Added support for tensor chips

6 files changed, 491 insertions, 2 deletions

diff --git a/unsupported/Eigen/CXX11/Tensor b/unsupported/Eigen/CXX11/Tensor
index b1bd2f676..5a6246a03 100644
--- a/unsupported/Eigen/CXX11/Tensor
+++ b/unsupported/Eigen/CXX11/Tensor
@@ -47,6 +47,7 @@
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h"
+#include "unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h"
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
index d4b7846a0..cadeb3b19 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
@@ -254,6 +254,11 @@ class TensorBase<Derived, ReadOnlyAccessors>
     slice(const StartIndices& startIndices, const Sizes& sizes) const {
       return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
     }
+    template <std::size_t DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<DimId, const Derived>
+    chip(const Index offset) const {
+       return TensorChippingOp<DimId, const Derived>(derived(), offset);
+    }
     template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
     const TensorPaddingOp<const PaddingDimensions, const Derived>
     pad(const PaddingDimensions& padding) const {
@@ -327,7 +332,7 @@ class TensorBase<Derived, WriteAccessors> : public TensorBase<Derived, ReadOnlyA
 
     template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
     TensorReshapingOp<const NewDimensions, Derived>
-    reshape(const NewDimensions& newDimensions) {
+    reshape(const NewDimensions& newDimensions) const {
       return TensorReshapingOp<const NewDimensions, Derived>(derived(), newDimensions);
     }
     template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -335,6 +340,11 @@ class TensorBase<Derived, WriteAccessors> : public TensorBase<Derived, ReadOnlyA
     slice(const StartIndices& startIndices, const Sizes& sizes) const {
       return TensorSlicingOp<const StartIndices, const Sizes, Derived>(derived(), startIndices, sizes);
     }
+    template <std::size_t DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorChippingOp<DimId, Derived>
+    chip(const Index offset) const {
+       return TensorChippingOp<DimId, Derived>(derived(), offset);
+    }
     template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
     TensorShufflingOp<const Shuffle, Derived>
     shuffle(const Shuffle& shuffle) const {
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h b/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
new file mode 100644
index 000000000..9ecea9108
--- /dev/null
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
@@ -0,0 +1,232 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// 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/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
+
+namespace Eigen {
+
+/** \class TensorKChippingReshaping
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief A chip is a thin slice, corresponding to a column or a row in a 2-d tensor.
+  *
+  *
+  */
+
+namespace internal {
+template<std::size_t DimId, typename XprType>
+struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type Packet;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+};
+
+template<std::size_t DimId, typename XprType>
+struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
+{
+  typedef const TensorChippingOp<DimId, XprType>& type;
+};
+
+template<std::size_t DimId, typename XprType>
+struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
+{
+  typedef TensorChippingOp<DimId, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<std::size_t DimId, typename XprType>
+class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorChippingOp>::Scalar Scalar;
+  typedef typename Eigen::internal::traits<TensorChippingOp>::Packet Packet;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+  typedef typename Eigen::internal::nested<TensorChippingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset)
+      : m_xpr(expr), m_offset(offset) {}
+
+    EIGEN_DEVICE_FUNC
+    const Index offset() const { return m_offset; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorChippingOp& operator = (const OtherDerived& other)
+    {
+      typedef TensorAssignOp<TensorChippingOp, const OtherDerived> Assign;
+      Assign assign(*this, other);
+      internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
+      return *this;
+    }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Index m_offset;
+};
+
+
+// Eval as rvalue
+template<std::size_t DimId, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
+{
+  typedef TensorChippingOp<DimId, ArgType> XprType;
+  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static const int NumDims = NumInputDims-1;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  enum {
+    // Alignment can't be guaranteed at compile time since it depends on the
+    // slice offsets.
+    IsAligned = false,
+    PacketAccess = false,  // not yet implemented
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_device(device)
+  {
+    // We could also support the case where NumInputDims==1 if needed.
+    EIGEN_STATIC_ASSERT(NumInputDims >= 2, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT(NumInputDims > DimId, YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    int j = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (i != DimId) {
+        m_dimensions[j] = input_dims[i];
+        ++j;
+      }
+    }
+
+     m_stride = 1;
+     m_inputStride = 1;
+     for (int i = 0; i < DimId; ++i) {
+       m_stride *= input_dims[i];
+       m_inputStride *= input_dims[i];
+     }
+     m_inputStride *= input_dims[DimId];
+     m_inputOffset = m_stride * op.offset();
+  }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_impl.coeff(srcCoeff(index));
+  }
+
+  /* to be done
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+
+  }*/
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const { return NULL; }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
+  {
+    Index inputIndex;
+    if (DimId == 0) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(m_stride == 1);
+      inputIndex = index * m_inputStride + m_inputOffset;
+    } else if (DimId == NumInputDims-1) {
+      // m_stride is aways greater than index, so let's avoid the integer division.
+      eigen_assert(m_stride > index);
+      inputIndex = index + m_inputOffset;
+    } else {
+      const Index idx = index / m_stride;
+      inputIndex = idx * m_inputStride + m_inputOffset;
+      index -= idx * m_stride;
+      inputIndex += index;
+    }
+    return inputIndex;
+  }
+
+  Dimensions m_dimensions;
+  Index m_stride;
+  Index m_inputOffset;
+  Index m_inputStride;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Device& m_device;
+};
+
+
+// Eval as lvalue
+template<std::size_t DimId, typename ArgType, typename Device>
+struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
+  : public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base;
+  typedef TensorChippingOp<DimId, ArgType> XprType;
+  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static const int NumDims = NumInputDims-1;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : Base(op, device)
+    { }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
+  {
+    return this->m_impl.coeffRef(this->srcCoeff(index));
+  }
+
+  /* to be done
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+  } */
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
index bc67586a4..86ddd1ae8 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
@@ -21,11 +21,12 @@ template<typename NullaryOp, typename PlainObjectType> class TensorCwiseNullaryO
 template<typename UnaryOp, typename XprType> class TensorCwiseUnaryOp;
 template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp;
 template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
-template<typename Broadcast, typename XprType> class TensorBroadcastingOp;
 template<typename Op, typename Dims, typename XprType> class TensorReductionOp;
 template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp;
 template<typename Dimensions, typename LeftXprType, typename RightXprType> class TensorContractionOp;
 template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp;
+template<typename Broadcast, typename XprType> class TensorBroadcastingOp;
+template<std::size_t DimId, typename XprType> class TensorChippingOp;
 template<typename NewDimensions, typename XprType> class TensorReshapingOp;
 template<typename StartIndices, typename Sizes, typename XprType> class TensorSlicingOp;
 template<typename PaddingDimensions, typename XprType> class TensorPaddingOp;
diff --git a/unsupported/test/CMakeLists.txt b/unsupported/test/CMakeLists.txt
index ac2ccaf27..48435eb9c 100644
--- a/unsupported/test/CMakeLists.txt
+++ b/unsupported/test/CMakeLists.txt
@@ -115,6 +115,7 @@ if(EIGEN_TEST_CXX11)
   ei_add_test(cxx11_tensor_lvalue "-std=c++0x")
   ei_add_test(cxx11_tensor_map "-std=c++0x")
   ei_add_test(cxx11_tensor_broadcasting "-std=c++0x")
+  ei_add_test(cxx11_tensor_chipping "-std=c++0x")
   ei_add_test(cxx11_tensor_concatenation "-std=c++0x")
   ei_add_test(cxx11_tensor_morphing "-std=c++0x")
   ei_add_test(cxx11_tensor_padding "-std=c++0x")
diff --git a/unsupported/test/cxx11_tensor_chipping.cpp b/unsupported/test/cxx11_tensor_chipping.cpp
new file mode 100644
index 000000000..8c8a0cec2
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_chipping.cpp
@@ -0,0 +1,244 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// 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/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+
+static void test_simple_chip()
+{
+  Tensor<float, 5> tensor(2,3,5,7,11);
+  tensor.setRandom();
+
+  Tensor<float, 4> chip1;
+  chip1 = tensor.chip<0>(1);
+  VERIFY_IS_EQUAL(chip1.dimension(0), 3);
+  VERIFY_IS_EQUAL(chip1.dimension(1), 5);
+  VERIFY_IS_EQUAL(chip1.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip1.dimension(3), 11);
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1,i,j,k,l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4> chip2 = tensor.chip<1>(1);
+  VERIFY_IS_EQUAL(chip2.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip2.dimension(1), 5);
+  VERIFY_IS_EQUAL(chip2.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip2.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1,j,k,l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4> chip3 = tensor.chip<2>(2);
+  VERIFY_IS_EQUAL(chip3.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip3.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip3.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip3.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2,k,l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4> chip4(tensor.chip<3>(5));
+  VERIFY_IS_EQUAL(chip4.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip4.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip4.dimension(2), 5);
+  VERIFY_IS_EQUAL(chip4.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5,l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4> chip5(tensor.chip<4>(7));
+  VERIFY_IS_EQUAL(chip5.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip5.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip5.dimension(2), 5);
+  VERIFY_IS_EQUAL(chip5.dimension(3), 7);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(chip5(i,j,k,l), tensor(i,j,k,l,7));
+        }
+      }
+    }
+  }
+}
+
+
+static void test_chip_in_expr() {
+  Tensor<float, 5> input1(2,3,5,7,11);
+  input1.setRandom();
+  Tensor<float, 4> input2(3,5,7,11);
+  input2.setRandom();
+
+  Tensor<float, 4> result = input1.chip<0>(0) + input2;
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          float expected = input1(0,i,j,k,l) + input2(i,j,k,l);
+          VERIFY_IS_EQUAL(result(i,j,k,l), expected);
+        }
+      }
+    }
+  }
+
+  Tensor<float, 3> input3(3,7,11);
+  input3.setRandom();
+  Tensor<float, 3> result2 = input1.chip<0>(0).chip<1>(2) + input3;
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      for (int k = 0; k < 11; ++k) {
+        float expected = input1(0,i,2,j,k) + input3(i,j,k);
+        VERIFY_IS_EQUAL(result2(i,j,k), expected);
+      }
+    }
+  }
+}
+
+
+static void test_chip_as_lvalue()
+{
+  Tensor<float, 5> input1(2,3,5,7,11);
+  input1.setRandom();
+
+  Tensor<float, 4> input2(3,5,7,11);
+  input2.setRandom();
+  Tensor<float, 5> tensor = input1;
+  tensor.chip<0>(1) = input2;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (i != 1) {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input2(j,k,l,m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4> input3(2,5,7,11);
+  input3.setRandom();
+  tensor = input1;
+  tensor.chip<1>(1) = input3;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (j != 1) {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input3(i,k,l,m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4> input4(2,3,7,11);
+  input4.setRandom();
+  tensor = input1;
+  tensor.chip<2>(3) = input4;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (k != 3) {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input4(i,j,l,m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4> input5(2,3,5,11);
+  input5.setRandom();
+  tensor = input1;
+  tensor.chip<3>(4) = input5;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (l != 4) {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input5(i,j,k,m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4> input6(2,3,5,7);
+  input6.setRandom();
+  tensor = input1;
+  tensor.chip<4>(5) = input6;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (m != 5) {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input6(i,j,k,l));
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+
+void test_cxx11_tensor_chipping()
+{
+  CALL_SUBTEST(test_simple_chip());
+  CALL_SUBTEST(test_chip_in_expr());
+  CALL_SUBTEST(test_chip_as_lvalue());
+}