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Vectorized the evaluation of expressions involving tensor slices.

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This commit is contained in:
Benoit Steiner 2014-07-10 11:09:46 -07:00
parent 25b2f6624d
commit ffd3654f67
1 changed files with 98 additions and 10 deletions
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108
unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
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@ -273,8 +273,10 @@ struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Devi
static const int NumDims = internal::array_size<Sizes>::value;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/false,
// Alignment can't be guaranteed at compile time since it depends on the
// slice offsets and sizes.
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
@ -329,11 +331,40 @@ struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Devi
return m_impl.coeff(inputIndex);
}
/* template<int LoadMode>
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_impl.template packet<LoadMode>(index);
}*/
static 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());
Index inputIndices[] = {0, 0};
Index indices[] = {index, index + packetSize - 1};
for (int i = NumDims - 1; i > 0; --i) {
const Index idx0 = indices[0] / m_outputStrides[i];
const Index idx1 = indices[1] / m_outputStrides[i];
inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
indices[0] -= idx0 * m_outputStrides[i];
indices[1] -= idx1 * m_outputStrides[i];
}
inputIndices[0] += (indices[0] + m_offsets[0]);
inputIndices[1] += (indices[1] + m_offsets[0]);
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
return rslt;
}
else {
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[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
private:
Dimensions m_dimensions;
@ -353,8 +384,8 @@ struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
static const int NumDims = internal::array_size<Sizes>::value;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/false,
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
@ -409,11 +440,38 @@ struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
return m_impl.coeff(inputIndex);
}
/* template<int LoadMode>
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_impl.template packet<LoadMode>(index);
}*/
static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
Index inputIndices[] = {0, 0};
Index indices[] = {index, index + packetSize - 1};
for (int i = NumDims - 1; i > 0; --i) {
const Index idx0 = indices[0] / m_outputStrides[i];
const Index idx1 = indices[1] / m_outputStrides[i];
inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
indices[0] -= idx0 * m_outputStrides[i];
indices[1] -= idx1 * m_outputStrides[i];
}
inputIndices[0] += (indices[0] + m_offsets[0]);
inputIndices[1] += (indices[1] + m_offsets[0]);
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
PacketReturnType rslt = m_impl.template packet<LoadMode>(inputIndices[0]);
return rslt;
}
else {
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[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
{
@ -427,6 +485,36 @@ struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
return m_impl.coeffRef(inputIndex);
}
template <int StoreMode> EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
Index inputIndices[] = {0, 0};
Index indices[] = {index, index + packetSize - 1};
for (int i = NumDims - 1; i > 0; --i) {
const Index idx0 = indices[0] / m_outputStrides[i];
const Index idx1 = indices[1] / m_outputStrides[i];
inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
indices[0] -= idx0 * m_outputStrides[i];
indices[1] -= idx1 * m_outputStrides[i];
}
inputIndices[0] += (indices[0] + m_offsets[0]);
inputIndices[1] += (indices[1] + m_offsets[0]);
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
m_impl.template writePacket<StoreMode>(inputIndices[0], x);
}
else {
CoeffReturnType values[packetSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
m_impl.coeffRef(inputIndices[0]) = values[0];
m_impl.coeffRef(inputIndices[1]) = values[packetSize-1];
for (int i = 1; i < packetSize-1; ++i) {
coeffRef(index+i) = values[i];
}
}
}
private:
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;