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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 "GEMM.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta)
{
// Create reference
SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 };
// Compute reference
const int M = a.shape().y();
const int N = b.shape().x();
const int K = a.shape().x();
const int D = a.shape().z(); // Number of matrices in a batch
const int W = a.shape()[3]; // Number of batched-gemm (Winograd case)
const int a_stride_z = K * M;
const int a_stride_w = K * M * D;
const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions
const int b_stride_w = b.shape().num_dimensions() > 3 ? K * N * D : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions
const int c_stride_z = N * M;
const int c_stride_w = N * M * D;
#if defined(_OPENMP) && !( defined(__arm__) && defined(__ANDROID__))
#pragma omp parallel for collapse(2)
#endif /* _OPENMP */
for(int w = 0; w < W; ++w)
{
for(int depth = 0; depth < D; ++depth)
{
const int base_addr_a = depth * a_stride_z + w * a_stride_w;
const int base_addr_b = depth * b_stride_z + w * b_stride_w;
const int base_addr_c = depth * c_stride_z + w * c_stride_w;
for(int row = 0; row < M; ++row)
{
for(int col = 0; col < N; ++col)
{
T acc(0);
for(int k = 0; k < K; ++k)
{
acc += a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N];
}
// Finalize the result: alpha * A * B + beta * C
dst[base_addr_c + col + row * N] = alpha * acc + beta * c[base_addr_c + col + row * N];
}
}
}
}
return dst;
}
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta)
{
// GEMM mixed-precision combines F32 accumulators with F16 multiplications
// Create reference
SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 };
// Compute reference
const int M = a.shape().y();
const int N = b.shape().x();
const int K = a.shape().x();
const int D = a.shape().z(); // Number of matrices in a batch
const int W = a.shape()[3]; // Number of batched-gemm (Winograd case)
const int a_stride_z = K * M;
const int a_stride_w = K * M * D;
const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions
const int b_stride_w = b.shape().num_dimensions() > 3 ? K * N * D : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions
const int c_stride_z = N * M;
const int c_stride_w = N * M * D;
#if defined(_OPENMP) && !( defined(__arm__) && defined(__ANDROID__))
#pragma omp parallel for collapse(2)
#endif /* _OPENMP */
for(int w = 0; w < W; ++w)
{
for(int depth = 0; depth < D; ++depth)
{
const int base_addr_a = depth * a_stride_z + w * a_stride_w;
const int base_addr_b = depth * b_stride_z + w * b_stride_w;
const int base_addr_c = depth * c_stride_z + w * c_stride_w;
for(int row = 0; row < M; ++row)
{
for(int col = 0; col < N; ++col)
{
float acc(0);
for(int k = 0; k < K; ++k)
{
acc += static_cast<float>(a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N]);
}
// Finalize the result: alpha * A * B + beta * C
dst[base_addr_c + col + row * N] = static_cast<T>(alpha * acc + beta * c[base_addr_c + col + row * N]);
}
}
}
}
return dst;
}
template SimpleTensor<float> gemm(const SimpleTensor<float> &a, const SimpleTensor<float> &b, const SimpleTensor<float> &c, float alpha, float beta);
template SimpleTensor<half> gemm(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta);
template SimpleTensor<half> gemm_mixed_precision(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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