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author | Manjunath Kudlur <keveman@gmail.com> | 2015-11-06 16:27:58 -0800 |
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committer | Manjunath Kudlur <keveman@gmail.com> | 2015-11-06 16:27:58 -0800 |
commit | f41959ccb2d9d4c722fe8fc3351401d53bcf4900 (patch) | |
tree | ef0ca22cb2a5ac4bdec9d080d8e0788a53ed496d /third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h |
TensorFlow: Initial commit of TensorFlow library.
TensorFlow is an open source software library for numerical computation
using data flow graphs.
Base CL: 107276108
Diffstat (limited to 'third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h')
-rw-r--r-- | third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h | 352 |
1 files changed, 352 insertions, 0 deletions
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h new file mode 100644 index 0000000000..7e6d00fad6 --- /dev/null +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h @@ -0,0 +1,352 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H +#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H + +namespace Eigen { + +/** \class TensorBroadcasting + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor broadcasting class. + * + * + */ +namespace internal { +template<typename Broadcast, typename XprType> +struct traits<TensorBroadcastingOp<Broadcast, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename packet_traits<Scalar>::type Packet; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename Broadcast, typename XprType> +struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense> +{ + typedef const TensorBroadcastingOp<Broadcast, XprType>& type; +}; + +template<typename Broadcast, typename XprType> +struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type> +{ + typedef TensorBroadcastingOp<Broadcast, XprType> type; +}; + +} // end namespace internal + + + +template<typename Broadcast, typename XprType> +class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar; + typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Packet Packet; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename XprType::PacketReturnType PacketReturnType; + typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast) + : m_xpr(expr), m_broadcast(broadcast) {} + + EIGEN_DEVICE_FUNC + const Broadcast& broadcast() const { return m_broadcast; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const Broadcast m_broadcast; +}; + + +// Eval as rvalue +template<typename Broadcast, typename ArgType, typename Device> +struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device> +{ + typedef TensorBroadcastingOp<Broadcast, ArgType> XprType; + 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 TensorEvaluator<ArgType, Device>::Dimensions InputDimensions; + EIGEN_STATIC_ASSERT(NumDims == internal::array_size<Broadcast>::value, "Broadcast cannot change rank") + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + const Broadcast& broadcast = op.broadcast(); + for (int i = 0; i < NumDims; ++i) { + eigen_assert(input_dims[i] > 0); + m_dimensions[i] = input_dims[i] * broadcast[i]; + } + + if (NumDims > 0) { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_inputStrides[0] = 1; + m_outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; + m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; + } + } else { + // NumDims is always > 0 here, but use max to avoid compiler warning + m_inputStrides[numext::maxi(0, NumDims-1)] = 1; + m_outputStrides[numext::maxi(0, NumDims-1)] = 1; + for (int i = NumDims-2; i >= 0; --i) { + m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; + m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1]; + } + } + } + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename XprType::PacketReturnType PacketReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const + { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return coeffColMajor(index); + } else { + return coeffRowMajor(index); + } + } + + // TODO: attempt to speed this up. The integer divisions and modulo are slow + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const + { + Index inputIndex = 0; + if (NumDims > 0) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStrides[i]; + if (internal::index_statically_eq<Broadcast>()(i, 1)) { + eigen_assert(idx < m_impl.dimensions()[i]); + inputIndex += idx * m_inputStrides[i]; + } else { + if (internal::index_statically_eq<InputDimensions>()(i, 1)) { + eigen_assert(idx % m_impl.dimensions()[i] == 0); + } else { + inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; + } + } + index -= idx * m_outputStrides[i]; + } + if (internal::index_statically_eq<Broadcast>()(0, 1)) { + eigen_assert(index < m_impl.dimensions()[0]); + inputIndex += index; + } else { + if (internal::index_statically_eq<InputDimensions>()(0, 1)) { + eigen_assert(index % m_impl.dimensions()[0] == 0); + } else { + inputIndex += (index % m_impl.dimensions()[0]); + } + } + } + return m_impl.coeff(inputIndex); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const + { + Index inputIndex = 0; + if (NumDims > 0) { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStrides[i]; + if (internal::index_statically_eq<Broadcast>()(i, 1)) { + eigen_assert(idx < m_impl.dimensions()[i]); + inputIndex += idx * m_inputStrides[i]; + } else { + if (internal::index_statically_eq<InputDimensions>()(i, 1)) { + eigen_assert(idx % m_impl.dimensions()[i] == 0); + } else { + inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; + } + } + index -= idx * m_outputStrides[i]; + } + if (internal::index_statically_eq<Broadcast>()(NumDims-1, 1)) { + eigen_assert(index < m_impl.dimensions()[NumDims-1]); + inputIndex += index; + } else { + if (internal::index_statically_eq<InputDimensions>()(NumDims-1, 1)) { + eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0); + } else { + inputIndex += (index % m_impl.dimensions()[NumDims-1]); + } + } + } + return m_impl.coeff(inputIndex); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const + { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return packetColMajor<LoadMode>(index); + } else { + return packetRowMajor<LoadMode>(index); + } + } + + // Ignore the LoadMode and always use unaligned loads since we can't guarantee + // the alignment at compile time. + template<int LoadMode> + 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()); + + const Index originalIndex = index; + + Index inputIndex = 0; + Index innermostLoc = 0; + if (NumDims > 0) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStrides[i]; + if (internal::index_statically_eq<Broadcast>()(i, 1)) { + eigen_assert(idx < m_impl.dimensions()[i]); + inputIndex += idx * m_inputStrides[i]; + } else { + if (internal::index_statically_eq<InputDimensions>()(i, 1)) { + eigen_assert(idx % m_impl.dimensions()[i] == 0); + } else { + inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; + } + } + index -= idx * m_outputStrides[i]; + } + if (internal::index_statically_eq<Broadcast>()(0, 1)) { + eigen_assert(index < m_impl.dimensions()[0]); + innermostLoc = index; + } else { + if (internal::index_statically_eq<InputDimensions>()(0, 1)) { + eigen_assert(innermostLoc % m_impl.dimensions()[0] == 0); + innermostLoc = 0; + } else { + innermostLoc = index % m_impl.dimensions()[0]; + } + } + inputIndex += innermostLoc; + } + + // Todo: this could be extended to the second dimension if we're not + // broadcasting alongside the first dimension, and so on. + if (innermostLoc + packetSize <= m_impl.dimensions()[0]) { + return m_impl.template packet<Unaligned>(inputIndex); + } else { + EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize]; + values[0] = m_impl.coeff(inputIndex); + for (int i = 1; i < packetSize; ++i) { + values[i] = coeffColMajor(originalIndex+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + + template<int LoadMode> + 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()); + + const Index originalIndex = index; + + Index inputIndex = 0; + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStrides[i]; + if (internal::index_statically_eq<Broadcast>()(i, 1)) { + eigen_assert(idx < m_impl.dimensions()[i]); + inputIndex += idx * m_inputStrides[i]; + } else { + if (internal::index_statically_eq<InputDimensions>()(i, 1)) { + eigen_assert(idx % m_impl.dimensions()[i] == 0); + } else { + inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; + } + } + index -= idx * m_outputStrides[i]; + } + Index innermostLoc; + if (internal::index_statically_eq<Broadcast>()(NumDims-1, 1)) { + eigen_assert(index < m_impl.dimensions()[NumDims-1]); + innermostLoc = index; + } else { + if (internal::index_statically_eq<InputDimensions>()(NumDims-1, 1)) { + eigen_assert(innermostLoc % m_impl.dimensions()[NumDims-1] == 0); + innermostLoc = 0; + } else { + innermostLoc = index % m_impl.dimensions()[NumDims-1]; + } + } + inputIndex += innermostLoc; + + // Todo: this could be extended to the second dimension if we're not + // broadcasting alongside the first dimension, and so on. + if (innermostLoc + packetSize <= m_impl.dimensions()[NumDims-1]) { + return m_impl.template packet<Unaligned>(inputIndex); + } else { + EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize]; + values[0] = m_impl.coeff(inputIndex); + for (int i = 1; i < packetSize; ++i) { + values[i] = coeffRowMajor(originalIndex+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + Dimensions m_dimensions; + array<Index, NumDims> m_outputStrides; + array<Index, NumDims> m_inputStrides; + TensorEvaluator<ArgType, Device> m_impl; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H |