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

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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
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30
unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
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@@ -85,6 +85,10 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1;
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 = false,
@@ -137,9 +141,6 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
}
}
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*/) {
@@ -183,12 +184,11 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, 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());
Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
Index indices[2] = {index, index + packetSize - 1};
Index indices[2] = {index, index + PacketSize - 1};
Index patchIndices[2] = {indices[0] / m_outputStrides[output_stride_index],
indices[1] / m_outputStrides[output_stride_index]};
Index patchOffsets[2] = {indices[0] - patchIndices[0] * m_outputStrides[output_stride_index],
@@ -229,15 +229,15 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
inputIndices[0] += (patchIndices[0] + patchOffsets[0]);
inputIndices[1] += (patchIndices[1] + patchOffsets[1]);
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
return rslt;
}
else {
EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
values[0] = m_impl.coeff(inputIndices[0]);
values[packetSize-1] = m_impl.coeff(inputIndices[1]);
for (int i = 1; i < packetSize-1; ++i) {
values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
for (int i = 1; i < PacketSize-1; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
@@ -245,6 +245,14 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
const double compute_cost = NumDims * (TensorOpCost::DivCost<Index>() +
TensorOpCost::MulCost<Index>() +
2 * TensorOpCost::AddCost<Index>());
return m_impl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected: