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/*
* Copyright (c) 2017-2018 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 "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
using namespace arm_compute;
using namespace arm_compute::misc::shape_calculator;
CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _mm_kernel(), _mtx_a_reshape_kernel(), _mtx_b_reshape_kernel(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _offset_contribution_kernel(),
_vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _a_offset(0), _b_offset(0), _is_interleaved_transposed(true), _is_first_run(true), _reshape_b_only_on_first_run(false)
{
}
void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
ARM_COMPUTE_UNUSED(gemm_info);
ARM_COMPUTE_ERROR_THROW_ON(CLGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), output->info(), gemm_info));
_reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
_a_offset = a->info()->quantization_info().offset;
_b_offset = b->info()->quantization_info().offset;
// If the input tensor has less than 16 rows, we run a special version of GEMMLowp without reshaping the input tensors
_is_interleaved_transposed = (a->info()->dimension(1)) > 16 && (CLScheduler::get().target() != GPUTarget::BIFROST);
// Set the target for the matrix multiply kernel
_mm_kernel.set_target(CLScheduler::get().target());
const ICLTensor *matrix_a = a;
const ICLTensor *matrix_b = b;
if(_is_interleaved_transposed)
{
matrix_a = &_tmp_a;
matrix_b = &_tmp_b;
TensorInfo info_a(compute_interleaved_shape(*a->info()), 1, a->info()->data_type());
TensorInfo info_b(compute_transpose1xW_shape(*b->info()), 1, b->info()->data_type());
_tmp_a.allocator()->init(info_a);
_tmp_b.allocator()->init(info_b);
_memory_group.manage(&_tmp_a);
_memory_group.manage(&_tmp_b);
// Configure interleave kernel
_mtx_a_reshape_kernel.configure(a, &_tmp_a);
// Configure transpose kernel
_mtx_b_reshape_kernel.configure(b, &_tmp_b);
}
// Configure matrix multiply kernel
_mm_kernel.configure(matrix_a, matrix_b, output, _is_interleaved_transposed);
// Initialize matrix B reduction kernel only if _a_offset is not equal to 0
if(_a_offset != 0)
{
TensorInfo info_vector_sum_col(compute_reductionA_shape(*b->info()), 1, DataType::S32);
_vector_sum_col.allocator()->init(info_vector_sum_col);
_memory_group.manage(&_vector_sum_col);
// Configure Matrix B reduction kernel
_mtx_b_reduction_kernel.configure(b, &_vector_sum_col);
}
// Initialize Matrix A reduction kernel only if _b_offset is not equal to 0
if(_b_offset != 0)
{
TensorInfo info_vector_sum_row(compute_reductionB_shape(*a->info()), 1, DataType::S32);
_vector_sum_row.allocator()->init(info_vector_sum_row);
_memory_group.manage(&_vector_sum_row);
// Configure matrix A reduction kernel
_mtx_a_reduction_kernel.configure(a, &_vector_sum_row);
}
// Configure offset contribution kernel
_offset_contribution_kernel.configure(output, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, a->info()->dimension(0), _a_offset, _b_offset);
// Allocate tensors
if(_is_interleaved_transposed)
{
_tmp_a.allocator()->allocate();
_tmp_b.allocator()->allocate();
}
if(_a_offset != 0)
{
_vector_sum_col.allocator()->allocate();
}
if(_b_offset != 0)
{
_vector_sum_row.allocator()->allocate();
}
}
Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(0) != (b)->dimension(1),
"The product AB is defined only if the number of columns in A is equal to the number of rows in B");
ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(1) != (output)->dimension(1),
"The output matrix must have the same number of rows as the matrix A");
ARM_COMPUTE_RETURN_ERROR_ON_MSG((b)->dimension(0) != (output)->dimension(0),
"The output matrix must have the same number of columns as the matrix B");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
int32_t a_offset = a->quantization_info().offset;
int32_t b_offset = b->quantization_info().offset;
bool is_interleaved_transposed = (a->dimension(1)) > 16 && (CLScheduler::get().target() != GPUTarget::BIFROST);
if(is_interleaved_transposed)
{
TensorInfo info_a(compute_interleaved_shape(*a), 1, a->data_type());
TensorInfo info_b(compute_transpose1xW_shape(*b), 1, b->data_type());
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMInterleave4x4Kernel::validate(a, &info_a, 1));
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMTranspose1xWKernel::validate(b, &info_b, 1));
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(&info_a, &info_b, output));
}
else
{
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(a, b, output));
}
TensorInfo info_vector_sum_col, info_vector_sum_row;
// Validate matrix B reduction kernel only if _a_offset is not equal to 0
if(a_offset != 0)
{
info_vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32);
// Configure Matrix B reduction kernel
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixBReductionKernel::validate(b, &info_vector_sum_col));
}
// Validate Matrix A reduction kernel only if _b_offset is not equal to 0
if(b_offset != 0)
{
info_vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32);
// Configure matrix A reduction kernel
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(a, &info_vector_sum_row));
}
// Validate offset contribution kernel
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOffsetContributionKernel::validate(output,
a_offset == 0 ? nullptr : &info_vector_sum_col,
b_offset == 0 ? nullptr : &info_vector_sum_row,
a_offset, b_offset));
return Status{};
}
void CLGEMMLowpMatrixMultiplyCore::run()
{
_memory_group.acquire();
if(_is_interleaved_transposed)
{
// Run reshape matrix A
CLScheduler::get().enqueue(_mtx_a_reshape_kernel, false);
if(_is_first_run || !_reshape_b_only_on_first_run)
{
// Run reshape matrix B
CLScheduler::get().enqueue(_mtx_b_reshape_kernel, false);
}
}
// Note: if _reshape_b_only_on_first_run = true, the reduction kernel can be executed only once
if(_is_first_run || !_reshape_b_only_on_first_run)
{
// Run matrix B reduction kernel only if _a_offset is not equal to 0
if(_a_offset != 0)
{
CLScheduler::get().enqueue(_mtx_b_reduction_kernel, false);
}
}
// Run matrix multiply
CLScheduler::get().enqueue(_mm_kernel, false);
// Run matrix A reduction kernel only if _b_offset is not equal to 0
if(_b_offset != 0)
{
CLScheduler::get().enqueue(_mtx_a_reduction_kernel, false);
}
// Run offset contribution kernel
CLScheduler::get().enqueue(_offset_contribution_kernel, true);
_memory_group.release();
_is_first_run = false;
}
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