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

 /*
  * Copyright (c) 2017-2019 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/CLGEMM.h"

 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/GPUTarget.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "arm_compute/runtime/CL/gemm_reshaped/CLGEMMReshapedConfiguration.h"
 #include "arm_compute/runtime/ITensorAllocator.h"

 namespace arm_compute
 {
 using namespace arm_compute::misc::shape_calculator;
 using namespace arm_compute::cl_gemm;

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

     if(gpu_target_is_in(gpu_target, GPUTarget::G52, GPUTarget::G52LIT, GPUTarget::G71, GPUTarget::G72, GPUTarget::G76))
     {
         if((m > 1) && n < 16)
         {
             flag = true;
         }
         else
         {
             // COMPMID-852
             if(k > 256 && m > 4 && is_data_type_float(data_type) && reshape_b_only_on_first_run)
             {
                 constexpr float alpha = 3.2f;
                 constexpr float fact0 = 1.51f;
                 constexpr float fact1 = 1.66f;
                 constexpr float ops   = 12.0f;
                 const float     scale = k > 1024 ? 1.07f : 1.0f;
                 flag                  = alpha + ((n * fact0) / ops) < ((fact1 * n * scale) / ops);
             }
             else
             {
                 flag = false;
             }
         }
     }
     else
     {
         // We reshape the matrices only if we do not have the vector-by-matrix case and we reshape the matrix B only once
         flag = m != 1 && reshape_b_only_on_first_run;
     }

     return flag;
 }
 } // namespace

 CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)),
       _mm_kernel(),
       _ma_kernel(),
       _reshape_lhs_kernel(),
       _reshape_rhs_kernel(),
       _mm_reshaped_kernel(),
       _tmp_a(),
       _tmp_b(),
       _original_b(nullptr),
       _is_interleaved_transposed(false),
       _run_addition(false),
       _reshape_b_only_on_first_run(false),
       _is_prepared(false),
       _is_new_gemm_reshaped(false)
 {
 }

 void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), alpha, beta, gemm_info));

     // Check if we need to reshape the matrix B only on the first run
     _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
     _is_prepared                 = gemm_info.retain_internal_weights();
     _original_b                  = b;

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

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

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

     // 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
     DataType           data_type                 = a->info()->data_type();
     bool               reinterpret_input_as_3d   = gemm_info.reinterpret_input_as_3d();
     const unsigned int m                         = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
     const unsigned int n                         = b->info()->dimension(0);
     const unsigned int k                         = a->info()->dimension(0);
     const unsigned int batch_size                = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2);
     const int          depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
     int                mult_transpose1xW_width   = 1;
     int                mult_interleave4x4_height = 1;

     if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
     {
         mult_transpose1xW_width   = 4;
         mult_interleave4x4_height = 2;
     }
     GEMMRHSMatrixInfo rhs_info;
     rhs_info.n0         = 16 / b->info()->element_size();
     rhs_info.k0         = 1;
     rhs_info.h0         = mult_transpose1xW_width;
     rhs_info.interleave = false;
     rhs_info.transpose  = false;

     GEMMLHSMatrixInfo lhs_info;
     lhs_info.m0         = 4;
     lhs_info.k0         = 4;
     lhs_info.v0         = mult_interleave4x4_height;
     lhs_info.interleave = true;
     lhs_info.transpose  = true;

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

     // Check if we can run the new reshaped GEMM
     const auto workload   = static_cast<float>((m * n) / 20.0f);
     _is_new_gemm_reshaped = (workload > 1600.0f) && (get_arch_from_target(gpu_target) == GPUTarget::BIFROST) && _is_interleaved_transposed && (data_type == DataType::F32);

     const bool add_matrix_c  = (beta != 0.f && c != nullptr);
     const bool is_beta_one   = std::abs(1.0f - beta) < 0.00001f;
     const bool use_fused_add = is_beta_one && (c != nullptr && c->info()->num_dimensions() == 1) && !_is_new_gemm_reshaped;

     // if _is_interleaved_transposed is set, force reinterpret_input_as_3d to be false as the output of CLGEMMInterleaveKernel will be 2D
     if(_is_interleaved_transposed)
     {
         reinterpret_input_as_3d = false;

         matrix_a = &_tmp_a;
         matrix_b = &_tmp_b;

         // Manage intermediate buffers
         _memory_group.manage(&_tmp_a);
         if(!_reshape_b_only_on_first_run)
         {
             _memory_group.manage(&_tmp_b);
         }
         // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel

         if(_is_new_gemm_reshaped)
         {
             GEMMLHSMatrixInfo lhs_info;

             // Pick up the GEMM configuration
             std::tie(lhs_info, rhs_info) = CLGEMMReshapedConfigurationFactory::create()->configure(m, n, k, batch_size, data_type);

             _reshape_lhs_kernel.configure(a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d());
             _reshape_rhs_kernel.configure(b, &_tmp_b, rhs_info);

             // Configure and tune matrix multiply kernel
             _mm_reshaped_kernel.configure(matrix_a, matrix_b, output, alpha, lhs_info, rhs_info, GEMMReshapeInfo(m, n, k, 1, 1,
                                                                                                                  depth_output_gemm3d, reinterpret_input_as_3d));
         }
         else
         {
             // Configure interleave kernel
             _reshape_lhs_kernel.configure(a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d());
             // Configure transpose kernel
             _reshape_rhs_kernel.configure(b, &_tmp_b, rhs_info);
         }
     }

     if(!_is_new_gemm_reshaped)
     {
         // Configure and tune matrix multiply kernel
         _mm_kernel.configure(matrix_a, matrix_b, (add_matrix_c && !use_fused_add) ? nullptr : c, output, alpha, beta, _is_interleaved_transposed,
                              GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, reinterpret_input_as_3d),
                              gemm_info.fp_mixed_precision());
         CLScheduler::get().tune_kernel_static(_mm_kernel);
     }

     if(_is_interleaved_transposed)
     {
         // Allocate intermediate tensors
         _tmp_a.allocator()->allocate();
         if(!_reshape_b_only_on_first_run)
         {
             _tmp_b.allocator()->allocate();
         }
     }

     // Configure matrix addition kernel
     if(add_matrix_c && !use_fused_add)
     {
         _ma_kernel.configure(c, output, beta);
         _run_addition = true;
     }
 }

 Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_UNUSED(alpha);
     ARM_COMPUTE_UNUSED(output);

     // Check if we need to reshape the matrix B only on the first run
     const bool reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();

     const ITensorInfo *matrix_a_info = a;
     const ITensorInfo *matrix_b_info = b;

     TensorInfo tmp_a_info{};
     TensorInfo tmp_b_info{};

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

     // 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
     DataType           data_type                 = a->data_type();
     bool               reinterpret_input_as_3d   = gemm_info.reinterpret_input_as_3d();
     const unsigned int m                         = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
     const unsigned int n                         = b->dimension(0);
     const unsigned int k                         = a->dimension(0);
     const unsigned int batch_size                = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
     int                mult_transpose1xW_width   = 1;
     int                mult_interleave4x4_height = 1;
     const int          depth_output_gemm3d       = gemm_info.depth_output_gemm3d();

     if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
     {
         mult_transpose1xW_width   = 4;
         mult_interleave4x4_height = 2;
     }

     GEMMRHSMatrixInfo rhs_info;
     rhs_info.n0         = 16 / b->element_size();
     rhs_info.k0         = 1;
     rhs_info.h0         = mult_transpose1xW_width;
     rhs_info.interleave = false;
     rhs_info.transpose  = false;

     GEMMLHSMatrixInfo lhs_info;
     lhs_info.m0         = 4;
     lhs_info.k0         = 4;
     lhs_info.v0         = mult_interleave4x4_height;
     lhs_info.interleave = true;
     lhs_info.transpose  = true;

     // Check if we need to reshape the matrix A and matrix B
     const bool run_interleave_transpose = is_interleaved_transposed(m, n, k, a->data_type(), reshape_b_only_on_first_run, gpu_target);

     // Check if we can run the new reshaped GEMM
     const auto workload             = static_cast<float>((m * n) / 20.0f);
     const bool is_new_gemm_reshaped = (workload > 1600.f) && (get_arch_from_target(gpu_target) == GPUTarget::BIFROST) && run_interleave_transpose && (data_type == DataType::F32);

     const bool add_matrix_c  = (beta != 0.f && c != nullptr);
     const bool is_beta_one   = std::abs(1.0f - beta) < 0.00001f;
     const bool use_fused_add = is_beta_one && (c != nullptr && c->num_dimensions() == 1) && !is_new_gemm_reshaped;

     // if _is_interleaved_transposed is set, force reinterpret_input_as_3d to be false as the output of CLGEMMInterleaveKernel will be 2D
     if(run_interleave_transpose)
     {
         reinterpret_input_as_3d = false;
     }

     const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, reinterpret_input_as_3d);

     if(run_interleave_transpose)
     {
         matrix_a_info = &tmp_a_info;
         matrix_b_info = &tmp_b_info;

         if(is_new_gemm_reshaped)
         {
             GEMMLHSMatrixInfo lhs_info;

             // Pick up the GEMM configuration
             std::tie(lhs_info, rhs_info) = CLGEMMReshapedConfigurationFactory::create()->configure(m, n, k, batch_size, data_type);

             auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
             ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));

             auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
             ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));

             // Validate matrix multiply
             ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedKernel::validate(matrix_a_info, matrix_b_info, output, alpha, lhs_info, rhs_info, GEMMReshapeInfo(m, n, k, 1, 1,
                                                                                      depth_output_gemm3d, reinterpret_input_as_3d)));
         }
         else
         {
             // Validate interleave kernel
             auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
             ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
             // Validate transpose kernel
             auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
             ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
         }
     }

     if(!is_new_gemm_reshaped)
     {
         // Validate matrix multiply
         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, (add_matrix_c && !use_fused_add) ? nullptr : c, output, alpha, beta,
                                                                          run_interleave_transpose, reshape_info, gpu_target, gemm_info.fp_mixed_precision()));
     }

     if(add_matrix_c && !use_fused_add)
     {
         // Validate matrix addition kernel
         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixAdditionKernel::validate(c, output, beta));
     }

     return Status{};
 }

 void CLGEMM::run()
 {
     prepare();

     _memory_group.acquire();

     if(_is_interleaved_transposed)
     {
         // Run interleave kernel
         CLScheduler::get().enqueue(_reshape_lhs_kernel, false);

         if(!_reshape_b_only_on_first_run)
         {
             // Run transpose kernel
             CLScheduler::get().enqueue(_reshape_rhs_kernel, false);
         }
     }

     // Run matrix multiply kernel
     if(_is_new_gemm_reshaped)
     {
         CLScheduler::get().enqueue(_mm_reshaped_kernel, !_run_addition);
     }
     else
     {
         CLScheduler::get().enqueue(_mm_kernel, !_run_addition);
     }

     // Run matrix addition kernel
     if(_run_addition)
     {
         CLScheduler::get().enqueue(_ma_kernel);
     }

     _memory_group.release();
 }

 void CLGEMM::prepare()
 {
     if(!_is_prepared)
     {
         if(_is_interleaved_transposed && _reshape_b_only_on_first_run)
         {
             // Run transpose kernel and mark original weights tensor as unused
             _tmp_b.allocator()->allocate();
             CLScheduler::get().enqueue(_reshape_rhs_kernel, false);
             _original_b->mark_as_unused();
         }
         CLScheduler::get().queue().finish();
         _is_prepared = true;
     }
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2019 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/CLGEMM.h"

	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/GPUTarget.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/runtime/CL/CLScheduler.h"
	#include "arm_compute/runtime/CL/gemm_reshaped/CLGEMMReshapedConfiguration.h"
	#include "arm_compute/runtime/ITensorAllocator.h"

	namespace arm_compute
	{
	using namespace arm_compute::misc::shape_calculator;
	using namespace arm_compute::cl_gemm;

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

	if(gpu_target_is_in(gpu_target, GPUTarget::G52, GPUTarget::G52LIT, GPUTarget::G71, GPUTarget::G72, GPUTarget::G76))
	{
	if((m > 1) && n < 16)
	{
	flag = true;
	}
	else
	{
	// COMPMID-852
	if(k > 256 && m > 4 && is_data_type_float(data_type) && reshape_b_only_on_first_run)
	{
	constexpr float alpha = 3.2f;
	constexpr float fact0 = 1.51f;
	constexpr float fact1 = 1.66f;
	constexpr float ops = 12.0f;
	const float scale = k > 1024 ? 1.07f : 1.0f;
	flag = alpha + ((n * fact0) / ops) < ((fact1 * n * scale) / ops);
	}
	else
	{
	flag = false;
	}
	}
	}
	else
	{
	// We reshape the matrices only if we do not have the vector-by-matrix case and we reshape the matrix B only once
	flag = m != 1 && reshape_b_only_on_first_run;
	}

	return flag;
	}
	} // namespace

	CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)),
	_mm_kernel(),
	_ma_kernel(),
	_reshape_lhs_kernel(),
	_reshape_rhs_kernel(),
	_mm_reshaped_kernel(),
	_tmp_a(),
	_tmp_b(),
	_original_b(nullptr),
	_is_interleaved_transposed(false),
	_run_addition(false),
	_reshape_b_only_on_first_run(false),
	_is_prepared(false),
	_is_new_gemm_reshaped(false)
	{
	}

	void CLGEMM::configure(const ICLTensor a, const ICLTensor b, const ICLTensor c, ICLTensor output, float alpha, float beta, const GEMMInfo &gemm_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), alpha, beta, gemm_info));

	// Check if we need to reshape the matrix B only on the first run
	_reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
	_is_prepared = gemm_info.retain_internal_weights();
	_original_b = b;

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

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

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

	// 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
	DataType data_type = a->info()->data_type();
	bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
	const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
	const unsigned int n = b->info()->dimension(0);
	const unsigned int k = a->info()->dimension(0);
	const unsigned int batch_size = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2);
	const int depth_output_gemm3d = gemm_info.depth_output_gemm3d();
	int mult_transpose1xW_width = 1;
	int mult_interleave4x4_height = 1;

	if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
	{
	mult_transpose1xW_width = 4;
	mult_interleave4x4_height = 2;
	}
	GEMMRHSMatrixInfo rhs_info;
	rhs_info.n0 = 16 / b->info()->element_size();
	rhs_info.k0 = 1;
	rhs_info.h0 = mult_transpose1xW_width;
	rhs_info.interleave = false;
	rhs_info.transpose = false;

	GEMMLHSMatrixInfo lhs_info;
	lhs_info.m0 = 4;
	lhs_info.k0 = 4;
	lhs_info.v0 = mult_interleave4x4_height;
	lhs_info.interleave = true;
	lhs_info.transpose = true;

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

	// Check if we can run the new reshaped GEMM
	const auto workload = static_cast<float>((m * n) / 20.0f);
	_is_new_gemm_reshaped = (workload > 1600.0f) && (get_arch_from_target(gpu_target) == GPUTarget::BIFROST) && _is_interleaved_transposed && (data_type == DataType::F32);

	const bool add_matrix_c = (beta != 0.f && c != nullptr);
	const bool is_beta_one = std::abs(1.0f - beta) < 0.00001f;
	const bool use_fused_add = is_beta_one && (c != nullptr && c->info()->num_dimensions() == 1) && !_is_new_gemm_reshaped;

	// if _is_interleaved_transposed is set, force reinterpret_input_as_3d to be false as the output of CLGEMMInterleaveKernel will be 2D
	if(_is_interleaved_transposed)
	{
	reinterpret_input_as_3d = false;

	matrix_a = &_tmp_a;
	matrix_b = &_tmp_b;

	// Manage intermediate buffers
	_memory_group.manage(&_tmp_a);
	if(!_reshape_b_only_on_first_run)
	{
	_memory_group.manage(&_tmp_b);
	}
	// _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel

	if(_is_new_gemm_reshaped)
	{
	GEMMLHSMatrixInfo lhs_info;

	// Pick up the GEMM configuration
	std::tie(lhs_info, rhs_info) = CLGEMMReshapedConfigurationFactory::create()->configure(m, n, k, batch_size, data_type);

	_reshape_lhs_kernel.configure(a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d());
	_reshape_rhs_kernel.configure(b, &_tmp_b, rhs_info);

	// Configure and tune matrix multiply kernel
	_mm_reshaped_kernel.configure(matrix_a, matrix_b, output, alpha, lhs_info, rhs_info, GEMMReshapeInfo(m, n, k, 1, 1,
	depth_output_gemm3d, reinterpret_input_as_3d));
	}
	else
	{
	// Configure interleave kernel
	_reshape_lhs_kernel.configure(a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d());
	// Configure transpose kernel
	_reshape_rhs_kernel.configure(b, &_tmp_b, rhs_info);
	}
	}

	if(!_is_new_gemm_reshaped)
	{
	// Configure and tune matrix multiply kernel
	_mm_kernel.configure(matrix_a, matrix_b, (add_matrix_c && !use_fused_add) ? nullptr : c, output, alpha, beta, _is_interleaved_transposed,
	GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, reinterpret_input_as_3d),
	gemm_info.fp_mixed_precision());
	CLScheduler::get().tune_kernel_static(_mm_kernel);
	}

	if(_is_interleaved_transposed)
	{
	// Allocate intermediate tensors
	_tmp_a.allocator()->allocate();
	if(!_reshape_b_only_on_first_run)
	{
	_tmp_b.allocator()->allocate();
	}
	}

	// Configure matrix addition kernel
	if(add_matrix_c && !use_fused_add)
	{
	_ma_kernel.configure(c, output, beta);
	_run_addition = true;
	}
	}

	Status CLGEMM::validate(const ITensorInfo a, const ITensorInfo b, const ITensorInfo c, const ITensorInfo output, float alpha, float beta, const GEMMInfo &gemm_info)
	{
	ARM_COMPUTE_UNUSED(alpha);
	ARM_COMPUTE_UNUSED(output);

	// Check if we need to reshape the matrix B only on the first run
	const bool reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();

	const ITensorInfo *matrix_a_info = a;
	const ITensorInfo *matrix_b_info = b;

	TensorInfo tmp_a_info{};
	TensorInfo tmp_b_info{};

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

	// 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
	DataType data_type = a->data_type();
	bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
	const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
	const unsigned int n = b->dimension(0);
	const unsigned int k = a->dimension(0);
	const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
	int mult_transpose1xW_width = 1;
	int mult_interleave4x4_height = 1;
	const int depth_output_gemm3d = gemm_info.depth_output_gemm3d();

	if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
	{
	mult_transpose1xW_width = 4;
	mult_interleave4x4_height = 2;
	}

	GEMMRHSMatrixInfo rhs_info;
	rhs_info.n0 = 16 / b->element_size();
	rhs_info.k0 = 1;
	rhs_info.h0 = mult_transpose1xW_width;
	rhs_info.interleave = false;
	rhs_info.transpose = false;

	GEMMLHSMatrixInfo lhs_info;
	lhs_info.m0 = 4;
	lhs_info.k0 = 4;
	lhs_info.v0 = mult_interleave4x4_height;
	lhs_info.interleave = true;
	lhs_info.transpose = true;

	// Check if we need to reshape the matrix A and matrix B
	const bool run_interleave_transpose = is_interleaved_transposed(m, n, k, a->data_type(), reshape_b_only_on_first_run, gpu_target);

	// Check if we can run the new reshaped GEMM
	const auto workload = static_cast<float>((m * n) / 20.0f);
	const bool is_new_gemm_reshaped = (workload > 1600.f) && (get_arch_from_target(gpu_target) == GPUTarget::BIFROST) && run_interleave_transpose && (data_type == DataType::F32);

	const bool add_matrix_c = (beta != 0.f && c != nullptr);
	const bool is_beta_one = std::abs(1.0f - beta) < 0.00001f;
	const bool use_fused_add = is_beta_one && (c != nullptr && c->num_dimensions() == 1) && !is_new_gemm_reshaped;

	// if _is_interleaved_transposed is set, force reinterpret_input_as_3d to be false as the output of CLGEMMInterleaveKernel will be 2D
	if(run_interleave_transpose)
	{
	reinterpret_input_as_3d = false;
	}

	const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, reinterpret_input_as_3d);

	if(run_interleave_transpose)
	{
	matrix_a_info = &tmp_a_info;
	matrix_b_info = &tmp_b_info;

	if(is_new_gemm_reshaped)
	{
	GEMMLHSMatrixInfo lhs_info;

	// Pick up the GEMM configuration
	std::tie(lhs_info, rhs_info) = CLGEMMReshapedConfigurationFactory::create()->configure(m, n, k, batch_size, data_type);

	auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));

	auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));

	// Validate matrix multiply
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedKernel::validate(matrix_a_info, matrix_b_info, output, alpha, lhs_info, rhs_info, GEMMReshapeInfo(m, n, k, 1, 1,
	depth_output_gemm3d, reinterpret_input_as_3d)));
	}
	else
	{
	// Validate interleave kernel
	auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
	// Validate transpose kernel
	auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
	}
	}

	if(!is_new_gemm_reshaped)
	{
	// Validate matrix multiply
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, (add_matrix_c && !use_fused_add) ? nullptr : c, output, alpha, beta,
	run_interleave_transpose, reshape_info, gpu_target, gemm_info.fp_mixed_precision()));
	}

	if(add_matrix_c && !use_fused_add)
	{
	// Validate matrix addition kernel
	ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixAdditionKernel::validate(c, output, beta));
	}

	return Status{};
	}

	void CLGEMM::run()
	{
	prepare();

	_memory_group.acquire();

	if(_is_interleaved_transposed)
	{
	// Run interleave kernel
	CLScheduler::get().enqueue(_reshape_lhs_kernel, false);

	if(!_reshape_b_only_on_first_run)
	{
	// Run transpose kernel
	CLScheduler::get().enqueue(_reshape_rhs_kernel, false);
	}
	}

	// Run matrix multiply kernel
	if(_is_new_gemm_reshaped)
	{
	CLScheduler::get().enqueue(_mm_reshaped_kernel, !_run_addition);
	}
	else
	{
	CLScheduler::get().enqueue(_mm_kernel, !_run_addition);
	}

	// Run matrix addition kernel
	if(_run_addition)
	{
	CLScheduler::get().enqueue(_ma_kernel);
	}

	_memory_group.release();
	}

	void CLGEMM::prepare()
	{
	if(!_is_prepared)
	{
	if(_is_interleaved_transposed && _reshape_b_only_on_first_run)
	{
	// Run transpose kernel and mark original weights tensor as unused
	_tmp_b.allocator()->allocate();
	CLScheduler::get().enqueue(_reshape_rhs_kernel, false);
	_original_b->mark_as_unused();
	}
	CLScheduler::get().queue().finish();
	_is_prepared = true;
	}
	}
	} // namespace arm_compute