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/*
* Copyright (c) 2017-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "GEMMLowp.h"
#include "arm_compute/core/Types.h"
#include "tests/validation/reference/UtilsQuantizedAsymm.h"
#include "support/ToolchainSupport.h"
#include <limits>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
namespace
{
template <typename T>
struct DataTypeExtractor
{
static DataType data_type()
{
DataType data_type = DataType::UNKNOWN;
if(std::is_same<T, int8_t>::value)
{
data_type = DataType::QASYMM8_SIGNED;
}
else if(std::is_same<T, uint8_t>::value)
{
data_type = DataType::QASYMM8;
}
else if(std::is_same<T, int16_t>::value)
{
data_type = DataType::QSYMM16;
}
return data_type;
}
};
template <typename TIn, typename TOut>
void quantize_down_scale(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min, int32_t max)
{
const int cols_in = in->shape().x();
const bool is_per_channel = result_mult_int.size() > 1;
#if defined(_OPENMP)
#pragma omp parallel for
#endif /* _OPENMP */
for(int i = 0; i < in->num_elements(); ++i)
{
int32_t result = ((*in)[i] + result_offset);
if(bias != nullptr)
{
result += (*bias)[i % cols_in];
}
result *= (is_per_channel) ? result_mult_int[i % cols_in] : result_mult_int[0];
result >>= (is_per_channel) ? result_shift[i % cols_in] : result_shift[0];
// Bounded ReLu
if(min != max)
{
result = std::max(min, std::min(max, result));
}
(*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
}
}
template <typename TIn, typename TOut>
void quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, std::vector<int32_t> result_fixedpoint_multiplier,
std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
{
const int cols_in = in->shape().x();
const bool is_per_channel = result_fixedpoint_multiplier.size() > 1;
#if defined(_OPENMP)
#pragma omp parallel for
#endif /* _OPENMP */
for(int i = 0; i < in->num_elements(); ++i)
{
TIn result = (*in)[i];
if(bias != nullptr)
{
result += (*bias)[i % cols_in];
}
// Fixed point multiplication
const int32_t multiplier = (is_per_channel) ? result_fixedpoint_multiplier[i % cols_in] : result_fixedpoint_multiplier[0];
const int32_t shift = (is_per_channel) ? result_shift[i % cols_in] : result_shift[0];
if(shift < 0)
{
result = asymm_int_mult(result * (1 << (-shift)), multiplier);
}
else
{
result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, multiplier), shift);
}
result += result_offset_after_shift;
// Bounded ReLu
if(min != max)
{
result = std::max(min, std::min(max, result));
}
(*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
}
}
template <typename TIn, typename TOut>
void quantize_down_scale_by_float(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, std::vector<float_t> result_real_multiplier,
int32_t result_offset, int32_t min, int32_t max)
{
const int cols_in = in->shape().x();
const bool is_per_channel = result_real_multiplier.size() > 1;
#if defined(_OPENMP)
#pragma omp parallel for
#endif /* _OPENMP */
for(int i = 0; i < in->num_elements(); ++i)
{
TIn result = (*in)[i];
if(bias != nullptr)
{
result += (*bias)[i % cols_in];
}
// Float multiplication
const float_t multiplier = (is_per_channel) ? result_real_multiplier[i % cols_in] : result_real_multiplier[0];
float_t result_f = static_cast<float_t>(result) * multiplier + static_cast<float_t>(result_offset);
result = static_cast<TIn>(support::cpp11::round(result_f));
// Bounded ReLu
if(min != max)
{
result = std::max(min, std::min(max, result));
}
(*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
}
}
} // namespace
template <typename T_out, typename T_in, typename T_in_1>
SimpleTensor<T_out> gemmlowp_matrix_multiply_core(const SimpleTensor<T_in> &a, const SimpleTensor<T_in_1> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
{
static_assert(std::is_same<typename std::decay<T_out>::type, int32_t>::value, "Only int32_t is allowed for the output");
DataType dt = std::is_same<T_out, int32_t>::value ? DataType::S32 : DataType::U32;
SimpleTensor<T_out> c(shape_c, dt);
const int K = a.shape().x();
const int M = a.shape().y();
const int N = b.shape().x();
const int D = a.shape().z(); // Number of matrices in a batch
const int a_stride_z = K * M;
// Do not slide the matrix B along the 3rd dimension in case matrix B has less than 3 dimensions
const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0;
const int c_stride_z = N * M;
std::vector<T_out> acc;
acc.resize(N);
for(int depth = 0; depth < D; ++depth)
{
const int base_addr_a = depth * a_stride_z;
const int base_addr_b = depth * b_stride_z;
const int base_addr_c = depth * c_stride_z;
for(int i = 0; i < M; ++i)
{
for(int j = 0; j < N; ++j)
{
acc[j] = 0;
}
for(int k = 0; k < K; ++k)
{
const T_out tmp_a = a_offset + static_cast<T_out>(a[base_addr_a + k + i * K]);
for(int j = 0; j < N; ++j)
{
const T_out tmp_b = b_offset + static_cast<T_out>(b[base_addr_b + j + k * N]);
const T_out mult_as_int = tmp_a * tmp_b;
acc[j] += mult_as_int;
}
}
for(int j = 0; j < N; ++j)
{
c[base_addr_c + j + i * N] = acc[j];
}
}
}
return c;
}
// used to validate assembly kernels which don't know anything about offsets
template <typename T1, typename T2, typename T3>
SimpleTensor<T1> gemmlowp(const SimpleTensor<T2> &a, const SimpleTensor<T3> &b, TensorShape shape_c)
{
return gemmlowp_matrix_multiply_core<T1, T2, T3>(a, b, shape_c, 0, 0);
}
template <typename TIn, typename TOut>
SimpleTensor<TOut> gemmlowp_quantize_down_scale(const SimpleTensor<TIn> &in, int32_t result_offset, std::vector<int32_t> result_mult_int, std::vector<int32_t> result_shift,
int32_t min, int32_t max)
{
SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());
quantize_down_scale<TIn, TOut>(&in, nullptr, &dst, result_offset, result_mult_int, result_shift, min, max);
return dst;
}
template <typename TIn, typename TOut>
SimpleTensor<TOut> gemmlowp_quantize_down_scale(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min, int32_t max)
{
SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());
quantize_down_scale<TIn, TOut>(&in, &bias, &dst, result_offset, result_mult_int, result_shift, min, max);
return dst;
}
template <typename TIn, typename TOut>
SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift,
int32_t result_offset_after_shift, int32_t min, int32_t max)
{
SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());
quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
return dst;
}
template <typename TIn, typename TOut>
SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier,
std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
{
SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());
quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
return dst;
}
template <typename TIn, typename TOut>
SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias,
std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max)
{
SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());
quantize_down_scale_by_float<TIn, TOut>(&in, &bias, &dst, result_real_multiplier, result_offset, min, max);
return dst;
}
template <typename TIn, typename TOut>
SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in,
std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max)
{
SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());
quantize_down_scale_by_float<TIn, TOut>(&in, nullptr, &dst, result_real_multiplier, result_offset, min, max);
return dst;
}
template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a,
std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a,
std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
std::vector<int32_t> result_fixedpoint_multiplier,
std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
std::vector<int32_t> result_fixedpoint_multiplier,
std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
std::vector<int32_t> result_fixedpoint_multiplier,
std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min, int32_t max);
template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min, int32_t max);
template SimpleTensor<int8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min, int32_t max);
template SimpleTensor<int8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min, int32_t max);
template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
template SimpleTensor<int32_t> gemmlowp<int32_t, int8_t, int8_t>(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c);
template SimpleTensor<int32_t> gemmlowp<int32_t, uint8_t, uint8_t>(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c);
template SimpleTensor<int32_t> gemmlowp<int32_t, uint8_t, int8_t>(const SimpleTensor<uint8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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