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/*
* Copyright (c) 2023 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 "src/runtime/heuristics/matmul_native/ClMatMulNativeHelpers.h"
#include "arm_compute/core/KernelDescriptors.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/TensorShape.h"
#include "src/gpu/cl/kernels/ClMatMulNativeKernel.h"
#include <limits>
#include <utility>
namespace arm_compute
{
namespace cl_matmul
{
MatMulKernelInfo select_info(const MatMulKernelInfo &info0,
const MatMulKernelInfo &info1,
unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type, bool rhs_lock_padding)
{
ARM_COMPUTE_ERROR_ON_MSG(info1.export_rhs_to_cl_image == true, "The fallback MatMul configuration cannot have export_to_cl_image = true");
ARM_COMPUTE_ERROR_ON_MSG(info0.adj_lhs != info1.adj_lhs, "The MatMul configurations must have the same adj_lhs value");
ARM_COMPUTE_ERROR_ON_MSG(info0.adj_rhs != info1.adj_rhs, "The MatMul configurations must have the same adj_rhs value");
const bool adj_lhs = info0.adj_lhs;
const bool adj_rhs = info0.adj_rhs;
TensorInfo lhs_info = !adj_lhs ? TensorInfo(TensorShape(k, m, b), 1, data_type) : TensorInfo(TensorShape(m, k, b), 1, data_type);
TensorInfo rhs_info = !adj_rhs ? TensorInfo(TensorShape(n, k, b), 1, data_type) : TensorInfo(TensorShape(k, n, b), 1, data_type);
TensorInfo dst_info;
if(rhs_lock_padding == false)
{
if(bool(opencl::kernels::ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &dst_info, info0)))
{
return info0;
}
else
{
return info1;
}
}
else
{
return info1;
}
}
MatMulKernelInfo find_info(const MatMulNativeConfigsMatrix &configs, bool adj_lhs, bool adj_rhs, unsigned int m, unsigned int n, unsigned int k, unsigned int b)
{
size_t min_acc = std::numeric_limits<size_t>::max();
size_t min_idx = 0;
ARM_COMPUTE_ERROR_ON(configs.size() == 0);
const size_t num_rows = configs.size();
const size_t num_cols = configs[0].size();
ARM_COMPUTE_ERROR_ON_MSG(num_cols != 8U, "The entry should have 8 integer values representing: M, N, K, B, M0, N0. K0, IMG_RHS");
ARM_COMPUTE_UNUSED(num_cols);
// Find nearest GeMM workload
// Note: the workload does not depend on the K dimension
for(size_t y = 0; y < num_rows; ++y)
{
size_t mc0 = static_cast<size_t>(configs[y][0]);
size_t nc0 = static_cast<size_t>(configs[y][1]);
size_t kc0 = static_cast<size_t>(configs[y][2]);
size_t bc0 = static_cast<size_t>(configs[y][3]);
size_t acc = 0;
acc += (m - mc0) * (m - mc0);
acc += (n - nc0) * (n - nc0);
acc += (k - kc0) * (k - kc0);
acc += (b - bc0) * (b - bc0);
acc = std::sqrt(acc);
if(acc < min_acc)
{
min_acc = acc;
min_idx = y;
}
}
// Get the configuration from the nearest GeMM shape
MatMulKernelInfo desc;
desc.adj_lhs = adj_lhs;
desc.adj_rhs = adj_rhs;
desc.m0 = configs[min_idx][4];
desc.n0 = configs[min_idx][5];
desc.k0 = configs[min_idx][6];
desc.export_rhs_to_cl_image = configs[min_idx][7];
return desc;
}
} // namespace cl_matmul
} // namespace arm_compute
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