diff options
author | Mehdi Goli <mehdi.goli@codeplay.com> | 2018-08-16 00:07:02 +0100 |
---|---|---|
committer | Mehdi Goli <mehdi.goli@codeplay.com> | 2018-08-16 00:07:02 +0100 |
commit | 161dcbae9bceb337ed7046a5105b3ea715b9601b (patch) | |
tree | 66b95279de3707e85ef1fb0a7df0d2c0055638a2 /unsupported | |
parent | a97aaa2bcf3ff27ddb62a919d52d570d8fbb82da (diff) | |
parent | f197c3f55b3a04ab24dfee8057b1d510c7483fc3 (diff) |
Using PointerType struct and specializing it per device for TensorCustomOp.h
Diffstat (limited to 'unsupported')
-rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h | 86 | ||||
-rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h | 10 | ||||
-rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h | 90 | ||||
-rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h | 8 | ||||
-rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h | 12 | ||||
-rw-r--r-- | unsupported/test/cxx11_tensor_block_access.cpp | 135 | ||||
-rw-r--r-- | unsupported/test/cxx11_tensor_contraction.cpp | 6 | ||||
-rw-r--r-- | unsupported/test/cxx11_tensor_convolution.cpp | 2 | ||||
-rw-r--r-- | unsupported/test/cxx11_tensor_index_list.cpp | 1 | ||||
-rw-r--r-- | unsupported/test/cxx11_tensor_thread_pool.cpp | 2 | ||||
-rw-r--r-- | unsupported/test/kronecker_product.cpp | 24 |
11 files changed, 169 insertions, 207 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h index 21a6b66e8..24a6343e8 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h @@ -73,7 +73,7 @@ struct TensorOpResourceRequirements { // expression tree (like reductions) to communicate resources // requirements based on local state (like the total number of reductions // to be computed). - TensorOpResourceRequirements(internal::TensorBlockShapeType shape, + TensorOpResourceRequirements(TensorBlockShapeType shape, const Index size) : block_shape(shape), block_total_size(size) {} }; @@ -90,9 +90,9 @@ EIGEN_STRONG_INLINE void MergeResourceRequirements( *block_shape = resources[0].block_shape; *block_total_size = resources[0].block_total_size; for (std::vector<TensorOpResourceRequirements>::size_type i = 1; i < resources.size(); ++i) { - if (resources[i].block_shape == TensorBlockShapeType::kSkewedInnerDims && - *block_shape != TensorBlockShapeType::kSkewedInnerDims) { - *block_shape = TensorBlockShapeType::kSkewedInnerDims; + if (resources[i].block_shape == kSkewedInnerDims && + *block_shape != kSkewedInnerDims) { + *block_shape = kSkewedInnerDims; } *block_total_size = numext::maxi(*block_total_size, resources[i].block_total_size); @@ -152,11 +152,11 @@ struct TensorBlockCopyOp { const Scalar* src_base = &src_data[src_index]; Scalar* dst_base = &dst_data[dst_index]; - typedef const Eigen::Array<Scalar, Dynamic, 1> Src; - typedef Eigen::Array<Scalar, Dynamic, 1> Dst; + typedef const Array<Scalar, Dynamic, 1> Src; + typedef Array<Scalar, Dynamic, 1> Dst; - typedef Eigen::Map<Src, 0, InnerStride<> > SrcMap; - typedef Eigen::Map<Dst, 0, InnerStride<> > DstMap; + typedef Map<Src, 0, InnerStride<> > SrcMap; + typedef Map<Dst, 0, InnerStride<> > DstMap; const SrcMap src(src_base, num_coeff_to_copy, InnerStride<>(src_stride)); DstMap dst(dst_base, num_coeff_to_copy, InnerStride<>(dst_stride)); @@ -178,10 +178,8 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout, bool BlockRead> class TensorBlockIO { public: - typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout> - TensorBlock; - typedef typename internal::TensorBlockCopyOp<Scalar, StorageIndex> - TensorBlockCopyOp; + typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block; + typedef TensorBlockCopyOp<Scalar, StorageIndex> BlockCopyOp; protected: struct BlockIteratorState { @@ -194,7 +192,7 @@ class TensorBlockIO { }; static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Copy( - const TensorBlock& block, StorageIndex first_coeff_index, + const Block& block, StorageIndex first_coeff_index, const array<StorageIndex, NumDims>& tensor_to_block_dim_map, const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data, Scalar* dst_data) { @@ -290,8 +288,8 @@ class TensorBlockIO { const StorageIndex block_total_size = NumDims == 0 ? 1 : block.block_sizes().TotalSize(); for (StorageIndex i = 0; i < block_total_size; i += block_inner_dim_size) { - TensorBlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride, - dst_data, inputIndex, input_stride, src_data); + BlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride, + dst_data, inputIndex, input_stride, src_data); // Update index. for (int j = 0; j < num_squeezed_dims; ++j) { if (++block_iter_state[j].count < block_iter_state[j].size) { @@ -320,13 +318,11 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout> class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true> { public: - typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout> - TensorBlock; - typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true> - Base; + typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block; + typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true> Base; static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run( - TensorBlock* block, const Scalar* src_data) { + Block* block, const Scalar* src_data) { array<StorageIndex, NumDims> tensor_to_block_dim_map; for (int i = 0; i < NumDims; ++i) { tensor_to_block_dim_map[i] = i; @@ -336,7 +332,7 @@ class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims, } static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run( - TensorBlock* block, StorageIndex first_coeff_index, + Block* block, StorageIndex first_coeff_index, const array<StorageIndex, NumDims>& tensor_to_block_dim_map, const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data) { Base::Copy(*block, first_coeff_index, tensor_to_block_dim_map, @@ -357,13 +353,11 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout> class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false> { public: - typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout> - TensorBlock; - typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false> - Base; + typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block; + typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false> Base; static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run( - const TensorBlock& block, Scalar* dst_data) { + const Block& block, Scalar* dst_data) { array<StorageIndex, NumDims> tensor_to_block_dim_map; for (int i = 0; i < NumDims; ++i) { tensor_to_block_dim_map[i] = i; @@ -373,7 +367,7 @@ class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims, } static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run( - const TensorBlock& block, StorageIndex first_coeff_index, + const Block& block, StorageIndex first_coeff_index, const array<StorageIndex, NumDims>& tensor_to_block_dim_map, const array<StorageIndex, NumDims>& tensor_strides, Scalar* dst_data) { Base::Copy(block, first_coeff_index, tensor_to_block_dim_map, @@ -401,13 +395,13 @@ struct TensorBlockCwiseBinaryOp { const StorageIndex left_stride, const LeftScalar* left_data, const StorageIndex right_index, const StorageIndex right_stride, const RightScalar* right_data) { - typedef const Eigen::Array<LeftScalar, Dynamic, 1> Lhs; - typedef const Eigen::Array<RightScalar, Dynamic, 1> Rhs; - typedef Eigen::Array<OutputScalar, Dynamic, 1> Out; + typedef const Array<LeftScalar, Dynamic, 1> Lhs; + typedef const Array<RightScalar, Dynamic, 1> Rhs; + typedef Array<OutputScalar, Dynamic, 1> Out; - typedef Eigen::Map<Lhs, 0, InnerStride<> > LhsMap; - typedef Eigen::Map<Rhs, 0, InnerStride<> > RhsMap; - typedef Eigen::Map<Out, 0, InnerStride<> > OutMap; + typedef Map<Lhs, 0, InnerStride<> > LhsMap; + typedef Map<Rhs, 0, InnerStride<> > RhsMap; + typedef Map<Out, 0, InnerStride<> > OutMap; const LeftScalar* lhs_base = &left_data[left_index]; const RightScalar* rhs_base = &right_data[right_index]; @@ -417,8 +411,7 @@ struct TensorBlockCwiseBinaryOp { const RhsMap rhs(rhs_base, num_coeff, InnerStride<>(right_stride)); OutMap out(out_base, num_coeff, InnerStride<>(output_stride)); - out = - Eigen::CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor); + out = CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor); } }; @@ -434,8 +427,7 @@ struct TensorBlockCwiseBinaryOp { template <typename BinaryFunctor, typename StorageIndex, typename OutputScalar, int NumDims, int Layout> struct TensorBlockCwiseBinaryIO { - typedef typename internal::TensorBlock<OutputScalar, StorageIndex, NumDims, - Layout>::Dimensions Dimensions; + typedef typename TensorBlock<OutputScalar, StorageIndex, NumDims, Layout>::Dimensions Dimensions; struct BlockIteratorState { StorageIndex output_stride, output_span; @@ -627,8 +619,7 @@ struct TensorBlockView { template <typename Scalar, typename StorageIndex, int NumDims, int Layout> class TensorBlockMapper { public: - typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout> - TensorBlock; + typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block; typedef DSizes<StorageIndex, NumDims> Dimensions; TensorBlockMapper(const Dimensions& dims, @@ -663,7 +654,7 @@ class TensorBlockMapper { } } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block GetBlockForIndex(StorageIndex block_index, Scalar* data) const { StorageIndex first_coeff_index = 0; DSizes<StorageIndex, NumDims> coords; @@ -711,8 +702,7 @@ class TensorBlockMapper { } } - return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides, - data); + return Block(first_coeff_index, sizes, strides, m_tensor_strides, data); } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const { @@ -742,7 +732,7 @@ class TensorBlockMapper { block_dim_sizes[i] = 1; } } else if (block_dim_sizes.TotalSize() > min_target_size) { - if (block_shape == TensorBlockShapeType::kUniformAllDims) { + if (block_shape == kUniformAllDims) { // Tensor will not fit within 'min_target_size' budget: calculate tensor // block dimension sizes based on "square" dimension size target. const size_t dim_size_target = static_cast<const size_t>( @@ -773,7 +763,7 @@ class TensorBlockMapper { total_size = total_size_other_dims * block_dim_sizes[dim]; } } - } else if (block_shape == TensorBlockShapeType::kSkewedInnerDims) { + } else if (block_shape == kSkewedInnerDims) { StorageIndex coeff_to_allocate = min_target_size; for (int i = 0; i < NumDims; ++i) { const int dim = cond<Layout>()(i, NumDims - i - 1); @@ -818,8 +808,7 @@ class TensorBlockMapper { template <typename Scalar, typename StorageIndex, int NumDims, int Layout> class TensorSliceBlockMapper { public: - typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout> - TensorBlock; + typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block; typedef DSizes<StorageIndex, NumDims> Dimensions; TensorSliceBlockMapper(const Dimensions& tensor_dims, @@ -860,7 +849,7 @@ class TensorSliceBlockMapper { } } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block GetBlockForIndex(StorageIndex block_index, Scalar* data) const { StorageIndex first_coeff_index = 0; DSizes<StorageIndex, NumDims> coords; @@ -917,8 +906,7 @@ class TensorSliceBlockMapper { } } - return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides, - data); + return Block(first_coeff_index, sizes, strides, m_tensor_strides, data); } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const { diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h b/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h index e604456e8..5d619efd8 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h @@ -152,13 +152,7 @@ struct TensorContractionParams { // 1. Elementwise Relu transformation following Conv2D. // 2. AddBias to the Conv2D output channels dimension. // -// See expected implementation in NoOpOutputKernel. -struct OutputKernel { - template <typename Index, typename Scalar> - using OutputMapper = internal::blas_data_mapper<Scalar, Index, ColMajor>; -}; - -// Output kernel that does absolutely nothing. +// The NoOpOutputKernel implements an output kernel that does absolutely nothing. struct NoOpOutputKernel { /** * Tensor contraction evaluator calls this kernel after finishing each block @@ -177,7 +171,7 @@ struct NoOpOutputKernel { */ template <typename Index, typename Scalar> EIGEN_ALWAYS_INLINE void operator()( - const OutputKernel::OutputMapper<Index, Scalar>& /*output_mapper*/, + const internal::blas_data_mapper<Scalar, Index, ColMajor>& /*output_mapper*/, const TensorContractionParams& /*params*/, Index /*i*/, Index /*j*/, Index /*num_rows*/, Index /*num_cols*/) const {} }; diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h b/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h index 39410e63d..ab5990c14 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h @@ -20,8 +20,8 @@ namespace Eigen { * */ namespace internal { -template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_> -struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> > +template<typename CustomUnaryFunc, typename XprType> +struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType> > { typedef typename XprType::Scalar Scalar; typedef typename XprType::StorageKind StorageKind; @@ -31,34 +31,26 @@ struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> > static const int NumDimensions = traits<XprType>::NumDimensions; static const int Layout = traits<XprType>::Layout; - template <class T> struct MakePointer { - // Intermediate typedef to workaround MSVC issue. - typedef MakePointer_<T> MakePointerT; - typedef typename MakePointerT::Type Type; - typedef typename MakePointerT::RefType RefType; - typedef typename MakePointerT::ScalarType ScalarType; - }; - typedef typename MakePointer<typename internal::remove_const<typename XprType::CoeffReturnType>::type>::Type PointerType; }; -template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_> -struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, Eigen::Dense> +template<typename CustomUnaryFunc, typename XprType> +struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense> { - typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>& type; + typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType>& type; }; -template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_> -struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> > +template<typename CustomUnaryFunc, typename XprType> +struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType> > { - typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> type; + typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type; }; } // end namespace internal -template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_> -class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, ReadOnlyAccessors> +template<typename CustomUnaryFunc, typename XprType> +class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors> { public: typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar; @@ -85,10 +77,10 @@ class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFun // Eval as rvalue -template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_, typename Device> -struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, Device> +template<typename CustomUnaryFunc, typename XprType, typename Device> +struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device> { - typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> ArgType; + typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType; typedef typename internal::traits<ArgType>::Index Index; static const int NumDims = internal::traits<ArgType>::NumDimensions; typedef DSizes<Index, NumDims> Dimensions; @@ -96,7 +88,7 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; static const int PacketSize = PacketType<CoeffReturnType, Device>::size; - typedef typename Eigen::internal::traits<ArgType>::PointerType PointerType; + typedef typename PointerType<CoeffReturnType, Device>::Type PointerT; enum { IsAligned = false, @@ -115,12 +107,12 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerType data) { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerT data) { if (data) { evalTo(data); return false; } else { - m_result = static_cast<PointerType>( + m_result = static_cast<PointerT>( m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar))); evalTo(m_result); return true; @@ -148,14 +140,14 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize); } - EIGEN_DEVICE_FUNC PointerType data() const { return m_result; } + EIGEN_DEVICE_FUNC PointerT data() const { return m_result; } #ifdef EIGEN_USE_SYCL EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { return m_device; } #endif protected: - EIGEN_DEVICE_FUNC void evalTo(PointerType data) { + EIGEN_DEVICE_FUNC void evalTo(PointerT data) { TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(data, m_dimensions); m_op.func().eval(m_op.expression(), result, m_device); } @@ -163,7 +155,7 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP Dimensions m_dimensions; const ArgType m_op; const Device& m_device; - PointerType m_result; + PointerT m_result; }; @@ -176,8 +168,8 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP * */ namespace internal { -template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_> -struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_> > +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> +struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> > { typedef typename internal::promote_storage_type<typename LhsXprType::Scalar, typename RhsXprType::Scalar>::ret Scalar; @@ -194,34 +186,26 @@ struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, Mak static const int NumDimensions = traits<LhsXprType>::NumDimensions; static const int Layout = traits<LhsXprType>::Layout; - template <class T> struct MakePointer { - // Intermediate typedef to workaround MSVC issue. - typedef MakePointer_<T> MakePointerT; - typedef typename MakePointerT::Type Type; - typedef typename MakePointerT::RefType RefType; - typedef typename MakePointerT::ScalarType ScalarType; - }; - typedef typename MakePointer<CoeffReturnType>::Type PointerType; }; -template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_> -struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_>, Eigen::Dense> +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> +struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Eigen::Dense> { typedef const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>& type; }; -template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_> -struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_> > +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> +struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> > { - typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_> type; + typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> type; }; } // end namespace internal -template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType,template <class> class MakePointer_> -class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_>, ReadOnlyAccessors> +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> +class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, ReadOnlyAccessors> { public: typedef typename internal::traits<TensorCustomBinaryOp>::Scalar Scalar; @@ -254,10 +238,10 @@ class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinary // Eval as rvalue -template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_, typename Device> -struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_>, Device> +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, typename Device> +struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Device> { - typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType, MakePointer_> XprType; + typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> XprType; typedef typename internal::traits<XprType>::Index Index; static const int NumDims = internal::traits<XprType>::NumDimensions; typedef DSizes<Index, NumDims> Dimensions; @@ -265,7 +249,7 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; static const int PacketSize = PacketType<CoeffReturnType, Device>::size; - typedef typename Eigen::internal::traits<XprType>::PointerType PointerType; + typedef typename PointerType<CoeffReturnType, Device>::Type PointerT; enum { IsAligned = false, @@ -284,12 +268,12 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerType data) { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerT data) { if (data) { evalTo(data); return false; } else { - m_result = static_cast<PointerType>(m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType))); + m_result = static_cast<PointerT>(m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType))); evalTo(m_result); return true; } @@ -316,14 +300,14 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize); } - EIGEN_DEVICE_FUNC PointerType data() const { return m_result; } + EIGEN_DEVICE_FUNC PointerT data() const { return m_result; } #ifdef EIGEN_USE_SYCL EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { return m_device; } #endif protected: - EIGEN_DEVICE_FUNC void evalTo(PointerType data) { + EIGEN_DEVICE_FUNC void evalTo(PointerT data) { TensorMap<Tensor<CoeffReturnType, NumDims, Layout> > result(data, m_dimensions); m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device); } @@ -331,7 +315,7 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, Dimensions m_dimensions; const XprType m_op; const Device& m_device; - PointerType m_result; + PointerT m_result; }; diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h index 0cefe42dd..9b9587de5 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h @@ -132,7 +132,7 @@ class TensorExecutor<Expression, DefaultDevice, Vectorizable, if (needs_assign) { // Size tensor blocks to fit in cache (or requested target block size). Index block_total_size = numext::mini(cache_size, total_size); - TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims; + TensorBlockShapeType block_shape = kSkewedInnerDims; // Query expression tree for desired block size/shape. std::vector<TensorOpResourceRequirements> resources; evaluator.getResourceRequirements(&resources); @@ -229,10 +229,6 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable> { Evaluator evaluator(expr, device); const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL); if (needs_assign) { - const StorageIndex PacketSize = - Vectorizable - ? unpacket_traits<typename Evaluator::PacketReturnType>::size - : 1; const StorageIndex size = array_prod(evaluator.dimensions()); device.parallelFor(size, evaluator.costPerCoeff(Vectorizable), EvalRange::alignBlockSize, @@ -272,7 +268,7 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL); if (needs_assign) { - TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims; + TensorBlockShapeType block_shape = kSkewedInnerDims; Index block_total_size = 0; // Query expression tree for desired block size/shape. std::vector<internal::TensorOpResourceRequirements> resources; diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h index da0751039..93a3b0e14 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h @@ -24,6 +24,14 @@ template<typename T> struct MakePointer { typedef T ScalarType; }; +// The PointerType class is a container of the device specefic pointer +// used for refering to a Pointer on TensorEvaluator class. While the TensorExpression +// is a device-agnostic type and need MakePointer class for type conversion, +// the TensorEvaluator calss can be specialized for a device, hence it is possible +// to construct different types of temproray storage memory in TensorEvaluator +// for different devices by specializing the following PointerType class. +template<typename T, typename Device> struct PointerType : MakePointer<T>{}; + namespace internal{ template<typename A, typename B> struct Pointer_type_promotion { static const bool val=false; @@ -89,8 +97,8 @@ template<typename LeftXprType, typename RightXprType> class TensorAssignOp; template<typename Op, typename XprType> class TensorScanOp; template<typename Dims, typename XprType> class TensorTraceOp; -template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_ = MakePointer> class TensorCustomUnaryOp; -template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, template <class> class MakePointer_ = MakePointer> class TensorCustomBinaryOp; +template<typename CustomUnaryFunc, typename XprType> class TensorCustomUnaryOp; +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> class TensorCustomBinaryOp; template<typename XprType, template <class> class MakePointer_ = MakePointer> class TensorEvalToOp; template<typename XprType> class TensorForcedEvalOp; diff --git a/unsupported/test/cxx11_tensor_block_access.cpp b/unsupported/test/cxx11_tensor_block_access.cpp index 6feeff231..417b72201 100644 --- a/unsupported/test/cxx11_tensor_block_access.cpp +++ b/unsupported/test/cxx11_tensor_block_access.cpp @@ -10,6 +10,8 @@ #include "main.h" +#include <algorithm> +#include <random> #include <set> #include <Eigen/CXX11/Tensor> @@ -19,17 +21,16 @@ using Eigen::Index; using Eigen::RowMajor; using Eigen::ColMajor; -using internal::TensorBlockShapeType; template<typename T> static const T& choose(int layout, const T& col, const T& row) { return layout == ColMajor ? col : row; } -static const TensorBlockShapeType RandomShape() { +static internal::TensorBlockShapeType RandomShape() { return internal::random<bool>() - ? internal::TensorBlockShapeType::kUniformAllDims - : internal::TensorBlockShapeType::kSkewedInnerDims; + ? internal::kUniformAllDims + : internal::kSkewedInnerDims; } template <int NumDims> @@ -44,7 +45,7 @@ static DSizes<Index, NumDims> RandomDims() { dims[i] = internal::random<int>(1, 20); } return DSizes<Index, NumDims>(dims); -}; +} /** Dummy data type to test TensorBlock copy ops. */ struct Data { @@ -91,21 +92,19 @@ static void Debug(DSizes<Index, NumDims> dims) { template <int Layout> static void test_block_mapper_sanity() { - using T = int; - using TensorBlock = internal::TensorBlock<T, Index, 2, Layout>; - using TensorBlockMapper = internal::TensorBlockMapper<T, Index, 2, Layout>; + typedef internal::TensorBlockMapper<int, Index, 2, Layout> TensorBlockMapper; DSizes<Index, 2> tensor_dims(100, 100); // Test uniform blocks. TensorBlockMapper uniform_block_mapper( - tensor_dims, internal::TensorBlockShapeType::kUniformAllDims, 100); + tensor_dims, internal::kUniformAllDims, 100); VERIFY_IS_EQUAL(uniform_block_mapper.total_block_count(), 100); VERIFY_IS_EQUAL(uniform_block_mapper.block_dims_total_size(), 100); // 10x10 blocks - auto uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, nullptr); + auto uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(0), 10); VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(1), 10); // Depending on a layout we stride by cols rows. @@ -117,13 +116,13 @@ static void test_block_mapper_sanity() // Test skewed to inner dims blocks. TensorBlockMapper skewed_block_mapper( - tensor_dims, internal::TensorBlockShapeType::kSkewedInnerDims, 100); + tensor_dims, internal::kSkewedInnerDims, 100); VERIFY_IS_EQUAL(skewed_block_mapper.total_block_count(), 100); VERIFY_IS_EQUAL(skewed_block_mapper.block_dims_total_size(), 100); // 1x100 (100x1) rows/cols depending on a tensor layout. - auto skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, nullptr); + auto skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(0), choose(Layout, 100, 1)); VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(1), choose(Layout, 1, 100)); // Depending on a layout we stride by cols rows. @@ -158,9 +157,8 @@ static void UpdateCoeffSet( template <typename T, int NumDims, int Layout> static void test_block_mapper_maps_every_element() { - using TensorBlock = internal::TensorBlock<T, Index, NumDims, Layout>; - using TensorBlockMapper = - internal::TensorBlockMapper<T, Index, NumDims, Layout>; + typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock; + typedef internal::TensorBlockMapper<T, Index, NumDims, Layout> TensorBlockMapper; DSizes<Index, NumDims> dims = RandomDims<NumDims>(); @@ -171,7 +169,7 @@ static void test_block_mapper_maps_every_element() { TensorBlockMapper block_mapper(dims, RandomShape(), RandomTargetSize(dims)); for (int i = 0; i < block_mapper.total_block_count(); ++i) { - TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(i, NULL); UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(), choose(Layout, NumDims - 1, 0), &coeff_set); @@ -187,9 +185,8 @@ static void test_block_mapper_maps_every_element() { template <typename T, int NumDims, int Layout> static void test_slice_block_mapper_maps_every_element() { - using TensorBlock = internal::TensorBlock<T, Index, NumDims, Layout>; - using TensorSliceBlockMapper = - internal::TensorSliceBlockMapper<T, Index, NumDims, Layout>; + typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock; + typedef internal::TensorSliceBlockMapper<T, Index, NumDims, Layout> TensorSliceBlockMapper; DSizes<Index, NumDims> tensor_dims = RandomDims<NumDims>(); DSizes<Index, NumDims> tensor_slice_offsets = RandomDims<NumDims>(); @@ -219,7 +216,7 @@ static void test_slice_block_mapper_maps_every_element() { DimensionList<Index, NumDims>()); for (int i = 0; i < block_mapper.total_block_count(); ++i) { - TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(i, NULL); UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(), choose(Layout, NumDims - 1, 0), &coeff_set); @@ -647,17 +644,16 @@ static void test_block_cwise_binary_io_zero_strides() { template <int Layout> static void test_uniform_block_shape() { - using T = int; - typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock; - typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper; + typedef internal::TensorBlock<int, Index, 5, Layout> TensorBlock; + typedef internal::TensorBlockMapper<int, Index, 5, Layout> TensorBlockMapper; { // Test shape 'UniformAllDims' with uniform 'max_coeff count'. DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 5 * 5 * 5 * 5 * 5; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); for (int i = 0; i < 5; ++i) { VERIFY_IS_EQUAL(5, block.block_sizes()[i]); } @@ -669,9 +665,9 @@ static void test_uniform_block_shape() if (Layout == ColMajor) { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 7 * 5 * 5 * 5 * 5; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[0]); for (int i = 1; i < 5; ++i) { VERIFY_IS_EQUAL(5, block.block_sizes()[i]); @@ -680,9 +676,9 @@ static void test_uniform_block_shape() } else { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 5 * 5 * 5 * 5 * 6; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(6, block.block_sizes()[4]); for (int i = 3; i >= 0; --i) { VERIFY_IS_EQUAL(5, block.block_sizes()[i]); @@ -695,9 +691,9 @@ static void test_uniform_block_shape() if (Layout == ColMajor) { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 11 * 5 * 5 * 5 * 5; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(11, block.block_sizes()[0]); for (int i = 1; i < 5; ++i) { VERIFY_IS_EQUAL(5, block.block_sizes()[i]); @@ -706,9 +702,9 @@ static void test_uniform_block_shape() } else { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 5 * 5 * 5 * 5 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[4]); for (int i = 3; i >= 0; --i) { VERIFY_IS_EQUAL(5, block.block_sizes()[i]); @@ -721,9 +717,9 @@ static void test_uniform_block_shape() if (Layout == ColMajor) { DSizes<Index, 5> dims(7, 5, 6, 17, 7); const size_t max_coeff_count = 7 * 5 * 6 * 7 * 5; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[0]); VERIFY_IS_EQUAL(5, block.block_sizes()[1]); VERIFY_IS_EQUAL(6, block.block_sizes()[2]); @@ -733,9 +729,9 @@ static void test_uniform_block_shape() } else { DSizes<Index, 5> dims(7, 5, 6, 9, 7); const size_t max_coeff_count = 5 * 5 * 5 * 6 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[4]); VERIFY_IS_EQUAL(6, block.block_sizes()[3]); VERIFY_IS_EQUAL(5, block.block_sizes()[2]); @@ -748,9 +744,9 @@ static void test_uniform_block_shape() if (Layout == ColMajor) { DSizes<Index, 5> dims(7, 5, 6, 17, 7); const size_t max_coeff_count = 7 * 5 * 6 * 17 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[0]); VERIFY_IS_EQUAL(5, block.block_sizes()[1]); VERIFY_IS_EQUAL(6, block.block_sizes()[2]); @@ -760,9 +756,9 @@ static void test_uniform_block_shape() } else { DSizes<Index, 5> dims(7, 5, 6, 9, 7); const size_t max_coeff_count = 7 * 5 * 6 * 9 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims, + TensorBlockMapper block_mapper(dims, internal::kUniformAllDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[4]); VERIFY_IS_EQUAL(9, block.block_sizes()[3]); VERIFY_IS_EQUAL(6, block.block_sizes()[2]); @@ -775,17 +771,16 @@ static void test_uniform_block_shape() template <int Layout> static void test_skewed_inner_dim_block_shape() { - using T = int; - typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock; - typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper; + typedef internal::TensorBlock<int, Index, 5, Layout> TensorBlock; + typedef internal::TensorBlockMapper<int, Index, 5, Layout> TensorBlockMapper; // Test shape 'SkewedInnerDims' with partial allocation to inner-most dim. if (Layout == ColMajor) { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 10 * 1 * 1 * 1 * 1; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(10, block.block_sizes()[0]); for (int i = 1; i < 5; ++i) { VERIFY_IS_EQUAL(1, block.block_sizes()[i]); @@ -794,9 +789,9 @@ static void test_skewed_inner_dim_block_shape() } else { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 1 * 1 * 1 * 1 * 6; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(6, block.block_sizes()[4]); for (int i = 3; i >= 0; --i) { VERIFY_IS_EQUAL(1, block.block_sizes()[i]); @@ -808,9 +803,9 @@ static void test_skewed_inner_dim_block_shape() if (Layout == ColMajor) { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 11 * 1 * 1 * 1 * 1; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(11, block.block_sizes()[0]); for (int i = 1; i < 5; ++i) { VERIFY_IS_EQUAL(1, block.block_sizes()[i]); @@ -819,9 +814,9 @@ static void test_skewed_inner_dim_block_shape() } else { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 1 * 1 * 1 * 1 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[4]); for (int i = 3; i >= 0; --i) { VERIFY_IS_EQUAL(1, block.block_sizes()[i]); @@ -834,9 +829,9 @@ static void test_skewed_inner_dim_block_shape() if (Layout == ColMajor) { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 11 * 3 * 1 * 1 * 1; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(11, block.block_sizes()[0]); VERIFY_IS_EQUAL(3, block.block_sizes()[1]); for (int i = 2; i < 5; ++i) { @@ -846,9 +841,9 @@ static void test_skewed_inner_dim_block_shape() } else { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 1 * 1 * 1 * 15 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[4]); VERIFY_IS_EQUAL(15, block.block_sizes()[3]); for (int i = 2; i >= 0; --i) { @@ -862,9 +857,9 @@ static void test_skewed_inner_dim_block_shape() if (Layout == ColMajor) { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 11 * 5 * 5 * 1 * 1; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(11, block.block_sizes()[0]); VERIFY_IS_EQUAL(5, block.block_sizes()[1]); VERIFY_IS_EQUAL(5, block.block_sizes()[2]); @@ -875,9 +870,9 @@ static void test_skewed_inner_dim_block_shape() } else { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 1 * 1 * 5 * 17 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[4]); VERIFY_IS_EQUAL(17, block.block_sizes()[3]); VERIFY_IS_EQUAL(5, block.block_sizes()[2]); @@ -891,9 +886,9 @@ static void test_skewed_inner_dim_block_shape() if (Layout == ColMajor) { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(11, block.block_sizes()[0]); VERIFY_IS_EQUAL(5, block.block_sizes()[1]); VERIFY_IS_EQUAL(6, block.block_sizes()[2]); @@ -903,9 +898,9 @@ static void test_skewed_inner_dim_block_shape() } else { DSizes<Index, 5> dims(11, 5, 6, 17, 7); const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7; - TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims, + TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims, max_coeff_count); - TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr); + TensorBlock block = block_mapper.GetBlockForIndex(0, NULL); VERIFY_IS_EQUAL(7, block.block_sizes()[4]); VERIFY_IS_EQUAL(17, block.block_sizes()[3]); VERIFY_IS_EQUAL(6, block.block_sizes()[2]); @@ -918,15 +913,13 @@ static void test_skewed_inner_dim_block_shape() template <int Layout> static void test_empty_dims(const internal::TensorBlockShapeType block_shape) { - using T = int; - // Test blocking of tensors with zero dimensions: // - we must not crash on asserts and divisions by zero // - we must not return block with zero dimensions // (recipe for overflows/underflows, divisions by zero and NaNs later) // - total block count must be zero { - typedef internal::TensorBlockMapper<T, Index, 1, Layout> TensorBlockMapper; + typedef internal::TensorBlockMapper<int, Index, 1, Layout> TensorBlockMapper; DSizes<Index, 1> dims(0); for (int max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) { TensorBlockMapper block_mapper(dims, block_shape, max_coeff_count); @@ -936,7 +929,7 @@ static void test_empty_dims(const internal::TensorBlockShapeType block_shape) } { - typedef internal::TensorBlockMapper<T, Index, 2, Layout> TensorBlockMapper; + typedef internal::TensorBlockMapper<int, Index, 2, Layout> TensorBlockMapper; for (int dim1 = 0; dim1 < 3; ++dim1) { for (int dim2 = 0; dim2 < 3; ++dim2) { DSizes<Index, 2> dims(dim1, dim2); @@ -987,9 +980,9 @@ EIGEN_DECLARE_TEST(cxx11_tensor_block_access) { TEST_LAYOUTS(test_block_cwise_binary_io_zero_strides); TEST_LAYOUTS(test_uniform_block_shape); TEST_LAYOUTS(test_skewed_inner_dim_block_shape); - TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kUniformAllDims); - TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kSkewedInnerDims); + TEST_LAYOUTS_WITH_ARG(test_empty_dims, internal::kUniformAllDims); + TEST_LAYOUTS_WITH_ARG(test_empty_dims, internal::kSkewedInnerDims); } #undef TEST_LAYOUTS -#undef TEST_LAYOUTS_WITH_ARG
\ No newline at end of file +#undef TEST_LAYOUTS_WITH_ARG diff --git a/unsupported/test/cxx11_tensor_contraction.cpp b/unsupported/test/cxx11_tensor_contraction.cpp index d4cfbd0da..928d20f6e 100644 --- a/unsupported/test/cxx11_tensor_contraction.cpp +++ b/unsupported/test/cxx11_tensor_contraction.cpp @@ -471,7 +471,7 @@ static void test_tensor_product() mat1.setRandom(); mat2.setRandom(); - Tensor<float, 4, DataLayout> result = mat1.contract(mat2, Eigen::array<DimPair, 0>{{}}); + Tensor<float, 4, DataLayout> result = mat1.contract(mat2, Eigen::array<DimPair, 0>{}); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_EQUAL(result.dimension(1), 3); @@ -514,7 +514,7 @@ static void test_const_inputs() struct SqrtOutputKernel { template <typename Index, typename Scalar> EIGEN_ALWAYS_INLINE void operator()( - const OutputKernel::OutputMapper<Index, Scalar>& output_mapper, + const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper, const TensorContractionParams&, Index, Index, Index num_rows, Index num_cols) const { for (int i = 0; i < num_rows; ++i) { @@ -553,7 +553,7 @@ static void test_large_contraction_with_output_kernel() { m_result = m_left * m_right; - for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) { + for (std::ptrdiff_t i = 0; i < t_result.dimensions().TotalSize(); i++) { VERIFY(&t_result.data()[i] != &m_result.data()[i]); VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i])); } diff --git a/unsupported/test/cxx11_tensor_convolution.cpp b/unsupported/test/cxx11_tensor_convolution.cpp index 01bc77bc1..9fe980648 100644 --- a/unsupported/test/cxx11_tensor_convolution.cpp +++ b/unsupported/test/cxx11_tensor_convolution.cpp @@ -25,7 +25,7 @@ static void test_evals() Tensor<float, 2, DataLayout> result(2,3); result.setZero(); - Eigen::array<Tensor<float, 2>::Index, 1> dims3{{0}}; + Eigen::array<Tensor<float, 2>::Index, 1> dims3{0}; typedef TensorEvaluator<decltype(input.convolve(kernel, dims3)), DefaultDevice> Evaluator; Evaluator eval(input.convolve(kernel, dims3), DefaultDevice()); diff --git a/unsupported/test/cxx11_tensor_index_list.cpp b/unsupported/test/cxx11_tensor_index_list.cpp index e81fa5e40..294677a4d 100644 --- a/unsupported/test/cxx11_tensor_index_list.cpp +++ b/unsupported/test/cxx11_tensor_index_list.cpp @@ -170,7 +170,6 @@ static void test_type2indexpair_list() typedef Eigen::IndexPairList<Eigen::type2indexpair<0,10>, Eigen::IndexPair<DenseIndex>, Eigen::type2indexpair<2,12>> Dims2_b; typedef Eigen::IndexPairList<Eigen::IndexPair<DenseIndex>, Eigen::type2indexpair<1,11>, Eigen::IndexPair<DenseIndex>> Dims2_c; - Dims0 d0; Dims2_a d2_a; Dims2_b d2_b; diff --git a/unsupported/test/cxx11_tensor_thread_pool.cpp b/unsupported/test/cxx11_tensor_thread_pool.cpp index dd163c18a..7606b0abf 100644 --- a/unsupported/test/cxx11_tensor_thread_pool.cpp +++ b/unsupported/test/cxx11_tensor_thread_pool.cpp @@ -255,7 +255,7 @@ void test_multithread_contraction_agrees_with_singlethread() { struct SqrtOutputKernel { template <typename Index, typename Scalar> EIGEN_ALWAYS_INLINE void operator()( - const OutputKernel::OutputMapper<Index, Scalar>& output_mapper, + const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper, const TensorContractionParams&, Index, Index, Index num_rows, Index num_cols) const { for (int i = 0; i < num_rows; ++i) { diff --git a/unsupported/test/kronecker_product.cpp b/unsupported/test/kronecker_product.cpp index 4f143b6de..b5b764c65 100644 --- a/unsupported/test/kronecker_product.cpp +++ b/unsupported/test/kronecker_product.cpp @@ -9,6 +9,7 @@ // 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/. + #ifdef EIGEN_TEST_PART_1 #include "sparse.h" @@ -95,7 +96,7 @@ EIGEN_DECLARE_TEST(kronecker_product) SM_a.insert(1,0) = DM_a.coeffRef(1,0) = -0.9076572187376921; SM_a.insert(1,1) = DM_a.coeffRef(1,1) = 0.6469156566545853; SM_a.insert(1,2) = DM_a.coeffRef(1,2) = -0.3658010398782789; - + MatrixXd DM_b(3,2); SparseMatrix<double> SM_b(3,2); SM_b.insert(0,0) = DM_b.coeffRef(0,0) = 0.9004440976767099; @@ -165,7 +166,7 @@ EIGEN_DECLARE_TEST(kronecker_product) SM_a.insert(0,3) = -0.2; SM_a.insert(2,4) = 0.3; SM_a.finalize(); - + SM_b.insert(0,0) = 0.4; SM_b.insert(2,1) = -0.5; SM_b.finalize(); @@ -183,7 +184,7 @@ EIGEN_DECLARE_TEST(kronecker_product) DM_b2.resize(4,8); DM_ab2 = kroneckerProduct(DM_a2,DM_b2); CALL_SUBTEST(check_dimension(DM_ab2,10*4,9*8)); - + for(int i = 0; i < g_repeat; i++) { double density = Eigen::internal::random<double>(0.01,0.5); @@ -196,35 +197,35 @@ EIGEN_DECLARE_TEST(kronecker_product) MatrixXf dA(ra,ca), dB(rb,cb), dC; initSparse(density, dA, sA); initSparse(density, dB, sB); - + sC = kroneckerProduct(sA,sB); dC = kroneckerProduct(dA,dB); VERIFY_IS_APPROX(MatrixXf(sC),dC); - + sC = kroneckerProduct(sA.transpose(),sB); dC = kroneckerProduct(dA.transpose(),dB); VERIFY_IS_APPROX(MatrixXf(sC),dC); - + sC = kroneckerProduct(sA.transpose(),sB.transpose()); dC = kroneckerProduct(dA.transpose(),dB.transpose()); VERIFY_IS_APPROX(MatrixXf(sC),dC); - + sC = kroneckerProduct(sA,sB.transpose()); dC = kroneckerProduct(dA,dB.transpose()); VERIFY_IS_APPROX(MatrixXf(sC),dC); - + sC2 = kroneckerProduct(sA,sB); dC = kroneckerProduct(dA,dB); VERIFY_IS_APPROX(MatrixXf(sC2),dC); - + sC2 = kroneckerProduct(dA,sB); dC = kroneckerProduct(dA,dB); VERIFY_IS_APPROX(MatrixXf(sC2),dC); - + sC2 = kroneckerProduct(sA,dB); dC = kroneckerProduct(dA,dB); VERIFY_IS_APPROX(MatrixXf(sC2),dC); - + sC2 = kroneckerProduct(2*sA,sB); dC = kroneckerProduct(2*dA,dB); VERIFY_IS_APPROX(MatrixXf(sC2),dC); @@ -236,7 +237,6 @@ EIGEN_DECLARE_TEST(kronecker_product) #ifdef EIGEN_TEST_PART_2 // simply check that for a dense kronecker product, sparse module is not needed - #include "main.h" #include <Eigen/KroneckerProduct> |