Added support for argmax/argmin

1 files changed, 288 insertions, 0 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h b/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
new file mode 100644
index 000000000..ee3bf7fe3
--- /dev/null
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
@@ -0,0 +1,288 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
+//                    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_ARG_MAX_H
+#define EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
+
+namespace Eigen {
+namespace internal {
+
+/** \class TensorIndexTuple
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor + Index Tuple class.
+  *
+  *
+  */
+template<typename XprType>
+struct traits<TensorIndexTupleOp<XprType> > : public traits<XprType>
+{
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef Tuple<Index, typename XprTraits::Scalar> Scalar;
+  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 XprType>
+struct eval<TensorIndexTupleOp<XprType>, Eigen::Dense>
+{
+  typedef const TensorIndexTupleOp<XprType>& type;
+};
+
+template<typename XprType>
+struct nested<TensorIndexTupleOp<XprType>, 1,
+              typename eval<TensorIndexTupleOp<XprType> >::type>
+{
+  typedef TensorIndexTupleOp<XprType> type;
+};
+
+}  // end namespace internal
+
+template<typename XprType>
+class TensorIndexTupleOp : public TensorBase<TensorIndexTupleOp<XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename Eigen::internal::nested<TensorIndexTupleOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Index Index;
+  typedef Tuple<Index, typename XprType::CoeffReturnType> CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIndexTupleOp(const XprType& expr)
+      : m_xpr(expr) {}
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename XprType::Nested>::type&
+  expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+};
+
+// Eval as rvalue
+template<typename ArgType, typename Device>
+struct TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device>
+{
+  typedef TensorIndexTupleOp<ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  static const int NumDims = internal::array_size<Dimensions>::value;
+
+  enum {
+    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
+    PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
+    BlockAccess = false,
+    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) { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
+    return m_impl.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 CoeffReturnType(index, m_impl.coeff(index));
+  }
+
+  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+namespace internal {
+
+/** \class TensorTupleIndex
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Converts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index>.
+  *
+  */
+template<typename ReduceOp, typename Dims, typename XprType>
+struct traits<TensorTupleReducerOp<ReduceOp, Dims, XprType> > : public traits<XprType>
+{
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef Index Scalar;
+  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 ReduceOp, typename Dims, typename XprType>
+struct eval<TensorTupleReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense>
+{
+  typedef const TensorTupleReducerOp<ReduceOp, Dims, XprType>& type;
+};
+
+template<typename ReduceOp, typename Dims, typename XprType>
+struct nested<TensorTupleReducerOp<ReduceOp, Dims, XprType>, 1,
+              typename eval<TensorTupleReducerOp<ReduceOp, Dims, XprType> >::type>
+{
+  typedef TensorTupleReducerOp<ReduceOp, Dims, XprType> type;
+};
+
+}  // end namespace internal
+
+template<typename ReduceOp, typename Dims, typename XprType>
+class TensorTupleReducerOp : public TensorBase<TensorTupleReducerOp<ReduceOp, Dims, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename Eigen::internal::nested<TensorTupleReducerOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Index Index;
+  typedef Index CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTupleReducerOp(const XprType& expr,
+                                                          const ReduceOp& reduce_op,
+                                                          const int return_dim,
+                                                          const Dims& reduce_dims)
+      : m_xpr(expr), m_reduce_op(reduce_op), m_return_dim(return_dim), m_reduce_dims(reduce_dims) {}
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename XprType::Nested>::type&
+  expression() const { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC
+  const ReduceOp& reduce_op() const { return m_reduce_op; }
+
+  EIGEN_DEVICE_FUNC
+  const Dims& reduce_dims() const { return m_reduce_dims; }
+
+  EIGEN_DEVICE_FUNC
+  int return_dim() const { return m_return_dim; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const ReduceOp m_reduce_op;
+    const int m_return_dim;
+    const Dims m_reduce_dims;
+};
+
+// Eval as rvalue
+template<typename ReduceOp, typename Dims, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorTupleReducerOp<ReduceOp, Dims, ArgType>, Device>
+{
+  typedef TensorTupleReducerOp<ReduceOp, Dims, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename TensorIndexTupleOp<ArgType>::CoeffReturnType TupleType;
+  typedef typename TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Dimensions Dimensions;
+  typedef typename TensorEvaluator<const TensorIndexTupleOp<ArgType> , Device>::Dimensions InputDimensions;
+  static const int NumDims = internal::array_size<InputDimensions>::value;
+  typedef array<Index, NumDims> StrideDims;
+
+  enum {
+    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
+    PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
+    BlockAccess = false,
+    Layout = TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_orig_impl(op.expression(), device),
+        m_impl(op.expression().index_tuples().reduce(op.reduce_dims(), op.reduce_op()), device),
+        m_return_dim(op.return_dim()),
+        m_strides(gen_strides(m_orig_impl.dimensions())),
+        m_stride_mod(gen_stride_mod(m_orig_impl.dimensions())),
+        m_stride_div(gen_stride_div()) { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
+    return m_impl.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 {
+    const TupleType v = m_impl.coeff(index);
+    return (m_return_dim < 0) ? v.first : (v.first % m_stride_mod) / m_stride_div;
+  }
+
+  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
+
+ private:
+  EIGEN_DEVICE_FUNC StrideDims gen_strides(const InputDimensions& dims) {
+    StrideDims strides;
+    if (m_return_dim < 0) return strides;  // Won't be using these.
+    eigen_assert(m_return_dim < NumDims &&
+                 "Asking to convert index to a dimension outside of the rank");
+
+    // Calculate m_stride_div and m_stride_mod, which are used to
+    // calculate the value of an index w.r.t. the m_return_dim.
+    if (Layout == static_cast<int>(ColMajor)) {
+      strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        strides[i] = strides[i-1] * dims[i-1];
+      }
+    } else {
+      strides[NumDims-1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        strides[i] = strides[i+1] * dims[i+1];
+      }
+    }
+    return strides;
+  }
+
+  EIGEN_DEVICE_FUNC Index gen_stride_mod(const InputDimensions& dims) {
+    if (Layout == static_cast<int>(ColMajor)) {
+      return (m_return_dim < NumDims - 1) ? m_strides[m_return_dim + 1] : dims.TotalSize();
+    } else {
+      return (m_return_dim > 0) ? m_strides[m_return_dim - 1] : dims.TotalSize();
+    }
+  }
+
+  EIGEN_DEVICE_FUNC Index gen_stride_div() {
+    return m_strides[m_return_dim];
+  }
+
+ protected:
+  TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device> m_orig_impl;
+  TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device> m_impl;
+  const int m_return_dim;
+  const StrideDims m_strides;
+  const Index m_stride_mod;
+  const Index m_stride_div;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H