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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/core/Utils.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "src/common/utils/Log.h"
#include "src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h"
#include "src/gpu/cl/kernels/ClMatMulLowpNativeMMULKernel.h"
#include "src/gpu/cl/kernels/ClMatMulNativeKernel.h"
#include "src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h"
#include "src/runtime/heuristics/matmul_native/ClMatMulNativeDefaultConfigValhall.h"
#include "src/runtime/heuristics/matmul_native/ClMatMulNativeKernelConfig.h"
#include "src/runtime/heuristics/matmul_native/IClMatMulNativeKernelConfig.h"
using namespace arm_compute::cl_matmul;
namespace arm_compute
{
namespace opencl
{
namespace
{
enum class MatMulKernelType
{
/** Native matrix multiplication for FP types */
NATIVE_FP,
/** Native matrix multiplication for quantized types */
NATIVE_QUANTIZED,
/** Native matrix multiplication using MMUL extension for FP types */
NATIVE_MMUL_FP,
/** Native matrix multiplication using MMUL extension for Quantized types */
NATIVE_MMUL_QUANTIZED
};
MatMulKernelType get_matmul_kernel(const ITensorInfo *lhs,
const ITensorInfo *rhs,
const MatMulInfo &matmul_info,
const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_UNUSED(lhs, rhs, matmul_info, act_info);
const bool is_quantized = is_data_type_quantized_asymmetric(lhs->data_type());
const bool is_mmul_supported = arm_matrix_multiply_supported(CLKernelLibrary::get().get_device());
const int k = matmul_info.adj_lhs() ? lhs->tensor_shape().y() : lhs->tensor_shape().x();
if (is_quantized)
{
// MMUL kernel works only when K is a multiple of 16
if (is_mmul_supported && !act_info.enabled() && k % 16 == 0)
{
return MatMulKernelType::NATIVE_MMUL_QUANTIZED;
}
return MatMulKernelType::NATIVE_QUANTIZED;
}
else
{
// MMUL kernel works only when K is a multiple of 4
if (is_mmul_supported && !act_info.enabled() && k % 4 == 0)
{
return MatMulKernelType::NATIVE_MMUL_FP;
}
return MatMulKernelType::NATIVE_FP;
}
}
} // namespace
using namespace arm_compute::opencl::kernels;
ClMatMul::ClMatMul()
{
}
Status ClMatMul::validate(const ITensorInfo *lhs,
const ITensorInfo *rhs,
const ITensorInfo *dst,
const MatMulInfo &matmul_info,
const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(rhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
DataType::F16, DataType::F32);
const GPUTarget gpu_target = CLScheduler::get().target();
std::unique_ptr<IClMatMulNativeKernelConfig> t = ClMatMulNativeKernelConfigurationFactory::create(gpu_target);
const MatMulKernelInfo kernel_info = t->configure(lhs, rhs, matmul_info);
switch (get_matmul_kernel(lhs, rhs, matmul_info, act_info))
{
case MatMulKernelType::NATIVE_FP:
return ClMatMulNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
case MatMulKernelType::NATIVE_MMUL_FP:
return ClMatMulNativeMMULKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info);
case MatMulKernelType::NATIVE_QUANTIZED:
return ClMatMulLowpNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
case MatMulKernelType::NATIVE_MMUL_QUANTIZED:
return ClMatMulLowpNativeMMULKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
default:
ARM_COMPUTE_ERROR("Unsupported MatMul Kernel!");
}
}
void ClMatMul::configure(const CLCompileContext &compile_context,
ITensorInfo *lhs,
ITensorInfo *rhs,
ITensorInfo *dst,
const MatMulInfo &matmul_info,
const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, matmul_info);
// Perform validation step
ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, dst, matmul_info));
const GPUTarget gpu_target = CLScheduler::get().target();
const auto kernel_config = ClMatMulNativeKernelConfigurationFactory::create(gpu_target);
const MatMulKernelInfo kernel_info = kernel_config->configure(lhs, rhs, matmul_info);
switch (get_matmul_kernel(lhs, rhs, matmul_info, act_info))
{
case MatMulKernelType::NATIVE_FP:
{
auto kernel = std::make_unique<ClMatMulNativeKernel>();
kernel->set_target(gpu_target);
kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
_matmul_kernel = std::move(kernel);
}
break;
case MatMulKernelType::NATIVE_MMUL_FP:
{
auto kernel = std::make_unique<ClMatMulNativeMMULKernel>();
kernel->set_target(gpu_target);
kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info);
_matmul_kernel = std::move(kernel);
}
break;
case MatMulKernelType::NATIVE_QUANTIZED:
{
auto kernel = std::make_unique<ClMatMulLowpNativeKernel>();
kernel->set_target(gpu_target);
kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
_matmul_kernel = std::move(kernel);
}
break;
case MatMulKernelType::NATIVE_MMUL_QUANTIZED:
{
auto kernel = std::make_unique<ClMatMulLowpNativeMMULKernel>();
kernel->set_target(gpu_target);
kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
_matmul_kernel = std::move(kernel);
}
break;
default:
ARM_COMPUTE_ERROR("Unsupported MatMul Kernel!");
}
}
void ClMatMul::run(ITensorPack &tensors)
{
CLScheduler::get().enqueue_op(*_matmul_kernel, tensors, /* flush */ true);
}
} // namespace opencl
} // namespace arm_compute
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