mirror of
https://gitlab.com/libeigen/eigen.git
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Using PointerType struct and specializing it per device for TensorCustomOp.h
This commit is contained in:
Mehdi Goli
commit
161dcbae9b
@ -73,7 +73,7 @@ struct TensorOpResourceRequirements {
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// expression tree (like reductions) to communicate resources
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// requirements based on local state (like the total number of reductions
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// to be computed).
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TensorOpResourceRequirements(internal::TensorBlockShapeType shape,
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TensorOpResourceRequirements(TensorBlockShapeType shape,
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const Index size)
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: block_shape(shape), block_total_size(size) {}
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};
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@ -90,9 +90,9 @@ EIGEN_STRONG_INLINE void MergeResourceRequirements(
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*block_shape = resources[0].block_shape;
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*block_total_size = resources[0].block_total_size;
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for (std::vector<TensorOpResourceRequirements>::size_type i = 1; i < resources.size(); ++i) {
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if (resources[i].block_shape == TensorBlockShapeType::kSkewedInnerDims &&
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*block_shape != TensorBlockShapeType::kSkewedInnerDims) {
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*block_shape = TensorBlockShapeType::kSkewedInnerDims;
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if (resources[i].block_shape == kSkewedInnerDims &&
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*block_shape != kSkewedInnerDims) {
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*block_shape = kSkewedInnerDims;
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}
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*block_total_size =
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numext::maxi(*block_total_size, resources[i].block_total_size);
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@ -152,11 +152,11 @@ struct TensorBlockCopyOp {
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const Scalar* src_base = &src_data[src_index];
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Scalar* dst_base = &dst_data[dst_index];
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typedef const Eigen::Array<Scalar, Dynamic, 1> Src;
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typedef Eigen::Array<Scalar, Dynamic, 1> Dst;
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typedef const Array<Scalar, Dynamic, 1> Src;
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typedef Array<Scalar, Dynamic, 1> Dst;
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typedef Eigen::Map<Src, 0, InnerStride<> > SrcMap;
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typedef Eigen::Map<Dst, 0, InnerStride<> > DstMap;
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typedef Map<Src, 0, InnerStride<> > SrcMap;
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typedef Map<Dst, 0, InnerStride<> > DstMap;
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const SrcMap src(src_base, num_coeff_to_copy, InnerStride<>(src_stride));
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DstMap dst(dst_base, num_coeff_to_copy, InnerStride<>(dst_stride));
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@ -178,10 +178,8 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout,
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bool BlockRead>
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class TensorBlockIO {
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public:
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typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
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TensorBlock;
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typedef typename internal::TensorBlockCopyOp<Scalar, StorageIndex>
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TensorBlockCopyOp;
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typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
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typedef TensorBlockCopyOp<Scalar, StorageIndex> BlockCopyOp;
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protected:
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struct BlockIteratorState {
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@ -194,7 +192,7 @@ class TensorBlockIO {
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};
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static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Copy(
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const TensorBlock& block, StorageIndex first_coeff_index,
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const Block& block, StorageIndex first_coeff_index,
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const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
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const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data,
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Scalar* dst_data) {
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@ -290,8 +288,8 @@ class TensorBlockIO {
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const StorageIndex block_total_size =
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NumDims == 0 ? 1 : block.block_sizes().TotalSize();
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for (StorageIndex i = 0; i < block_total_size; i += block_inner_dim_size) {
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TensorBlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride,
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dst_data, inputIndex, input_stride, src_data);
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BlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride,
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dst_data, inputIndex, input_stride, src_data);
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// Update index.
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for (int j = 0; j < num_squeezed_dims; ++j) {
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if (++block_iter_state[j].count < block_iter_state[j].size) {
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@ -320,13 +318,11 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
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class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims,
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Layout, /*BlockRead=*/true> {
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public:
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typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
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TensorBlock;
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typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true>
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Base;
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typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
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typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true> Base;
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static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
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TensorBlock* block, const Scalar* src_data) {
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Block* block, const Scalar* src_data) {
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array<StorageIndex, NumDims> tensor_to_block_dim_map;
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for (int i = 0; i < NumDims; ++i) {
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tensor_to_block_dim_map[i] = i;
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@ -336,7 +332,7 @@ class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims,
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}
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static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
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TensorBlock* block, StorageIndex first_coeff_index,
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Block* block, StorageIndex first_coeff_index,
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const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
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const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data) {
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Base::Copy(*block, first_coeff_index, tensor_to_block_dim_map,
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@ -357,13 +353,11 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
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class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims,
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Layout, /*BlockRead=*/false> {
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public:
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typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
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TensorBlock;
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typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false>
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Base;
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typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
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typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false> Base;
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static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
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const TensorBlock& block, Scalar* dst_data) {
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const Block& block, Scalar* dst_data) {
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array<StorageIndex, NumDims> tensor_to_block_dim_map;
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for (int i = 0; i < NumDims; ++i) {
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tensor_to_block_dim_map[i] = i;
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@ -373,7 +367,7 @@ class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims,
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}
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static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
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const TensorBlock& block, StorageIndex first_coeff_index,
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const Block& block, StorageIndex first_coeff_index,
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const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
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const array<StorageIndex, NumDims>& tensor_strides, Scalar* dst_data) {
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Base::Copy(block, first_coeff_index, tensor_to_block_dim_map,
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@ -401,13 +395,13 @@ struct TensorBlockCwiseBinaryOp {
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const StorageIndex left_stride, const LeftScalar* left_data,
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const StorageIndex right_index, const StorageIndex right_stride,
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const RightScalar* right_data) {
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typedef const Eigen::Array<LeftScalar, Dynamic, 1> Lhs;
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typedef const Eigen::Array<RightScalar, Dynamic, 1> Rhs;
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typedef Eigen::Array<OutputScalar, Dynamic, 1> Out;
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typedef const Array<LeftScalar, Dynamic, 1> Lhs;
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typedef const Array<RightScalar, Dynamic, 1> Rhs;
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typedef Array<OutputScalar, Dynamic, 1> Out;
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typedef Eigen::Map<Lhs, 0, InnerStride<> > LhsMap;
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typedef Eigen::Map<Rhs, 0, InnerStride<> > RhsMap;
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typedef Eigen::Map<Out, 0, InnerStride<> > OutMap;
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typedef Map<Lhs, 0, InnerStride<> > LhsMap;
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typedef Map<Rhs, 0, InnerStride<> > RhsMap;
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typedef Map<Out, 0, InnerStride<> > OutMap;
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const LeftScalar* lhs_base = &left_data[left_index];
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const RightScalar* rhs_base = &right_data[right_index];
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@ -417,8 +411,7 @@ struct TensorBlockCwiseBinaryOp {
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const RhsMap rhs(rhs_base, num_coeff, InnerStride<>(right_stride));
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OutMap out(out_base, num_coeff, InnerStride<>(output_stride));
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out =
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Eigen::CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor);
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out = CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor);
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}
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};
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@ -434,8 +427,7 @@ struct TensorBlockCwiseBinaryOp {
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template <typename BinaryFunctor, typename StorageIndex, typename OutputScalar,
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int NumDims, int Layout>
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struct TensorBlockCwiseBinaryIO {
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typedef typename internal::TensorBlock<OutputScalar, StorageIndex, NumDims,
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Layout>::Dimensions Dimensions;
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typedef typename TensorBlock<OutputScalar, StorageIndex, NumDims, Layout>::Dimensions Dimensions;
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struct BlockIteratorState {
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StorageIndex output_stride, output_span;
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@ -627,8 +619,7 @@ struct TensorBlockView {
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template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
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class TensorBlockMapper {
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public:
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typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
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TensorBlock;
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typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
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typedef DSizes<StorageIndex, NumDims> Dimensions;
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TensorBlockMapper(const Dimensions& dims,
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@ -663,7 +654,7 @@ class TensorBlockMapper {
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block
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GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
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StorageIndex first_coeff_index = 0;
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DSizes<StorageIndex, NumDims> coords;
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@ -711,8 +702,7 @@ class TensorBlockMapper {
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}
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}
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return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides,
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data);
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return Block(first_coeff_index, sizes, strides, m_tensor_strides, data);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {
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@ -742,7 +732,7 @@ class TensorBlockMapper {
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block_dim_sizes[i] = 1;
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}
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} else if (block_dim_sizes.TotalSize() > min_target_size) {
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if (block_shape == TensorBlockShapeType::kUniformAllDims) {
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if (block_shape == kUniformAllDims) {
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// Tensor will not fit within 'min_target_size' budget: calculate tensor
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// block dimension sizes based on "square" dimension size target.
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const size_t dim_size_target = static_cast<const size_t>(
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@ -773,7 +763,7 @@ class TensorBlockMapper {
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total_size = total_size_other_dims * block_dim_sizes[dim];
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}
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}
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} else if (block_shape == TensorBlockShapeType::kSkewedInnerDims) {
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} else if (block_shape == kSkewedInnerDims) {
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StorageIndex coeff_to_allocate = min_target_size;
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for (int i = 0; i < NumDims; ++i) {
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const int dim = cond<Layout>()(i, NumDims - i - 1);
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@ -818,8 +808,7 @@ class TensorBlockMapper {
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template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
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class TensorSliceBlockMapper {
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public:
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typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
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TensorBlock;
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typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
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typedef DSizes<StorageIndex, NumDims> Dimensions;
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TensorSliceBlockMapper(const Dimensions& tensor_dims,
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@ -860,7 +849,7 @@ class TensorSliceBlockMapper {
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block
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GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
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StorageIndex first_coeff_index = 0;
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DSizes<StorageIndex, NumDims> coords;
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@ -917,8 +906,7 @@ class TensorSliceBlockMapper {
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}
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}
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return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides,
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data);
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return Block(first_coeff_index, sizes, strides, m_tensor_strides, data);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {
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@ -152,13 +152,7 @@ struct TensorContractionParams {
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// 1. Elementwise Relu transformation following Conv2D.
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// 2. AddBias to the Conv2D output channels dimension.
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//
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// See expected implementation in NoOpOutputKernel.
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struct OutputKernel {
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template <typename Index, typename Scalar>
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using OutputMapper = internal::blas_data_mapper<Scalar, Index, ColMajor>;
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};
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// Output kernel that does absolutely nothing.
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// The NoOpOutputKernel implements an output kernel that does absolutely nothing.
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struct NoOpOutputKernel {
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/**
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* Tensor contraction evaluator calls this kernel after finishing each block
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@ -177,7 +171,7 @@ struct NoOpOutputKernel {
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*/
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template <typename Index, typename Scalar>
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EIGEN_ALWAYS_INLINE void operator()(
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const OutputKernel::OutputMapper<Index, Scalar>& /*output_mapper*/,
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const internal::blas_data_mapper<Scalar, Index, ColMajor>& /*output_mapper*/,
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const TensorContractionParams& /*params*/, Index /*i*/,
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Index /*j*/, Index /*num_rows*/, Index /*num_cols*/) const {}
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};
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@ -20,8 +20,8 @@ namespace Eigen {
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*
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*/
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namespace internal {
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template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_>
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struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> >
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template<typename CustomUnaryFunc, typename XprType>
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struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
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{
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::StorageKind StorageKind;
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@ -31,34 +31,26 @@ struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> >
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static const int NumDimensions = traits<XprType>::NumDimensions;
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static const int Layout = traits<XprType>::Layout;
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template <class T> struct MakePointer {
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// Intermediate typedef to workaround MSVC issue.
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typedef MakePointer_<T> MakePointerT;
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typedef typename MakePointerT::Type Type;
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typedef typename MakePointerT::RefType RefType;
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typedef typename MakePointerT::ScalarType ScalarType;
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};
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typedef typename MakePointer<typename internal::remove_const<typename XprType::CoeffReturnType>::type>::Type PointerType;
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};
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template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_>
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struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, Eigen::Dense>
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template<typename CustomUnaryFunc, typename XprType>
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struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense>
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{
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typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>& type;
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typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType>& type;
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};
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template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_>
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struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> >
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template<typename CustomUnaryFunc, typename XprType>
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struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
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{
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typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> type;
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typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type;
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};
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} // end namespace internal
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template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_>
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class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, ReadOnlyAccessors>
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template<typename CustomUnaryFunc, typename XprType>
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class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors>
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{
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public:
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typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar;
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@ -85,10 +77,10 @@ class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFun
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// Eval as rvalue
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template<typename CustomUnaryFunc, typename XprType, template <class> class MakePointer_, typename Device>
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struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_>, Device>
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template<typename CustomUnaryFunc, typename XprType, typename Device>
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struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device>
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{
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typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakePointer_> ArgType;
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typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType;
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typedef typename internal::traits<ArgType>::Index Index;
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static const int NumDims = internal::traits<ArgType>::NumDimensions;
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typedef DSizes<Index, NumDims> Dimensions;
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@ -96,7 +88,7 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
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typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
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typedef typename Eigen::internal::traits<ArgType>::PointerType PointerType;
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typedef typename PointerType<CoeffReturnType, Device>::Type PointerT;
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enum {
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IsAligned = false,
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@ -115,12 +107,12 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerType data) {
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerT data) {
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if (data) {
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evalTo(data);
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return false;
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} else {
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m_result = static_cast<PointerType>(
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m_result = static_cast<PointerT>(
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m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar)));
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evalTo(m_result);
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return true;
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@ -148,14 +140,14 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
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return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
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}
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EIGEN_DEVICE_FUNC PointerType data() const { return m_result; }
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EIGEN_DEVICE_FUNC PointerT data() const { return m_result; }
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#ifdef EIGEN_USE_SYCL
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { return m_device; }
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#endif
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protected:
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EIGEN_DEVICE_FUNC void evalTo(PointerType data) {
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EIGEN_DEVICE_FUNC void evalTo(PointerT data) {
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TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(data, m_dimensions);
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m_op.func().eval(m_op.expression(), result, m_device);
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}
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@ -163,7 +155,7 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType, MakeP
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Dimensions m_dimensions;
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const ArgType m_op;
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const Device& m_device;
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PointerType m_result;
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PointerT m_result;
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};
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||||
|
||||
@ -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;
|
||||
};
|
||||
|
||||
|
||||
|
||||
@ -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;
|
||||
|
||||
@ -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;
|
||||
|
||||
@ -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
|
||||
#undef TEST_LAYOUTS_WITH_ARG
|
||||
|
||||
@ -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]));
|
||||
}
|
||||
|
||||
@ -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());
|
||||
|
||||
@ -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;
|
||||
|
||||
@ -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) {
|
||||
|
||||
@ -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);
|
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VERIFY_IS_APPROX(MatrixXf(sC2),dC);
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sC2 = kroneckerProduct(dA,sB);
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dC = kroneckerProduct(dA,dB);
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VERIFY_IS_APPROX(MatrixXf(sC2),dC);
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||||
|
||||
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sC2 = kroneckerProduct(sA,dB);
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||||
dC = kroneckerProduct(dA,dB);
|
||||
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
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||||
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||||
|
||||
sC2 = kroneckerProduct(2*sA,sB);
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dC = kroneckerProduct(2*dA,dB);
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VERIFY_IS_APPROX(MatrixXf(sC2),dC);
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||||
@ -236,7 +237,6 @@ EIGEN_DECLARE_TEST(kronecker_product)
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#ifdef EIGEN_TEST_PART_2
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||||
|
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// simply check that for a dense kronecker product, sparse module is not needed
|
||||
|
||||
#include "main.h"
|
||||
#include <Eigen/KroneckerProduct>
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user