devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

62
unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
View File

@@ -87,6 +87,10 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<PaddingDimensions>::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 = false,
@@ -129,10 +133,6 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, 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*) {
@@ -224,21 +224,51 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
return m_impl.coeff(inputIndex);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
TensorOpCost cost = m_impl.costPerCoeff(vectorized);
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = 0; i < NumDims; ++i)
updateCostPerDimension(cost, i, i == 0);
} else {
for (int i = NumDims - 1; i >= 0; --i)
updateCostPerDimension(cost, i, i == NumDims - 1);
}
return cost;
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
private:
void updateCostPerDimension(TensorOpCost& cost, int i, bool first) const {
const double in = static_cast<double>(m_impl.dimensions()[i]);
const double out = in + m_padding[i].first + m_padding[i].second;
if (out == 0)
return;
const double reduction = in / out;
cost *= reduction;
if (first) {
cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() +
reduction * (1 * TensorOpCost::AddCost<Index>()));
} else {
cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() +
2 * TensorOpCost::MulCost<Index>() +
reduction * (2 * TensorOpCost::MulCost<Index>() +
1 * TensorOpCost::DivCost<Index>()));
}
}
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(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());
const Index initialIndex = index;
Index inputIndex = 0;
for (int i = NumDims - 1; i > 0; --i) {
const Index first = index;
const Index last = index + packetSize - 1;
const Index last = index + PacketSize - 1;
const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i];
const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i];
const Index lastPaddedRight = m_outputStrides[i+1];
@@ -263,7 +293,7 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
}
}
const Index last = index + packetSize - 1;
const Index last = index + PacketSize - 1;
const Index first = index;
const Index lastPaddedLeft = m_padding[0].first;
const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second);
@@ -288,16 +318,15 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(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());
const Index initialIndex = index;
Index inputIndex = 0;
for (int i = 0; i < NumDims - 1; ++i) {
const Index first = index;
const Index last = index + packetSize - 1;
const Index last = index + PacketSize - 1;
const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i+1];
const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i+1];
const Index lastPaddedRight = m_outputStrides[i];
@@ -322,7 +351,7 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
}
}
const Index last = index + packetSize - 1;
const Index last = index + PacketSize - 1;
const Index first = index;
const Index lastPaddedLeft = m_padding[NumDims-1].first;
const Index firstPaddedRight = (m_dimensions[NumDims-1] - m_padding[NumDims-1].second);
@@ -347,9 +376,8 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
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);