src/runtime/heuristics/matmul_native/ClMatMulNativeDefaultConfigValhall.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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/runtime/heuristics/matmul_native/ClMatMulNativeDefaultConfigValhall.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/GPUTarget.h"
 #include "arm_compute/core/KernelDescriptors.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "src/gpu/cl/kernels/ClMatMulNativeKernel.h"
 #include <utility>

 #include "src/runtime/heuristics/matmul_native/ClMatMulNativeHelpers.h"

 namespace arm_compute
 {
 namespace cl_matmul
 {
 ClMatMulNativeDefaultConfigValhall::ClMatMulNativeDefaultConfigValhall(GPUTarget gpu)
     : IClMatMulNativeKernelConfig(gpu)
 {
 }

 MatMulKernelInfo ClMatMulNativeDefaultConfigValhall::configure(const ITensorInfo *lhs, const ITensorInfo *rhs, const MatMulInfo &info)
 {
     using ConfigurationFunctionExecutorPtr = MatMulKernelInfo (ClMatMulNativeDefaultConfigValhall::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b, bool rhs_lock_padding, const MatMulInfo & info);

     ClMatMulNativeConfigArray<ConfigurationFunctionExecutorPtr> configs_G710(&ClMatMulNativeDefaultConfigValhall::configure_G710_f32,
                                                                              &ClMatMulNativeDefaultConfigValhall::configure_G710_f16,
                                                                              &ClMatMulNativeDefaultConfigValhall::configure_G710_u8);

     ConfigurationFunctionExecutorPtr func = nullptr;
     switch(_target)
     {
         case GPUTarget::G710:
         default:
             func = configs_G710.get_function(lhs->data_type());
             break;
     }

     const bool adj_lhs = info.adj_lhs();
     const bool adj_rhs = info.adj_rhs();

     TensorShape lhs_shape = lhs->tensor_shape();
     TensorShape rhs_shape = rhs->tensor_shape();

     const bool is_batched = lhs_shape.num_dimensions() > 2;

     if(is_batched == true)
     {
         lhs_shape.collapse_from(2);
     }

     const unsigned int m = adj_lhs ? lhs_shape.x() : lhs_shape.y();
     const unsigned int n = adj_rhs ? rhs_shape.y() : rhs_shape.x();
     const unsigned int k = adj_lhs ? lhs_shape.y() : lhs_shape.x();
     const unsigned int b = lhs_shape.z();

     ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not supported for matmul native");
     return (this->*func)(m, n, k, b, rhs->lock_paddings(), info);
 }

 MatMulKernelInfo ClMatMulNativeDefaultConfigValhall::configure_G710_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b, bool rhs_lock_padding, const MatMulInfo &info)
 {
     const MatMulNativeConfigsMatrix configs_mnkb_best_nt_nt =
     {
         { 3136, 64, 64, 36, 4, 4, 16, 1 },
         { 4096, 48, 32, 36, 4, 4, 4, 1 },
         { 688, 92, 68, 32, 2, 8, 4, 1 },
         { 24, 464, 412, 24, 2, 8, 4, 1 },
         { 112, 184, 144, 28, 4, 4, 16, 1 },
         { 5776, 64, 32, 36, 2, 4, 16, 1 },
         { 1568, 64, 40, 36, 2, 8, 8, 1 },
         { 2920, 64, 64, 24, 4, 4, 16, 1 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_fallback_nt_nt =
     {
         { 3136, 64, 64, 36, 4, 4, 8, 0 },
         { 4096, 48, 32, 36, 4, 4, 8, 0 },
         { 688, 92, 68, 32, 5, 4, 4, 0 },
         { 24, 464, 412, 24, 6, 2, 8, 0 },
         { 112, 184, 144, 28, 6, 4, 4, 0 },
         { 5776, 64, 32, 36, 5, 4, 4, 0 },
         { 1568, 64, 40, 36, 4, 4, 8, 0 },
         { 2920, 64, 64, 24, 4, 4, 8, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_nt_t =
     {
         { 3136, 64, 64, 36, 4, 4, 4, 1 },
         { 4096, 48, 32, 36, 2, 2, 16, 1 },
         { 688, 92, 68, 32, 4, 4, 4, 1 },
         { 24, 464, 412, 24, 6, 2, 8, 1 },
         { 112, 184, 144, 28, 4, 2, 16, 1 },
         { 5776, 64, 32, 36, 4, 4, 4, 1 },
         { 1568, 64, 40, 36, 4, 4, 8, 1 },
         { 2920, 64, 64, 24, 4, 4, 4, 1 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_fallback_nt_t =
     {
         { 3136, 64, 64, 36, 5, 4, 4, 0 },
         { 4096, 48, 32, 36, 5, 4, 4, 0 },
         { 688, 92, 68, 32, 5, 4, 4, 0 },
         { 24, 464, 412, 24, 6, 2, 4, 0 },
         { 112, 184, 144, 28, 5, 4, 4, 0 },
         { 5776, 64, 32, 36, 5, 4, 4, 0 },
         { 1568, 64, 40, 36, 5, 4, 4, 0 },
         { 2920, 64, 64, 24, 6, 2, 4, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_t_nt =
     {
         { 3136, 64, 64, 36, 4, 4, 16, 1 },
         { 4096, 48, 32, 36, 4, 4, 4, 1 },
         { 688, 92, 68, 32, 2, 8, 4, 1 },
         { 24, 464, 412, 24, 2, 8, 4, 1 },
         { 112, 184, 144, 28, 4, 4, 16, 1 },
         { 5776, 64, 32, 36, 2, 8, 8, 1 },
         { 1568, 64, 40, 36, 4, 4, 8, 1 },
         { 2920, 64, 64, 24, 4, 4, 16, 1 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_fallback_t_nt =
     {
         { 3136, 64, 64, 36, 4, 4, 4, 0 },
         { 4096, 48, 32, 36, 4, 4, 4, 0 },
         { 688, 92, 68, 32, 4, 4, 4, 0 },
         { 24, 464, 412, 24, 4, 4, 4, 0 },
         { 112, 184, 144, 28, 4, 4, 4, 0 },
         { 5776, 64, 32, 36, 4, 4, 8, 0 },
         { 1568, 64, 40, 36, 4, 4, 4, 0 },
         { 2920, 64, 64, 24, 4, 4, 4, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_t_t =
     {
         { 3136, 64, 64, 36, 4, 4, 4, 1 },
         { 4096, 48, 32, 36, 4, 4, 4, 1 },
         { 688, 92, 68, 32, 4, 4, 4, 1 },
         { 24, 464, 412, 24, 2, 2, 16, 1 },
         { 112, 184, 144, 28, 4, 4, 4, 1 },
         { 5776, 64, 32, 36, 4, 4, 4, 1 },
         { 1568, 64, 40, 36, 4, 4, 4, 1 },
         { 2920, 64, 64, 24, 4, 4, 4, 1 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_fallback_t_t =
     {
         { 3136, 64, 64, 36, 4, 4, 4, 0 },
         { 4096, 48, 32, 36, 4, 4, 4, 0 },
         { 688, 92, 68, 32, 4, 4, 4, 0 },
         { 24, 464, 412, 24, 4, 2, 8, 0 },
         { 112, 184, 144, 28, 4, 4, 4, 0 },
         { 5776, 64, 32, 36, 4, 4, 4, 0 },
         { 1568, 64, 40, 36, 4, 4, 4, 0 },
         { 2920, 64, 64, 24, 4, 4, 4, 0 }
     };

     const bool adj_lhs = info.adj_lhs();
     const bool adj_rhs = info.adj_rhs();

     const MatMulNativeConfigsMatrix *configs_best_to_use     = nullptr;
     const MatMulNativeConfigsMatrix *configs_fallback_to_use = nullptr;

     if((adj_lhs == false) && (adj_rhs == false))
     {
         configs_best_to_use     = &configs_mnkb_best_nt_nt;
         configs_fallback_to_use = &configs_mnkb_fallback_nt_nt;
     }
     else if((adj_lhs == false) && (adj_rhs == true))
     {
         configs_best_to_use     = &configs_mnkb_best_nt_t;
         configs_fallback_to_use = &configs_mnkb_fallback_nt_t;
     }
     else if((adj_lhs == true) && (adj_rhs == false))
     {
         configs_best_to_use     = &configs_mnkb_best_t_nt;
         configs_fallback_to_use = &configs_mnkb_fallback_t_nt;
     }
     else
     {
         configs_best_to_use     = &configs_mnkb_best_t_t;
         configs_fallback_to_use = &configs_mnkb_fallback_t_t;
     }

     MatMulKernelInfo desc0 = find_info(*configs_best_to_use, adj_lhs, adj_rhs, m, n, k, b);
     MatMulKernelInfo desc1 = find_info(*configs_fallback_to_use, adj_lhs, adj_rhs, m, n, k, b);

     return select_info(desc0,
                        desc1,
                        m, n, k, b, DataType::F32, rhs_lock_padding);
 }

 MatMulKernelInfo ClMatMulNativeDefaultConfigValhall::configure_G710_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b, bool rhs_lock_padding, const MatMulInfo &info)
 {
     const MatMulNativeConfigsMatrix configs_mnkb_best_nt_nt =
     {
         { 3136, 64, 64, 36, 4, 4, 16, 1 },
         { 4096, 48, 32, 36, 4, 4, 8, 1 },
         { 688, 92, 68, 32, 4, 4, 16, 1 },
         { 24, 464, 412, 24, 4, 4, 4, 1 },
         { 112, 184, 144, 28, 4, 4, 16, 1 },
         { 5776, 64, 32, 36, 4, 4, 8, 1 },
         { 1568, 64, 40, 36, 4, 4, 8, 1 },
         { 2920, 64, 64, 24, 4, 4, 16, 1 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_fallback_nt_nt =
     {
         { 3136, 64, 64, 36, 6, 4, 8, 0 },
         { 4096, 48, 32, 36, 6, 4, 8, 0 },
         { 688, 92, 68, 32, 6, 4, 8, 0 },
         { 24, 464, 412, 24, 4, 4, 8, 0 },
         { 112, 184, 144, 28, 6, 4, 8, 0 },
         { 5776, 64, 32, 36, 6, 4, 8, 0 },
         { 1568, 64, 40, 36, 6, 4, 8, 0 },
         { 2920, 64, 64, 24, 6, 4, 8, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_nt_t =
     {
         { 3136, 64, 64, 36, 6, 4, 8, 1 },
         { 4096, 48, 32, 36, 6, 4, 8, 1 },
         { 688, 92, 68, 32, 4, 4, 4, 1 },
         { 24, 464, 412, 24, 6, 2, 4, 1 },
         { 112, 184, 144, 28, 4, 2, 16, 1 },
         { 5776, 64, 32, 36, 6, 4, 8, 1 },
         { 1568, 64, 40, 36, 6, 4, 8, 1 },
         { 2920, 64, 64, 24, 6, 4, 8, 1 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_fallback_nt_t =
     {
         { 3136, 64, 64, 36, 6, 2, 16, 0 },
         { 4096, 48, 32, 36, 5, 4, 8, 0 },
         { 688, 92, 68, 32, 6, 2, 16, 0 },
         { 24, 464, 412, 24, 6, 2, 16, 0 },
         { 112, 184, 144, 28, 6, 2, 16, 0 },
         { 5776, 64, 32, 36, 5, 4, 8, 0 },
         { 1568, 64, 40, 36, 5, 4, 8, 0 },
         { 2920, 64, 64, 24, 6, 2, 16, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_t_nt =
     {
         { 3136, 64, 64, 36, 4, 4, 16, 1 },
         { 4096, 48, 32, 36, 4, 4, 4, 1 },
         { 688, 92, 68, 32, 4, 4, 4, 1 },
         { 24, 464, 412, 24, 4, 4, 4, 1 },
         { 112, 184, 144, 28, 4, 4, 4, 1 },
         { 5776, 64, 32, 36, 4, 4, 4, 1 },
         { 1568, 64, 40, 36, 4, 4, 4, 1 },
         { 2920, 64, 64, 24, 4, 4, 4, 1 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_fallback_t_nt =
     {
         { 3136, 64, 64, 36, 4, 4, 4, 0 },
         { 4096, 48, 32, 36, 4, 4, 4, 0 },
         { 688, 92, 68, 32, 4, 4, 4, 0 },
         { 24, 464, 412, 24, 4, 4, 4, 0 },
         { 112, 184, 144, 28, 4, 4, 4, 0 },
         { 5776, 64, 32, 36, 4, 4, 4, 0 },
         { 1568, 64, 40, 36, 4, 4, 4, 0 },
         { 2920, 64, 64, 24, 4, 4, 4, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_t_t =
     {
         { 3136, 64, 64, 36, 4, 4, 16, 1 },
         { 4096, 48, 32, 36, 4, 4, 8, 1 },
         { 688, 92, 68, 32, 4, 4, 4, 1 },
         { 24, 464, 412, 24, 4, 2, 8, 1 },
         { 112, 184, 144, 28, 4, 2, 16, 1 },
         { 5776, 64, 32, 36, 4, 4, 16, 1 },
         { 1568, 64, 40, 36, 4, 4, 8, 1 },
         { 2920, 64, 64, 24, 4, 4, 16, 1 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_fallback_t_t =
     {
         { 3136, 64, 64, 36, 4, 4, 8, 0 },
         { 4096, 48, 32, 36, 4, 4, 8, 0 },
         { 688, 92, 68, 32, 4, 4, 8, 0 },
         { 24, 464, 412, 24, 4, 4, 8, 0 },
         { 112, 184, 144, 28, 4, 4, 8, 0 },
         { 5776, 64, 32, 36, 4, 4, 8, 0 },
         { 1568, 64, 40, 36, 4, 4, 8, 0 },
         { 2920, 64, 64, 24, 4, 4, 8, 0 }
     };

     const bool adj_lhs = info.adj_lhs();
     const bool adj_rhs = info.adj_rhs();

     const MatMulNativeConfigsMatrix *configs_best_to_use     = nullptr;
     const MatMulNativeConfigsMatrix *configs_fallback_to_use = nullptr;

     if((adj_lhs == false) && (adj_rhs == false))
     {
         configs_best_to_use     = &configs_mnkb_best_nt_nt;
         configs_fallback_to_use = &configs_mnkb_fallback_nt_nt;
     }
     else if((adj_lhs == false) && (adj_rhs == true))
     {
         configs_best_to_use     = &configs_mnkb_best_nt_t;
         configs_fallback_to_use = &configs_mnkb_fallback_nt_t;
     }
     else if((adj_lhs == true) && (adj_rhs == false))
     {
         configs_best_to_use     = &configs_mnkb_best_t_nt;
         configs_fallback_to_use = &configs_mnkb_fallback_t_nt;
     }
     else
     {
         configs_best_to_use     = &configs_mnkb_best_t_t;
         configs_fallback_to_use = &configs_mnkb_fallback_t_t;
     }

     MatMulKernelInfo desc0 = find_info(*configs_best_to_use, adj_lhs, adj_rhs, m, n, k, b);
     MatMulKernelInfo desc1 = find_info(*configs_fallback_to_use, adj_lhs, adj_rhs, m, n, k, b);

     return select_info(desc0,
                        desc1,
                        m, n, k, b, DataType::F16, rhs_lock_padding);
 }

 MatMulKernelInfo ClMatMulNativeDefaultConfigValhall::configure_G710_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b, bool rhs_lock_padding, const MatMulInfo &info)
 {
     ARM_COMPUTE_UNUSED(rhs_lock_padding);

     const MatMulNativeConfigsMatrix configs_mnkb_best_nt_nt =
     {
         { 3136, 64, 64, 36, 6, 4, 4, 0 },
         { 4096, 48, 32, 36, 6, 4, 4, 0 },
         { 688, 92, 68, 32, 2, 8, 4, 0 },
         { 24, 464, 412, 24, 4, 4, 4, 0 },
         { 112, 184, 144, 28, 6, 4, 4, 0 },
         { 5776, 64, 32, 36, 6, 4, 4, 0 },
         { 1568, 64, 40, 36, 6, 4, 4, 0 },
         { 2920, 64, 64, 24, 5, 4, 4, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_nt_t =
     {
         { 3136, 64, 64, 36, 4, 4, 16, 0 },
         { 4096, 48, 32, 36, 4, 4, 16, 0 },
         { 688, 92, 68, 32, 4, 4, 16, 0 },
         { 24, 464, 412, 24, 6, 2, 16, 0 },
         { 112, 184, 144, 28, 4, 4, 16, 0 },
         { 5776, 64, 32, 36, 4, 4, 16, 0 },
         { 1568, 64, 40, 36, 6, 4, 4, 0 },
         { 2920, 64, 64, 24, 4, 4, 16, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_t_nt =
     {
         { 3136, 64, 64, 36, 4, 4, 8, 0 },
         { 4096, 48, 32, 36, 4, 4, 8, 0 },
         { 688, 92, 68, 32, 4, 4, 4, 0 },
         { 24, 464, 412, 24, 4, 4, 4, 0 },
         { 112, 184, 144, 28, 4, 4, 8, 0 },
         { 5776, 64, 32, 36, 4, 4, 8, 0 },
         { 1568, 64, 40, 36, 4, 4, 8, 0 },
         { 2920, 64, 64, 24, 4, 4, 8, 0 }
     };

     const MatMulNativeConfigsMatrix configs_mnkb_best_t_t =
     {
         { 3136, 64, 64, 36, 4, 2, 16, 0 },
         { 4096, 48, 32, 36, 4, 4, 4, 0 },
         { 688, 92, 68, 32, 4, 4, 8, 0 },
         { 24, 464, 412, 24, 4, 2, 16, 0 },
         { 112, 184, 144, 28, 4, 2, 16, 0 },
         { 5776, 64, 32, 36, 4, 4, 4, 0 },
         { 1568, 64, 40, 36, 4, 4, 8, 0 },
         { 2920, 64, 64, 24, 4, 2, 16, 0 }
     };

     const bool adj_lhs = info.adj_lhs();
     const bool adj_rhs = info.adj_rhs();

     if((adj_lhs == false) && (adj_rhs == false))
     {
         return find_info(configs_mnkb_best_nt_nt, adj_lhs, adj_rhs, m, n, k, b);
     }
     else if((adj_lhs == false) && (adj_rhs == true))
     {
         return find_info(configs_mnkb_best_nt_t, adj_lhs, adj_rhs, m, n, k, b);
     }
     else if((adj_lhs == true) && (adj_rhs == false))
     {
         return find_info(configs_mnkb_best_t_nt, adj_lhs, adj_rhs, m, n, k, b);
     }
     else
     {
         return find_info(configs_mnkb_best_t_t, adj_lhs, adj_rhs, m, n, k, b);
     }
 }
 } // namespace opencl
 } // namespace arm_compute
	/*
	* 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/runtime/heuristics/matmul_native/ClMatMulNativeDefaultConfigValhall.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/GPUTarget.h"
	#include "arm_compute/core/KernelDescriptors.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "src/gpu/cl/kernels/ClMatMulNativeKernel.h"
	#include <utility>

	#include "src/runtime/heuristics/matmul_native/ClMatMulNativeHelpers.h"

	namespace arm_compute
	{
	namespace cl_matmul
	{
	ClMatMulNativeDefaultConfigValhall::ClMatMulNativeDefaultConfigValhall(GPUTarget gpu)
	: IClMatMulNativeKernelConfig(gpu)
	{
	}

	MatMulKernelInfo ClMatMulNativeDefaultConfigValhall::configure(const ITensorInfo lhs, const ITensorInfo rhs, const MatMulInfo &info)
	{
	using ConfigurationFunctionExecutorPtr = MatMulKernelInfo (ClMatMulNativeDefaultConfigValhall::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b, bool rhs_lock_padding, const MatMulInfo & info);

	ClMatMulNativeConfigArray<ConfigurationFunctionExecutorPtr> configs_G710(&ClMatMulNativeDefaultConfigValhall::configure_G710_f32,
	&ClMatMulNativeDefaultConfigValhall::configure_G710_f16,
	&ClMatMulNativeDefaultConfigValhall::configure_G710_u8);

	ConfigurationFunctionExecutorPtr func = nullptr;
	switch(_target)
	{
	case GPUTarget::G710:
	default:
	func = configs_G710.get_function(lhs->data_type());
	break;
	}

	const bool adj_lhs = info.adj_lhs();
	const bool adj_rhs = info.adj_rhs();

	TensorShape lhs_shape = lhs->tensor_shape();
	TensorShape rhs_shape = rhs->tensor_shape();

	const bool is_batched = lhs_shape.num_dimensions() > 2;

	if(is_batched == true)
	{
	lhs_shape.collapse_from(2);
	}

	const unsigned int m = adj_lhs ? lhs_shape.x() : lhs_shape.y();
	const unsigned int n = adj_rhs ? rhs_shape.y() : rhs_shape.x();
	const unsigned int k = adj_lhs ? lhs_shape.y() : lhs_shape.x();
	const unsigned int b = lhs_shape.z();

	ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not supported for matmul native");
	return (this->*func)(m, n, k, b, rhs->lock_paddings(), info);
	}

	MatMulKernelInfo ClMatMulNativeDefaultConfigValhall::configure_G710_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b, bool rhs_lock_padding, const MatMulInfo &info)
	{
	const MatMulNativeConfigsMatrix configs_mnkb_best_nt_nt =
	{
	{ 3136, 64, 64, 36, 4, 4, 16, 1 },
	{ 4096, 48, 32, 36, 4, 4, 4, 1 },
	{ 688, 92, 68, 32, 2, 8, 4, 1 },
	{ 24, 464, 412, 24, 2, 8, 4, 1 },
	{ 112, 184, 144, 28, 4, 4, 16, 1 },
	{ 5776, 64, 32, 36, 2, 4, 16, 1 },
	{ 1568, 64, 40, 36, 2, 8, 8, 1 },
	{ 2920, 64, 64, 24, 4, 4, 16, 1 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_fallback_nt_nt =
	{
	{ 3136, 64, 64, 36, 4, 4, 8, 0 },
	{ 4096, 48, 32, 36, 4, 4, 8, 0 },
	{ 688, 92, 68, 32, 5, 4, 4, 0 },
	{ 24, 464, 412, 24, 6, 2, 8, 0 },
	{ 112, 184, 144, 28, 6, 4, 4, 0 },
	{ 5776, 64, 32, 36, 5, 4, 4, 0 },
	{ 1568, 64, 40, 36, 4, 4, 8, 0 },
	{ 2920, 64, 64, 24, 4, 4, 8, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_nt_t =
	{
	{ 3136, 64, 64, 36, 4, 4, 4, 1 },
	{ 4096, 48, 32, 36, 2, 2, 16, 1 },
	{ 688, 92, 68, 32, 4, 4, 4, 1 },
	{ 24, 464, 412, 24, 6, 2, 8, 1 },
	{ 112, 184, 144, 28, 4, 2, 16, 1 },
	{ 5776, 64, 32, 36, 4, 4, 4, 1 },
	{ 1568, 64, 40, 36, 4, 4, 8, 1 },
	{ 2920, 64, 64, 24, 4, 4, 4, 1 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_fallback_nt_t =
	{
	{ 3136, 64, 64, 36, 5, 4, 4, 0 },
	{ 4096, 48, 32, 36, 5, 4, 4, 0 },
	{ 688, 92, 68, 32, 5, 4, 4, 0 },
	{ 24, 464, 412, 24, 6, 2, 4, 0 },
	{ 112, 184, 144, 28, 5, 4, 4, 0 },
	{ 5776, 64, 32, 36, 5, 4, 4, 0 },
	{ 1568, 64, 40, 36, 5, 4, 4, 0 },
	{ 2920, 64, 64, 24, 6, 2, 4, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_t_nt =
	{
	{ 3136, 64, 64, 36, 4, 4, 16, 1 },
	{ 4096, 48, 32, 36, 4, 4, 4, 1 },
	{ 688, 92, 68, 32, 2, 8, 4, 1 },
	{ 24, 464, 412, 24, 2, 8, 4, 1 },
	{ 112, 184, 144, 28, 4, 4, 16, 1 },
	{ 5776, 64, 32, 36, 2, 8, 8, 1 },
	{ 1568, 64, 40, 36, 4, 4, 8, 1 },
	{ 2920, 64, 64, 24, 4, 4, 16, 1 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_fallback_t_nt =
	{
	{ 3136, 64, 64, 36, 4, 4, 4, 0 },
	{ 4096, 48, 32, 36, 4, 4, 4, 0 },
	{ 688, 92, 68, 32, 4, 4, 4, 0 },
	{ 24, 464, 412, 24, 4, 4, 4, 0 },
	{ 112, 184, 144, 28, 4, 4, 4, 0 },
	{ 5776, 64, 32, 36, 4, 4, 8, 0 },
	{ 1568, 64, 40, 36, 4, 4, 4, 0 },
	{ 2920, 64, 64, 24, 4, 4, 4, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_t_t =
	{
	{ 3136, 64, 64, 36, 4, 4, 4, 1 },
	{ 4096, 48, 32, 36, 4, 4, 4, 1 },
	{ 688, 92, 68, 32, 4, 4, 4, 1 },
	{ 24, 464, 412, 24, 2, 2, 16, 1 },
	{ 112, 184, 144, 28, 4, 4, 4, 1 },
	{ 5776, 64, 32, 36, 4, 4, 4, 1 },
	{ 1568, 64, 40, 36, 4, 4, 4, 1 },
	{ 2920, 64, 64, 24, 4, 4, 4, 1 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_fallback_t_t =
	{
	{ 3136, 64, 64, 36, 4, 4, 4, 0 },
	{ 4096, 48, 32, 36, 4, 4, 4, 0 },
	{ 688, 92, 68, 32, 4, 4, 4, 0 },
	{ 24, 464, 412, 24, 4, 2, 8, 0 },
	{ 112, 184, 144, 28, 4, 4, 4, 0 },
	{ 5776, 64, 32, 36, 4, 4, 4, 0 },
	{ 1568, 64, 40, 36, 4, 4, 4, 0 },
	{ 2920, 64, 64, 24, 4, 4, 4, 0 }
	};

	const bool adj_lhs = info.adj_lhs();
	const bool adj_rhs = info.adj_rhs();

	const MatMulNativeConfigsMatrix *configs_best_to_use = nullptr;
	const MatMulNativeConfigsMatrix *configs_fallback_to_use = nullptr;

	if((adj_lhs == false) && (adj_rhs == false))
	{
	configs_best_to_use = &configs_mnkb_best_nt_nt;
	configs_fallback_to_use = &configs_mnkb_fallback_nt_nt;
	}
	else if((adj_lhs == false) && (adj_rhs == true))
	{
	configs_best_to_use = &configs_mnkb_best_nt_t;
	configs_fallback_to_use = &configs_mnkb_fallback_nt_t;
	}
	else if((adj_lhs == true) && (adj_rhs == false))
	{
	configs_best_to_use = &configs_mnkb_best_t_nt;
	configs_fallback_to_use = &configs_mnkb_fallback_t_nt;
	}
	else
	{
	configs_best_to_use = &configs_mnkb_best_t_t;
	configs_fallback_to_use = &configs_mnkb_fallback_t_t;
	}

	MatMulKernelInfo desc0 = find_info(*configs_best_to_use, adj_lhs, adj_rhs, m, n, k, b);
	MatMulKernelInfo desc1 = find_info(*configs_fallback_to_use, adj_lhs, adj_rhs, m, n, k, b);

	return select_info(desc0,
	desc1,
	m, n, k, b, DataType::F32, rhs_lock_padding);
	}

	MatMulKernelInfo ClMatMulNativeDefaultConfigValhall::configure_G710_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b, bool rhs_lock_padding, const MatMulInfo &info)
	{
	const MatMulNativeConfigsMatrix configs_mnkb_best_nt_nt =
	{
	{ 3136, 64, 64, 36, 4, 4, 16, 1 },
	{ 4096, 48, 32, 36, 4, 4, 8, 1 },
	{ 688, 92, 68, 32, 4, 4, 16, 1 },
	{ 24, 464, 412, 24, 4, 4, 4, 1 },
	{ 112, 184, 144, 28, 4, 4, 16, 1 },
	{ 5776, 64, 32, 36, 4, 4, 8, 1 },
	{ 1568, 64, 40, 36, 4, 4, 8, 1 },
	{ 2920, 64, 64, 24, 4, 4, 16, 1 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_fallback_nt_nt =
	{
	{ 3136, 64, 64, 36, 6, 4, 8, 0 },
	{ 4096, 48, 32, 36, 6, 4, 8, 0 },
	{ 688, 92, 68, 32, 6, 4, 8, 0 },
	{ 24, 464, 412, 24, 4, 4, 8, 0 },
	{ 112, 184, 144, 28, 6, 4, 8, 0 },
	{ 5776, 64, 32, 36, 6, 4, 8, 0 },
	{ 1568, 64, 40, 36, 6, 4, 8, 0 },
	{ 2920, 64, 64, 24, 6, 4, 8, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_nt_t =
	{
	{ 3136, 64, 64, 36, 6, 4, 8, 1 },
	{ 4096, 48, 32, 36, 6, 4, 8, 1 },
	{ 688, 92, 68, 32, 4, 4, 4, 1 },
	{ 24, 464, 412, 24, 6, 2, 4, 1 },
	{ 112, 184, 144, 28, 4, 2, 16, 1 },
	{ 5776, 64, 32, 36, 6, 4, 8, 1 },
	{ 1568, 64, 40, 36, 6, 4, 8, 1 },
	{ 2920, 64, 64, 24, 6, 4, 8, 1 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_fallback_nt_t =
	{
	{ 3136, 64, 64, 36, 6, 2, 16, 0 },
	{ 4096, 48, 32, 36, 5, 4, 8, 0 },
	{ 688, 92, 68, 32, 6, 2, 16, 0 },
	{ 24, 464, 412, 24, 6, 2, 16, 0 },
	{ 112, 184, 144, 28, 6, 2, 16, 0 },
	{ 5776, 64, 32, 36, 5, 4, 8, 0 },
	{ 1568, 64, 40, 36, 5, 4, 8, 0 },
	{ 2920, 64, 64, 24, 6, 2, 16, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_t_nt =
	{
	{ 3136, 64, 64, 36, 4, 4, 16, 1 },
	{ 4096, 48, 32, 36, 4, 4, 4, 1 },
	{ 688, 92, 68, 32, 4, 4, 4, 1 },
	{ 24, 464, 412, 24, 4, 4, 4, 1 },
	{ 112, 184, 144, 28, 4, 4, 4, 1 },
	{ 5776, 64, 32, 36, 4, 4, 4, 1 },
	{ 1568, 64, 40, 36, 4, 4, 4, 1 },
	{ 2920, 64, 64, 24, 4, 4, 4, 1 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_fallback_t_nt =
	{
	{ 3136, 64, 64, 36, 4, 4, 4, 0 },
	{ 4096, 48, 32, 36, 4, 4, 4, 0 },
	{ 688, 92, 68, 32, 4, 4, 4, 0 },
	{ 24, 464, 412, 24, 4, 4, 4, 0 },
	{ 112, 184, 144, 28, 4, 4, 4, 0 },
	{ 5776, 64, 32, 36, 4, 4, 4, 0 },
	{ 1568, 64, 40, 36, 4, 4, 4, 0 },
	{ 2920, 64, 64, 24, 4, 4, 4, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_t_t =
	{
	{ 3136, 64, 64, 36, 4, 4, 16, 1 },
	{ 4096, 48, 32, 36, 4, 4, 8, 1 },
	{ 688, 92, 68, 32, 4, 4, 4, 1 },
	{ 24, 464, 412, 24, 4, 2, 8, 1 },
	{ 112, 184, 144, 28, 4, 2, 16, 1 },
	{ 5776, 64, 32, 36, 4, 4, 16, 1 },
	{ 1568, 64, 40, 36, 4, 4, 8, 1 },
	{ 2920, 64, 64, 24, 4, 4, 16, 1 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_fallback_t_t =
	{
	{ 3136, 64, 64, 36, 4, 4, 8, 0 },
	{ 4096, 48, 32, 36, 4, 4, 8, 0 },
	{ 688, 92, 68, 32, 4, 4, 8, 0 },
	{ 24, 464, 412, 24, 4, 4, 8, 0 },
	{ 112, 184, 144, 28, 4, 4, 8, 0 },
	{ 5776, 64, 32, 36, 4, 4, 8, 0 },
	{ 1568, 64, 40, 36, 4, 4, 8, 0 },
	{ 2920, 64, 64, 24, 4, 4, 8, 0 }
	};

	const bool adj_lhs = info.adj_lhs();
	const bool adj_rhs = info.adj_rhs();

	const MatMulNativeConfigsMatrix *configs_best_to_use = nullptr;
	const MatMulNativeConfigsMatrix *configs_fallback_to_use = nullptr;

	if((adj_lhs == false) && (adj_rhs == false))
	{
	configs_best_to_use = &configs_mnkb_best_nt_nt;
	configs_fallback_to_use = &configs_mnkb_fallback_nt_nt;
	}
	else if((adj_lhs == false) && (adj_rhs == true))
	{
	configs_best_to_use = &configs_mnkb_best_nt_t;
	configs_fallback_to_use = &configs_mnkb_fallback_nt_t;
	}
	else if((adj_lhs == true) && (adj_rhs == false))
	{
	configs_best_to_use = &configs_mnkb_best_t_nt;
	configs_fallback_to_use = &configs_mnkb_fallback_t_nt;
	}
	else
	{
	configs_best_to_use = &configs_mnkb_best_t_t;
	configs_fallback_to_use = &configs_mnkb_fallback_t_t;
	}

	MatMulKernelInfo desc0 = find_info(*configs_best_to_use, adj_lhs, adj_rhs, m, n, k, b);
	MatMulKernelInfo desc1 = find_info(*configs_fallback_to_use, adj_lhs, adj_rhs, m, n, k, b);

	return select_info(desc0,
	desc1,
	m, n, k, b, DataType::F16, rhs_lock_padding);
	}

	MatMulKernelInfo ClMatMulNativeDefaultConfigValhall::configure_G710_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b, bool rhs_lock_padding, const MatMulInfo &info)
	{
	ARM_COMPUTE_UNUSED(rhs_lock_padding);

	const MatMulNativeConfigsMatrix configs_mnkb_best_nt_nt =
	{
	{ 3136, 64, 64, 36, 6, 4, 4, 0 },
	{ 4096, 48, 32, 36, 6, 4, 4, 0 },
	{ 688, 92, 68, 32, 2, 8, 4, 0 },
	{ 24, 464, 412, 24, 4, 4, 4, 0 },
	{ 112, 184, 144, 28, 6, 4, 4, 0 },
	{ 5776, 64, 32, 36, 6, 4, 4, 0 },
	{ 1568, 64, 40, 36, 6, 4, 4, 0 },
	{ 2920, 64, 64, 24, 5, 4, 4, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_nt_t =
	{
	{ 3136, 64, 64, 36, 4, 4, 16, 0 },
	{ 4096, 48, 32, 36, 4, 4, 16, 0 },
	{ 688, 92, 68, 32, 4, 4, 16, 0 },
	{ 24, 464, 412, 24, 6, 2, 16, 0 },
	{ 112, 184, 144, 28, 4, 4, 16, 0 },
	{ 5776, 64, 32, 36, 4, 4, 16, 0 },
	{ 1568, 64, 40, 36, 6, 4, 4, 0 },
	{ 2920, 64, 64, 24, 4, 4, 16, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_t_nt =
	{
	{ 3136, 64, 64, 36, 4, 4, 8, 0 },
	{ 4096, 48, 32, 36, 4, 4, 8, 0 },
	{ 688, 92, 68, 32, 4, 4, 4, 0 },
	{ 24, 464, 412, 24, 4, 4, 4, 0 },
	{ 112, 184, 144, 28, 4, 4, 8, 0 },
	{ 5776, 64, 32, 36, 4, 4, 8, 0 },
	{ 1568, 64, 40, 36, 4, 4, 8, 0 },
	{ 2920, 64, 64, 24, 4, 4, 8, 0 }
	};

	const MatMulNativeConfigsMatrix configs_mnkb_best_t_t =
	{
	{ 3136, 64, 64, 36, 4, 2, 16, 0 },
	{ 4096, 48, 32, 36, 4, 4, 4, 0 },
	{ 688, 92, 68, 32, 4, 4, 8, 0 },
	{ 24, 464, 412, 24, 4, 2, 16, 0 },
	{ 112, 184, 144, 28, 4, 2, 16, 0 },
	{ 5776, 64, 32, 36, 4, 4, 4, 0 },
	{ 1568, 64, 40, 36, 4, 4, 8, 0 },
	{ 2920, 64, 64, 24, 4, 2, 16, 0 }
	};

	const bool adj_lhs = info.adj_lhs();
	const bool adj_rhs = info.adj_rhs();

	if((adj_lhs == false) && (adj_rhs == false))
	{
	return find_info(configs_mnkb_best_nt_nt, adj_lhs, adj_rhs, m, n, k, b);
	}
	else if((adj_lhs == false) && (adj_rhs == true))
	{
	return find_info(configs_mnkb_best_nt_t, adj_lhs, adj_rhs, m, n, k, b);
	}
	else if((adj_lhs == true) && (adj_rhs == false))
	{
	return find_info(configs_mnkb_best_t_nt, adj_lhs, adj_rhs, m, n, k, b);
	}
	else
	{
	return find_info(configs_mnkb_best_t_t, adj_lhs, adj_rhs, m, n, k, b);
	}
	}
	} // namespace opencl
	} // namespace arm_compute