src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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;

 namespace
 {
 inline bool is_interleaved_transposed(int m, int n, int k, bool reshape_b_only_on_first_run, GPUTarget gpu_target)
 {
     bool flag = true;

     if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
     {
         // COMPMID-852
         if(k > 256 && m > 4 && reshape_b_only_on_first_run)
         {
             flag = ((0.72f + n * 0.10766f) < (n * 0.1284f));
         }
         else
         {
             flag = false;
         }
     }

     return flag;
 }
 } // namespace

 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;

     // Get the GPU target
     const GPUTarget gpu_target = CLScheduler::get().target();

     // Set the target for the kernels
     _mtx_a_reshape_kernel.set_target(gpu_target);
     _mm_kernel.set_target(gpu_target);

     const ICLTensor *matrix_a = a;
     const ICLTensor *matrix_b = b;

     // Arguments used by GEMMReshapeInfo
     // If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo
     // in order to know how the matrices have been reshaped
     const int     m                         = a->info()->dimension(1);
     const int     n                         = b->info()->dimension(0);
     const int     k                         = a->info()->dimension(0);
     constexpr int mult_transpose1xW_width   = 1;
     constexpr int mult_interleave4x4_height = 1;

     // Check if we need to reshape the matrix A and matrix B
     _is_interleaved_transposed = is_interleaved_transposed(m, n, k, _reshape_b_only_on_first_run, gpu_target);

     if(_is_interleaved_transposed)
     {
         matrix_a = &_tmp_a;
         matrix_b = &_tmp_b;

         _memory_group.manage(&_tmp_a);
         if(!_reshape_b_only_on_first_run)
         {
             _memory_group.manage(&_tmp_b);
         }

         // Configure interleave kernel
         _mtx_a_reshape_kernel.configure(a, &_tmp_a, mult_interleave4x4_height);

         // Configure transpose kernel
         _mtx_b_reshape_kernel.configure(b, &_tmp_b, mult_transpose1xW_width);
     }

     // Configure matrix multiply kernel
     _mm_kernel.configure(matrix_a, matrix_b, output, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height));

     // 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);
         if(!_reshape_b_only_on_first_run)
         {
             _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;

     const int             m                         = a->dimension(1);
     const int             n                         = b->dimension(0);
     const int             k                         = a->dimension(0);
     constexpr int         mult_transpose1xW_width   = 1;
     constexpr int         mult_interleave4x4_height = 1;
     const GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height);

     bool reshape_matrices = is_interleaved_transposed(m, n, k, gemm_info.reshape_b_only_on_first_run(), CLScheduler::get().target());

     if(reshape_matrices)
     {
         TensorInfo info_a(compute_interleaved_shape(*a, mult_interleave4x4_height), 1, a->data_type());
         TensorInfo info_b(compute_transpose1xW_with_element_size_shape(*b, mult_transpose1xW_width), 1, b->data_type());

         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMInterleave4x4Kernel::validate(a, &info_a, mult_interleave4x4_height));
         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMTranspose1xWKernel::validate(b, &info_b, mult_transpose1xW_width));
         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(&info_a, &info_b, output, reshape_matrices, reshape_info));
     }
     else
     {
         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(a, b, output, reshape_matrices, reshape_info));
     }

     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;
 }
	/*
	* 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;

	namespace
	{
	inline bool is_interleaved_transposed(int m, int n, int k, bool reshape_b_only_on_first_run, GPUTarget gpu_target)
	{
	bool flag = true;

	if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
	{
	// COMPMID-852
	if(k > 256 && m > 4 && reshape_b_only_on_first_run)
	{
	flag = ((0.72f + n * 0.10766f) < (n * 0.1284f));
	}
	else
	{
	flag = false;
	}
	}

	return flag;
	}
	} // namespace

	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;

	// Get the GPU target
	const GPUTarget gpu_target = CLScheduler::get().target();

	// Set the target for the kernels
	_mtx_a_reshape_kernel.set_target(gpu_target);
	_mm_kernel.set_target(gpu_target);

	const ICLTensor *matrix_a = a;
	const ICLTensor *matrix_b = b;

	// Arguments used by GEMMReshapeInfo
	// If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo
	// in order to know how the matrices have been reshaped
	const int m = a->info()->dimension(1);
	const int n = b->info()->dimension(0);
	const int k = a->info()->dimension(0);
	constexpr int mult_transpose1xW_width = 1;
	constexpr int mult_interleave4x4_height = 1;

	// Check if we need to reshape the matrix A and matrix B
	_is_interleaved_transposed = is_interleaved_transposed(m, n, k, _reshape_b_only_on_first_run, gpu_target);

	if(_is_interleaved_transposed)
	{
	matrix_a = &_tmp_a;
	matrix_b = &_tmp_b;

	_memory_group.manage(&_tmp_a);
	if(!_reshape_b_only_on_first_run)
	{
	_memory_group.manage(&_tmp_b);
	}

	// Configure interleave kernel
	_mtx_a_reshape_kernel.configure(a, &_tmp_a, mult_interleave4x4_height);

	// Configure transpose kernel
	_mtx_b_reshape_kernel.configure(b, &_tmp_b, mult_transpose1xW_width);
	}

	// Configure matrix multiply kernel
	_mm_kernel.configure(matrix_a, matrix_b, output, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height));

	// 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);
	if(!_reshape_b_only_on_first_run)
	{
	_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;

	const int m = a->dimension(1);
	const int n = b->dimension(0);
	const int k = a->dimension(0);
	constexpr int mult_transpose1xW_width = 1;
	constexpr int mult_interleave4x4_height = 1;
	const GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height);

	bool reshape_matrices = is_interleaved_transposed(m, n, k, gemm_info.reshape_b_only_on_first_run(), CLScheduler::get().target());

	if(reshape_matrices)
	{
	TensorInfo info_a(compute_interleaved_shape(*a, mult_interleave4x4_height), 1, a->data_type());
	TensorInfo info_b(compute_transpose1xW_with_element_size_shape(*b, mult_transpose1xW_width), 1, b->data_type());

	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMInterleave4x4Kernel::validate(a, &info_a, mult_interleave4x4_height));
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMTranspose1xWKernel::validate(b, &info_b, mult_transpose1xW_width));
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(&info_a, &info_b, output, reshape_matrices, reshape_info));
	}
	else
	{
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(a, b, output, reshape_matrices, reshape_info));
	}

	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;
	}