Improved support for RowMajor tensors

Misc fixes and API cleanups.

1 files changed, 161 insertions, 47 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h b/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
index b862a8fd3..bc336e488 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
@@ -21,34 +21,61 @@ namespace Eigen {
   */
 
 namespace internal {
-template<std::size_t DimId, typename XprType>
+template<DenseIndex 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 traits<XprType> XprTraits;
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
   typedef typename XprType::Nested Nested;
   typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions - 1;
+  static const int Layout = XprTraits::Layout;
 };
 
-template<std::size_t DimId, typename XprType>
+template<DenseIndex DimId, typename XprType>
 struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
 {
   typedef const TensorChippingOp<DimId, XprType>& type;
 };
 
-template<std::size_t DimId, typename XprType>
+template<DenseIndex DimId, typename XprType>
 struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
 {
   typedef TensorChippingOp<DimId, XprType> type;
 };
 
+template <DenseIndex DimId>
+struct DimensionId
+{
+  DimensionId(DenseIndex dim) {
+    eigen_assert(dim == DimId);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
+    return DimId;
+  }
+};
+template <>
+struct DimensionId<Dynamic>
+{
+  DimensionId(DenseIndex dim) : actual_dim(dim) {
+    eigen_assert(dim >= 0);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
+    return actual_dim;
+  }
+ private:
+  const DenseIndex actual_dim;
+};
+
+
 }  // end namespace internal
 
 
 
-template<std::size_t DimId, typename XprType>
+template<DenseIndex DimId, typename XprType>
 class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
 {
   public:
@@ -61,34 +88,39 @@ class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
   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 EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset, const Index dim)
+      : m_xpr(expr), m_offset(offset), m_dim(dim) {
+  }
 
-    EIGEN_DEVICE_FUNC
-    const Index offset() const { return m_offset; }
+  EIGEN_DEVICE_FUNC
+  const Index offset() const { return m_offset; }
+  EIGEN_DEVICE_FUNC
+  const Index dim() const { return m_dim.actualDim(); }
 
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
+  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;
-    }
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE TensorChippingOp& operator = (const OtherDerived& other)
+  {
+    typedef TensorAssignOp<TensorChippingOp, const OtherDerived> Assign;
+    Assign assign(*this, other);
+    static const bool Vectorize = TensorEvaluator<const Assign, DefaultDevice>::PacketAccess;
+    internal::TensorExecutor<const Assign, DefaultDevice, Vectorize>::run(assign, DefaultDevice());
+    return *this;
+  }
 
   protected:
     typename XprType::Nested m_xpr;
     const Index m_offset;
+    const internal::DimensionId<DimId> m_dim;
 };
 
 
 // Eval as rvalue
-template<std::size_t DimId, typename ArgType, typename Device>
+template<DenseIndex DimId, typename ArgType, typename Device>
 struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
 {
   typedef TensorChippingOp<DimId, ArgType> XprType;
@@ -96,41 +128,50 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
   static const int NumDims = NumInputDims-1;
   typedef typename XprType::Index Index;
   typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
 
   enum {
     // Alignment can't be guaranteed at compile time since it depends on the
     // slice offsets.
     IsAligned = false,
-    PacketAccess = false,  // not yet implemented
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
   };
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_device(device)
+      : m_impl(op.expression(), device), m_dim(op.dim()), 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);
+    eigen_assert(NumInputDims > m_dim.actualDim());
 
     const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
     int j = 0;
     for (int i = 0; i < NumInputDims; ++i) {
-      if (i != DimId) {
+      if (i != m_dim.actualDim()) {
         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();
+    m_stride = 1;
+    m_inputStride = 1;
+    if (Layout == ColMajor) {
+      for (int i = 0; i < m_dim.actualDim(); ++i) {
+        m_stride *= input_dims[i];
+        m_inputStride *= input_dims[i];
+      }
+    } else {
+      for (int i = NumInputDims-1; i > m_dim.actualDim(); --i) {
+        m_stride *= input_dims[i];
+        m_inputStride *= input_dims[i];
+      }
+    }
+    m_inputStride *= input_dims[m_dim.actualDim()];
+    m_inputOffset = m_stride * op.offset();
   }
 
-  typedef typename XprType::Scalar Scalar;
   typedef typename XprType::CoeffReturnType CoeffReturnType;
   typedef typename XprType::PacketReturnType PacketReturnType;
 
@@ -150,16 +191,52 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
     return m_impl.coeff(srcCoeff(index));
   }
 
-  /* to be done
   template<int LoadMode>
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
   {
+    const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
+    EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+packetSize-1 < dimensions().TotalSize());
 
-  }*/
+    if ((Layout == ColMajor && m_dim.actualDim() == 0) ||
+        (Layout == RowMajor && m_dim.actualDim() == NumInputDims-1)) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(m_stride == 1);
+      Index inputIndex = index * m_inputStride + m_inputOffset;
+      EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
+      for (int i = 0; i < packetSize; ++i) {
+        values[i] = m_impl.coeff(inputIndex);
+        inputIndex += m_inputStride;
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    } else if ((Layout == ColMajor && m_dim.actualDim() == NumInputDims - 1) ||
+               (Layout == RowMajor && m_dim.actualDim() == 0)) {
+      // m_stride is aways greater than index, so let's avoid the integer division.
+      eigen_assert(m_stride > index);
+      return m_impl.template packet<LoadMode>(index + m_inputOffset);
+    } else {
+      const Index idx = index / m_stride;
+      const Index rem = index - idx * m_stride;
+      if (rem + packetSize <= m_stride) {
+        Index inputIndex = idx * m_inputStride + m_inputOffset + rem;
+        return m_impl.template packet<LoadMode>(inputIndex);
+      } else {
+        // Cross the stride boundary. Fallback to slow path.
+        EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
+        for (int i = 0; i < packetSize; ++i) {
+          values[i] = coeff(index);
+          ++index;
+        }
+        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+        return rslt;
+      }
+    }
+  }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const {
     Scalar* result = m_impl.data();
-    if (DimId == NumDims && result) {
+    if (m_dim.actualDim() == NumDims && result) {
       return result + m_inputOffset;
     } else {
       return NULL;
@@ -170,11 +247,13 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
   {
     Index inputIndex;
-    if (DimId == 0) {
+    if ((Layout == ColMajor && m_dim.actualDim() == 0) ||
+        (Layout == RowMajor && m_dim.actualDim() == NumInputDims-1)) {
       // 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) {
+    } else if ((Layout == ColMajor && m_dim.actualDim() == NumInputDims-1) ||
+               (Layout == RowMajor && m_dim.actualDim() == 0)) {
       // m_stride is aways greater than index, so let's avoid the integer division.
       eigen_assert(m_stride > index);
       inputIndex = index + m_inputOffset;
@@ -192,12 +271,13 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
   Index m_inputOffset;
   Index m_inputStride;
   TensorEvaluator<ArgType, Device> m_impl;
+  const internal::DimensionId<DimId> m_dim;
   const Device& m_device;
 };
 
 
 // Eval as lvalue
-template<std::size_t DimId, typename ArgType, typename Device>
+template<DenseIndex DimId, typename ArgType, typename Device>
 struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
   : public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
 {
@@ -207,17 +287,17 @@ struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
   static const int NumDims = NumInputDims-1;
   typedef typename XprType::Index Index;
   typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
 
   enum {
     IsAligned = false,
-    PacketAccess = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
   };
 
   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;
 
@@ -226,11 +306,45 @@ struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
     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)
   {
-  } */
+    static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
+    EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+
+    if ((this->Layout == ColMajor && this->m_dim.actualDim() == 0) ||
+        (this->Layout == RowMajor && this->m_dim.actualDim() == NumInputDims-1)) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(this->m_stride == 1);
+      EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
+      internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+      Index inputIndex = index * this->m_inputStride + this->m_inputOffset;
+      for (int i = 0; i < packetSize; ++i) {
+        this->m_impl.coeffRef(inputIndex) = values[i];
+        inputIndex += this->m_inputStride;
+      }
+    } else if ((this->Layout == ColMajor && this->m_dim.actualDim() == NumInputDims-1) ||
+               (this->Layout == RowMajor && this->m_dim.actualDim() == 0)) {
+      // m_stride is aways greater than index, so let's avoid the integer division.
+      eigen_assert(this->m_stride > index);
+      this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x);
+    } else {
+      const Index idx = index / this->m_stride;
+      const Index rem = index - idx * this->m_stride;
+      if (rem + packetSize <= this->m_stride) {
+        const Index inputIndex = idx * this->m_inputStride + this->m_inputOffset + rem;
+        this->m_impl.template writePacket<StoreMode>(inputIndex, x);
+      } else {
+        // Cross stride boundary. Fallback to slow path.
+        EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
+        internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+        for (int i = 0; i < packetSize; ++i) {
+          this->coeffRef(index) = values[i];
+          ++index;
+        }
+      }
+    }
+  }
 };