diff options
Diffstat (limited to 'unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h')
| -rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h | 325 |
1 files changed, 266 insertions, 59 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h b/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h index 113c060e3..e5e5efdee 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h @@ -31,6 +31,7 @@ struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType> typedef typename remove_reference<Nested>::type _Nested; static const int NumDimensions = XprTraits::NumDimensions; static const int Layout = XprTraits::Layout; + typedef typename XprTraits::PointerType PointerType; }; template<typename Shuffle, typename XprType> @@ -53,15 +54,16 @@ template<typename Shuffle, typename XprType> class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> > { public: - typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar; - typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested; - typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind; - typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index; + typedef TensorBase<TensorShufflingOp<Shuffle, XprType> > Base; + typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shuffle) - : m_xpr(expr), m_shuffle(shuffle) {} + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shfl) + : m_xpr(expr), m_shuffle(shfl) {} EIGEN_DEVICE_FUNC const Shuffle& shufflePermutation() const { return m_shuffle; } @@ -70,24 +72,8 @@ class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; } - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const TensorShufflingOp& other) - { - typedef TensorAssignOp<TensorShufflingOp, const TensorShufflingOp> Assign; - Assign assign(*this, other); - internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); - return *this; - } + EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorShufflingOp) - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const OtherDerived& other) - { - typedef TensorAssignOp<TensorShufflingOp, const OtherDerived> Assign; - Assign assign(*this, other); - internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); - return *this; - } protected: typename XprType::Nested m_xpr; @@ -99,6 +85,7 @@ class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> template<typename Shuffle, typename ArgType, typename Device> struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> { + typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Self; typedef TensorShufflingOp<Shuffle, ArgType> XprType; typedef typename XprType::Index Index; static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; @@ -106,100 +93,246 @@ struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> 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; + static const int PacketSize = PacketType<CoeffReturnType, Device>::size; + typedef StorageMemory<CoeffReturnType, Device> Storage; + typedef typename Storage::Type EvaluatorPointerType; enum { - IsAligned = false, - PacketAccess = (internal::packet_traits<Scalar>::size > 1), - Layout = TensorEvaluator<ArgType, Device>::Layout, - CoordAccess = false, // to be implemented - RawAccess = false + IsAligned = false, + PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1), + BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess, + PreferBlockAccess = true, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false }; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) - : m_impl(op.expression(), device) + typedef typename internal::remove_const<Scalar>::type ScalarNoConst; + + //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// + typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc; + typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch; + + typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, + Layout, Index> + TensorBlock; + //===--------------------------------------------------------------------===// + + EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_device(device), + m_impl(op.expression(), device) { const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); const Shuffle& shuffle = op.shufflePermutation(); + m_is_identity = true; for (int i = 0; i < NumDims; ++i) { + m_shuffle[i] = static_cast<int>(shuffle[i]); m_dimensions[i] = input_dims[shuffle[i]]; + m_inverseShuffle[shuffle[i]] = i; + if (m_is_identity && shuffle[i] != i) { + m_is_identity = false; + } } - array<Index, NumDims> inputStrides; - if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { - inputStrides[0] = 1; + m_unshuffledInputStrides[0] = 1; m_outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { - inputStrides[i] = inputStrides[i - 1] * input_dims[i - 1]; + m_unshuffledInputStrides[i] = + m_unshuffledInputStrides[i - 1] * input_dims[i - 1]; m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1]; + m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>( + m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1)); } } else { - inputStrides[NumDims - 1] = 1; + m_unshuffledInputStrides[NumDims - 1] = 1; m_outputStrides[NumDims - 1] = 1; for (int i = NumDims - 2; i >= 0; --i) { - inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1]; + m_unshuffledInputStrides[i] = + m_unshuffledInputStrides[i + 1] * input_dims[i + 1]; m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1]; + m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>( + m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1)); } } for (int i = 0; i < NumDims; ++i) { - m_inputStrides[i] = inputStrides[shuffle[i]]; + m_inputStrides[i] = m_unshuffledInputStrides[shuffle[i]]; } } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { m_impl.evalSubExprsIfNeeded(NULL); return true; } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + +#ifdef EIGEN_USE_THREADS + template <typename EvalSubExprsCallback> + EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync( + EvaluatorPointerType, EvalSubExprsCallback done) { + m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); }); + } +#endif // EIGEN_USE_THREADS + + EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { - return m_impl.coeff(srcCoeff(index)); + if (m_is_identity) { + return m_impl.coeff(index); + } else { + return m_impl.coeff(srcCoeff(index)); + } } + template <int LoadMode, typename Self, bool ImplPacketAccess> + struct PacketLoader { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + static PacketReturnType Run(const Self& self, Index index) { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + EIGEN_UNROLL_LOOP + for (int i = 0; i < PacketSize; ++i) { + values[i] = self.coeff(index + i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + }; + + template<int LoadMode, typename Self> + struct PacketLoader<LoadMode, Self, true> { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + static PacketReturnType Run(const Self& self, Index index) { + if (self.m_is_identity) { + return self.m_impl.template packet<LoadMode>(index); + } else { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + EIGEN_UNROLL_LOOP + for (int i = 0; i < PacketSize; ++i) { + values[i] = self.coeff(index + i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + }; + template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const { EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) - eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + eigen_assert(index + PacketSize - 1 < dimensions().TotalSize()); + return PacketLoader<LoadMode, Self, TensorEvaluator<ArgType, Device>::PacketAccess>::Run(*this, index); + } - EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; - for (int i = 0; i < PacketSize; ++i) { - values[i] = coeff(index+i); + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + internal::TensorBlockResourceRequirements getResourceRequirements() const { + static const int inner_dim = + Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1; + + const size_t target_size = m_device.firstLevelCacheSize(); + const bool inner_dim_shuffled = m_shuffle[inner_dim] != inner_dim; + + // Shuffled inner dimensions leads to a random memory access, which is not + // captured by default cost model bytes loaded/stored. We add this cost + // explicitly. The number of cycles picked based on the benchmarks. + // TODO(ezhulenev): This number was picked based on a very questionable + // benchmarks, add benchmarks that are representative of real workloads. + using BlockRequirements = internal::TensorBlockResourceRequirements; + if (inner_dim_shuffled) { + return BlockRequirements::uniform<Scalar>(target_size) + .addCostPerCoeff({0, 0, NumDims * 28}); + } else { + return BlockRequirements::skewed<Scalar>(target_size); } - PacketReturnType rslt = internal::pload<PacketReturnType>(values); - return rslt; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock + block(TensorBlockDesc& desc, TensorBlockScratch& scratch, + bool root_of_expr_ast = false) const { + assert(m_impl.data() != NULL); + + typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout> + TensorBlockIO; + typedef typename TensorBlockIO::Dst TensorBlockIODst; + typedef typename TensorBlockIO::Src TensorBlockIOSrc; + + const typename TensorBlock::Storage block_storage = + TensorBlock::prepareStorage( + desc, scratch, /*allow_strided_storage=*/root_of_expr_ast); + + typename TensorBlockIO::Dimensions input_strides(m_unshuffledInputStrides); + TensorBlockIOSrc src(input_strides, m_impl.data(), srcCoeff(desc.offset())); + + TensorBlockIODst dst(block_storage.dimensions(), block_storage.strides(), + block_storage.data()); + + typename TensorBlockIO::DimensionsMap dst_to_src_dim_map(m_shuffle); + TensorBlockIO::Copy(dst, src, dst_to_src_dim_map); + + return block_storage.AsTensorMaterializedBlock(); } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { - const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + + const double compute_cost = m_is_identity ? TensorOpCost::AddCost<Index>() : + NumDims * (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>()); return m_impl.costPerCoeff(vectorized) + - TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize); + TensorOpCost(0, 0, compute_cost, m_is_identity /* vectorized */, PacketSize); } - EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; } +#ifdef EIGEN_USE_SYCL + // binding placeholder accessors to a command group handler for SYCL + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const { + m_impl.bind(cgh); + } +#endif protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index GetBlockOutputIndex( + Index input_index, + const DSizes<Index, NumDims>& input_block_strides, + const DSizes<Index, NumDims>& output_block_strides, + const DSizes<internal::TensorIntDivisor<Index>, NumDims>& fast_input_block_strides) const { + Index output_index = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = input_index / fast_input_block_strides[i]; + output_index += idx * output_block_strides[m_inverseShuffle[i]]; + input_index -= idx * input_block_strides[i]; + } + return output_index + input_index * + output_block_strides[m_inverseShuffle[0]]; + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = input_index / fast_input_block_strides[i]; + output_index += idx * output_block_strides[m_inverseShuffle[i]]; + input_index -= idx * input_block_strides[i]; + } + return output_index + input_index * + output_block_strides[m_inverseShuffle[NumDims - 1]]; + } + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const { Index inputIndex = 0; if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { for (int i = NumDims - 1; i > 0; --i) { - const Index idx = index / m_outputStrides[i]; + const Index idx = index / m_fastOutputStrides[i]; inputIndex += idx * m_inputStrides[i]; index -= idx * m_outputStrides[i]; } return inputIndex + index * m_inputStrides[0]; } else { for (int i = 0; i < NumDims - 1; ++i) { - const Index idx = index / m_outputStrides[i]; + const Index idx = index / m_fastOutputStrides[i]; inputIndex += idx * m_inputStrides[i]; index -= idx * m_outputStrides[i]; } @@ -208,8 +341,15 @@ struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> } Dimensions m_dimensions; + bool m_is_identity; + array<int, NumDims> m_shuffle; + array<Index, NumDims> m_inverseShuffle; // TODO(ezhulenev): Make it int type. array<Index, NumDims> m_outputStrides; + array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides; array<Index, NumDims> m_inputStrides; + array<Index, NumDims> m_unshuffledInputStrides; + + const Device EIGEN_DEVICE_REF m_device; TensorEvaluator<ArgType, Device> m_impl; }; @@ -228,15 +368,24 @@ struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device> 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; + static const int PacketSize = PacketType<CoeffReturnType, Device>::size; enum { - IsAligned = false, - PacketAccess = (internal::packet_traits<Scalar>::size > 1), - RawAccess = false + IsAligned = false, + PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1), + BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess, + PreferBlockAccess = true, + Layout = TensorEvaluator<ArgType, Device>::Layout, + RawAccess = false }; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + typedef typename internal::remove_const<Scalar>::type ScalarNoConst; + + //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// + typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc; + //===--------------------------------------------------------------------===// + + EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) { } @@ -252,10 +401,68 @@ struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device> EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; internal::pstore<CoeffReturnType, PacketReturnType>(values, x); + EIGEN_UNROLL_LOOP for (int i = 0; i < PacketSize; ++i) { this->coeffRef(index+i) = values[i]; } } + + template <typename TensorBlock> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock( + const TensorBlockDesc& desc, const TensorBlock& block) { + eigen_assert(this->m_impl.data() != NULL); + + typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout> + TensorBlockIO; + typedef typename TensorBlockIO::Dst TensorBlockIODst; + typedef typename TensorBlockIO::Src TensorBlockIOSrc; + + const Scalar* block_buffer = block.data(); + + // TODO(ezhulenev): TensorBlockIO should be able to read from any Eigen + // expression with coefficient and packet access as `src`. + void* mem = NULL; + if (block_buffer == NULL) { + mem = this->m_device.allocate(desc.size() * sizeof(Scalar)); + ScalarNoConst* buf = static_cast<ScalarNoConst*>(mem); + + typedef internal::TensorBlockAssignment< + ScalarNoConst, NumDims, typename TensorBlock::XprType, Index> + TensorBlockAssignment; + + TensorBlockAssignment::Run( + TensorBlockAssignment::target( + desc.dimensions(), internal::strides<Layout>(desc.dimensions()), + buf), + block.expr()); + + block_buffer = buf; + } + + // Read from block. + TensorBlockIOSrc src(internal::strides<Layout>(desc.dimensions()), + block_buffer); + + // Write to the output buffer. + typename TensorBlockIO::Dimensions output_strides( + this->m_unshuffledInputStrides); + typename TensorBlockIO::Dimensions output_dimensions; + for (int i = 0; i < NumDims; ++i) { + output_dimensions[this->m_shuffle[i]] = desc.dimension(i); + } + TensorBlockIODst dst(output_dimensions, output_strides, this->m_impl.data(), + this->srcCoeff(desc.offset())); + + // Reorder dimensions according to the shuffle. + typename TensorBlockIO::DimensionsMap dst_to_src_dim_map; + for (int i = 0; i < NumDims; ++i) { + dst_to_src_dim_map[i] = static_cast<int>(this->m_inverseShuffle[i]); + } + TensorBlockIO::Copy(dst, src, dst_to_src_dim_map); + + // Deallocate temporary buffer used for the block materialization. + if (mem != NULL) this->m_device.deallocate(mem); + } }; |