From 235e83aba608cf3d94b033bfbf551f8c136a3fab Mon Sep 17 00:00:00 2001
From: Rasmus Munk Larsen <rmlarsen@google.com>
Date: Thu, 14 Apr 2016 13:57:35 -0700
Subject: Eigen cost model part 1. This implements a basic recursive framework
 to estimate the cost of evaluating tensor expressions.

---
 unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

(limited to 'unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h')

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h b/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
index 1fb27a65b..893542d29 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
@@ -88,6 +88,10 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType>, Device>
   typedef TensorEvalToOp<ArgType> XprType;
   typedef typename ArgType::Scalar Scalar;
   typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef typename XprType::Index Index;
+  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
 
   enum {
     IsAligned = true,
@@ -104,10 +108,6 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType>, Device>
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~TensorEvaluator() {
   }
 
-  typedef typename XprType::Index Index;
-  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-
   EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* scalar) {
@@ -138,6 +138,13 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType>, Device>
     return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
   }
 
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // We assume that evalPacket or evalScalar is called to perform the
+    // assignment and account for the cost of the write here.
+    return m_impl.costPerCoeff(vectorized) +
+        TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
+  }
+
   EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_buffer; }
 
  private:
-- 
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