Fix several integer conversion and sign-compare warnings

6 files changed, 80 insertions, 81 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
index 24a6343e8..aa500eb70 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
@@ -212,11 +212,11 @@ class TensorBlockIO {
         num_size_one_inner_dims, NumDims - num_size_one_inner_dims - 1);
     const StorageIndex block_dim_for_tensor_stride1_dim =
         NumDims == 0 ? 1 : tensor_to_block_dim_map[tensor_stride1_dim];
-    size_t block_inner_dim_size =
+    StorageIndex block_inner_dim_size =
         NumDims == 0 ? 1
                      : block.block_sizes()[block_dim_for_tensor_stride1_dim];
-    for (int i = num_size_one_inner_dims + 1; i < NumDims; ++i) {
-      const int dim = cond<Layout>()(i, NumDims - i - 1);
+    for (Index i = num_size_one_inner_dims + 1; i < NumDims; ++i) {
+      const Index dim = cond<Layout>()(i, NumDims - i - 1);
       const StorageIndex block_stride =
           block.block_strides()[tensor_to_block_dim_map[dim]];
       if (block_inner_dim_size == block_stride &&
@@ -258,8 +258,8 @@ class TensorBlockIO {
 
     // Initialize block iterator state. Squeeze away any dimension of size 1.
     int num_squeezed_dims = 0;
-    for (int i = num_size_one_inner_dims; i < NumDims - 1; ++i) {
-      const int dim = cond<Layout>()(i + 1, NumDims - i - 2);
+    for (Index i = num_size_one_inner_dims; i < NumDims - 1; ++i) {
+      const Index dim = cond<Layout>()(i + 1, NumDims - i - 2);
       const StorageIndex size = block.block_sizes()[tensor_to_block_dim_map[dim]];
       if (size == 1) {
         continue;
@@ -626,7 +626,7 @@ class TensorBlockMapper {
                     const TensorBlockShapeType block_shape,
                     Index min_target_size)
       : m_dimensions(dims),
-        m_block_dim_sizes(BlockDimensions(dims, block_shape, min_target_size)) {
+        m_block_dim_sizes(BlockDimensions(dims, block_shape, internal::convert_index<StorageIndex>(min_target_size))) {
     // Calculate block counts by dimension and total block count.
     DSizes<StorageIndex, NumDims> block_count;
     for (Index i = 0; i < block_count.rank(); ++i) {
@@ -717,8 +717,8 @@ class TensorBlockMapper {
  private:
   static Dimensions BlockDimensions(const Dimensions& tensor_dims,
                                     const TensorBlockShapeType block_shape,
-                                    Index min_target_size) {
-    min_target_size = numext::maxi<Index>(1, min_target_size);
+                                    StorageIndex min_target_size) {
+    min_target_size = numext::maxi<StorageIndex>(1, min_target_size);
 
     // If tensor fully fits into the target size, we'll treat it a single block.
     Dimensions block_dim_sizes = tensor_dims;
@@ -735,16 +735,15 @@ class TensorBlockMapper {
       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>(
+        const StorageIndex dim_size_target = internal::convert_index<StorageIndex>(
             std::pow(static_cast<float>(min_target_size),
                      1.0f / static_cast<float>(block_dim_sizes.rank())));
-        for (size_t i = 0; i < block_dim_sizes.rank(); ++i) {
+        for (Index i = 0; i < block_dim_sizes.rank(); ++i) {
           // TODO(andydavis) Adjust the inner most 'block_dim_size' to make it
           // a multiple of the packet size. Note that reducing
           // 'block_dim_size' in this manner can increase the number of
           // blocks, and so will amplify any per-block overhead.
-          block_dim_sizes[i] = numext::mini(
-              dim_size_target, static_cast<size_t>(tensor_dims[i]));
+          block_dim_sizes[i] = numext::mini(dim_size_target, tensor_dims[i]);
         }
         // Add any un-allocated coefficients to inner dimension(s).
         StorageIndex total_size = block_dim_sizes.TotalSize();
@@ -781,7 +780,7 @@ class TensorBlockMapper {
 
     eigen_assert(
         block_dim_sizes.TotalSize() >=
-        numext::mini<size_t>(min_target_size, tensor_dims.TotalSize()));
+        numext::mini<Index>(min_target_size, tensor_dims.TotalSize()));
 
     return block_dim_sizes;
   }
@@ -824,7 +823,7 @@ class TensorSliceBlockMapper {
         m_total_block_count(1) {
     // Calculate block counts by dimension and total block count.
     DSizes<StorageIndex, NumDims> block_count;
-    for (size_t i = 0; i < block_count.rank(); ++i) {
+    for (Index i = 0; i < block_count.rank(); ++i) {
       block_count[i] = divup(m_tensor_slice_extents[i], m_block_dim_sizes[i]);
     }
     m_total_block_count = array_prod(block_count);
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
index 4f973a5b7..ce91bc2a6 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
@@ -32,12 +32,12 @@ namespace Eigen {
 // Boilerplate code
 namespace internal {
 
-template<std::size_t n, typename Dimension> struct dget {
+template<std::ptrdiff_t n, typename Dimension> struct dget {
   static const std::ptrdiff_t value = get<n, Dimension>::value;
 };
 
 
-template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
+template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
 struct fixed_size_tensor_index_linearization_helper
 {
   template <typename Dimensions> EIGEN_DEVICE_FUNC
@@ -50,7 +50,7 @@ struct fixed_size_tensor_index_linearization_helper
   }
 };
 
-template<typename Index, std::size_t NumIndices, bool RowMajor>
+template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
 struct fixed_size_tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
 {
   template <typename Dimensions> EIGEN_DEVICE_FUNC
@@ -60,7 +60,7 @@ struct fixed_size_tensor_index_linearization_helper<Index, NumIndices, 0, RowMaj
   }
 };
 
-template<typename Index, std::size_t n>
+template<typename Index, std::ptrdiff_t n>
 struct fixed_size_tensor_index_extraction_helper
 {
   template <typename Dimensions> EIGEN_DEVICE_FUNC
@@ -94,7 +94,7 @@ struct Sizes {
   typedef internal::numeric_list<std::ptrdiff_t, Indices...> Base;
   const Base t = Base();
   static const std::ptrdiff_t total_size = internal::arg_prod(Indices...);
-  static const size_t count = Base::count;
+  static const ptrdiff_t count = Base::count;
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t rank() const {
     return Base::count;
@@ -121,16 +121,16 @@ struct Sizes {
     return *this;
   }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t operator[] (const std::size_t index) const {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t operator[] (const std::ptrdiff_t index) const {
     return internal::fixed_size_tensor_index_extraction_helper<std::ptrdiff_t, Base::count>::run(index, t);
   }
 
   template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  size_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
+  ptrdiff_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
     return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(indices, t);
   }
   template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  size_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
+  ptrdiff_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
     return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(indices, t);
   }
 };
@@ -144,25 +144,25 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes<Indi
 
 #else
 
-template <std::size_t n>
+template <std::ptrdiff_t n>
 struct non_zero_size {
-  typedef internal::type2val<std::size_t, n> type;
+  typedef internal::type2val<std::ptrdiff_t, n> type;
 };
 template <>
 struct non_zero_size<0> {
   typedef internal::null_type type;
 };
 
-template <std::size_t V1=0, std::size_t V2=0, std::size_t V3=0, std::size_t V4=0, std::size_t V5=0> struct Sizes {
+template <std::ptrdiff_t V1=0, std::ptrdiff_t V2=0, std::ptrdiff_t V3=0, std::ptrdiff_t V4=0, std::ptrdiff_t V5=0> struct Sizes {
   typedef typename internal::make_type_list<typename non_zero_size<V1>::type, typename non_zero_size<V2>::type, typename non_zero_size<V3>::type, typename non_zero_size<V4>::type, typename non_zero_size<V5>::type >::type Base;
-  static const size_t count = Base::count;
-  static const std::size_t total_size = internal::arg_prod<Base>::value;
+  static const std::ptrdiff_t count = Base::count;
+  static const std::ptrdiff_t total_size = internal::arg_prod<Base>::value;
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptrdiff_t rank() const {
     return count;
   }
 
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t TotalSize() {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptrdiff_t TotalSize() {
     return internal::arg_prod<Base>::value;
   }
 
@@ -178,7 +178,7 @@ template <std::size_t V1=0, std::size_t V2=0, std::size_t V3=0, std::size_t V4=0
 
 #if EIGEN_HAS_VARIADIC_TEMPLATES
   template <typename... DenseIndex> Sizes(DenseIndex... /*indices*/) { }
-  explicit Sizes(std::initializer_list<std::size_t>) {
+  explicit Sizes(std::initializer_list<std::ptrdiff_t>) {
     // todo: add assertion
   }
 #else
@@ -213,18 +213,18 @@ template <std::size_t V1=0, std::size_t V2=0, std::size_t V3=0, std::size_t V4=0
   }
 
   template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  size_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
+  ptrdiff_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
     return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(indices, *reinterpret_cast<const Base*>(this));
   }
   template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  size_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
+  ptrdiff_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
     return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(indices, *reinterpret_cast<const Base*>(this));
   }
 };
 
 namespace internal {
-template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_prod(const Sizes<V1, V2, V3, V4, V5>&) {
+template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes<V1, V2, V3, V4, V5>&) {
   return Sizes<V1, V2, V3, V4, V5>::total_size;
 }
 }
@@ -233,7 +233,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_prod(const Sizes<V1, V2,
 
 // Boilerplate
 namespace internal {
-template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
+template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
 struct tensor_index_linearization_helper
 {
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -245,7 +245,7 @@ struct tensor_index_linearization_helper
   }
 };
 
-template<typename Index, std::size_t NumIndices, bool RowMajor>
+template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
 struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
 {
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -264,7 +264,7 @@ struct DSizes : array<DenseIndex, NumDims> {
   typedef array<DenseIndex, NumDims> Base;
   static const int count = NumDims;
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const {
     return NumDims;
   }
 
@@ -298,7 +298,7 @@ struct DSizes : array<DenseIndex, NumDims> {
     }
   }
 #else
-  template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5>
+  template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5>
   EIGEN_DEVICE_FUNC DSizes(const Sizes<V1, V2, V3, V4, V5>& a) {
     for (int i = 0 ; i < NumDims; ++i) {
       (*this)[i] = a[i];
@@ -359,7 +359,7 @@ struct DSizes : array<DenseIndex, NumDims> {
 
 // Boilerplate
 namespace internal {
-template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
+template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
 struct tensor_vsize_index_linearization_helper
 {
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -371,7 +371,7 @@ struct tensor_vsize_index_linearization_helper
   }
 };
 
-template<typename Index, std::size_t NumIndices, bool RowMajor>
+template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
 struct tensor_vsize_index_linearization_helper<Index, NumIndices, 0, RowMajor>
 {
   static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -386,10 +386,10 @@ struct tensor_vsize_index_linearization_helper<Index, NumIndices, 0, RowMajor>
 namespace internal {
 
 template <typename DenseIndex, int NumDims> struct array_size<const DSizes<DenseIndex, NumDims> > {
-  static const size_t value = NumDims;
+  static const ptrdiff_t value = NumDims;
 };
 template <typename DenseIndex, int NumDims> struct array_size<DSizes<DenseIndex, NumDims> > {
-  static const size_t value = NumDims;
+  static const ptrdiff_t value = NumDims;
 };
 #ifndef EIGEN_EMULATE_CXX11_META_H
 template <typename std::ptrdiff_t... Indices> struct array_size<const Sizes<Indices...> > {
@@ -399,33 +399,33 @@ template <typename std::ptrdiff_t... Indices> struct array_size<Sizes<Indices...
 static const std::ptrdiff_t value = Sizes<Indices...>::count;
 };
 template <std::ptrdiff_t n, typename std::ptrdiff_t... Indices> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<Indices...>&) {
-  return get<n, internal::numeric_list<std::size_t, Indices...> >::value;
+  return get<n, internal::numeric_list<std::ptrdiff_t, Indices...> >::value;
 }
 template <std::ptrdiff_t n> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<>&) {
   eigen_assert(false && "should never be called");
   return -1;
 }
 #else
-template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> struct array_size<const Sizes<V1,V2,V3,V4,V5> > {
-  static const size_t value = Sizes<V1,V2,V3,V4,V5>::count;
+template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> struct array_size<const Sizes<V1,V2,V3,V4,V5> > {
+  static const ptrdiff_t value = Sizes<V1,V2,V3,V4,V5>::count;
 };
-template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> struct array_size<Sizes<V1,V2,V3,V4,V5> > {
-  static const size_t value = Sizes<V1,V2,V3,V4,V5>::count;
+template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> struct array_size<Sizes<V1,V2,V3,V4,V5> > {
+  static const ptrdiff_t value = Sizes<V1,V2,V3,V4,V5>::count;
 };
-template <std::size_t n, std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_get(const Sizes<V1,V2,V3,V4,V5>&) {
+template <std::ptrdiff_t n, std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<V1,V2,V3,V4,V5>&) {
   return get<n, typename Sizes<V1,V2,V3,V4,V5>::Base>::value;
 }
 
 #endif
 
 
-template <typename Dims1, typename Dims2, size_t n, size_t m>
+template <typename Dims1, typename Dims2, ptrdiff_t n, ptrdiff_t m>
 struct sizes_match_below_dim {
   static EIGEN_DEVICE_FUNC  EIGEN_STRONG_INLINE bool run(Dims1&, Dims2&) {
     return false;
   }
 };
-template <typename Dims1, typename Dims2, size_t n>
+template <typename Dims1, typename Dims2, ptrdiff_t n>
 struct sizes_match_below_dim<Dims1, Dims2, n, n> {
   static EIGEN_DEVICE_FUNC  EIGEN_STRONG_INLINE bool run(Dims1& dims1, Dims2& dims2) {
     return (array_get<n-1>(dims1) == array_get<n-1>(dims2)) &
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
index 9b9587de5..b756be3b3 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
@@ -256,8 +256,8 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
     typedef TensorBlockMapper<ScalarNoConst, StorageIndex, NumDims, Evaluator::Layout> TensorBlockMapper;
 
     Evaluator evaluator(expr, device);
-    StorageIndex total_size = array_prod(evaluator.dimensions());
-    StorageIndex cache_size = device.firstLevelCacheSize() / sizeof(Scalar);
+    Index total_size = array_prod(evaluator.dimensions());
+    Index cache_size = device.firstLevelCacheSize() / sizeof(Scalar);
     if (total_size < cache_size) {
       // TODO(andydavis) Reduce block management overhead for small tensors.
       internal::TensorExecutor<Expression, ThreadPoolDevice, Vectorizable,
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h b/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
index 98ad661ca..3f7d26b18 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
@@ -84,7 +84,7 @@ template<DenseIndex n> struct NumTraits<type2index<n> >
 namespace internal {
 template <typename T>
 EIGEN_DEVICE_FUNC void update_value(T& val, DenseIndex new_val) {
-  val = new_val;
+  val = internal::convert_index<T>(new_val);
 }
 template <DenseIndex n>
 EIGEN_DEVICE_FUNC void update_value(type2index<n>& val, DenseIndex new_val) {
diff --git a/unsupported/test/cxx11_tensor_block_access.cpp b/unsupported/test/cxx11_tensor_block_access.cpp
index f572e496d..24a95ab12 100644
--- a/unsupported/test/cxx11_tensor_block_access.cpp
+++ b/unsupported/test/cxx11_tensor_block_access.cpp
@@ -33,8 +33,8 @@ static internal::TensorBlockShapeType RandomShape() {
 }
 
 template <int NumDims>
-static std::size_t RandomTargetSize(const DSizes<Index, NumDims>& dims) {
-  return internal::random<int>(1, dims.TotalSize());
+static Index RandomTargetSize(const DSizes<Index, NumDims>& dims) {
+  return internal::random<Index>(1, dims.TotalSize());
 }
 
 template <int NumDims>
@@ -178,7 +178,7 @@ static void test_block_mapper_maps_every_element() {
   // Verify that every coefficient in the original Tensor is accessible through
   // TensorBlock only once.
   Index total_coeffs = dims.TotalSize();
-  VERIFY_IS_EQUAL(coeff_set.size(), total_coeffs);
+  VERIFY_IS_EQUAL(Index(coeff_set.size()), total_coeffs);
   VERIFY_IS_EQUAL(*coeff_set.begin(), 0);
   VERIFY_IS_EQUAL(*coeff_set.rbegin(), total_coeffs - 1);
 }
@@ -208,7 +208,7 @@ static void test_slice_block_mapper_maps_every_element() {
   // Pick a random dimension sizes for the tensor blocks.
   DSizes<Index, NumDims> block_sizes;
   for (int i = 0; i < NumDims; ++i) {
-    block_sizes[i] = internal::random<int>(1, tensor_slice_extents[i]);
+    block_sizes[i] = internal::random<Index>(1, tensor_slice_extents[i]);
   }
 
   TensorSliceBlockMapper block_mapper(tensor_dims, tensor_slice_offsets,
@@ -222,7 +222,7 @@ static void test_slice_block_mapper_maps_every_element() {
                                        &coeff_set);
   }
 
-  VERIFY_IS_EQUAL(coeff_set.size(), total_coeffs);
+  VERIFY_IS_EQUAL(Index(coeff_set.size()), total_coeffs);
 }
 
 template <typename T, int NumDims, int Layout>
@@ -262,14 +262,14 @@ static void test_block_io_copy_data_from_source_to_target() {
 }
 
 template <int Layout, int NumDims>
-static int GetInputIndex(Index output_index,
+static Index GetInputIndex(Index output_index,
                          const array<Index, NumDims>& output_to_input_dim_map,
                          const array<Index, NumDims>& input_strides,
                          const array<Index, NumDims>& output_strides) {
   int input_index = 0;
   if (Layout == ColMajor) {
     for (int i = NumDims - 1; i > 0; --i) {
-      const int idx = output_index / output_strides[i];
+      const Index idx = output_index / output_strides[i];
       input_index += idx * input_strides[output_to_input_dim_map[i]];
       output_index -= idx * output_strides[i];
     }
@@ -277,7 +277,7 @@ static int GetInputIndex(Index output_index,
            output_index * input_strides[output_to_input_dim_map[0]];
   } else {
     for (int i = 0; i < NumDims - 1; ++i) {
-      const int idx = output_index / output_strides[i];
+      const Index idx = output_index / output_strides[i];
       input_index += idx * input_strides[output_to_input_dim_map[i]];
       output_index -= idx * output_strides[i];
     }
@@ -650,7 +650,7 @@ static void test_uniform_block_shape()
   {
     // 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;
+    const Index max_coeff_count = 5 * 5 * 5 * 5 * 5;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -664,7 +664,7 @@ static void test_uniform_block_shape()
   // partially into first inner-most dimension.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 7 * 5 * 5 * 5 * 5;
+    const Index max_coeff_count = 7 * 5 * 5 * 5 * 5;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -675,7 +675,7 @@ static void test_uniform_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 5 * 5 * 5 * 5 * 6;
+    const Index max_coeff_count = 5 * 5 * 5 * 5 * 6;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -690,7 +690,7 @@ static void test_uniform_block_shape()
   // fully into first inner-most dimension.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 11 * 5 * 5 * 5 * 5;
+    const Index max_coeff_count = 11 * 5 * 5 * 5 * 5;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -701,7 +701,7 @@ static void test_uniform_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 5 * 5 * 5 * 5 * 7;
+    const Index max_coeff_count = 5 * 5 * 5 * 5 * 7;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -716,7 +716,7 @@ static void test_uniform_block_shape()
   // fully into first few inner-most dimensions.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(7, 5, 6, 17, 7);
-    const size_t max_coeff_count = 7 * 5 * 6 * 7 * 5;
+    const Index max_coeff_count = 7 * 5 * 6 * 7 * 5;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -728,7 +728,7 @@ static void test_uniform_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(7, 5, 6, 9, 7);
-    const size_t max_coeff_count = 5 * 5 * 5 * 6 * 7;
+    const Index max_coeff_count = 5 * 5 * 5 * 6 * 7;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -743,7 +743,7 @@ static void test_uniform_block_shape()
   // Test shape 'UniformAllDims' with full allocation to all dims.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(7, 5, 6, 17, 7);
-    const size_t max_coeff_count = 7 * 5 * 6 * 17 * 7;
+    const Index max_coeff_count = 7 * 5 * 6 * 17 * 7;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -755,7 +755,7 @@ static void test_uniform_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(7, 5, 6, 9, 7);
-    const size_t max_coeff_count = 7 * 5 * 6 * 9 * 7;
+    const Index max_coeff_count = 7 * 5 * 6 * 9 * 7;
     TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -777,7 +777,7 @@ static void test_skewed_inner_dim_block_shape()
   // 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;
+    const Index max_coeff_count = 10 * 1 * 1 * 1 * 1;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -788,7 +788,7 @@ static void test_skewed_inner_dim_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 1 * 1 * 1 * 1 * 6;
+    const Index max_coeff_count = 1 * 1 * 1 * 1 * 6;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -802,7 +802,7 @@ static void test_skewed_inner_dim_block_shape()
   // Test shape 'SkewedInnerDims' with full allocation to inner-most dim.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 11 * 1 * 1 * 1 * 1;
+    const Index max_coeff_count = 11 * 1 * 1 * 1 * 1;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -813,7 +813,7 @@ static void test_skewed_inner_dim_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 1 * 1 * 1 * 1 * 7;
+    const Index max_coeff_count = 1 * 1 * 1 * 1 * 7;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -828,7 +828,7 @@ static void test_skewed_inner_dim_block_shape()
   // and partial allocation to second inner-dim.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 11 * 3 * 1 * 1 * 1;
+    const Index max_coeff_count = 11 * 3 * 1 * 1 * 1;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -840,7 +840,7 @@ static void test_skewed_inner_dim_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 1 * 1 * 1 * 15 * 7;
+    const Index max_coeff_count = 1 * 1 * 1 * 15 * 7;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -856,7 +856,7 @@ static void test_skewed_inner_dim_block_shape()
   // and partial allocation to third inner-dim.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 11 * 5 * 5 * 1 * 1;
+    const Index max_coeff_count = 11 * 5 * 5 * 1 * 1;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -869,7 +869,7 @@ static void test_skewed_inner_dim_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 1 * 1 * 5 * 17 * 7;
+    const Index max_coeff_count = 1 * 1 * 5 * 17 * 7;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -885,7 +885,7 @@ static void test_skewed_inner_dim_block_shape()
   // Test shape 'SkewedInnerDims' with full allocation to all dims.
   if (Layout == ColMajor) {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7;
+    const Index max_coeff_count = 11 * 5 * 6 * 17 * 7;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
@@ -897,7 +897,7 @@ static void test_skewed_inner_dim_block_shape()
     VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
   } else {
     DSizes<Index, 5> dims(11, 5, 6, 17, 7);
-    const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7;
+    const Index max_coeff_count = 11 * 5 * 6 * 17 * 7;
     TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
                                    max_coeff_count);
     TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
diff --git a/unsupported/test/cxx11_tensor_thread_pool.cpp b/unsupported/test/cxx11_tensor_thread_pool.cpp
index 7606b0abf..6d8e58214 100644
--- a/unsupported/test/cxx11_tensor_thread_pool.cpp
+++ b/unsupported/test/cxx11_tensor_thread_pool.cpp
@@ -300,7 +300,7 @@ static void test_multithread_contraction_with_output_kernel() {
 
   m_result = m_left * m_right;
 
-  for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
+  for (Index 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]));
   }