xref: /external/tensorflow/tensorflow/lite/experimental/ruy/internal_matrix.h

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Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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    http://www.apache.org/licenses/LICENSE-2.0

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distributed under the License is distributed on an "AS IS" BASIS,
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See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// Internal types and helpers for matrices.
//
// Ruy has a couple slightly different notions of matrices, besides the
// Matrix<T> class that we expose to the user-facing API.
//
// TODO(silvasean): Put parts of this architecture description somewhere more
// prominent.
//
// The 4 main matrix types are:
// - Matrix<T>: This is a user-facing type on Ruy's external API boundary. It is
// also used internally.
// - DMatrix: This is a type-erased version of Matrix<T>. "D" = "dynamic".
// - PMatrix: This represents a packed matrix, which requires tracking kernel
// layout and row/column sums for quantization. It is type-erased.
// - PackedMatrix<T>: This is a statically typed variant of PMatrix for
// convenience inside typed routines.
//
// Note that Matrix<T> is *not* implemented in terms of the internal types. It
// is an independent, simple, and user-facing type.
//
// The use of type-erasure might seem surprising for a library like Ruy with a
// heavily-templated entry point, but it is motivated by the desire for most of
// Ruy's "middle-end" to be non-templated. Ruy can be thought of as having 3
// main parts:
// - "front-end" (dispatch.h) - this is the highly templated ruy::Mul entry
// point, along with routines that select RunKernel and RunPack implementations
// statically based on those template parameters.
// - "back-end" (kernel.h, pack.h)- this consists of the implementations of
// RunKernel and RunPack, often in assembly code, which are the building blocks
// that Ruy calls to perform matrix multiplication.  These are templated so that
// only the requested types/Path's are actually emitted by the compiler.
// - "middle-end" (trmul.h) - this is the part of Ruy that orchestrates the
// calls to the "back-end" optimized building blocks. This layer has to deal
// with issues like cache locality and low-overhead multi-threading.
//
// There is a desire for the "middle-end" to be non-templated in order to
// simplify the implementation and reduce code-size. We type-erase when going
// from the "front-end" to the "middle-end", and un-type-erase going from the
// "middle-end" to the "back-end". The un-type-erasure is possible because the
// "front-end" is responsible for instantiating the needed "back-end" templates,
// and thus the static type information is still present.
//
// Each layer of Ruy uses matrix types:
// - "front-end": Matrix<T>
// - "middle-end": DMatrix, PMatrix
// - "back-end": Matrix<T>, PackedMatrix<T>
//
// The use of separate types for packed matrices is not essential, but makes it
// obvious at a glance whether a matrix is a packed matrix or not. We would
// reconsider this decision if there was significant duplication between packed
// and unpacked matrices, but that doesn't seem to be the case at the moment.
//
// Another goal is to keep the user-facing Matrix<T> as simple and
// understandable as possible. Ideally, a user should be able to read the struct
// definition for Matrix<T> and see a very simple definition with no internal
// details like sums and kernel block layout.
//
// To present another structured view of our various matrix types, here's a
// table:
//                Plain matrices   Packed matrices
//             +----------------------------------
// Templated   |  Matrix<T>        PackedMatrix<T>
// Type-erased |  DMatrix          PMatrix
//
//
// There is 1 additional matrix type not mentioned above, due to its low
// importance:
// - PrepackedMatrix: This is a user-facing version of PMatrix. It has the bare
// minimum of fields needed for representing the raw data and sums buffers of a
// packed matrix for the "advanced" explicit pre-packing API. This type plays no
// role in Ruy's internals and can generally by ignored. The only reason it
// exists is so that PMatrix is not exposed to users -- we prefer to keep the
// internal matrix types hidden, even from "advanced" users.

#ifndef TENSORFLOW_LITE_EXPERIMENTAL_RUY_INTERNAL_MATRIX_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_RUY_INTERNAL_MATRIX_H_

#include <cstddef>
#include <cstdint>
#include <type_traits>
#include <utility>

#include "tensorflow/lite/experimental/ruy/check_macros.h"
#include "tensorflow/lite/experimental/ruy/common.h"
#include "tensorflow/lite/experimental/ruy/matrix.h"
#include "tensorflow/lite/experimental/ruy/size_util.h"

namespace ruy {

// KernelLayout describes small-scale block structure in a packed matrix layout.
// It's a runtime (as opposed to compile-time-constant) version of the
// FixedKernelLayout struct used to declare kernel layouts.
//
// This is is sometimes known as "tiling" in other contexts.
//
// For example, consider a packed matrix in column-major format with a
// column-major KernelLayout. The matrix logically has a shape of
// `[cols, rows]`. However, the matrix is laid out as though it were a 4D array
// of shape `[cols / kcols, rows / krows, kcols, krows]`.
//
// Note that in the case of kcols=1, krows=1, this degenerates to
// `[cols, rows, 1, 1]` which is equivalent to having no small-scale block
// structure.
struct KernelLayout {
  Order order = Order::kColMajor;
  std::uint8_t rows = 1;
  std::uint8_t cols = 1;
};

// A packed matrix has a small-scale block structure that is not present in in
// the input matrices. This block structure is necessary for the kernels to
// process data efficiently.
//
// This struct is very similar to Layout, but has the extra KernelLayout field.
struct PackedLayout {
  std::int32_t rows = 0;
  std::int32_t cols = 0;
  // Stride is the offset between two adjacent matrix elements
  // in the non-contiguous direction.
  std::int32_t stride = 0;
  Order order = Order::kColMajor;
  // Small scale layout shuffling, potentially departing from
  // linear row-major or column-major storage. See KernelLayout.
  KernelLayout kernel;
};

// Dynamic representation for a type.
//
// The most important field in this struct is the size, which Ruy uses to know
// how much memory to allocate without having to be templated on a type.
// Signed-ness and floating-point-ness are mainly present as debugging checks.
//
// Note: Ruy does not use this struct to to dynamically dispatch between
// different typed implementations. As described in the comment at the top of
// this file, Ruy's "front-end", which is templated, instantiates all the
// necessary "back-end" routines with complete static knowledge of all the
// types.
struct Type {
  template <typename T>
  static Type Create() {
    Type ret;
    ret.is_signed = std::is_signed<T>::value;
    ret.is_floating_point = std::is_floating_point<T>::value;
    ret.size = sizeof(T);
    return ret;
  }

  template <typename T>
  void AssertIs() const {
    RUY_DCHECK_EQ(is_signed, Create<T>().is_signed);
    RUY_DCHECK_EQ(is_floating_point, Create<T>().is_floating_point);
    RUY_DCHECK_EQ(size, Create<T>().size);
  }

  bool is_signed = false;
  bool is_floating_point = false;
  std::uint8_t size = 0;
};

// Type-erased matrix.
struct DMatrix {
  Type data_type;
  void* data = nullptr;
  Layout layout;
  std::int32_t zero_point = 0;
};

// Type-erased packed matrix.
struct PMatrix {
  Type data_type;
  void* data = nullptr;
  Type sums_type;
  void* sums = nullptr;
  PackedLayout layout;
  std::int32_t zero_point = 0;
};

// Convenient typed helper for packed matrices.
template <typename Scalar>
struct PackedMatrix {
  // The row/column sums needed for quantized matrix multiplication when
  // the opposite operand of the multiplication uses a non-symmetric zero
  // point.
  // This member is only relevant for packed matrices.
  // Additionally, Ruy always uses 32-bit signed accumulators for quantized
  // matrix multiplication.
  // For floating point types, there is no quantization, so this pointer
  // will always be null. We still need code referencing it to compile
  // though, even if it is always branched around. Hence we use Scalar*
  // itself as the type in that case.
  using SumsType =
      typename std::conditional<std::is_floating_point<Scalar>::value, Scalar,
                                std::int32_t>::type;

  Scalar* data = nullptr;
  SumsType* sums = nullptr;
  PackedLayout layout;
  std::int32_t zero_point = 0;
};

template <typename T>
DMatrix ToDMatrix(const Matrix<T>& matrix) {
  DMatrix ret;
  ret.data_type = Type::Create<T>();
  ret.data = ToVoidPtr(matrix.data.get());
  ret.layout = matrix.layout;
  ret.zero_point = matrix.zero_point;
  return ret;
}

template <typename T>
Matrix<T> ToMatrix(const DMatrix& dmatrix) {
  dmatrix.data_type.AssertIs<T>();
  Matrix<T> ret;
  ret.data = static_cast<T*>(dmatrix.data);
  ret.layout = dmatrix.layout;
  ret.zero_point = dmatrix.zero_point;
  return ret;
}

template <typename T>
PackedMatrix<T> ToPackedMatrix(const PMatrix& pmatrix) {
  using SumsType = typename PackedMatrix<T>::SumsType;
  pmatrix.data_type.AssertIs<T>();
  pmatrix.sums_type.AssertIs<SumsType>();
  PackedMatrix<T> ret;
  ret.data = static_cast<T*>(pmatrix.data);
  ret.sums = static_cast<SumsType*>(pmatrix.sums);
  ret.layout = pmatrix.layout;
  ret.zero_point = pmatrix.zero_point;
  return ret;
}

// Helpers for Layout / PackedLayout.

inline bool IsPacked(const Layout& layout) {
  if (layout.order == Order::kColMajor) {
    return layout.stride == layout.rows;
  } else {
    return layout.stride == layout.cols;
  }
}

inline bool IsRowMajor(const Layout& layout) {
  return layout.order == Order::kRowMajor;
}

template <typename LayoutOrPackedLayout>
inline bool IsColMajor(const LayoutOrPackedLayout& layout) {
  return layout.order == Order::kColMajor;
}

template <typename LayoutOrPackedLayout>
inline int FlatSize(const LayoutOrPackedLayout& layout) {
  const int outerdim =
      layout.order == Order::kColMajor ? layout.cols : layout.rows;
  return layout.stride * outerdim;
}

// TODO(b/130417400) add a unit test
inline int Offset(const Layout& layout, int row, int col) {
  // TODO(benoitjacob)  - should check this but this make the _slow tests take
  // 5x longer.  Find a mitigation like in Eigen with an 'internal' variant
  // bypassing the check?
  // RUY_DCHECK_GE(row, 0);
  // RUY_DCHECK_GE(col, 0);
  // RUY_DCHECK_LT(row, layout.rows);
  // RUY_DCHECK_LT(col, layout.cols);
  int row_stride = layout.order == Order::kColMajor ? 1 : layout.stride;
  int col_stride = layout.order == Order::kRowMajor ? 1 : layout.stride;
  return row * row_stride + col * col_stride;
}

// TODO(b/130417400) add a unit test
inline int Offset(const PackedLayout& layout, int row, int col) {
  RUY_DCHECK(is_pot(layout.kernel.rows));
  RUY_DCHECK(is_pot(layout.kernel.cols));
  int row_outer = row & ~(layout.kernel.rows - 1);
  int col_outer = col & ~(layout.kernel.cols - 1);
  int row_stride_outer =
      layout.order == Order::kColMajor ? layout.kernel.cols : layout.stride;
  int col_stride_outer =
      layout.order == Order::kRowMajor ? layout.kernel.rows : layout.stride;
  int offset_outer =
      row_outer * row_stride_outer + col_outer * col_stride_outer;
  int row_inner = row - row_outer;
  int col_inner = col - col_outer;
  int row_stride_inner =
      layout.kernel.order == Order::kColMajor ? 1 : layout.kernel.cols;
  int col_stride_inner =
      layout.kernel.order == Order::kRowMajor ? 1 : layout.kernel.rows;
  int offset_inner =
      row_inner * row_stride_inner + col_inner * col_stride_inner;
  return offset_outer + offset_inner;
}

// Helpers for Matrix<T>.

template <typename Scalar>
const Scalar* ElementPtr(const Matrix<Scalar>& mat, int row, int col) {
  return mat.data.get() + Offset(mat.layout, row, col);
}

template <typename Scalar>
Scalar* ElementPtr(Matrix<Scalar>* mat, int row, int col) {
  return mat->data.get() + Offset(mat->layout, row, col);
}

template <typename Scalar>
Scalar Element(const Matrix<Scalar>& mat, int row, int col) {
  return *ElementPtr(mat, row, col);
}

// Helpers for PackedMatrix<T>.
// Duplicated from Matrix<T>, but the duplication seems acceptable.

template <typename Scalar>
const Scalar* ElementPtr(const PackedMatrix<Scalar>& mat, int row, int col) {
  return mat.data + Offset(mat.layout, row, col);
}

template <typename Scalar>
Scalar* ElementPtr(PackedMatrix<Scalar>* mat, int row, int col) {
  return mat->data + Offset(mat->layout, row, col);
}

template <typename Scalar>
Scalar Element(const PackedMatrix<Scalar>& mat, int row, int col) {
  return *ElementPtr(mat, row, col);
}

// Helpers for PMatrix.

inline std::size_t DataSize(const PMatrix& packed) {
  return FlatSize(packed.layout) * packed.data_type.size;
}

inline std::size_t SumsSize(const PMatrix& packed) {
  // Packed matrices are only relevant for Ruy's TrMul implementations. For
  // TrMul, the number of sums is always equal to the number of columns.
  return packed.layout.cols * packed.sums_type.size;
}

// Transpose helpers.

inline void Transpose(Order* order) {
  *order = *order == Order::kColMajor ? Order::kRowMajor : Order::kColMajor;
}

inline void Transpose(Layout* layout) {
  Transpose(&layout->order);
  std::swap(layout->rows, layout->cols);
}

template <typename Scalar>
inline void Transpose(Matrix<Scalar>* matrix) {
  Transpose(&matrix->layout);
}

// Helpers for KernelLayout.

template <typename FixedKernelLayout>
KernelLayout ToKernelLayout() {
  KernelLayout ret;
  ret.order = FixedKernelLayout::kOrder;
  ret.rows = FixedKernelLayout::kRows;
  ret.cols = FixedKernelLayout::kCols;
  return ret;
}

}  // namespace ruy

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_RUY_INTERNAL_MATRIX_H_