceRequirements> resources;
       evaluator.getResourceRequirements(&resources);
@@ -229,10 +229,6 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable> {
     Evaluator evaluator(expr, device);
     const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
     if (needs_assign) {
-      const StorageIndex PacketSize =
-          Vectorizable
-              ? unpacket_traits<typename Evaluator::PacketReturnType>::size
-              : 1;
       const StorageIndex size = array_prod(evaluator.dimensions());
       device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),
                          EvalRange::alignBlockSize,
@@ -272,7 +268,7 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
 
     const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
     if (needs_assign) {
-      TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims;
+      TensorBlockShapeType block_shape = kSkewedInnerDims;
       Index block_total_size = 0;
       // Query expression tree for desired block size/shape.
       std::vector<internal::TensorOpResourceRequirements> resources;
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
index da0751039..93a3b0e14 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
@@ -24,6 +24,14 @@ template<typename T> struct MakePointer {
   typedef T ScalarType;
 };
 
+// The PointerType class is a container of the device specefic pointer
+// used for refering to a Pointer on TensorEvaluator class. While the TensorExpression
+// is a device-agnostic type and need MakePointer class for type conversion,
+// the TensorEvaluator calss can be specialized for a device, hence it is possible
+// to construct different types of temproray storage memory in TensorEvaluator
+// for different devices by specializing the following PointerType class.
+template<typename T, typename Device> struct PointerType : MakePointer<T>{};
+
 namespace internal{
 template<typename A, typename B> struct Pointer_type_promotion {
   static const bool val=false;
@@ -89,8 +97,8 @@ template<typename LeftXprType, typename RightXprType> class TensorAssignOp;
 template<typename Op, typename XprType> class TensorScanOp;
 template<typename Dims, typename XprType> class TensorTraceOp;
 
-template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_ = MakePointer> class TensorCustomUnaryOp;
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_ = MakePointer> class TensorCustomBinaryOp;
+template<typename CustomUnaryFunc, typename XprType> class TensorCustomUnaryOp;
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> class TensorCustomBinaryOp;
 
 template<typename XprType, template <class> class MakePointer_ = MakePointer> class TensorEvalToOp;
 template<typename XprType> class TensorForcedEvalOp;
diff --git a/unsupported/test/cxx11_tensor_block_access.cpp b/unsupported/test/cxx11_tensor_block_access.cpp
index 6feeff231..417b72201 100644
--- a/unsupported/test/cxx11_tensor_block_access.cpp
+++ b/unsupported/test/cxx11_tensor_block_access.cpp
@@ -10,6 +10,8 @@
 
 #include "main.h"
 
+#include <algorithm>
+#include <random>
 #include <set>
 
 #include <Eigen/CXX11/Tensor>
@@ -19,17 +21,16 @@ using Eigen::Index;
 using Eigen::RowMajor;
 using Eigen::ColMajor;
 
-using internal::TensorBlockShapeType;
 
 template<typename T>
 static const T& choose(int layout, const T& col, const T& row) {
   return layout == ColMajor ? col : row;
 }
 
-static const TensorBlockShapeType RandomShape() {
+static internal::TensorBlockShapeType RandomShape() {
   return internal::random<bool>()
-             ? internal::TensorBlockShapeType::kUniformAllDims
-             : internal::TensorBlockShapeType::kSkewedInnerDims;
+             ? internal::kUniformAllDims
+             : internal::kSkewedInnerDims;
 }
 
 template <int NumDims>
@@ -44,7 +45,7 @@ static DSizes<Index, NumDims> RandomDims() {
     dims[i] = internal::random<int>(1, 20);
   }
   return DSizes<Index, NumDims>(dims);
-};
+}
 
 /** Dummy data type to test TensorBlock copy ops. */
 struct Data {
@@ -91,21 +92,19 @@ static void Debug(DSizes<Index, NumDims> dims) {
 template <int Layout>
 static void test_block_mapper_sanity()
 {
-  using T = int;
-  using TensorBlock = internal::TensorBlock<T, Index, 2, Layout>;
-  using TensorBlockMapper = internal::TensorBlockMapper<T, Index, 2, Layout>;
+  typedef internal::TensorBlockMapper<int, Index, 2, Layout> TensorBlockMapper;
 
   DSizes<Index, 2> tensor_dims(100, 100);
 
   // Test uniform blocks.
   TensorBlockMapper uniform_block_mapper(
-      tensor_dims, internal::TensorBlockShapeType::kUniformAllDims, 100);
+      tensor_dims, internal::kUniformAllDims, 100);
 
   VERIFY_IS_EQUAL(uniform_block_mapper.total_block_count(), 100);
   VERIFY_IS_EQUAL(uniform_block_mapper.block_dims_total_size(), 100);
 
   // 10x10 blocks
-  auto uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, nullptr);
+  auto uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, NULL);
   VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(0), 10);
   VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(1), 10);
   // Depending on a layout we stride by cols rows.
@@ -117,13 +116,13 @@ static void test_block_mapper_sanity()
 
   // Test skewed to inner dims blocks.
   TensorBlockMapper skewed_block_mapper(
-      tensor_dims, internal::TensorBlockShapeType::kSkewedInnerDims, 100);
+      tensor_dims, internal::kSkewedInnerDims, 100);
 
   VERIFY_IS_EQUAL(skewed_block_mapper.total_block_count(), 100);
   VERIFY_IS_EQUAL(skewed_block_mapper.block_dims_total_size(), 100);
 
   // 1x100 (100x1) rows/cols depending on a tensor layout.
-  auto skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, nullptr);
+  auto skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, NULL);
   VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(0), choose(Layout, 100, 1));
   VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(1), choose(Layout, 1, 100));
   // Depending on a layout we stride by cols rows.
@@ -158,9 +157,8 @@ static void UpdateCoeffSet(
 
 template <typename T, int NumDims, int Layout>
 static void test_block_mapper_maps_every_element() {
-  using TensorBlock = internal::TensorBlock<T, Index, NumDims, Layout>;
-  using TensorBlockMapper =
-      internal::TensorBlockMapper<T, Index, NumDims, Layout>;
+  typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock;
+  typedef internal::TensorBlockMapper<T, Index, NumDims, Layout> TensorBlockMapper;
 
   DSizes<Index, NumDims> dims = RandomDims<NumDims>();
 
@@ -171,7 +169,7 @@ static void test_block_mapper_maps_every_element() {
   TensorBlockMapper block_mapper(dims, RandomShape(), RandomTargetSize(dims));
 
   for (int i = 0; i < block_mapper.total_block_count(); ++i) {
-    TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(i, NULL);
     UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(),
                                        choose(Layout, NumDims - 1, 0),
                                        &coeff_set);
@@ -187,9 +185,8 @@ static void test_block_mapper_maps_every_element() {
 
 template <typename T, int NumDims, int Layout>
 static void test_slice_block_mapper_maps_every_element() {
-  using TensorBlock = internal::TensorBlock<T, Index, NumDims, Layout>;
-  using TensorSliceBlockMapper =
-      internal::TensorSliceBlockMapper<T, Index, NumDims, Layout>;
+  typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock;
+  typedef internal::TensorSliceBlockMapper<T, Index, NumDims, Layout> TensorSliceBlockMapper;
 
   DSizes<Index, NumDims> tensor_dims = RandomDims<NumDims>();
   DSizes<Index, NumDims> tensor_slice_offsets = RandomDims<NumDims>();
@@ -219,7 +216,7 @@ static void test_slice_block_mapper_maps_every_element() {
                                       DimensionList<Index, NumDims>());
 
   for (int i = 0; i < block_mapper.total_block_count(); ++i) {
-    TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(i, NULL);
     UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(),
                                        choose(Layout, NumDims - 1, 0),
                                        &coeff_set);
@@ -647,17 +644,16 @@ static void test_block_cwise_binary_io_zero_strides() {
 template <int Layout>
 static void test_uniform_block_shape()
 {
-  using T = int;
-  typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock;
-  typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper;
+  typedef internal::TensorBlock<int, Index, 5, Layout> TensorBlock;
+  typedef internal::TensorBlockMapper<int, Index, 5, Layout> TensorBlockMapper;
 
   {
     // Test shape 'UniformAllDims' with uniform 'max_coeff count'.
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 5 * 5 * 5 * 5 * 5;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     for (int i = 0; i < 5; ++i) {
       VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
     }
@@ -669,9 +665,9 @@ static void test_uniform_block_shape()
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 7 * 5 * 5 * 5 * 5;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
     for (int i = 1; i < 5; ++i) {
       VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
@@ -680,9 +676,9 @@ static void test_uniform_block_shape()
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 5 * 5 * 5 * 5 * 6;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(6, block.block_sizes()[4]);
     for (int i = 3; i >= 0; --i) {
       VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
@@ -695,9 +691,9 @@ static void test_uniform_block_shape()
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 11 * 5 * 5 * 5 * 5;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
     for (int i = 1; i < 5; ++i) {
       VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
@@ -706,9 +702,9 @@ static void test_uniform_block_shape()
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 5 * 5 * 5 * 5 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
     for (int i = 3; i >= 0; --i) {
       VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
@@ -721,9 +717,9 @@ static void test_uniform_block_shape()
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(7, 5, 6, 17, 7);
     const size_t max_coeff_count = 7 * 5 * 6 * 7 * 5;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
     VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
     VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@@ -733,9 +729,9 @@ static void test_uniform_block_shape()
   } else {
     DSizes<Index, 5> dims(7, 5, 6, 9, 7);
     const size_t max_coeff_count = 5 * 5 * 5 * 6 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
     VERIFY_IS_EQUAL(6, block.block_sizes()[3]);
     VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
@@ -748,9 +744,9 @@ static void test_uniform_block_shape()
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(7, 5, 6, 17, 7);
     const size_t max_coeff_count = 7 * 5 * 6 * 17 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
     VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
     VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@@ -760,9 +756,9 @@ static void test_uniform_block_shape()
   } else {
     DSizes<Index, 5> dims(7, 5, 6, 9, 7);
     const size_t max_coeff_count = 7 * 5 * 6 * 9 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
+    TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
     VERIFY_IS_EQUAL(9, block.block_sizes()[3]);
     VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@@ -775,17 +771,16 @@ static void test_uniform_block_shape()
 template <int Layout>
 static void test_skewed_inner_dim_block_shape()
 {
-  using T = int;
-  typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock;
-  typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper;
+  typedef internal::TensorBlock<int, Index, 5, Layout> TensorBlock;
+  typedef internal::TensorBlockMapper<int, Index, 5, Layout> TensorBlockMapper;
 
   // Test shape 'SkewedInnerDims' with partial allocation to inner-most dim.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 10 * 1 * 1 * 1 * 1;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(10, block.block_sizes()[0]);
     for (int i = 1; i < 5; ++i) {
       VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
@@ -794,9 +789,9 @@ static void test_skewed_inner_dim_block_shape()
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 1 * 1 * 1 * 1 * 6;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(6, block.block_sizes()[4]);
     for (int i = 3; i >= 0; --i) {
       VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
@@ -808,9 +803,9 @@ static void test_skewed_inner_dim_block_shape()
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 11 * 1 * 1 * 1 * 1;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
     for (int i = 1; i < 5; ++i) {
       VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
@@ -819,9 +814,9 @@ static void test_skewed_inner_dim_block_shape()
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 1 * 1 * 1 * 1 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
     for (int i = 3; i >= 0; --i) {
       VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
@@ -834,9 +829,9 @@ static void test_skewed_inner_dim_block_shape()
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 11 * 3 * 1 * 1 * 1;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
     VERIFY_IS_EQUAL(3, block.block_sizes()[1]);
     for (int i = 2; i < 5; ++i) {
@@ -846,9 +841,9 @@ static void test_skewed_inner_dim_block_shape()
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 1 * 1 * 1 * 15 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
     VERIFY_IS_EQUAL(15, block.block_sizes()[3]);
     for (int i = 2; i >= 0; --i) {
@@ -862,9 +857,9 @@ static void test_skewed_inner_dim_block_shape()
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 11 * 5 * 5 * 1 * 1;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
     VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
     VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
@@ -875,9 +870,9 @@ static void test_skewed_inner_dim_block_shape()
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 1 * 1 * 5 * 17 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
     VERIFY_IS_EQUAL(17, block.block_sizes()[3]);
     VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
@@ -891,9 +886,9 @@ static void test_skewed_inner_dim_block_shape()
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
     VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
     VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@@ -903,9 +898,9 @@ static void test_skewed_inner_dim_block_shape()
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
     const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7;
-    TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
+    TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
-    TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
+    TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
     VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
     VERIFY_IS_EQUAL(17, block.block_sizes()[3]);
     VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@@ -918,15 +913,13 @@ static void test_skewed_inner_dim_block_shape()
 template <int Layout>
 static void test_empty_dims(const internal::TensorBlockShapeType block_shape)
 {
-  using T = int;
-
   // Test blocking of tensors with zero dimensions:
   //  - we must not crash on asserts and divisions by zero
   //  - we must not return block with zero dimensions
   //    (recipe for overflows/underflows, divisions by zero and NaNs later)
   //  - total block count must be zero
   {
-    typedef internal::TensorBlockMapper<T, Index, 1, Layout> TensorBlockMapper;
+    typedef internal::TensorBlockMapper<int, Index, 1, Layout> TensorBlockMapper;
     DSizes<Index, 1> dims(0);
     for (int max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
       TensorBlockMapper block_mapper(dims, block_shape, max_coeff_count);
@@ -936,7 +929,7 @@ static void test_empty_dims(const internal::TensorBlockShapeType block_shape)
   }
 
   {
-    typedef internal::TensorBlockMapper<T, Index, 2, Layout> TensorBlockMapper;
+    typedef internal::TensorBlockMapper<int, Index, 2, Layout> TensorBlockMapper;
     for (int dim1 = 0; dim1 < 3; ++dim1) {
       for (int dim2 = 0; dim2 < 3; ++dim2) {
         DSizes<Index, 2> dims(dim1, dim2);
@@ -987,9 +980,9 @@ EIGEN_DECLARE_TEST(cxx11_tensor_block_access) {
   TEST_LAYOUTS(test_block_cwise_binary_io_zero_strides);
   TEST_LAYOUTS(test_uniform_block_shape);
   TEST_LAYOUTS(test_skewed_inner_dim_block_shape);
-  TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kUniformAllDims);
-  TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kSkewedInnerDims);
+  TEST_LAYOUTS_WITH_ARG(test_empty_dims, internal::kUniformAllDims);
+  TEST_LAYOUTS_WITH_ARG(test_empty_dims, internal::kSkewedInnerDims);
 }
 
 #undef TEST_LAYOUTS
-#undef TEST_LAYOUTS_WITH_ARG
\ No newline at end of file
+#undef TEST_LAYOUTS_WITH_ARG
diff --git a/unsupported/test/cxx11_tensor_contraction.cpp b/unsupported/test/cxx11_tensor_contraction.cpp
index d4cfbd0da..928d20f6e 100644
--- a/unsupported/test/cxx11_tensor_contraction.cpp
+++ b/unsupported/test/cxx11_tensor_contraction.cpp
@@ -471,7 +471,7 @@ static void test_tensor_product()
   mat1.setRandom();
   mat2.setRandom();
 
-  Tensor<float, 4, DataLayout> result = mat1.contract(mat2, Eigen::array<DimPair, 0>{{}});
+  Tensor<float, 4, DataLayout> result = mat1.contract(mat2, Eigen::array<DimPair, 0>{});
 
   VERIFY_IS_EQUAL(result.dimension(0), 2);
   VERIFY_IS_EQUAL(result.dimension(1), 3);
@@ -514,7 +514,7 @@ static void test_const_inputs()
 struct SqrtOutputKernel {
   template <typename Index, typename Scalar>
   EIGEN_ALWAYS_INLINE void operator()(
-      const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
+      const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
       const TensorContractionParams&, Index, Index, Index num_rows,
       Index num_cols) const {
     for (int i = 0; i < num_rows; ++i) {
@@ -553,7 +553,7 @@ static void test_large_contraction_with_output_kernel() {
 
   m_result = m_left * m_right;
 
-  for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
+  for (std::ptrdiff_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
     VERIFY(&t_result.data()[i] != &m_result.data()[i]);
     VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
   }
diff --git a/unsupported/test/cxx11_tensor_convolution.cpp b/unsupported/test/cxx11_tensor_convolution.cpp
index 01bc77bc1..9fe980648 100644
--- a/unsupported/test/cxx11_tensor_convolution.cpp
+++ b/unsupported/test/cxx11_tensor_convolution.cpp
@@ -25,7 +25,7 @@ static void test_evals()
 
   Tensor<float, 2, DataLayout> result(2,3);
   result.setZero();
-  Eigen::array<Tensor<float, 2>::Index, 1> dims3{{0}};
+  Eigen::array<Tensor<float, 2>::Index, 1> dims3{0};
 
   typedef TensorEvaluator<decltype(input.convolve(kernel, dims3)), DefaultDevice> Evaluator;
   Evaluator eval(input.convolve(kernel, dims3), DefaultDevice());
diff --git a/unsupported/test/cxx11_tensor_index_list.cpp b/unsupported/test/cxx11_tensor_index_list.cpp
index e81fa5e40..294677a4d 100644
--- a/unsupported/test/cxx11_tensor_index_list.cpp
+++ b/unsupported/test/cxx11_tensor_index_list.cpp
@@ -170,7 +170,6 @@ static void test_type2indexpair_list()
   typedef Eigen::IndexPairList<Eigen::type2indexpair<0,10>, Eigen::IndexPair<DenseIndex>, Eigen::type2indexpair<2,12>> Dims2_b;
   typedef Eigen::IndexPairList<Eigen::IndexPair<DenseIndex>, Eigen::type2indexpair<1,11>, Eigen::IndexPair<DenseIndex>> Dims2_c;
 
-  Dims0 d0;
   Dims2_a d2_a;
 
   Dims2_b d2_b;
diff --git a/unsupported/test/cxx11_tensor_thread_pool.cpp b/unsupported/test/cxx11_tensor_thread_pool.cpp
index dd163c18a..7606b0abf 100644
--- a/unsupported/test/cxx11_tensor_thread_pool.cpp
+++ b/unsupported/test/cxx11_tensor_thread_pool.cpp
@@ -255,7 +255,7 @@ void test_multithread_contraction_agrees_with_singlethread() {
 struct SqrtOutputKernel {
   template <typename Index, typename Scalar>
   EIGEN_ALWAYS_INLINE void operator()(
-      const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
+      const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
       const TensorContractionParams&, Index, Index, Index num_rows,
       Index num_cols) const {
     for (int i = 0; i < num_rows; ++i) {
diff --git a/unsupported/test/kronecker_product.cpp b/unsupported/test/kronecker_product.cpp
index 4f143b6de..b5b764c65 100644
--- a/unsupported/test/kronecker_product.cpp
+++ b/unsupported/test/kronecker_product.cpp
@@ -9,6 +9,7 @@
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
+
 #ifdef EIGEN_TEST_PART_1
 
 #include "sparse.h"
@@ -95,7 +96,7 @@ EIGEN_DECLARE_TEST(kronecker_product)
   SM_a.insert(1,0) = DM_a.coeffRef(1,0) = -0.9076572187376921;
   SM_a.insert(1,1) = DM_a.coeffRef(1,1) =  0.6469156566545853;
   SM_a.insert(1,2) = DM_a.coeffRef(1,2) = -0.3658010398782789;
- 
+
   MatrixXd             DM_b(3,2);
   SparseMatrix<double> SM_b(3,2);
   SM_b.insert(0,0) = DM_b.coeffRef(0,0) =  0.9004440976767099;
@@ -165,7 +166,7 @@ EIGEN_DECLARE_TEST(kronecker_product)
   SM_a.insert(0,3) = -0.2;
   SM_a.insert(2,4) =  0.3;
   SM_a.finalize();
-  
+
   SM_b.insert(0,0) =  0.4;
   SM_b.insert(2,1) = -0.5;
   SM_b.finalize();
@@ -183,7 +184,7 @@ EIGEN_DECLARE_TEST(kronecker_product)
   DM_b2.resize(4,8);
   DM_ab2 = kroneckerProduct(DM_a2,DM_b2);
   CALL_SUBTEST(check_dimension(DM_ab2,10*4,9*8));
-  
+
   for(int i = 0; i < g_repeat; i++)
   {
     double density = Eigen::internal::random<double>(0.01,0.5);
@@ -196,35 +197,35 @@ EIGEN_DECLARE_TEST(kronecker_product)
     MatrixXf dA(ra,ca), dB(rb,cb), dC;
     initSparse(density, dA, sA);
     initSparse(density, dB, sB);
-    
+
     sC = kroneckerProduct(sA,sB);
     dC = kroneckerProduct(dA,dB);
     VERIFY_IS_APPROX(MatrixXf(sC),dC);
-    
+
     sC = kroneckerProduct(sA.transpose(),sB);
     dC = kroneckerProduct(dA.transpose(),dB);
     VERIFY_IS_APPROX(MatrixXf(sC),dC);
-    
+
     sC = kroneckerProduct(sA.transpose(),sB.transpose());
     dC = kroneckerProduct(dA.transpose(),dB.transpose());
     VERIFY_IS_APPROX(MatrixXf(sC),dC);
-    
+
     sC = kroneckerProduct(sA,sB.transpose());
     dC = kroneckerProduct(dA,dB.transpose());
     VERIFY_IS_APPROX(MatrixXf(sC),dC);
-    
+
     sC2 = kroneckerProduct(sA,sB);
     dC = kroneckerProduct(dA,dB);
     VERIFY_IS_APPROX(MatrixXf(sC2),dC);
-    
+
     sC2 = kroneckerProduct(dA,sB);
     dC = kroneckerProduct(dA,dB);
     VERIFY_IS_APPROX(MatrixXf(sC2),dC);
-    
+
     sC2 = kroneckerProduct(sA,dB);
     dC = kroneckerProduct(dA,dB);
     VERIFY_IS_APPROX(MatrixXf(sC2),dC);
-    
+
     sC2 = kroneckerProduct(2*sA,sB);
     dC = kroneckerProduct(2*dA,dB);
     VERIFY_IS_APPROX(MatrixXf(sC2),dC);
@@ -236,7 +237,6 @@ EIGEN_DECLARE_TEST(kronecker_product)
 #ifdef EIGEN_TEST_PART_2
 
 // simply check that for a dense kronecker product, sparse module is not needed
-
 #include "main.h"
 #include <Eigen/KroneckerProduct>