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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H
#define EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H
namespace Eigen {
namespace internal {
// Accumulate the product of 2 QInt8 inputs on 32 bits to prevent
// overflows
template<> struct scalar_product_traits<QInt8, QInt8>
{
enum {
Defined = 1
};
typedef QInt32 ReturnType;
};
// Accumulate the product of QInt8 inputs with QUint8 inputs on 32 bits
// to prevent overflows
template<> struct scalar_product_traits<QInt8, QUInt8>
{
enum {
Defined = 1
};
typedef QInt32 ReturnType;
};
// Description of the product implementation. It's pretty simple now since
// nothing is vectorized yet.
// This definition tackle the case where both lhs and rhs are encoded using
// signed 8bit integers
#ifndef EIGEN_USE_OPTIMIZED_INT8_INT8_MAT_MAT_PRODUCT
template<bool _ConjLhs, bool _ConjRhs>
class gebp_traits<QInt8, QInt8, _ConjLhs, _ConjRhs>
{
public:
typedef QInt8 LhsScalar;
typedef QInt8 RhsScalar;
typedef QInt32 ResScalar;
enum {
// register block size along the M and N directions
// One for the current implementation
nr = 1,
mr = 1,
// Progress made at each iteration of the product loop
// also 1 for the current implementation
LhsProgress = 1,
RhsProgress = 1
};
};
// The signed 8bit Mat-Mat product itself.
template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
struct gebp_kernel<QInt8, QInt8, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
{
EIGEN_DONT_INLINE
void operator()(const DataMapper& res, const QInt8* blockA, const QInt8* blockB,
Index rows, Index depth, Index cols, QInt32 alpha,
Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
};
template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
EIGEN_DONT_INLINE
void gebp_kernel<QInt8, QInt8, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
::operator()(const DataMapper& res, const QInt8* blockA, const QInt8* blockB,
Index rows, Index depth, Index cols, QInt32 alpha,
Index strideA, Index strideB, Index offsetA, Index offsetB)
{
EIGEN_STATIC_ASSERT(!ConjugateLhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(!ConjugateRhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(alpha.value == 1);
eigen_assert(strideA == -1);
eigen_assert(strideB == -1);
eigen_assert(offsetA == 0);
eigen_assert(offsetB == 0);
eigen_assert(rows > 0);
eigen_assert(cols > 0);
eigen_assert(depth > 0);
eigen_assert(blockA);
eigen_assert(blockB);
for (Index j = 0; j < cols; ++j) {
Index startB = j * depth;
for (Index i = 0; i < rows; ++i) {
Index startA = i * depth;
for (Index k = 0; k < depth; ++k) {
res(i, j) += blockA[startA + k] * blockB[startB + k];
}
}
}
}
#endif
// This definition tackle the case where the lhs is encoded using signed 8bit
// integers and the rhs using unsigned 8bit integers.
#ifndef EIGEN_USE_OPTIMIZED_INT8_UINT8_MAT_MAT_PRODUCT
template<bool _ConjLhs, bool _ConjRhs>
class gebp_traits<QInt8, QUInt8, _ConjLhs, _ConjRhs>
{
public:
typedef QInt8 LhsScalar;
typedef QUInt8 RhsScalar;
typedef QInt32 ResScalar;
enum {
// register block size along the M and N directions
// One for the current implementation
nr = 1,
mr = 1,
// Progress made at each iteration of the product loop
// also 1 for the current implementation
LhsProgress = 1,
RhsProgress = 1
};
};
// Mat-Mat product of a signed 8bit lhs with an unsigned 8bit rhs
template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
struct gebp_kernel<QInt8, QUInt8, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
{
EIGEN_DONT_INLINE
void operator()(const DataMapper& res, const QInt8* blockA, const QUInt8* blockB,
Index rows, Index depth, Index cols, QInt32 alpha,
Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
};
template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
EIGEN_DONT_INLINE
void gebp_kernel<QInt8, QUInt8, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
::operator()(const DataMapper& res, const QInt8* blockA, const QUInt8* blockB,
Index rows, Index depth, Index cols, QInt32 alpha,
Index strideA, Index strideB, Index offsetA, Index offsetB)
{
EIGEN_STATIC_ASSERT(!ConjugateLhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(!ConjugateRhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(alpha.value == 1);
eigen_assert(strideA == -1);
eigen_assert(strideB == -1);
eigen_assert(offsetA == 0);
eigen_assert(offsetB == 0);
eigen_assert(rows > 0);
eigen_assert(cols > 0);
eigen_assert(depth > 0);
eigen_assert(blockA);
eigen_assert(blockB);
for (Index j = 0; j < cols; ++j) {
Index startB = j * depth;
for (Index i = 0; i < rows; ++i) {
Index startA = i * depth;
for (Index k = 0; k < depth; ++k) {
res(i, j) += blockA[startA + k] * blockB[startB + k];
}
}
}
}
#endif
// This definition tackle the case where the khs is encoded using unsigned 8bit
// integers and the rhs using signed 8bit integers.
#ifndef EIGEN_USE_OPTIMIZED_UINT8_INT8_MAT_MAT_PRODUCT
template<bool _ConjLhs, bool _ConjRhs>
class gebp_traits<QUInt8, QInt8, _ConjLhs, _ConjRhs>
{
public:
typedef QUInt8 LhsScalar;
typedef QInt8 RhsScalar;
typedef QInt32 ResScalar;
enum {
// register block size along the M and N directions
// One for the current implementation
nr = 1,
mr = 1,
// Progress made at each iteration of the product loop
// also 1 for the current implementation
LhsProgress = 1,
RhsProgress = 1
};
};
// Mat-Mat product of an unsigned 8bit lhs with a signed 8bit rhs
template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
struct gebp_kernel<QUInt8, QInt8, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
{
EIGEN_DONT_INLINE
void operator()(const DataMapper& res, const QUInt8* blockA, const QInt8* blockB,
Index rows, Index depth, Index cols, QInt32 alpha,
Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
};
template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
EIGEN_DONT_INLINE
void gebp_kernel<QUInt8, QInt8, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
::operator()(const DataMapper& res, const QUInt8* blockA, const QInt8* blockB,
Index rows, Index depth, Index cols, QInt32 alpha,
Index strideA, Index strideB, Index offsetA, Index offsetB)
{
EIGEN_STATIC_ASSERT(!ConjugateLhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(!ConjugateRhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(alpha.value == 1);
eigen_assert(strideA == -1);
eigen_assert(strideB == -1);
eigen_assert(offsetA == 0);
eigen_assert(offsetB == 0);
eigen_assert(rows > 0);
eigen_assert(cols > 0);
eigen_assert(depth > 0);
eigen_assert(blockA);
eigen_assert(blockB);
for (Index j = 0; j < cols; ++j) {
Index startB = j * depth;
for (Index i = 0; i < rows; ++i) {
Index startA = i * depth;
for (Index k = 0; k < depth; ++k) {
res(i, j) += blockA[startA + k] * blockB[startB + k];
}
}
}
}
#endif
} // namespace internal
} // namespace Eigen
#endif // EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H
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