Eigen cost model part 1. This implements a basic recursive framework to estimate the cost of evaluating tensor expressions.

This commit is contained in:
Rasmus Munk Larsen
2016-04-14 13:57:35 -07:00
parent 3551dea887
commit 235e83aba6
28 changed files with 695 additions and 185 deletions

View File

@@ -81,6 +81,10 @@ struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device>
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 XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
enum {
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
@@ -123,11 +127,6 @@ struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device>
}
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
@@ -190,18 +189,30 @@ struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device>
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_STATIC_ASSERT(PacketSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
EIGEN_ALIGN_MAX 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 TensorOpCost costPerCoeff(bool vectorized) const {
const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() +
3 * TensorOpCost::MulCost<Index>() +
2 * TensorOpCost::AddCost<Index>());
const double input_size = m_impl.dimensions().TotalSize();
const double output_size = m_dimensions.TotalSize();
if (output_size == 0)
return TensorOpCost();
return m_impl.costPerCoeff(vectorized) +
TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0,
compute_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected: