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1560 lines
59 KiB
1560 lines
59 KiB
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
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#define EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
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namespace Eigen {
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namespace internal {
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// -------------------------------------------------------------------------- //
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// Forward declarations for templates defined below.
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template <typename Scalar, typename IndexType, int NumDims, int Layout>
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class TensorBlockIO;
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// -------------------------------------------------------------------------- //
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// Helper function to compute strides for densely stored buffer of given
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// dimensions.
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// TODO(ezhulenev): We compute strides 1000 times in different evaluators, use
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// this function instead everywhere.
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template <int Layout, typename IndexType, int NumDims>
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EIGEN_ALWAYS_INLINE DSizes<IndexType, NumDims> strides(
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const DSizes<IndexType, NumDims>& dimensions) {
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DSizes<IndexType, NumDims> strides;
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if (NumDims == 0) return strides;
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// TODO(ezhulenev): Use templates to unroll this loop (similar to
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// h_array_reduce in CXX11meta.h)? Benchmark it.
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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strides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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strides[i] = strides[i - 1] * dimensions[i - 1];
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}
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} else {
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strides[NumDims - 1] = 1;
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for (int i = NumDims - 2; i >= 0; --i) {
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strides[i] = strides[i + 1] * dimensions[i + 1];
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}
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}
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return strides;
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}
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template <int Layout, typename IndexType, size_t NumDims>
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EIGEN_ALWAYS_INLINE DSizes<IndexType, NumDims> strides(
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const Eigen::array<IndexType, NumDims>& dimensions) {
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return strides<Layout>(DSizes<IndexType, NumDims>(dimensions));
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}
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template <int Layout, std::ptrdiff_t... Indices>
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EIGEN_STRONG_INLINE DSizes<std::ptrdiff_t, sizeof...(Indices)> strides(
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const Sizes<Indices...>& sizes) {
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return strides<Layout>(DSizes<std::ptrdiff_t, sizeof...(Indices)>(sizes));
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}
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// -------------------------------------------------------------------------- //
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// Tensor block shape type defines what are the shape preference for the blocks
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// extracted from the larger tensor.
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//
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// Example: blocks of 100 elements from the large 100x100 tensor:
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// - tensor: 100x100
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// - target_block_size: 100
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//
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// TensorBlockShapeType:
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// - kUniformAllDims: 100 blocks of size 10x10
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// - kSkewedInnerDims: 100 blocks of size 100x1 (or 1x100 depending on a column
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// or row major layout)
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enum class TensorBlockShapeType { kUniformAllDims, kSkewedInnerDims };
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struct TensorBlockResourceRequirements {
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TensorBlockShapeType shape_type; // target block shape
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size_t size; // target block size
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TensorOpCost cost_per_coeff; // cost of computing a single block element
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#ifdef EIGEN_HIPCC
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// For HIPCC, we need to explicitly declare as a "device fun", the constructor
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// which is implicitly invoked in the "merge" / "any" routines. else HIPCC
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// errors out complaining about the lack of a matching constructor
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EIGEN_DEVICE_FUNC
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TensorBlockResourceRequirements(TensorBlockShapeType shape_type_, size_t size_,
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TensorOpCost cost_)
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: shape_type(shape_type_), size(size_), cost_per_coeff(cost_)
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{}
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#endif
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template <typename Scalar>
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EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(
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TensorBlockShapeType shape_type, size_t size_in_bytes,
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TensorOpCost cost) {
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const size_t size = numext::maxi(size_t(1), size_in_bytes / sizeof(Scalar));
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return {shape_type, size, cost};
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}
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template <typename Scalar>
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EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(
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TensorBlockShapeType shape_type, size_t size_in_bytes) {
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// This default cost per coefficient is valid for most materialized tensor
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// block evaluation implementations, because they typically just read
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// coefficients from the underlying tensor storage, and write to the tensor
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// block buffer (scratch or destination memory, reads and writes have linear
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// access pattern). We ignore the fixed cost of block evaluation, because in
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// practice it should negligible.
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//
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// Lazy block evaluation adds the cost of calling a functor for each
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// coefficient.
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//
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// All non-trivial block evaluation implementations must provide their own
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// cost approximation (e.g. shuffling inner dimension has a much higher cost
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// because it reads memory randomly, although the total number of moved
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// bytes is the same).
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return withShapeAndSize<Scalar>(shape_type, size_in_bytes,
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{/*bytes_loaded=*/sizeof(Scalar),
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/*bytes_stored=*/sizeof(Scalar),
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/*compute_cycles=*/0});
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}
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template <typename Scalar>
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EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements skewed(
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size_t size_in_bytes) {
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return withShapeAndSize<Scalar>(TensorBlockShapeType::kSkewedInnerDims,
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size_in_bytes);
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}
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template <typename Scalar>
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EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements uniform(
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size_t size_in_bytes) {
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return withShapeAndSize<Scalar>(TensorBlockShapeType::kUniformAllDims,
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size_in_bytes);
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}
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EIGEN_DEVICE_FUNC
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static EIGEN_STRONG_INLINE TensorBlockResourceRequirements
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merge(const TensorBlockResourceRequirements& lhs,
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const TensorBlockResourceRequirements& rhs) {
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return {merge(lhs.shape_type, rhs.shape_type), // shape_type
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merge(lhs.size, rhs.size), // size
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merge(lhs.cost_per_coeff, rhs.cost_per_coeff)}; // cost_per_coeff
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}
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EIGEN_DEVICE_FUNC TensorBlockResourceRequirements& addCostPerCoeff(
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TensorOpCost cost) {
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cost_per_coeff += cost;
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return *this;
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}
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// This is a resource requirement that should be returned from expressions
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// that do not have any block evaluation preference (e.g. default tensor
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// expression with raw buffer access).
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EIGEN_DEVICE_FUNC
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static EIGEN_STRONG_INLINE TensorBlockResourceRequirements any() {
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return {TensorBlockShapeType::kUniformAllDims, 1, {0, 0, 0}};
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}
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private:
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using Requirements = TensorBlockResourceRequirements;
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EIGEN_DEVICE_FUNC
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static EIGEN_STRONG_INLINE size_t merge(size_t lhs_size, size_t rhs_size) {
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return numext::maxi(lhs_size, rhs_size);
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}
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EIGEN_DEVICE_FUNC
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static EIGEN_STRONG_INLINE TensorBlockShapeType
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merge(TensorBlockShapeType lhs, TensorBlockShapeType rhs) {
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return (lhs == TensorBlockShapeType::kSkewedInnerDims ||
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rhs == TensorBlockShapeType::kSkewedInnerDims)
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? TensorBlockShapeType::kSkewedInnerDims
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: TensorBlockShapeType::kUniformAllDims;
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}
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EIGEN_DEVICE_FUNC
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static EIGEN_STRONG_INLINE TensorOpCost merge(TensorOpCost lhs_cost,
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TensorOpCost rhs_cost) {
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return lhs_cost + rhs_cost;
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}
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};
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// -------------------------------------------------------------------------- //
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// TensorBlockDescriptor specifies a block offset within a tensor and the block
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// sizes along each of the tensor dimensions.
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template <int NumDims, typename IndexType = Eigen::Index>
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class TensorBlockDescriptor {
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public:
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typedef DSizes<IndexType, NumDims> Dimensions;
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// If we evaluate a Tensor assignment, and expression on the left, already has
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// a memory buffer, then we might do performance optimization, and evaluate
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// the root expression directly into the final output memory. Some time it's
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// possible to reuse it for materializing subexpressions inside an expression
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// tree, to to avoid dynamic memory allocation.
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//
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// The pointer type of the underlying storage is erased, because passing
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// Scalar type through all the expression evaluation layers is way too many
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// templates. In practice destination buffer type should always match the
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// evaluated expression scalar type.
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class DestinationBuffer {
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public:
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enum DestinationBufferKind : int {
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// The above explicit specification of "int" as the enum basetype is
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// needed to get around a HIPCC link error ("the field type is not
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// amp-compatible")
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// which is issued for class members with the enum type.
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// TODO(rocm):
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// remove the "int" basetype once HIPCC has been fixed to not error out
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// in the above scenario.
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// Destination buffer is not defined (`m_data` == nullptr).
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kEmpty,
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// Tensor block defined by an owning tensor block descriptor can fit
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// contiguously into the destination buffer. In this case it's safe to
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// materialize tensor block in the destination buffer, wrap it in a
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// TensorMap, and use to build Eigen expression on top of it.
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kContiguous,
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// Destination buffer strides do not match strides of the contiguously
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// stored block, and it's impossible to define a TensorMap over this
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// buffer. However if we are evaluating a root of an expression tree, we
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// still can materialize an output into this destination, because we can
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// guarantee that no one will ever access it through block API.
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//
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// In theory it is possible to build valid TensorStriding<TensorMap>
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// expression on top of this destination buffer, however it has
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// inefficient coeff/packet access, and defeats the purpose of fast block
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// evaluation API.
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kStrided
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};
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template <typename Scalar>
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Scalar* data() const {
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eigen_assert(m_data_type_size == sizeof(Scalar));
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return static_cast<Scalar*>(m_data);
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}
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const Dimensions& strides() const { return m_strides; }
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const DestinationBufferKind& kind() const { return m_kind; }
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private:
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friend class TensorBlockDescriptor;
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DestinationBuffer() : m_data(NULL), m_data_type_size(0), m_kind(kEmpty) {}
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template <typename Scalar>
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DestinationBuffer(Scalar* data, const Dimensions& strides,
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DestinationBufferKind kind)
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: m_data(static_cast<void*>(data)),
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m_data_type_size(sizeof(Scalar)),
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m_strides(strides),
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m_kind(kind) {}
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template <int Layout, typename Scalar>
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static DestinationBuffer make(const TensorBlockDescriptor& desc,
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Scalar* data, const Dimensions& strides) {
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return DestinationBuffer(data, strides, kind<Layout>(desc, strides));
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}
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template <int Layout>
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static DestinationBufferKind kind(const TensorBlockDescriptor& desc,
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const Dimensions& strides) {
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const Dimensions& desc_dims = desc.dimensions();
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const Dimensions& desc_strides = internal::strides<Layout>(desc_dims);
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for (int i = 0; i < NumDims; ++i) {
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if (desc_dims[i] == 1) continue;
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if (desc_strides[i] != strides[i]) return kStrided;
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}
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return kContiguous;
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}
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// Storage pointer is type erased, to reduce template bloat, but we still
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// keep the size of the underlying element type for error checking.
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void* m_data;
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size_t m_data_type_size;
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// Destination buffer dimensions always match the dimensions of a tensor
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// block descriptor it belongs to, however strides might be different.
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Dimensions m_strides;
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DestinationBufferKind m_kind;
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};
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TensorBlockDescriptor(const IndexType offset, const Dimensions& dimensions,
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const DestinationBuffer& destination)
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: m_offset(offset),
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m_dimensions(dimensions),
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m_destination(destination) {}
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TensorBlockDescriptor(const IndexType offset, const Dimensions& dimensions)
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: m_offset(offset),
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m_dimensions(dimensions),
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m_destination(DestinationBuffer()) {}
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IndexType offset() const { return m_offset; }
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const Dimensions& dimensions() const { return m_dimensions; }
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IndexType dimension(int index) const { return m_dimensions[index]; }
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IndexType size() const { return array_prod<IndexType>(m_dimensions); }
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const DestinationBuffer& destination() const { return m_destination; }
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template <int Layout, typename Scalar>
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void AddDestinationBuffer(Scalar* dst_base, const Dimensions& dst_strides) {
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eigen_assert(dst_base != NULL);
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m_destination =
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DestinationBuffer::template make<Layout>(*this, dst_base, dst_strides);
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}
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template <int Layout, typename Scalar, typename DstStridesIndexType>
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void AddDestinationBuffer(
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Scalar* dst_base,
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const DSizes<DstStridesIndexType, NumDims>& dst_strides) {
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// DSizes constructor will do index type promotion if it's safe.
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AddDestinationBuffer<Layout>(dst_base, Dimensions(dst_strides));
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}
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TensorBlockDescriptor& DropDestinationBuffer() {
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m_destination.m_data = NULL;
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m_destination.m_kind = DestinationBuffer::kEmpty;
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return *this;
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}
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bool HasDestinationBuffer() const {
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return m_destination.kind() != DestinationBuffer::kEmpty;
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}
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// Returns a copy of `*this` with updated offset.
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TensorBlockDescriptor WithOffset(IndexType offset) const {
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return TensorBlockDescriptor(offset, m_dimensions, m_destination);
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}
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private:
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// Offset and dimensions are immutable after construction. Block descriptor
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// can only be mutated by adding or dropping destination.
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const IndexType m_offset;
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const Dimensions m_dimensions;
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DestinationBuffer m_destination;
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};
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// -------------------------------------------------------------------------- //
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// TensorBlockMapper is responsible for iterating over the blocks of a tensor.
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template <int NumDims, int Layout, typename IndexType = Eigen::Index>
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class TensorBlockMapper {
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typedef TensorBlockDescriptor<NumDims, IndexType> BlockDescriptor;
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public:
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typedef DSizes<IndexType, NumDims> Dimensions;
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TensorBlockMapper() = default;
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TensorBlockMapper(const DSizes<IndexType, NumDims>& dimensions,
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const TensorBlockResourceRequirements& requirements)
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: m_tensor_dimensions(dimensions), m_requirements(requirements) {
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// Compute block dimensions and the total number of blocks.
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InitializeBlockDimensions();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockCount() const {
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return m_total_block_count;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockTotalSize() const {
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return m_block_dimensions.TotalSize();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DSizes<IndexType, NumDims>&
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blockDimensions() const {
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return m_block_dimensions;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockDescriptor
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blockDescriptor(IndexType block_index) const {
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static const bool isColMajor = Layout == static_cast<int>(ColMajor);
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IndexType offset = 0;
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DSizes<IndexType, NumDims> dimensions;
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if (NumDims == 0) return BlockDescriptor(offset, dimensions);
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// Iterate outer -> inner dimensions.
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for (int i = NumDims - 1; i >= 0; --i) {
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const int dim = isColMajor ? i : NumDims - i - 1;
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const IndexType idx = block_index / m_block_strides[dim];
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block_index -= idx * m_block_strides[dim];
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const IndexType coord = idx * m_block_dimensions[dim];
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dimensions[dim] = numext::mini(m_tensor_dimensions[dim] - coord,
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m_block_dimensions[dim]);
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offset += coord * m_tensor_strides[dim];
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}
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return {offset, dimensions};
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}
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private:
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void InitializeBlockDimensions() {
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// Requested block shape and size.
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const TensorBlockShapeType shape_type = m_requirements.shape_type;
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IndexType target_block_size =
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numext::maxi<IndexType>(1, static_cast<IndexType>(m_requirements.size));
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IndexType tensor_size = m_tensor_dimensions.TotalSize();
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// Corner case: one of the dimensions is zero. Logic below is too complex
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// to handle this case on a general basis, just use unit block size.
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// Note: we must not yield blocks with zero dimensions (recipe for
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// overflows/underflows, divisions by zero and NaNs later).
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if (tensor_size == 0) {
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for (int i = 0; i < NumDims; ++i) {
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m_block_dimensions[i] = 1;
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}
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m_total_block_count = 0;
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return;
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}
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// If tensor fits into a target block size, evaluate it as a single block.
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if (tensor_size <= target_block_size) {
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m_block_dimensions = m_tensor_dimensions;
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m_total_block_count = 1;
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// The only valid block index is `0`, and in this case we do not need
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// to compute real strides for tensor or blocks (see blockDescriptor).
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for (int i = 0; i < NumDims; ++i) {
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m_tensor_strides[i] = 0;
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m_block_strides[i] = 1;
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}
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return;
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}
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static const bool isColMajor = Layout == static_cast<int>(ColMajor);
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// Block shape skewed towards inner dimension.
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if (shape_type == TensorBlockShapeType::kSkewedInnerDims) {
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IndexType coeff_to_allocate = target_block_size;
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for (int i = 0; i < NumDims; ++i) {
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const int dim = isColMajor ? i : NumDims - i - 1;
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m_block_dimensions[dim] =
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numext::mini(coeff_to_allocate, m_tensor_dimensions[dim]);
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coeff_to_allocate = divup(
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coeff_to_allocate,
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numext::maxi(static_cast<IndexType>(1), m_block_dimensions[dim]));
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}
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eigen_assert(coeff_to_allocate == 1);
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} else if (shape_type == TensorBlockShapeType::kUniformAllDims) {
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// Tensor will not fit within 'target_block_size' budget: calculate tensor
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// block dimension sizes based on "square" dimension size target.
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const IndexType dim_size_target = convert_index<IndexType>(
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std::pow(static_cast<float>(target_block_size),
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1.0f / static_cast<float>(m_block_dimensions.rank())));
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for (int i = 0; i < NumDims; ++i) {
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// TODO(andydavis) Adjust the inner most 'block_dim_size' to make it
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// a multiple of the packet size. Note that reducing
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// 'block_dim_size' in this manner can increase the number of
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// blocks, and so will amplify any per-block overhead.
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m_block_dimensions[i] =
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numext::mini(dim_size_target, m_tensor_dimensions[i]);
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}
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// Add any un-allocated coefficients to inner dimension(s).
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|
IndexType total_size = m_block_dimensions.TotalSize();
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for (int i = 0; i < NumDims; ++i) {
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const int dim = isColMajor ? i : NumDims - i - 1;
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if (m_block_dimensions[dim] < m_tensor_dimensions[dim]) {
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const IndexType total_size_other_dims =
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total_size / m_block_dimensions[dim];
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const IndexType alloc_avail =
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divup<IndexType>(target_block_size, total_size_other_dims);
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if (alloc_avail == m_block_dimensions[dim]) {
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// Insufficient excess coefficients to allocate.
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break;
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}
|
|
m_block_dimensions[dim] =
|
|
numext::mini(m_tensor_dimensions[dim], alloc_avail);
|
|
total_size = total_size_other_dims * m_block_dimensions[dim];
|
|
}
|
|
}
|
|
|
|
} else {
|
|
eigen_assert(false); // unknown block shape
|
|
}
|
|
|
|
eigen_assert(m_block_dimensions.TotalSize() >=
|
|
numext::mini<IndexType>(target_block_size,
|
|
m_tensor_dimensions.TotalSize()));
|
|
|
|
// Calculate block counts by dimension and total block count.
|
|
DSizes<IndexType, NumDims> block_count;
|
|
for (int i = 0; i < NumDims; ++i) {
|
|
block_count[i] = divup(m_tensor_dimensions[i], m_block_dimensions[i]);
|
|
}
|
|
m_total_block_count = array_prod(block_count);
|
|
|
|
// Calculate block strides (used for enumerating blocks).
|
|
m_tensor_strides = strides<Layout>(m_tensor_dimensions);
|
|
m_block_strides = strides<Layout>(block_count);
|
|
}
|
|
|
|
DSizes<IndexType, NumDims> m_tensor_dimensions;
|
|
TensorBlockResourceRequirements m_requirements;
|
|
|
|
DSizes<IndexType, NumDims> m_block_dimensions;
|
|
IndexType m_total_block_count;
|
|
|
|
DSizes<IndexType, NumDims> m_tensor_strides;
|
|
DSizes<IndexType, NumDims> m_block_strides;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorBlockScratchAllocator is responsible for allocating temporary buffers
|
|
// for block evaluation (output or input block materialization). Given that
|
|
// Eigen expression traversal order is deterministic, all temporary allocations
|
|
// are happening in the same order, and usually have exactly the same size.
|
|
// Scratch allocator keeps a trace of all dynamic allocations, and after the
|
|
// first block evaluation is completed, we should be able to reuse all the
|
|
// temporary buffers for the next block evaluation.
|
|
|
|
template <typename Device>
|
|
class TensorBlockScratchAllocator {
|
|
public:
|
|
explicit TensorBlockScratchAllocator(const Device& device)
|
|
: m_device(device), m_allocation_index(0) {}
|
|
|
|
~TensorBlockScratchAllocator() {
|
|
for (size_t i = 0; i < m_allocations.size(); ++i) {
|
|
m_device.deallocate(m_allocations[i].ptr);
|
|
}
|
|
}
|
|
|
|
void* allocate(size_t size) {
|
|
// TODO(ezhulenev): Remove when replaced with inlined vector.
|
|
if (m_allocations.capacity() == 0) m_allocations.reserve(8);
|
|
|
|
// Check if we already have an existing allocation att current index.
|
|
const int num_allocations = static_cast<int>(m_allocations.size());
|
|
const bool has_allocation = m_allocation_index < num_allocations;
|
|
|
|
// Allocation index can't be larger than the number of allocations.
|
|
eigen_assert(m_allocation_index <= num_allocations);
|
|
|
|
// If we have existing allocation, and its size is larger or equal to
|
|
// requested size, we do nothing.
|
|
|
|
// If current allocation can't fit requested size, we deallocate it, and
|
|
// replace with a larger allocation.
|
|
if (has_allocation && m_allocations[m_allocation_index].size < size) {
|
|
m_device.deallocate(m_allocations[m_allocation_index].ptr);
|
|
m_allocations[m_allocation_index].ptr = m_device.allocate(size);
|
|
m_allocations[m_allocation_index].size = size;
|
|
}
|
|
|
|
// Make a new allocation if we don't have and existing one.
|
|
if (!has_allocation) {
|
|
Allocation allocation;
|
|
allocation.ptr = m_device.allocate(size);
|
|
allocation.size = size;
|
|
m_allocations.push_back(allocation);
|
|
}
|
|
|
|
eigen_assert(m_allocations[m_allocation_index].ptr != NULL);
|
|
eigen_assert(m_allocations[m_allocation_index].size >= size);
|
|
|
|
return m_allocations[m_allocation_index++].ptr;
|
|
}
|
|
|
|
void reset() { m_allocation_index = 0; }
|
|
|
|
private:
|
|
struct Allocation {
|
|
void* ptr;
|
|
size_t size;
|
|
};
|
|
|
|
const Device& m_device;
|
|
int m_allocation_index;
|
|
// TODO(ezhulenev): This should be an inlined vector.
|
|
std::vector<Allocation> m_allocations;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorBlockKind represents all possible block kinds, that can be produced by
|
|
// TensorEvaluator::evalBlock function.
|
|
enum TensorBlockKind {
|
|
// Tensor block that is a lazy expression that must be assigned to a
|
|
// destination using TensorBlockAssign.
|
|
kExpr,
|
|
|
|
// Tensor block that is a view into a memory buffer owned by an underlying
|
|
// Tensor expression (e.g. it can be a view into a Tensor buffer).
|
|
kView,
|
|
|
|
// Tensor block that was materialized in a scratch memory buffer, allocated
|
|
// with TensorBlockScratchAllocator. This block must be copied to a
|
|
// destination, similar to a block of `kExpr` type.
|
|
kMaterializedInScratch,
|
|
|
|
// Tensor block that was materialized directly into the final output memory
|
|
// buffer. For example if the left side of an assignment is a Tensor, we can
|
|
// directly materialize the block in the destination memory.
|
|
//
|
|
// If strides in the output buffer do not match tensor block strides, the
|
|
// Tensor expression will be invalid, and should not be used by
|
|
// TensorBlockAssign or for constructing another block expression.
|
|
kMaterializedInOutput
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorBlockNotImplemented should be used to defined TensorBlock typedef in
|
|
// TensorEvaluators that do not support block evaluation.
|
|
|
|
class TensorBlockNotImplemented {
|
|
public:
|
|
typedef void XprType;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// XprScalar extracts Scalar type from the Eigen expressions (if expression type
|
|
// is not void). It's required to be able to define lazy block expression for
|
|
// argument types, that do not support block evaluation.
|
|
|
|
template <typename XprType>
|
|
struct XprScalar {
|
|
typedef typename XprType::Scalar type;
|
|
};
|
|
template <>
|
|
struct XprScalar<void> {
|
|
typedef void type;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorMaterializedBlock is a fully evaluated block of the original tensor,
|
|
// and XprType is just a TensorMap over the data. This block type is typically
|
|
// used to materialize blocks of tensor expressions, that can't be efficiently
|
|
// represented as lazy Tensor expressions with fast coeff/packet operations,
|
|
// e.g. we materialize all broadcasts into evaluated blocks.
|
|
//
|
|
// TensorMaterializedBlock does not own its memory buffer, it's either a memory
|
|
// buffer that backs the original expression (e.g. block is just a view into a
|
|
// Tensor), or a memory buffer allocated with scratch allocator, and in this
|
|
// case the scratch allocator will deallocate it at the end of block based
|
|
// expression execution.
|
|
//
|
|
// If the block was evaluated directly into the output buffer, and strides in
|
|
// the output buffer do not match block strides, the TensorMap expression will
|
|
// be invalid, and should never be used in block assignment or any other tensor
|
|
// expression.
|
|
|
|
template <typename Scalar, int NumDims, int Layout,
|
|
typename IndexType = Eigen::Index>
|
|
class TensorMaterializedBlock {
|
|
public:
|
|
typedef DSizes<IndexType, NumDims> Dimensions;
|
|
typedef TensorMap<const Tensor<Scalar, NumDims, Layout> > XprType;
|
|
|
|
TensorMaterializedBlock(TensorBlockKind kind, const Scalar* data,
|
|
const Dimensions& dimensions, bool valid_expr = true)
|
|
: m_kind(kind),
|
|
m_data(data),
|
|
m_dimensions(dimensions),
|
|
m_expr(m_data, m_dimensions),
|
|
m_valid_expr(valid_expr) {
|
|
eigen_assert(m_kind == internal::TensorBlockKind::kView ||
|
|
m_kind == internal::TensorBlockKind::kMaterializedInScratch ||
|
|
m_kind == internal::TensorBlockKind::kMaterializedInOutput);
|
|
}
|
|
|
|
TensorBlockKind kind() const { return m_kind; }
|
|
// NOTE(ezhulenev): Returning XprType by value like in other block types
|
|
// causes asan failures. The theory is that XprType::Nested doesn't work
|
|
// properly for TensorMap.
|
|
const XprType& expr() const {
|
|
eigen_assert(m_valid_expr);
|
|
return m_expr;
|
|
}
|
|
const Scalar* data() const { return m_data; }
|
|
void cleanup() {}
|
|
|
|
typedef internal::TensorBlockDescriptor<NumDims, IndexType> TensorBlockDesc;
|
|
|
|
// TensorMaterializedBlock can be backed by different types of storage:
|
|
//
|
|
// (1) Contiguous block of memory allocated with scratch allocator.
|
|
// (2) Contiguous block of memory reused from tensor block descriptor
|
|
// destination buffer.
|
|
// (3) Strided block of memory reused from tensor block descriptor
|
|
// destination buffer.
|
|
//
|
|
class Storage {
|
|
public:
|
|
Scalar* data() const { return m_data; }
|
|
const Dimensions& dimensions() const { return m_dimensions; }
|
|
const Dimensions& strides() const { return m_strides; }
|
|
|
|
TensorMaterializedBlock AsTensorMaterializedBlock() const {
|
|
return TensorMaterializedBlock(
|
|
m_materialized_in_output
|
|
? internal::TensorBlockKind::kMaterializedInOutput
|
|
: internal::TensorBlockKind::kMaterializedInScratch,
|
|
m_data, m_dimensions, !m_strided_storage);
|
|
}
|
|
|
|
private:
|
|
friend class TensorMaterializedBlock;
|
|
|
|
Storage(Scalar* data, const Dimensions& dimensions,
|
|
const Dimensions& strides, bool materialized_in_output,
|
|
bool strided_storage)
|
|
: m_data(data),
|
|
m_dimensions(dimensions),
|
|
m_strides(strides),
|
|
m_materialized_in_output(materialized_in_output),
|
|
m_strided_storage(strided_storage) {}
|
|
|
|
Scalar* m_data;
|
|
Dimensions m_dimensions;
|
|
Dimensions m_strides;
|
|
bool m_materialized_in_output;
|
|
bool m_strided_storage;
|
|
};
|
|
|
|
// Creates a storage for materialized block either from the block descriptor
|
|
// destination buffer, or allocates a new buffer with scratch allocator.
|
|
template <typename TensorBlockScratch>
|
|
EIGEN_STRONG_INLINE static Storage prepareStorage(
|
|
TensorBlockDesc& desc, TensorBlockScratch& scratch,
|
|
bool allow_strided_storage = false) {
|
|
// Try to reuse destination as an output block buffer.
|
|
typedef typename TensorBlockDesc::DestinationBuffer DestinationBuffer;
|
|
|
|
if (desc.destination().kind() == DestinationBuffer::kContiguous) {
|
|
Scalar* buffer = desc.destination().template data<Scalar>();
|
|
desc.DropDestinationBuffer();
|
|
return Storage(buffer, desc.dimensions(),
|
|
internal::strides<Layout>(desc.dimensions()),
|
|
/*materialized_in_output=*/true,
|
|
/*strided_storage=*/false);
|
|
|
|
} else if (desc.destination().kind() == DestinationBuffer::kStrided &&
|
|
allow_strided_storage) {
|
|
Scalar* buffer = desc.destination().template data<Scalar>();
|
|
desc.DropDestinationBuffer();
|
|
return Storage(buffer, desc.dimensions(), desc.destination().strides(),
|
|
/*materialized_in_output=*/true, /*strided_storage=*/true);
|
|
|
|
} else {
|
|
void* mem = scratch.allocate(desc.size() * sizeof(Scalar));
|
|
return Storage(static_cast<Scalar*>(mem), desc.dimensions(),
|
|
internal::strides<Layout>(desc.dimensions()),
|
|
/*materialized_in_output=*/false,
|
|
/*strided_storage=*/false);
|
|
}
|
|
}
|
|
|
|
// Creates a materialized block for the given descriptor from a memory buffer.
|
|
template <typename DataDimensions, typename TensorBlockScratch>
|
|
EIGEN_STRONG_INLINE static TensorMaterializedBlock materialize(
|
|
const Scalar* data, const DataDimensions& data_dims,
|
|
TensorBlockDesc& desc, TensorBlockScratch& scratch) {
|
|
eigen_assert(array_size<DataDimensions>::value == desc.dimensions().size());
|
|
|
|
// If a tensor block dimensions covers a contiguous block of the underlying
|
|
// memory, we can skip block buffer memory allocation, and construct a block
|
|
// from existing `data` memory buffer.
|
|
//
|
|
// Example: (RowMajor layout)
|
|
// data_dims: [11, 12, 13, 14]
|
|
// desc.dimensions(): [1, 1, 3, 14]
|
|
//
|
|
// In this case we can construct a TensorBlock starting at
|
|
// `data + desc.offset()`, with a `desc.dimensions()` block sizes.
|
|
static const bool is_col_major = Layout == ColMajor;
|
|
|
|
// Find out how many inner dimensions have a matching size.
|
|
int num_matching_inner_dims = 0;
|
|
for (int i = 0; i < NumDims; ++i) {
|
|
int dim = is_col_major ? i : NumDims - i - 1;
|
|
if (data_dims[dim] != desc.dimensions()[dim]) break;
|
|
++num_matching_inner_dims;
|
|
}
|
|
|
|
// All the outer dimensions must be of size `1`, except a single dimension
|
|
// before the matching inner dimension (`3` in the example above).
|
|
bool can_use_direct_access = true;
|
|
for (int i = num_matching_inner_dims + 1; i < NumDims; ++i) {
|
|
int dim = is_col_major ? i : NumDims - i - 1;
|
|
if (desc.dimension(dim) != 1) {
|
|
can_use_direct_access = false;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (can_use_direct_access) {
|
|
const Scalar* block_start = data + desc.offset();
|
|
return TensorMaterializedBlock(internal::TensorBlockKind::kView,
|
|
block_start, desc.dimensions());
|
|
|
|
} else {
|
|
// Reuse destination buffer or allocate new buffer with scratch allocator.
|
|
const Storage storage = prepareStorage(desc, scratch);
|
|
|
|
typedef internal::TensorBlockIO<Scalar, IndexType, NumDims, Layout>
|
|
TensorBlockIO;
|
|
typedef typename TensorBlockIO::Dst TensorBlockIODst;
|
|
typedef typename TensorBlockIO::Src TensorBlockIOSrc;
|
|
|
|
TensorBlockIOSrc src(internal::strides<Layout>(Dimensions(data_dims)),
|
|
data, desc.offset());
|
|
TensorBlockIODst dst(storage.dimensions(), storage.strides(),
|
|
storage.data());
|
|
|
|
TensorBlockIO::Copy(dst, src);
|
|
return storage.AsTensorMaterializedBlock();
|
|
}
|
|
}
|
|
|
|
private:
|
|
TensorBlockKind m_kind;
|
|
const Scalar* m_data;
|
|
Dimensions m_dimensions;
|
|
XprType m_expr;
|
|
bool m_valid_expr;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorCwiseUnaryBlock is a lazy tensor expression block that applies UnaryOp
|
|
// functor to the blocks produced by the underlying Tensor expression.
|
|
|
|
template <typename UnaryOp, typename ArgTensorBlock>
|
|
class TensorCwiseUnaryBlock {
|
|
static const bool NoArgBlockAccess =
|
|
internal::is_void<typename ArgTensorBlock::XprType>::value;
|
|
|
|
public:
|
|
typedef typename conditional<
|
|
NoArgBlockAccess, void,
|
|
TensorCwiseUnaryOp<UnaryOp, const typename ArgTensorBlock::XprType> >::
|
|
type XprType;
|
|
|
|
typedef typename XprScalar<XprType>::type Scalar;
|
|
|
|
TensorCwiseUnaryBlock(const ArgTensorBlock& arg_block, const UnaryOp& functor)
|
|
: m_arg_block(arg_block), m_functor(functor) {}
|
|
|
|
TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
|
|
|
|
XprType expr() const { return XprType(m_arg_block.expr(), m_functor); }
|
|
const Scalar* data() const { return NULL; }
|
|
void cleanup() { m_arg_block.cleanup(); }
|
|
|
|
private:
|
|
ArgTensorBlock m_arg_block;
|
|
UnaryOp m_functor;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorCwiseUnaryBlock is a lazy tensor expression block that applies BinaryOp
|
|
// functor to the blocks produced by the underlying Tensor expression.
|
|
|
|
template <typename BinaryOp, typename LhsTensorBlock, typename RhsTensorBlock>
|
|
class TensorCwiseBinaryBlock {
|
|
static const bool NoArgBlockAccess =
|
|
internal::is_void<typename LhsTensorBlock::XprType>::value ||
|
|
internal::is_void<typename RhsTensorBlock::XprType>::value;
|
|
|
|
public:
|
|
typedef typename conditional<
|
|
NoArgBlockAccess, void,
|
|
TensorCwiseBinaryOp<BinaryOp, const typename LhsTensorBlock::XprType,
|
|
const typename RhsTensorBlock::XprType> >::type
|
|
XprType;
|
|
|
|
typedef typename XprScalar<XprType>::type Scalar;
|
|
|
|
TensorCwiseBinaryBlock(const LhsTensorBlock& left_block,
|
|
const RhsTensorBlock& right_block,
|
|
const BinaryOp& functor)
|
|
: m_left_block(left_block),
|
|
m_right_block(right_block),
|
|
m_functor(functor) {}
|
|
|
|
TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
|
|
|
|
XprType expr() const {
|
|
return XprType(m_left_block.expr(), m_right_block.expr(), m_functor);
|
|
}
|
|
|
|
const Scalar* data() const { return NULL; }
|
|
|
|
void cleanup() {
|
|
m_left_block.cleanup();
|
|
m_right_block.cleanup();
|
|
}
|
|
|
|
private:
|
|
LhsTensorBlock m_left_block;
|
|
RhsTensorBlock m_right_block;
|
|
BinaryOp m_functor;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorUnaryExprBlock is a lazy tensor expression block that can construct
|
|
// an arbitrary tensor expression from a block of the underlying type (this is a
|
|
// generalization of the TensorCwiseUnaryBlock for arbitrary expressions).
|
|
|
|
template <typename BlockFactory, typename ArgTensorBlock>
|
|
class TensorUnaryExprBlock {
|
|
typedef typename ArgTensorBlock::XprType ArgXprType;
|
|
static const bool NoArgBlockAccess = internal::is_void<ArgXprType>::value;
|
|
|
|
public:
|
|
typedef typename conditional<
|
|
NoArgBlockAccess, void,
|
|
typename BlockFactory::template XprType<ArgXprType>::type>::type XprType;
|
|
|
|
typedef typename XprScalar<XprType>::type Scalar;
|
|
|
|
TensorUnaryExprBlock(const ArgTensorBlock& arg_block,
|
|
const BlockFactory& factory)
|
|
: m_arg_block(arg_block), m_factory(factory) {}
|
|
|
|
TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
|
|
XprType expr() const { return m_factory.expr(m_arg_block.expr()); }
|
|
const Scalar* data() const { return NULL; }
|
|
void cleanup() { m_arg_block.cleanup(); }
|
|
|
|
private:
|
|
ArgTensorBlock m_arg_block;
|
|
BlockFactory m_factory;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorTernaryExprBlock is a lazy tensor expression block that can construct
|
|
// an arbitrary tensor expression from three blocks of the underlying type.
|
|
|
|
template <typename BlockFactory, typename Arg1TensorBlock,
|
|
typename Arg2TensorBlock, typename Arg3TensorBlock>
|
|
class TensorTernaryExprBlock {
|
|
typedef typename Arg1TensorBlock::XprType Arg1XprType;
|
|
typedef typename Arg2TensorBlock::XprType Arg2XprType;
|
|
typedef typename Arg3TensorBlock::XprType Arg3XprType;
|
|
|
|
static const bool NoArgBlockAccess = internal::is_void<Arg1XprType>::value ||
|
|
internal::is_void<Arg2XprType>::value ||
|
|
internal::is_void<Arg3XprType>::value;
|
|
|
|
public:
|
|
typedef typename conditional<
|
|
NoArgBlockAccess, void,
|
|
typename BlockFactory::template XprType<Arg1XprType, Arg2XprType,
|
|
Arg3XprType>::type>::type XprType;
|
|
|
|
typedef typename XprScalar<XprType>::type Scalar;
|
|
|
|
TensorTernaryExprBlock(const Arg1TensorBlock& arg1_block,
|
|
const Arg2TensorBlock& arg2_block,
|
|
const Arg3TensorBlock& arg3_block,
|
|
const BlockFactory& factory)
|
|
: m_arg1_block(arg1_block),
|
|
m_arg2_block(arg2_block),
|
|
m_arg3_block(arg3_block),
|
|
m_factory(factory) {}
|
|
|
|
TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
|
|
XprType expr() const {
|
|
return m_factory.expr(m_arg1_block.expr(), m_arg2_block.expr(),
|
|
m_arg3_block.expr());
|
|
}
|
|
const Scalar* data() const { return NULL; }
|
|
void cleanup() {
|
|
m_arg1_block.cleanup();
|
|
m_arg2_block.cleanup();
|
|
m_arg3_block.cleanup();
|
|
}
|
|
|
|
private:
|
|
Arg1TensorBlock m_arg1_block;
|
|
Arg2TensorBlock m_arg2_block;
|
|
Arg3TensorBlock m_arg3_block;
|
|
BlockFactory m_factory;
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// StridedLinearBufferCopy provides a method to copy data between two linear
|
|
// buffers with different strides, with optimized paths for scatter/gather.
|
|
|
|
template <typename Scalar, typename IndexType>
|
|
class StridedLinearBufferCopy {
|
|
typedef typename packet_traits<Scalar>::type Packet;
|
|
enum {
|
|
Vectorizable = packet_traits<Scalar>::Vectorizable,
|
|
PacketSize = packet_traits<Scalar>::size
|
|
};
|
|
|
|
public:
|
|
// Specifying linear copy kind statically gives ~30% speedup for small sizes.
|
|
enum class Kind {
|
|
Linear = 0, // src_stride == 1 && dst_stride == 1
|
|
Scatter = 1, // src_stride == 1 && dst_stride != 1
|
|
FillLinear = 2, // src_stride == 0 && dst_stride == 1
|
|
FillScatter = 3, // src_stride == 0 && dst_stride != 1
|
|
Gather = 4, // dst_stride == 1
|
|
Random = 5 // everything else
|
|
};
|
|
|
|
struct Dst {
|
|
Dst(IndexType o, IndexType s, Scalar* d) : offset(o), stride(s), data(d) {}
|
|
|
|
IndexType offset;
|
|
IndexType stride;
|
|
Scalar* data;
|
|
};
|
|
|
|
struct Src {
|
|
Src(IndexType o, IndexType s, const Scalar* d)
|
|
: offset(o), stride(s), data(d) {}
|
|
|
|
IndexType offset;
|
|
IndexType stride;
|
|
const Scalar* data;
|
|
};
|
|
|
|
template <typename StridedLinearBufferCopy::Kind kind>
|
|
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Dst& dst,
|
|
const Src& src,
|
|
const size_t count) {
|
|
Run<kind>(count, dst.offset, dst.stride, dst.data, src.offset, src.stride,
|
|
src.data);
|
|
}
|
|
|
|
private:
|
|
template <typename StridedLinearBufferCopy::Kind kind>
|
|
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
|
|
const IndexType count, const IndexType dst_offset,
|
|
const IndexType dst_stride, Scalar* EIGEN_RESTRICT dst_data,
|
|
const IndexType src_offset, const IndexType src_stride,
|
|
const Scalar* EIGEN_RESTRICT src_data) {
|
|
const Scalar* src = &src_data[src_offset];
|
|
Scalar* dst = &dst_data[dst_offset];
|
|
|
|
if (!Vectorizable) {
|
|
for (Index i = 0; i < count; ++i) {
|
|
dst[i * dst_stride] = src[i * src_stride];
|
|
}
|
|
return;
|
|
}
|
|
|
|
const IndexType vectorized_size = count - PacketSize;
|
|
IndexType i = 0;
|
|
|
|
if (kind == StridedLinearBufferCopy::Kind::Linear) {
|
|
// ******************************************************************** //
|
|
// Linear copy from `src` to `dst`.
|
|
const IndexType unrolled_size = count - 4 * PacketSize;
|
|
eigen_assert(src_stride == 1 && dst_stride == 1);
|
|
for (; i <= unrolled_size; i += 4 * PacketSize) {
|
|
for (int j = 0; j < 4; ++j) {
|
|
Packet p = ploadu<Packet>(src + i + j * PacketSize);
|
|
pstoreu<Scalar, Packet>(dst + i + j * PacketSize, p);
|
|
}
|
|
}
|
|
for (; i <= vectorized_size; i += PacketSize) {
|
|
Packet p = ploadu<Packet>(src + i);
|
|
pstoreu<Scalar, Packet>(dst + i, p);
|
|
}
|
|
for (; i < count; ++i) {
|
|
dst[i] = src[i];
|
|
}
|
|
// ******************************************************************** //
|
|
} else if (kind == StridedLinearBufferCopy::Kind::Scatter) {
|
|
// Scatter from `src` to `dst`.
|
|
eigen_assert(src_stride == 1 && dst_stride != 1);
|
|
for (; i <= vectorized_size; i += PacketSize) {
|
|
Packet p = ploadu<Packet>(src + i);
|
|
pscatter<Scalar, Packet>(dst + i * dst_stride, p, dst_stride);
|
|
}
|
|
for (; i < count; ++i) {
|
|
dst[i * dst_stride] = src[i];
|
|
}
|
|
// ******************************************************************** //
|
|
} else if (kind == StridedLinearBufferCopy::Kind::FillLinear) {
|
|
// Fill `dst` with value at `*src`.
|
|
eigen_assert(src_stride == 0 && dst_stride == 1);
|
|
const IndexType unrolled_size = count - 4 * PacketSize;
|
|
Packet p = pload1<Packet>(src);
|
|
for (; i <= unrolled_size; i += 4 * PacketSize) {
|
|
for (int j = 0; j < 4; ++j) {
|
|
pstoreu<Scalar, Packet>(dst + i + j * PacketSize, p);
|
|
}
|
|
}
|
|
for (; i <= vectorized_size; i += PacketSize) {
|
|
pstoreu<Scalar, Packet>(dst + i, p);
|
|
}
|
|
for (; i < count; ++i) {
|
|
dst[i] = *src;
|
|
}
|
|
// ******************************************************************** //
|
|
} else if (kind == StridedLinearBufferCopy::Kind::FillScatter) {
|
|
// Scatter `*src` into `dst`.
|
|
eigen_assert(src_stride == 0 && dst_stride != 1);
|
|
Packet p = pload1<Packet>(src);
|
|
for (; i <= vectorized_size; i += PacketSize) {
|
|
pscatter<Scalar, Packet>(dst + i * dst_stride, p, dst_stride);
|
|
}
|
|
for (; i < count; ++i) {
|
|
dst[i * dst_stride] = *src;
|
|
}
|
|
// ******************************************************************** //
|
|
} else if (kind == StridedLinearBufferCopy::Kind::Gather) {
|
|
// Gather from `src` into `dst`.
|
|
eigen_assert(dst_stride == 1);
|
|
for (; i <= vectorized_size; i += PacketSize) {
|
|
Packet p = pgather<Scalar, Packet>(src + i * src_stride, src_stride);
|
|
pstoreu<Scalar, Packet>(dst + i, p);
|
|
}
|
|
for (; i < count; ++i) {
|
|
dst[i] = src[i * src_stride];
|
|
}
|
|
// ******************************************************************** //
|
|
} else if (kind == StridedLinearBufferCopy::Kind::Random) {
|
|
// Random.
|
|
for (; i < count; ++i) {
|
|
dst[i * dst_stride] = src[i * src_stride];
|
|
}
|
|
} else {
|
|
eigen_assert(false);
|
|
}
|
|
}
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorBlockIO copies data from `src` tensor block, to the `dst` tensor block.
|
|
// It's possible to specify src->dst dimension mapping for the copy operation.
|
|
// Dimensions of `dst` specify how many elements have to be copied, for the
|
|
// `src` we need to know only stride to navigate through source memory buffer.
|
|
|
|
template <typename Scalar, typename IndexType, int NumDims, int Layout>
|
|
class TensorBlockIO {
|
|
static const bool IsColMajor = (Layout == ColMajor);
|
|
|
|
typedef StridedLinearBufferCopy<Scalar, IndexType> LinCopy;
|
|
|
|
public:
|
|
typedef DSizes<IndexType, NumDims> Dimensions;
|
|
typedef DSizes<int, NumDims> DimensionsMap;
|
|
|
|
struct Dst {
|
|
Dst(const Dimensions& dst_dims, const Dimensions& dst_strides, Scalar* dst,
|
|
IndexType dst_offset = 0)
|
|
: dims(dst_dims), strides(dst_strides), data(dst), offset(dst_offset) {}
|
|
|
|
Dimensions dims;
|
|
Dimensions strides;
|
|
Scalar* data;
|
|
IndexType offset;
|
|
};
|
|
|
|
struct Src {
|
|
Src(const Dimensions& src_strides, const Scalar* src,
|
|
IndexType src_offset = 0)
|
|
: strides(src_strides), data(src), offset(src_offset) {}
|
|
|
|
Dimensions strides;
|
|
const Scalar* data;
|
|
IndexType offset;
|
|
};
|
|
|
|
// Copies data to `dst` from `src`, using provided dimensions mapping:
|
|
//
|
|
// src_dimension_index = dst_to_src_dim_map[dst_dimension_index]
|
|
//
|
|
// Returns the number of copied elements.
|
|
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType Copy(
|
|
const Dst& dst, const Src& src, const DimensionsMap& dst_to_src_dim_map) {
|
|
// Copy single scalar value from `src` to `dst`.
|
|
if (NumDims == 0) {
|
|
*(dst.data + dst.offset) = *(src.data + src.offset);
|
|
return 1;
|
|
}
|
|
|
|
// Both `dst` and `src` must have contiguous innermost dimension. We also
|
|
// accept the special case with stride '0', because it's used as a trick to
|
|
// implement broadcasting.
|
|
{
|
|
int inner_dim = IsColMajor ? 0 : NumDims - 1;
|
|
EIGEN_UNUSED_VARIABLE(inner_dim);
|
|
eigen_assert(dst.strides[inner_dim] == 1 || dst.strides[inner_dim] == 0);
|
|
eigen_assert(src.strides[inner_dim] == 1 || src.strides[inner_dim] == 0);
|
|
}
|
|
|
|
// Give a shorter name to `dst_to_src_dim_map`.
|
|
const DimensionsMap& dim_map = dst_to_src_dim_map;
|
|
|
|
// Do not squeeze reordered inner dimensions.
|
|
int num_squeezable_dims = NumSqueezableInnerDims(dim_map);
|
|
|
|
// NOTE: We find the innermost dimension (contiguous in memory) in the dst
|
|
// block, and we write data linearly into that dimension, reading it from
|
|
// the src. If dimensions are reordered, we might end up reading data from
|
|
// the src with `stride != 1`.
|
|
//
|
|
// NOTE: Random-Read/Linear-Write can be up to ~2X faster than
|
|
// Linear-Read/Random-Write: https://stackoverflow.com/a/54935680
|
|
|
|
// Find the innermost dimension in the dst whose size is not 1. This is the
|
|
// effective inner dim.
|
|
int num_size_one_inner_dims = 0;
|
|
for (int i = 0; i < num_squeezable_dims; ++i) {
|
|
const int dst_dim = IsColMajor ? i : NumDims - i - 1;
|
|
if (dst.dims[dst_dim] != 1) break;
|
|
num_size_one_inner_dims++;
|
|
}
|
|
|
|
// If all dimensions are of size 1, just copy a scalar from `src` to `dst`.
|
|
if (num_size_one_inner_dims == NumDims) {
|
|
*(dst.data + dst.offset) = *(src.data + src.offset);
|
|
return 1;
|
|
}
|
|
|
|
// Outermost dimension in the dst with `stride == 1` (contiguous in memory).
|
|
const int dst_stride1_dim = IsColMajor
|
|
? num_size_one_inner_dims
|
|
: NumDims - num_size_one_inner_dims - 1;
|
|
|
|
// Dimension in the src that corresponds to the dst innermost dimension.
|
|
const int src_dim_for_dst_stride1_dim =
|
|
NumDims == 0 ? 1 : dim_map[dst_stride1_dim];
|
|
|
|
// Size of the innermost dimension (length of contiguous blocks of memory).
|
|
IndexType dst_inner_dim_size = NumDims == 0 ? 1 : dst.dims[dst_stride1_dim];
|
|
|
|
// Squeeze multiple inner dims into one if they are contiguous in `dst` and
|
|
// `src` memory, so we can do less linear copy calls.
|
|
for (int i = num_size_one_inner_dims + 1; i < num_squeezable_dims; ++i) {
|
|
const int dst_dim = IsColMajor ? i : NumDims - i - 1;
|
|
const IndexType dst_stride = dst.strides[dst_dim];
|
|
const IndexType src_stride = src.strides[dim_map[dst_dim]];
|
|
if (dst_inner_dim_size == dst_stride && dst_stride == src_stride) {
|
|
dst_inner_dim_size *= dst.dims[dst_dim];
|
|
++num_size_one_inner_dims;
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Setup strides to read data from `src` and write to `dst`.
|
|
IndexType input_offset = src.offset;
|
|
IndexType output_offset = dst.offset;
|
|
IndexType input_stride =
|
|
NumDims == 0 ? 1 : src.strides[src_dim_for_dst_stride1_dim];
|
|
IndexType output_stride = NumDims == 0 ? 1 : dst.strides[dst_stride1_dim];
|
|
|
|
const int at_least_1_dim = NumDims <= 1 ? 1 : NumDims - 1;
|
|
array<BlockIteratorState, at_least_1_dim> it;
|
|
|
|
// Initialize block iterator state. Squeeze away any dimension of size 1.
|
|
int idx = 0; // currently initialized iterator state index
|
|
for (int i = num_size_one_inner_dims; i < NumDims - 1; ++i) {
|
|
const int dst_dim = IsColMajor ? i + 1 : NumDims - i - 2;
|
|
if (dst.dims[dst_dim] == 1) continue;
|
|
|
|
it[idx].size = dst.dims[dst_dim];
|
|
it[idx].input_stride = src.strides[dim_map[dst_dim]];
|
|
it[idx].output_stride = dst.strides[dst_dim];
|
|
|
|
it[idx].input_span = it[idx].input_stride * (it[idx].size - 1);
|
|
it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
|
|
|
|
idx++;
|
|
}
|
|
|
|
// Iterate copying data from src to dst.
|
|
const IndexType block_total_size = NumDims == 0 ? 1 : dst.dims.TotalSize();
|
|
|
|
#define COPY_INNER_DIM(KIND) \
|
|
IndexType num_copied = 0; \
|
|
for (num_copied = 0; num_copied < block_total_size; \
|
|
num_copied += dst_inner_dim_size) { \
|
|
LinCopy::template Run<KIND>( \
|
|
typename LinCopy::Dst(output_offset, output_stride, dst.data), \
|
|
typename LinCopy::Src(input_offset, input_stride, src.data), \
|
|
dst_inner_dim_size); \
|
|
\
|
|
for (int j = 0; j < idx; ++j) { \
|
|
if (++it[j].count < it[j].size) { \
|
|
input_offset += it[j].input_stride; \
|
|
output_offset += it[j].output_stride; \
|
|
break; \
|
|
} \
|
|
it[j].count = 0; \
|
|
input_offset -= it[j].input_span; \
|
|
output_offset -= it[j].output_span; \
|
|
} \
|
|
} \
|
|
return num_copied;
|
|
|
|
if (input_stride == 1 && output_stride == 1) {
|
|
COPY_INNER_DIM(LinCopy::Kind::Linear);
|
|
} else if (input_stride == 1 && output_stride != 1) {
|
|
COPY_INNER_DIM(LinCopy::Kind::Scatter);
|
|
} else if (input_stride == 0 && output_stride == 1) {
|
|
COPY_INNER_DIM(LinCopy::Kind::FillLinear);
|
|
} else if (input_stride == 0 && output_stride != 1) {
|
|
COPY_INNER_DIM(LinCopy::Kind::FillScatter);
|
|
} else if (output_stride == 1) {
|
|
COPY_INNER_DIM(LinCopy::Kind::Gather);
|
|
} else {
|
|
COPY_INNER_DIM(LinCopy::Kind::Random);
|
|
}
|
|
|
|
#undef COPY_INNER_DIM
|
|
}
|
|
|
|
// Copy from `src` to `dst` with an identity src->dst dimension map. Returns
|
|
// the number of copied elements.
|
|
static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexType Copy(const Dst& dst,
|
|
const Src& src) {
|
|
DimensionsMap dst_to_src_map;
|
|
for (int i = 0; i < NumDims; ++i) dst_to_src_map[i] = i;
|
|
return Copy(dst, src, dst_to_src_map);
|
|
}
|
|
|
|
private:
|
|
struct BlockIteratorState {
|
|
BlockIteratorState()
|
|
: size(0),
|
|
count(0),
|
|
input_stride(0),
|
|
output_stride(0),
|
|
input_span(0),
|
|
output_span(0) {}
|
|
|
|
IndexType size;
|
|
IndexType count;
|
|
IndexType input_stride;
|
|
IndexType output_stride;
|
|
IndexType input_span;
|
|
IndexType output_span;
|
|
};
|
|
|
|
// Compute how many inner dimensions it's allowed to squeeze when doing IO
|
|
// between two tensor blocks. It's safe to squeeze inner dimensions, only
|
|
// if they are not reordered.
|
|
static int NumSqueezableInnerDims(const DimensionsMap& dim_map) {
|
|
int num_squeezable_dims = 0;
|
|
for (int i = 0; i < NumDims; ++i) {
|
|
const int dim = IsColMajor ? i : NumDims - i - 1;
|
|
if (dim_map[dim] != dim) break;
|
|
num_squeezable_dims++;
|
|
}
|
|
return num_squeezable_dims;
|
|
}
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
// TensorBlockAssignment assigns a block expression of type `TensorBlockExpr` to
|
|
// a Tensor block defined by `desc`, backed by a memory buffer at `target`.
|
|
//
|
|
// Currently there is no way to write from a Tensor expression to a block of
|
|
// memory, if dimensions are reordered. If you need to do that, you should
|
|
// materialize a Tensor block expression into a memory buffer, and then use
|
|
// TensorBlockIO to copy data between two memory buffers with a custom
|
|
// `target->src` dimension map (see definition above).
|
|
//
|
|
// Also currently the innermost dimension of `target` must have a stride '1'
|
|
// (contiguous in memory). This restriction could be lifted with a `pscatter`,
|
|
// but in practice it's never needed, and there is a similar TensorBlockIO
|
|
// workaround for that.
|
|
//
|
|
// TODO(ezhulenev): TensorBlockAssignment is a special case of TensorBlockIO
|
|
// where `src` is a tensor expression. Explore if it is possible to rewrite IO
|
|
// to use expressions instead of pointers, and after that TensorBlockAssignment
|
|
// will become an alias to IO.
|
|
template <typename Scalar, int NumDims, typename TensorBlockExpr,
|
|
typename IndexType = Eigen::Index>
|
|
class TensorBlockAssignment {
|
|
// We will use coeff/packet path to evaluate block expressions.
|
|
typedef TensorEvaluator<const TensorBlockExpr, DefaultDevice>
|
|
TensorBlockEvaluator;
|
|
|
|
typedef DSizes<IndexType, NumDims> Dimensions;
|
|
|
|
enum {
|
|
Vectorizable = packet_traits<Scalar>::Vectorizable,
|
|
PacketSize = packet_traits<Scalar>::size
|
|
};
|
|
|
|
template <bool Vectorizable, typename Evaluator>
|
|
struct InnerDimAssign {
|
|
EIGEN_ALWAYS_INLINE static void Run(Scalar* target, IndexType count,
|
|
const Evaluator& eval,
|
|
IndexType eval_offset) {
|
|
for (IndexType i = 0; i < count; ++i) {
|
|
target[i] = eval.coeff(eval_offset + i);
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename Evaluator>
|
|
struct InnerDimAssign<true, Evaluator> {
|
|
EIGEN_ALWAYS_INLINE static void Run(Scalar* target, IndexType count,
|
|
const Evaluator& eval,
|
|
IndexType eval_offset) {
|
|
typedef typename packet_traits<Scalar>::type Packet;
|
|
|
|
const IndexType unrolled_size = count - 4 * PacketSize;
|
|
const IndexType vectorized_size = count - PacketSize;
|
|
IndexType i = 0;
|
|
|
|
for (; i <= unrolled_size; i += 4 * PacketSize) {
|
|
for (int j = 0; j < 4; ++j) {
|
|
const IndexType idx = eval_offset + i + j * PacketSize;
|
|
Packet p = eval.template packet<Unaligned>(idx);
|
|
pstoreu<Scalar>(target + i + j * PacketSize, p);
|
|
}
|
|
}
|
|
|
|
for (; i <= vectorized_size; i += PacketSize) {
|
|
Packet p = eval.template packet<Unaligned>(eval_offset + i);
|
|
pstoreu<Scalar>(target + i, p);
|
|
}
|
|
|
|
for (; i < count; ++i) {
|
|
target[i] = eval.coeff(eval_offset + i);
|
|
}
|
|
}
|
|
};
|
|
|
|
public:
|
|
struct Target {
|
|
Target(const Dimensions& target_dims, const Dimensions& target_strides,
|
|
Scalar* target_data, IndexType target_offset = 0)
|
|
: dims(target_dims),
|
|
strides(target_strides),
|
|
data(target_data),
|
|
offset(target_offset) {}
|
|
|
|
Dimensions dims;
|
|
Dimensions strides;
|
|
Scalar* data;
|
|
IndexType offset;
|
|
};
|
|
|
|
static Target target(const Dimensions& target_dims,
|
|
const Dimensions& target_strides, Scalar* target_data,
|
|
IndexType target_offset = 0) {
|
|
return Target(target_dims, target_strides, target_data, target_offset);
|
|
}
|
|
|
|
template <typename TargetDimsIndexType, typename TargetStridesIndexType>
|
|
static Target target(
|
|
const DSizes<TargetDimsIndexType, NumDims>& target_dims,
|
|
const DSizes<TargetStridesIndexType, NumDims>& target_strides,
|
|
Scalar* target_data, IndexType target_offset = 0) {
|
|
// DSizes constructor will do index type promotion if it's safe.
|
|
return Target(Dimensions(target_dims), Dimensions(target_strides),
|
|
target_data, target_offset);
|
|
}
|
|
|
|
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
|
|
const Target& target, const TensorBlockExpr& expr) {
|
|
// Prepare evaluator for block expression.
|
|
DefaultDevice default_device;
|
|
TensorBlockEvaluator eval(expr, default_device);
|
|
|
|
// Tensor block expression dimension should match destination dimensions.
|
|
eigen_assert(dimensions_match(target.dims, eval.dimensions()));
|
|
|
|
static const int Layout = TensorBlockEvaluator::Layout;
|
|
static const bool is_col_major = Layout == ColMajor;
|
|
|
|
// Initialize output inner dimension size based on a layout.
|
|
const IndexType output_size = NumDims == 0 ? 1 : target.dims.TotalSize();
|
|
const int inner_dim_idx = is_col_major ? 0 : NumDims - 1;
|
|
IndexType output_inner_dim_size = target.dims[inner_dim_idx];
|
|
|
|
// Target inner dimension stride must be '1'.
|
|
eigen_assert(target.strides[inner_dim_idx] == 1);
|
|
|
|
// Squeeze multiple inner dims into one if they are contiguous in `target`.
|
|
IndexType num_squeezed_dims = 0;
|
|
for (Index i = 1; i < NumDims; ++i) {
|
|
const Index dim = is_col_major ? i : NumDims - i - 1;
|
|
const IndexType target_stride = target.strides[dim];
|
|
|
|
if (output_inner_dim_size == target_stride) {
|
|
output_inner_dim_size *= target.dims[dim];
|
|
num_squeezed_dims++;
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Initialize output block iterator state. Dimension in this array are
|
|
// always in inner_most -> outer_most order (col major layout).
|
|
array<BlockIteratorState, NumDims> it;
|
|
|
|
int idx = 0; // currently initialized iterator state index
|
|
for (Index i = num_squeezed_dims; i < NumDims - 1; ++i) {
|
|
const Index dim = is_col_major ? i + 1 : NumDims - i - 2;
|
|
|
|
it[idx].count = 0;
|
|
it[idx].size = target.dims[dim];
|
|
it[idx].output_stride = target.strides[dim];
|
|
it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
|
|
idx++;
|
|
}
|
|
|
|
// We read block expression from the beginning, and start writing data to
|
|
// `target` at given offset.
|
|
IndexType input_offset = 0;
|
|
IndexType output_offset = target.offset;
|
|
|
|
// Iterate copying data from `eval` to `target`.
|
|
for (IndexType i = 0; i < output_size; i += output_inner_dim_size) {
|
|
// Assign to `target` at current offset.
|
|
InnerDimAssign<Vectorizable && TensorBlockEvaluator::PacketAccess,
|
|
TensorBlockEvaluator>::Run(target.data + output_offset,
|
|
output_inner_dim_size, eval,
|
|
input_offset);
|
|
|
|
// Move input offset forward by the number of assigned coefficients.
|
|
input_offset += output_inner_dim_size;
|
|
|
|
// Update index.
|
|
for (int j = 0; j < idx; ++j) {
|
|
if (++it[j].count < it[j].size) {
|
|
output_offset += it[j].output_stride;
|
|
break;
|
|
}
|
|
it[j].count = 0;
|
|
output_offset -= it[j].output_span;
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
struct BlockIteratorState {
|
|
BlockIteratorState()
|
|
: count(0), size(0), output_stride(0), output_span(0) {}
|
|
|
|
IndexType count;
|
|
IndexType size;
|
|
IndexType output_stride;
|
|
IndexType output_span;
|
|
};
|
|
};
|
|
|
|
// -------------------------------------------------------------------------- //
|
|
|
|
} // namespace internal
|
|
} // namespace Eigen
|
|
|
|
#endif // EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
|