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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/gpu/cl/operators/ClMatMul.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "src/common/utils/Log.h"
#include "src/gpu/cl/kernels/ClNativeMatMulKernel.h"
namespace arm_compute
{
namespace opencl
{
using namespace arm_compute::opencl::kernels;
ClMatMul::ClMatMul()
: _native_matmul_kernel(std::make_unique<ClNativeMatMulKernel>())
{
}
ClMatMul::~ClMatMul()
{
}
Status ClMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *output, const MatMulInfo &matmul_info)
{
MatMulKernelInfo kernel_info;
kernel_info.adj_lhs = matmul_info.adj_lhs();
kernel_info.adj_rhs = matmul_info.adj_rhs();
return ClNativeMatMulKernel::validate(lhs, rhs, output, kernel_info);
}
void ClMatMul::configure(const CLCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *output, const MatMulInfo &matmul_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, output);
ARM_COMPUTE_LOG_PARAMS(lhs, rhs, output, matmul_info);
// Perform validation step
ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, output, matmul_info));
const GPUTarget gpu_target = CLScheduler::get().target();
// Placeholder: Getting the heuristics calculated values for M0, N0, K0, and whether to export RHS to texture pipe
// Filling the MatMul Kernel info
MatMulKernelInfo kernel_info;
kernel_info.adj_lhs = matmul_info.adj_lhs();
kernel_info.adj_rhs = matmul_info.adj_rhs();
kernel_info.m0 = 1; // to be properly calculated from heuristics
kernel_info.n0 = 4; // to be properly calculated from heuristics
kernel_info.k0 = 4; // to be properly calculated from heuristics
kernel_info.export_rhs_to_cl_image = false; // to be properly determined from heuristics
// Set the target for the kernels
_native_matmul_kernel->set_target(gpu_target);
// Configure the native matrix multiply kernel
_native_matmul_kernel->configure(compile_context, lhs, rhs, output, kernel_info);
}
void ClMatMul::run(ITensorPack &tensors)
{
CLScheduler::get().enqueue_op(*_native_matmul_kernel, tensors, true);
}
} // namespace opencl
} // namespace arm_compute
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