text: /*
   * Copyright (c) 2017-2020 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.
   */
  #ifndef ARM_COMPUTE_TEST_GEMM_FIXTURE
  #define ARM_COMPUTE_TEST_GEMM_FIXTURE
  
  #include "arm_compute/core/KernelDescriptors.h"
  #include "arm_compute/core/TensorShape.h"
  #include "arm_compute/core/Types.h"
  #include "tests/AssetsLibrary.h"
  #include "tests/Globals.h"
  #include "tests/IAccessor.h"
  #include "tests/framework/Asserts.h"
  #include "tests/framework/Fixture.h"
  #include "tests/validation/Helpers.h"
  #include "tests/validation/reference/ActivationLayer.h"
  #include "tests/validation/reference/GEMM.h"
  
  #include <random>
  
  namespace arm_compute
  {
  namespace test
  {
  namespace validation
  {
  template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool disable_c = false, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
  class GEMMValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, TensorShape output_shape, float alpha, float beta, bool pretranspose, DataType data_type)
      {
          ARM_COMPUTE_UNUSED(pretranspose);
          _target    = compute_target(shape_a, shape_b, shape_c, output_shape, alpha, beta, data_type);
          _reference = compute_reference(shape_a, shape_b, output_shape, alpha, beta, data_type);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f)
      {
          switch(tensor.data_type())
          {
              case DataType::F16:
              case DataType::F32:
              {
                  std::uniform_real_distribution<> distribution(lo, hi);
                  library->fill(tensor, distribution, i);
                  break;
              }
              default:
                  library->fill_tensor_uniform(tensor, i);
          }
      }
  
      TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &output_shape, float alpha, float beta,
                                DataType data_type)
      {
          // Create tensors
          TensorType a   = create_tensor<TensorType>(shape_a, data_type, 1);
          TensorType b   = create_tensor<TensorType>(shape_b, data_type, 1);
          TensorType c   = create_tensor<TensorType>(shape_c, data_type, 1);
          TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1);
  
          // Create and configure function
          FunctionType gemm;
          // The GEMMinfo includes the values of the depth in case of reinterpreted 3d output.
          // If the output shape has the same number of dimensions of the input the method called is a 2D matrix multiplication (depth_output_reinterpreted_as_3D = 0),
          // in the other case we have to use the reinterpreted version of GEMM (depth_output_reinterpreted_as_3D = depth of the 3D output).
          gemm.configure(&a,
                         &b,
                         (disable_c) ? nullptr : &c,
                         &dst,
                         alpha, beta,
                         GEMMInfo(false, false, false, (reinterpret_output_as_3d ? output_shape[2] : 0), reinterpret_input_as_3d, false, GEMMLowpOutputStageInfo(), false, (reinterpret_input_as_3d
                                  || reinterpret_output_as_3d)));
          ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          a.allocator()->allocate();
          b.allocator()->allocate();
          c.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(a), 0);
          fill(AccessorType(b), 1);
          if(!disable_c)
          {
              fill(AccessorType(c), 2);
          }
  
          // Compute GEMM function
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, float alpha, float beta,
                                        DataType data_type)
      {
          TensorShape shape_a_to_use = shape_a;
  
          if(reinterpret_input_as_3d)
          {
              // Collapse the second and third dimension if the input is 3D
              shape_a_to_use.collapse(2U, 1U);
          }
  
          // Create reference
          SimpleTensor<T> a{ shape_a_to_use, data_type, 1 };
          SimpleTensor<T> b{ shape_b, data_type, 1 };
          SimpleTensor<T> c{ output_shape, data_type, 1 };
  
          // Fill reference
          fill(a, 0);
          fill(b, 1);
          fill(c, 2);
  
          if(reinterpret_input_as_3d || reinterpret_output_as_3d)
          {
              const int n          = shape_b[0];
              const int m          = reinterpret_output_as_3d ? output_shape[1] * output_shape[2] : output_shape[1];
              const int batch_size = reinterpret_output_as_3d ? output_shape[3] : output_shape[2];
  
              // In case of broadcast, we need simply copy the first into the following "M" ones
              for(int i = 1; i < m * batch_size; i++)
              {
                  memcpy(c.data() + i * n, c.data(), n * sizeof(T));
              }
          }
  
          // Setting beta to 0 will effectively disable C for the
          // computation of the reference: alpha * A * B + 0 * C
          return reference::gemm<T>(a, b, c, alpha, disable_c ? 0.f : beta);
      }
  
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
  class GEMMMatrixMultiplyValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, bool broadcast_bias, bool fp16_mixed_precision, const ActivationLayerInfo &act_info,
                 DataType data_type, GPUTarget gpu_arch)
      {
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n,
                                       broadcast_bias ? 1 : m,
                                       broadcast_bias ? 1 : batch_size);
  
          _target    = compute_target(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, fp16_mixed_precision, act_info, gpu_arch);
          _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
  
          // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
          std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
          library->fill_borders_with_garbage(tensor, distribution_inf, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
                                bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType dst;
  
          const unsigned int m = lhs_shape[1];
          const unsigned int n = rhs_shape[0];
          const unsigned int k = lhs_shape[0];
          GEMMReshapeInfo    reshape_info(m, n, k, 1, 1, 0, false, broadcast_bias);
  
          // The output tensor will be auto-initialized within the function
  
          // Create and configure function
          GEMMFunctionType gemm;
          gemm.configure(gpu_arch, &lhs, &rhs, &bias, &dst, alpha, beta, false, reshape_info, fp16_mixed_precision, act_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape[0]          = rhs_shape[0];
          dst_shape[1]          = lhs_shape[1];
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          if(broadcast_bias)
          {
              // In case of broadcast, we need simply copy the first into the following "M" ones
              for(int i = 1; i < m * batch_size; i++)
              {
                  memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
              }
          }
  
          return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
      }
  
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
  class GEMMMatrixMultiply3DValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, bool broadcast_bias, bool fp16_mixed_precision,
                 const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch)
      {
          ARM_COMPUTE_UNUSED(broadcast_bias);
  
          // In case of GEMM3D, m is the product between m_w and m_h
          const unsigned int m = m_w * m_h;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n, 1, 1);
  
          _target    = compute_target(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, fp16_mixed_precision, act_info, gpu_arch);
          _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, m_h, act_info);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
                                bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType dst;
  
          const unsigned int m = lhs_shape[1];
          const unsigned int n = rhs_shape[0];
          const unsigned int k = lhs_shape[0];
          GEMMReshapeInfo    reshape_info(m, n, k, 1, 1, m_h, false, true);
  
          // The output tensor will be auto-initialized within the function
  
          // Create and configure function
          GEMMFunctionType gemm;
          gemm.configure(gpu_arch, &lhs, &rhs, &bias, &dst, alpha, beta, false, reshape_info, fp16_mixed_precision, act_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape.set(0, rhs_shape[0]);
          dst_shape.set(1, lhs_shape[1] / m_h);
          dst_shape.set(2, m_h);
          dst_shape.set(3, lhs_shape[2]);
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          // In case of broadcast, we need simply copy the first into the following "M" ones
          for(int i = 1; i < m * batch_size; i++)
          {
              memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
          }
  
          return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
      }
  
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
  class GEMMMatrixMultiplyInterleavedTransposedValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, unsigned int v0, unsigned int h0, bool broadcast_bias, bool fp16_mixed_precision,
                 const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch)
      {
          GEMMLHSMatrixInfo lhs_info;
          lhs_info.m0         = 4;
          lhs_info.k0         = 4;
          lhs_info.v0         = v0;
          lhs_info.interleave = true;
          lhs_info.transpose  = true;
  
          GEMMRHSMatrixInfo rhs_info;
          rhs_info.n0         = 16 / sizeof(T);
          rhs_info.k0         = 1;
          rhs_info.h0         = h0;
          rhs_info.interleave = false;
          rhs_info.transpose  = false;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n,
                                       broadcast_bias ? 1 : m,
                                       broadcast_bias ? 1 : batch_size);
  
          _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, fp16_mixed_precision, act_info, gpu_arch);
          _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
  
          // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
          std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
          library->fill_borders_with_garbage(tensor, distribution_inf, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
                                DataType data_type, float alpha, float beta, bool broadcast_bias, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType lhs_reshaped;
          TensorType rhs_reshaped;
          TensorType dst;
  
          const unsigned int m = lhs_shape[1];
          const unsigned int n = rhs_shape[0];
          const unsigned int k = lhs_shape[0];
          GEMMReshapeInfo    reshape_info(m, n, k, rhs_info.h0, lhs_info.v0, 0, false, broadcast_bias);
  
          // The output tensor will be auto-initialized within the function
  
          // Create and configure function
          ReshapeLHSFunctionType reshape_lhs;
          ReshapeRHSFunctionType reshape_rhs;
          GEMMFunctionType       gemm;
          reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
          reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
          gemm.configure(gpu_arch, &lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, true, reshape_info, fp16_mixed_precision, act_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          lhs_reshaped.allocator()->allocate();
          rhs_reshaped.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          reshape_lhs.run();
          reshape_rhs.run();
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape[0]          = rhs_shape[0];
          dst_shape[1]          = lhs_shape[1];
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          if(broadcast_bias)
          {
              // In case of broadcast, we need simply copy the first into the following "M" ones
              for(int i = 1; i < m * batch_size; i++)
              {
                  memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
              }
          }
  
          return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
      }
  
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
  class GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, unsigned int v0, unsigned int h0, bool broadcast_bias,
                 bool fp16_mixed_precision, const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch)
      {
          ARM_COMPUTE_UNUSED(broadcast_bias);
  
          GEMMLHSMatrixInfo lhs_info;
          lhs_info.m0         = 4;
          lhs_info.k0         = 4;
          lhs_info.v0         = v0;
          lhs_info.interleave = true;
          lhs_info.transpose  = true;
  
          GEMMRHSMatrixInfo rhs_info;
          rhs_info.n0         = 16 / sizeof(T);
          rhs_info.k0         = 1;
          rhs_info.h0         = h0;
          rhs_info.interleave = false;
          rhs_info.transpose  = false;
  
          // In case of GEMM3D, m is the product between m_w and m_h
          const unsigned int m = m_w * m_h;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n, 1, 1);
  
          _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h, fp16_mixed_precision, act_info, gpu_arch);
          _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, m_h, act_info);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
                                DataType data_type, float alpha, float beta, unsigned int m_h, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType lhs_reshaped;
          TensorType rhs_reshaped;
          TensorType dst;
  
          const unsigned int m = lhs_shape[1];
          const unsigned int n = rhs_shape[0];
          const unsigned int k = lhs_shape[0];
          GEMMReshapeInfo    reshape_info(m, n, k, rhs_info.h0, lhs_info.v0, m_h, false, true);
  
          // The output tensor will be auto-initialized within the function
  
          // Create and configure function
          ReshapeLHSFunctionType reshape_lhs;
          ReshapeRHSFunctionType reshape_rhs;
          GEMMFunctionType       gemm;
          reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
          reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
          gemm.configure(gpu_arch, &lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, true, reshape_info, fp16_mixed_precision, act_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          lhs_reshaped.allocator()->allocate();
          rhs_reshaped.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          reshape_lhs.run();
          reshape_rhs.run();
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape.set(0, rhs_shape[0]);
          dst_shape.set(1, lhs_shape[1] / m_h);
          dst_shape.set(2, m_h);
          dst_shape.set(3, lhs_shape[2]);
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          // In case of broadcast, we need simply copy the first into the following "M" ones
          for(int i = 1; i < m * batch_size; i++)
          {
              memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
          }
  
          return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
      }
  
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType, bool fp_mixed_precision = false>
  class GEMMMatrixMultiplyReshapedValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs,
                 bool interleave_rhs, bool export_to_cl_image, DataType data_type, float alpha, float beta, bool broadcast_bias, bool lhs_transpose, const ActivationLayerInfo &act_info)
      {
          GEMMLHSMatrixInfo lhs_info;
          lhs_info.m0         = m0;
          lhs_info.k0         = k0;
          lhs_info.v0         = v0;
          lhs_info.interleave = interleave_lhs;
          lhs_info.transpose  = lhs_transpose;
  
          GEMMRHSMatrixInfo rhs_info;
          rhs_info.n0                 = n0;
          rhs_info.k0                 = k0;
          rhs_info.h0                 = h0;
          rhs_info.interleave         = interleave_rhs;
          rhs_info.transpose          = !lhs_transpose;
          rhs_info.export_to_cl_image = export_to_cl_image;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n,
                                       broadcast_bias ? 1 : m,
                                       broadcast_bias ? 1 : batch_size);
  
          _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info);
          if(validate_result)
          {
              _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info);
          }
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
  
          // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
          std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
          library->fill_borders_with_garbage(tensor, distribution_inf, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
                                DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType lhs_reshaped;
          TensorType rhs_reshaped;
          TensorType dst;
  
          const unsigned int M = lhs_shape[1];
          const unsigned int N = rhs_shape[0];
          const unsigned int K = lhs_shape[0];
          GEMMKernelInfo     kernel_info;
          kernel_info.m                       = M;
          kernel_info.n                       = N;
          kernel_info.k                       = K;
          kernel_info.depth_output_gemm3d     = 0;
          kernel_info.reinterpret_input_as_3d = false;
          kernel_info.broadcast_bias          = broadcast_bias;
          kernel_info.activation_info         = act_info;
          kernel_info.fp_mixed_precision      = fp_mixed_precision;
  
          // The output tensor will be auto-initialized within the function
  
          // Create and configure function
          ReshapeLHSFunctionType reshape_lhs;
          ReshapeRHSFunctionType reshape_rhs;
          GEMMFunctionType       gemm;
  
          validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
          validate_result = validate_result || !rhs_info.export_to_cl_image;
          if(!validate_result)
          {
              return nullptr;
          }
  
          reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
          reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
          gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          lhs_reshaped.allocator()->allocate();
          rhs_reshaped.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          reshape_lhs.run();
          reshape_rhs.run();
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape[0]          = rhs_shape[0];
          dst_shape[1]          = lhs_shape[1];
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          if(broadcast_bias)
          {
              // In case of broadcast, we need simply copy the first into the following "M" ones
              for(int i = 1; i < m * batch_size; i++)
              {
                  memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
              }
          }
  
          if(fp_mixed_precision)
          {
              return reference::activation_layer(reference::gemm_mixed_precision<T>(lhs, rhs, bias, alpha, beta), act_info);
          }
          else
          {
              return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
          }
      }
  
      bool            validate_result = true;
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType, bool fp_mixed_precision = false>
  class GEMMMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
                 bool interleave_lhs, bool interleave_rhs, bool export_to_cl_image, DataType data_type, float alpha, float beta, bool lhs_transpose, const ActivationLayerInfo &act_info)
      {
          GEMMLHSMatrixInfo lhs_info;
          lhs_info.m0         = m0;
          lhs_info.k0         = k0;
          lhs_info.v0         = v0;
          lhs_info.interleave = interleave_lhs;
          lhs_info.transpose  = lhs_transpose;
  
          GEMMRHSMatrixInfo rhs_info;
          rhs_info.n0                 = n0;
          rhs_info.k0                 = k0;
          rhs_info.h0                 = h0;
          rhs_info.interleave         = interleave_rhs;
          rhs_info.transpose          = !lhs_transpose;
          rhs_info.export_to_cl_image = export_to_cl_image;
  
          // In case of GEMM3D, m is the product between m_w and m_h
          const unsigned int m = m_w * m_h;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n, 1, 1);
  
          _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h, act_info);
          if(validate_result)
          {
              _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, m_h, act_info);
          }
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
                                DataType data_type, float alpha, float beta, unsigned int m_h, const ActivationLayerInfo &act_info)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType lhs_reshaped;
          TensorType rhs_reshaped;
          TensorType dst;
  
          const unsigned int M = lhs_shape[1];
          const unsigned int N = rhs_shape[0];
          const unsigned int K = lhs_shape[0];
          GEMMKernelInfo     kernel_info;
          kernel_info.m                       = M;
          kernel_info.n                       = N;
          kernel_info.k                       = K;
          kernel_info.depth_output_gemm3d     = m_h;
          kernel_info.reinterpret_input_as_3d = false;
          kernel_info.broadcast_bias          = true;
          kernel_info.activation_info         = act_info;
          kernel_info.fp_mixed_precision      = fp_mixed_precision;
  
          // The output tensor will be auto-initialized within the function
  
          // Create and configure function
          ReshapeLHSFunctionType reshape_lhs;
          ReshapeRHSFunctionType reshape_rhs;
          GEMMFunctionType       gemm;
  
          validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
          validate_result = validate_result || !rhs_info.export_to_cl_image;
          if(!validate_result)
          {
              return nullptr;
          }
  
          reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
          reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
          gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          lhs_reshaped.allocator()->allocate();
          rhs_reshaped.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          reshape_lhs.run();
          reshape_rhs.run();
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape.set(0, rhs_shape[0]);
          dst_shape.set(1, lhs_shape[1] / m_h);
          dst_shape.set(2, m_h);
          dst_shape.set(3, lhs_shape[2]);
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          // In case of broadcast, we need simply copy the first into the following "M" ones
          for(int i = 1; i < m * batch_size; i++)
          {
              memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
          }
  
          if(fp_mixed_precision)
          {
              return reference::activation_layer(reference::gemm_mixed_precision<T>(lhs, rhs, bias, alpha, beta), act_info);
          }
          else
          {
              return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
          }
      }
  
      bool            validate_result = true;
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
  class GEMMMatrixMultiplyReshapedOnlyRHSValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int h0,
                 bool interleave_rhs, bool transpose_rhs, bool export_to_cl_image, DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
      {
          GEMMLHSMatrixInfo lhs_info;
          lhs_info.m0 = m0;
          lhs_info.k0 = k0;
  
          GEMMRHSMatrixInfo rhs_info;
          rhs_info.n0                 = n0;
          rhs_info.k0                 = k0;
          rhs_info.h0                 = h0;
          rhs_info.interleave         = interleave_rhs;
          rhs_info.transpose          = transpose_rhs;
          rhs_info.export_to_cl_image = export_to_cl_image;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n,
                                       broadcast_bias ? 1 : m,
                                       broadcast_bias ? 1 : batch_size);
  
          _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info);
          if(validate_result)
          {
              _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info);
          }
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
  
          // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
          std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
          library->fill_borders_with_garbage(tensor, distribution_inf, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
                                DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType rhs_reshaped;
          TensorType dst;
  
          const unsigned int M = lhs_shape[1];
          const unsigned int N = rhs_shape[0];
          const unsigned int K = lhs_shape[0];
          GEMMKernelInfo     kernel_info;
          kernel_info.m                       = M;
          kernel_info.n                       = N;
          kernel_info.k                       = K;
          kernel_info.depth_output_gemm3d     = 0;
          kernel_info.reinterpret_input_as_3d = false;
          kernel_info.broadcast_bias          = broadcast_bias;
          kernel_info.activation_info         = act_info;
  
          // The output tensor will be auto-initialized within the function
  
          // Create and configure function
          ReshapeRHSFunctionType reshape_rhs;
          GEMMFunctionType       gemm;
  
          validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
          validate_result = validate_result || !rhs_info.export_to_cl_image;
          if(!validate_result)
          {
              return nullptr;
          }
  
          reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
          gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          rhs_reshaped.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          reshape_rhs.run();
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape[0]          = rhs_shape[0];
          dst_shape[1]          = lhs_shape[1];
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          if(broadcast_bias)
          {
              // In case of broadcast, we need simply copy the first into the following "M" ones
              for(int i = 1; i < m * batch_size; i++)
              {
                  memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
              }
          }
  
          return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
      }
  
      bool            validate_result = true;
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
  class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int h0,
                 bool interleave_rhs, bool transpose_rhs, bool export_to_cl_image, bool has_pad_y, DataType data_type, float alpha, float beta, const ActivationLayerInfo &act_info)
      {
          GEMMLHSMatrixInfo lhs_info;
          lhs_info.m0 = m0;
          lhs_info.k0 = k0;
  
          GEMMRHSMatrixInfo rhs_info;
          rhs_info.n0                 = n0;
          rhs_info.k0                 = k0;
          rhs_info.h0                 = h0;
          rhs_info.interleave         = interleave_rhs;
          rhs_info.transpose          = transpose_rhs;
          rhs_info.export_to_cl_image = export_to_cl_image;
  
          // In case of GEMM3D, m is the product between m_w and m_h
          const unsigned int m = m_w * m_h;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n, 1, 1);
  
          _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h, act_info, has_pad_y);
          if(validate_result)
          {
              _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, m_h, act_info);
          }
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
                                DataType data_type, float alpha, float beta,
                                unsigned int m_h, const ActivationLayerInfo &act_info, bool has_pad_y)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType rhs_reshaped;
          TensorType dst;
  
          const unsigned int M = lhs_shape[1];
          const unsigned int N = rhs_shape[0];
          const unsigned int K = lhs_shape[0];
          GEMMKernelInfo     kernel_info;
          kernel_info.m                       = M;
          kernel_info.n                       = N;
          kernel_info.k                       = K;
          kernel_info.depth_output_gemm3d     = m_h;
          kernel_info.reinterpret_input_as_3d = false;
          kernel_info.broadcast_bias          = true;
          kernel_info.activation_info         = act_info;
          kernel_info.has_pad_y               = has_pad_y;
  
          // The output tensor will be auto-initialized within the function
          // Create and configure function
          ReshapeRHSFunctionType reshape_rhs;
          GEMMFunctionType       gemm;
  
          validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
          validate_result = validate_result || !rhs_info.export_to_cl_image;
          if(!validate_result)
          {
              return nullptr;
          }
  
          reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
          gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
  
          if(has_pad_y)
          {
              // Add dummy padding into lhs to validate has_pad_y path
              lhs.info()->extend_padding(PaddingSize(2, 0, 2, 0));
              dst.info()->extend_padding(PaddingSize(2, 0, 1, 0));
          }
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          rhs_reshaped.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          reshape_rhs.run();
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape.set(0, rhs_shape[0]);
          dst_shape.set(1, lhs_shape[1] / m_h);
          dst_shape.set(2, m_h);
          dst_shape.set(3, lhs_shape[2]);
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          // In case of broadcast, we need simply copy the first into the following "M" ones
          for(int i = 1; i < m * batch_size; i++)
          {
              memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
          }
  
          return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
      }
  
      bool            validate_result = true;
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
  class GEMMMatrixMultiplyNativeValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta, bool broadcast_bias,
                 const ActivationLayerInfo &act_info)
      {
          GEMMLHSMatrixInfo lhs_info;
          lhs_info.m0 = m0;
          lhs_info.k0 = k0;
  
          GEMMRHSMatrixInfo rhs_info;
          rhs_info.n0 = n0;
          rhs_info.k0 = k0;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n,
                                       broadcast_bias ? 1 : m,
                                       broadcast_bias ? 1 : batch_size);
  
          _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info);
          _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
  
          // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
          std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
          library->fill_borders_with_garbage(tensor, distribution_inf, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
                                DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType dst;
  
          const unsigned int M = lhs_shape[1];
          const unsigned int N = rhs_shape[0];
          const unsigned int K = lhs_shape[0];
          GEMMKernelInfo     kernel_info;
          kernel_info.m                       = M;
          kernel_info.n                       = N;
          kernel_info.k                       = K;
          kernel_info.depth_output_gemm3d     = 0;
          kernel_info.reinterpret_input_as_3d = false;
          kernel_info.broadcast_bias          = broadcast_bias;
          kernel_info.activation_info         = act_info;
  
          // Create and configure function
          GEMMFunctionType gemm;
          gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape[0]          = rhs_shape[0];
          dst_shape[1]          = lhs_shape[1];
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          if(broadcast_bias)
          {
              // In case of broadcast, we need simply copy the first into the following "M" ones
              for(int i = 1; i < m * batch_size; i++)
              {
                  memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
              }
          }
  
          return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
      }
  
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
  class GEMMMatrixMultiplyNative3DValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta,
                 const ActivationLayerInfo &act_info)
      {
          GEMMLHSMatrixInfo lhs_info;
          lhs_info.m0 = m0;
          lhs_info.k0 = k0;
  
          GEMMRHSMatrixInfo rhs_info;
          rhs_info.n0 = n0;
          rhs_info.k0 = k0;
  
          // In case of GEMM3D, m is the product between m_w and m_h
          const unsigned int m = m_w * m_h;
  
          // Set the tensor shapes for LHS and RHS matrices
          const TensorShape lhs_shape(k, m, batch_size);
          const TensorShape rhs_shape(n, k, batch_size);
          const TensorShape bias_shape(n, 1, 1);
  
          _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h, act_info);
          _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, m_h, act_info);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
      }
  
      TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
                                DataType data_type, float alpha, float beta, unsigned int m_h, const ActivationLayerInfo &act_info)
      {
          // Create tensors
          TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
          TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
          TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
          TensorType dst;
  
          const unsigned int M = lhs_shape[1];
          const unsigned int N = rhs_shape[0];
          const unsigned int K = lhs_shape[0];
          GEMMKernelInfo     kernel_info;
          kernel_info.m                       = M;
          kernel_info.n                       = N;
          kernel_info.k                       = K;
          kernel_info.depth_output_gemm3d     = m_h;
          kernel_info.reinterpret_input_as_3d = false;
          kernel_info.broadcast_bias          = true;
          kernel_info.activation_info         = act_info;
  
          // The output tensor will be auto-initialized within the function
  
          // Create and configure function
          GEMMFunctionType gemm;
          gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
  
          ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          lhs.allocator()->allocate();
          rhs.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(lhs), 0);
          fill(AccessorType(rhs), 1);
          fill(AccessorType(bias), 2);
  
          // Compute GEMM
          gemm.run();
  
          return dst;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
                                        const ActivationLayerInfo &act_info)
      {
          TensorShape dst_shape = lhs_shape;
          dst_shape.set(0, rhs_shape[0]);
          dst_shape.set(1, lhs_shape[1] / m_h);
          dst_shape.set(2, m_h);
          dst_shape.set(3, lhs_shape[2]);
  
          // Create reference
          SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
          SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
          SimpleTensor<T> bias{ dst_shape, data_type, 1 };
  
          const int n          = rhs_shape[0];
          const int m          = lhs_shape[1];
          const int batch_size = lhs_shape[2];
  
          // Fill reference
          fill(lhs, 0);
          fill(rhs, 1);
          fill(bias, 2);
  
          // In case of broadcast, we need simply copy the first into the following "M" ones
          for(int i = 1; i < m * batch_size; i++)
          {
              memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
          }
  
          return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
      }
  
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  
  } // namespace validation
  } // namespace test
  } // namespace arm_compute
  #endif /* ARM_COMPUTE_TEST_GEMM_FIXTURE */
  