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/*
* Copyright (c) 2017-2022 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/cpu/kernels/CpuGemmMatrixMultiplyKernel.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/Validate.h"
#include "src/core/common/Registrars.h"
#include "src/core/CPP/Validate.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "src/cpu/kernels/gemm_matrix_mul/list.h"
namespace arm_compute
{
namespace cpu
{
namespace kernels
{
namespace
{
static const std::vector<CpuGemmMatrixMultiplyKernel::GemmMatrixMulKernel> available_kernels = {
{"neon_fp32_gemm_matrix_mul", [](const DataTypeISASelectorData &data) { return (data.dt == DataType::F32); },
REGISTER_FP32_NEON(neon_fp32_gemm_matrix_mul)},
{"neon_fp16_gemm_matrix_mul",
[](const DataTypeISASelectorData &data) { return (data.dt == DataType::F16) && data.isa.fp16; },
REGISTER_FP16_NEON(neon_fp16_gemm_matrix_mul)},
};
inline Status validate_arguments(const ITensorInfo *lhs,
const ITensorInfo *rhs,
const ITensorInfo *dst,
float alpha,
bool is_interleaved,
const GEMMReshapeInfo &reshape_info)
{
ARM_COMPUTE_UNUSED(alpha);
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(lhs);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs, dst);
if (!is_interleaved)
{
ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(0) != rhs->dimension(1));
if (dst->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON(rhs->dimension(0) != dst->dimension(0));
ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(1) != dst->dimension(1));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
}
}
else
{
const int m = reshape_info.m();
const int n = reshape_info.n();
const int k = reshape_info.k();
const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
/* Interleave */
TensorShape tensor_shape0{lhs->tensor_shape()};
tensor_shape0.set(0, k);
tensor_shape0.set(1, m);
const TensorInfo tensor_info0 = lhs->clone()->set_tensor_shape(tensor_shape0);
const TensorInfo tensor_info_reshaped0 = lhs->clone()->set_tensor_shape(
misc::shape_calculator::compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lhs, &tensor_info_reshaped0);
if (n != 0) /* Transpose */
{
TensorShape tensor_shape1{rhs->tensor_shape()};
tensor_shape1.set(0, n);
tensor_shape1.set(1, k);
const TensorInfo tensor_info1 = rhs->clone()->set_tensor_shape(tensor_shape1);
const TensorInfo tensor_info_reshaped1 =
rhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transpose1xW_with_element_size_shape(
tensor_info1, mult_transpose1xW_width));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(rhs, &tensor_info_reshaped1);
}
if (dst->total_size() != 0)
{
if (n != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(0) != static_cast<size_t>(n));
}
ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(1) != static_cast<size_t>(m));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
}
}
return Status{};
}
} // namespace
void CpuGemmMatrixMultiplyKernel::configure(const ITensorInfo *lhs,
const ITensorInfo *rhs,
ITensorInfo *dst,
float alpha,
bool is_interleaved,
const GEMMReshapeInfo &reshape_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
// dst tensor auto inizialitation if not yet initialized
TensorShape tensor_shape{lhs->tensor_shape()};
tensor_shape.set(0, is_interleaved ? reshape_info.n() : rhs->dimension(0));
tensor_shape.set(1, is_interleaved ? reshape_info.m() : lhs->dimension(1));
auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(tensor_shape));
// Perform validate step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info));
_alpha = alpha;
// Configure kernel window
Window win{};
// Check if the dst tensor is a vector. If so,the kernel runs the vector-matrix multiplication
const bool is_dst_vector = (dst->dimension(1) == 1);
if (is_dst_vector)
{
const unsigned int num_elems_processed_per_iteration_x = (lhs->data_type() == DataType::F32) ? 16 : 32;
win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x));
}
else
{
constexpr unsigned int num_elems_processed_per_iteration_x = 8;
constexpr unsigned int num_elems_processed_per_iteration_y = 4;
win =
calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
}
const auto uk = CpuGemmMatrixMultiplyKernel::get_implementation(
DataTypeISASelectorData{lhs->data_type(), CPUInfo::get().get_isa()});
ARM_COMPUTE_ERROR_ON_NULLPTR(uk);
_func = uk->ukernel;
ICPPKernel::configure(win);
}
Status CpuGemmMatrixMultiplyKernel::validate(const ITensorInfo *lhs,
const ITensorInfo *rhs,
const ITensorInfo *dst,
float alpha,
bool is_interleaved,
const GEMMReshapeInfo &reshape_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info));
return Status{};
}
void CpuGemmMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
ARM_COMPUTE_ERROR_ON(tensors.empty());
ARM_COMPUTE_ERROR_ON(_func == nullptr);
const ITensor *lhs = tensors.get_const_tensor(TensorType::ACL_SRC_0);
const ITensor *rhs = tensors.get_const_tensor(TensorType::ACL_SRC_1);
ITensor *dst = tensors.get_tensor(TensorType::ACL_DST);
const bool is_dst_vector = (dst->info()->dimension(1) == 1);
(*_func)(lhs, rhs, dst, window, info, _alpha, is_dst_vector);
}
const char *CpuGemmMatrixMultiplyKernel::name() const
{
return "CpuGemmMatrixMultiplyKernel";
}
const std::vector<CpuGemmMatrixMultiplyKernel::GemmMatrixMulKernel> &
CpuGemmMatrixMultiplyKernel::get_available_kernels()
{
return available_kernels;
}
} // namespace kernels
} // namespace cpu
} // namespace arm_compute
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