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diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
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+++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
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+// 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_SHUFFLING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
+
+namespace Eigen {
+
+/** \class TensorShuffling
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor shuffling class.
+  *
+  *
+  */
+namespace internal {
+template<typename Shuffle, typename XprType>
+struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  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;
+  static const int Layout = XprTraits::Layout;
+};
+
+template<typename Shuffle, typename XprType>
+struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense>
+{
+  typedef const TensorShufflingOp<Shuffle, XprType>& type;
+};
+
+template<typename Shuffle, typename XprType>
+struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type>
+{
+  typedef TensorShufflingOp<Shuffle, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename Shuffle, typename XprType>
+class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> >
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar;
+  typedef typename Eigen::internal::traits<TensorShufflingOp>::Packet Packet;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+  typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shuffle)
+      : m_xpr(expr), m_shuffle(shuffle) {}
+
+    EIGEN_DEVICE_FUNC
+    const Shuffle& shufflePermutation() const { return m_shuffle; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const TensorShufflingOp& other)
+    {
+      typedef TensorAssignOp<TensorShufflingOp, const TensorShufflingOp> Assign;
+      Assign assign(*this, other);
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(
+          assign, DefaultDevice());
+      return *this;
+    }
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const OtherDerived& other)
+    {
+      typedef TensorAssignOp<TensorShufflingOp, const OtherDerived> Assign;
+      Assign assign(*this, other);
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(
+          assign, DefaultDevice());
+      return *this;
+    }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Shuffle m_shuffle;
+};
+
+
+// Eval as rvalue
+template<typename Shuffle, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
+{
+  typedef TensorShufflingOp<Shuffle, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::remove_const<Scalar>::type ScalarNonConst;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = (internal::packet_traits<Scalar>::size > 1),
+    BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+  };
+
+  typedef typename internal::TensorBlock<
+    Index, typename internal::remove_const<Scalar>::type, NumDims,
+    TensorEvaluator<ArgType, Device>::Layout> TensorBlock;
+  typedef typename internal::TensorBlockReader<
+    Index, typename internal::remove_const<Scalar>::type, NumDims,
+    TensorEvaluator<ArgType, Device>::Layout, PacketAccess> TensorBlockReader;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_shuffle(op.shufflePermutation()), m_impl(op.expression(), device)
+  {
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    for (int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] = input_dims[m_shuffle[i]];
+      m_inverseShuffle[m_shuffle[i]] = i;
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_unshuffledInputStrides[0] = 1;
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_unshuffledInputStrides[i] =
+            m_unshuffledInputStrides[i - 1] * input_dims[i - 1];
+        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+      }
+    } else {
+      m_unshuffledInputStrides[NumDims - 1] = 1;
+      m_outputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_unshuffledInputStrides[i] =
+            m_unshuffledInputStrides[i + 1] * input_dims[i + 1];
+        m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+      }
+    }
+
+    for (int i = 0; i < NumDims; ++i) {
+      m_inputStrides[i] = m_unshuffledInputStrides[m_shuffle[i]];
+    }
+
+    m_block_total_size_max = numext::maxi(static_cast<std::size_t>(1),
+                                        device.firstLevelCacheSize() /
+                                        sizeof(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));
+  }
+
+  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());
+
+    EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
+    for (int i = 0; i < packetSize; ++i) {
+      values[i] = coeff(index+i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void getResourceRequirements(
+      std::vector<internal::TensorOpResourceRequirements>* resources) const {
+    resources->push_back(internal::TensorOpResourceRequirements(
+        internal::kUniformAllDims, m_block_total_size_max));
+    m_impl.getResourceRequirements(resources);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block(
+      TensorBlock* output_block) const {
+    if (m_impl.data() != NULL) {
+      // Fast path: we have direct access to the data, so shuffle as we read.
+      TensorBlockReader::Run(output_block,
+                             srcCoeff(output_block->first_coeff_index()),
+                             m_inverseShuffle,
+                             m_unshuffledInputStrides,
+                             m_impl.data());
+      return;
+    }
+
+    // Slow path: read unshuffled block from the input and shuffle in-place.
+    // Initialize input block sizes using input-to-output shuffle map.
+    DSizes<Index, NumDims> input_block_sizes;
+    for (Index i = 0; i < NumDims; ++i) {
+      input_block_sizes[i] = output_block->block_sizes()[m_inverseShuffle[i]];
+    }
+
+    // Calculate input block strides.
+    DSizes<Index, NumDims> input_block_strides;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      input_block_strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        input_block_strides[i] = input_block_strides[i - 1] *
+            input_block_sizes[i - 1];
+      }
+    } else {
+      input_block_strides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        input_block_strides[i] = input_block_strides[i + 1] *
+            input_block_sizes[i + 1];
+      }
+    }
+
+    // Read input block.
+    TensorBlock input_block(srcCoeff(output_block->first_coeff_index()),
+                            input_block_sizes,
+                            input_block_strides,
+                            m_unshuffledInputStrides,
+                            output_block->data());
+
+    m_impl.block(&input_block);
+
+    // Naive In-place shuffle: random IO but block size is O(L1 cache size).
+    // TODO(andydavis) Improve the performance of this in-place shuffle.
+    const Index total_size = input_block_sizes.TotalSize();
+    std::vector<bool> bitmap(total_size, false);
+    ScalarNonConst* data = const_cast<ScalarNonConst*>(output_block->data());
+    const DSizes<Index, NumDims>& output_block_strides =
+        output_block->block_strides();
+    for (Index input_index = 0; input_index < total_size; ++input_index) {
+      if (bitmap[input_index]) {
+        // Coefficient at this index has already been shuffled.
+        continue;
+      }
+
+      Index output_index = GetBlockOutputIndex(input_index,
+                                               input_block_strides,
+                                               output_block_strides);
+      if (output_index == input_index) {
+        // Coefficient already in place.
+        bitmap[output_index] = true;
+        continue;
+      }
+
+      // The following loop starts at 'input_index', and shuffles
+      // coefficients into their shuffled location at 'output_index'.
+      // It skips through the array shuffling coefficients by following
+      // the shuffle cycle starting and ending a 'start_index'.
+      ScalarNonConst evicted_value;
+      ScalarNonConst shuffled_value = data[input_index];
+      do {
+        evicted_value = data[output_index];
+        data[output_index] = shuffled_value;
+        shuffled_value = evicted_value;
+        bitmap[output_index] = true;
+        output_index = GetBlockOutputIndex(output_index,
+                                           input_block_strides,
+                                           output_block_strides);
+      } while (output_index != input_index);
+
+      data[output_index] = shuffled_value;
+      bitmap[output_index] = true;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index GetBlockOutputIndex(
+      Index input_index,
+      const DSizes<Index, NumDims>& input_block_strides,
+      const DSizes<Index, NumDims>& output_block_strides) const {
+    Index output_index = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = input_index / input_block_strides[i];
+        output_index += idx * output_block_strides[m_inverseShuffle[i]];
+        input_index -= idx * input_block_strides[i];
+      }
+      return output_index + input_index *
+          output_block_strides[m_inverseShuffle[0]];
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = input_index / input_block_strides[i];
+        output_index += idx * output_block_strides[m_inverseShuffle[i]];
+        input_index -= idx * input_block_strides[i];
+      }
+      return output_index + input_index *
+          output_block_strides[m_inverseShuffle[NumDims - 1]];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      return inputIndex + index * m_inputStrides[0];
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      return inputIndex + index * m_inputStrides[NumDims - 1];
+    }
+  }
+
+  const Shuffle& m_shuffle;
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_inverseShuffle;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  array<Index, NumDims> m_unshuffledInputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  std::size_t m_block_total_size_max;
+};
+
+
+// Eval as lvalue
+template<typename Shuffle, typename ArgType, typename Device>
+struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device>
+    : public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base;
+
+  typedef TensorShufflingOp<Shuffle, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = (internal::packet_traits<Scalar>::size > 1),
+    BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+  };
+
+  typedef typename internal::TensorBlock<
+    Index, typename internal::remove_const<Scalar>::type, NumDims,
+    TensorEvaluator<ArgType, Device>::Layout> TensorBlock;
+  typedef typename internal::TensorBlockWriter<
+    Index, typename internal::remove_const<Scalar>::type, NumDims,
+    TensorEvaluator<ArgType, Device>::Layout, PacketAccess> TensorBlockWriter;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : Base(op, device)
+  { }
+
+  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));
+  }
+
+  template <int StoreMode> 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)
+
+    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+i) = values[i];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
+      const TensorBlock& block) {
+    eigen_assert(this->m_impl.data() != NULL);
+    TensorBlockWriter::Run(block, this->srcCoeff(block.first_coeff_index()),
+                           this->m_inverseShuffle,
+                           this->m_unshuffledInputStrides, this->m_impl.data());
+  }
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H