tests/validation/fixtures/MatMulKernelFixture.h - 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.
  */
 #ifndef ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H
 #define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H

 #include "arm_compute/core/KernelDescriptors.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"

 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/framework/Asserts.h" // Required for ARM_COMPUTE_ASSERT
 #include "tests/framework/Fixture.h"
 #include "tests/validation/Helpers.h"
 #include "tests/validation/Validation.h"
 #include "tests/validation/reference/GEMM.h"
 #include "tests/validation/reference/GEMMLowp.h"
 #include "tests/validation/reference/Permute.h"
 #include "tests/validation/reference/ReshapeLayer.h"
 #include <cmath>
 #include <random>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 using namespace arm_compute::opencl::kernels;

 template <typename T, typename KernelType, bool use_mmul = false>
 class MatMulKernelGenericValidationFixture : public framework::Fixture
 {
 public:
     void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type,
                bool enable_bias)
     {
         // This hash is used by random generators. There may be hash collisions but
         // this is intentional as it's a very easy way to make the the current
         // random generation process almost different for many test configurations,
         // which were using the same set of values before.
         _hash = M0 + N0 + K0 + shape_a[0] + shape_a[1] + shape_b[0] + shape_b[1] + enable_bias + export_rhs_to_cl_image;

         // Flag to create a bias
         _enable_bias = enable_bias;

         // For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
         QuantizationInfo lhs_q_info;
         QuantizationInfo rhs_q_info;
         QuantizationInfo dst_q_info;

         if(is_data_type_quantized(data_type))
         {
             const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max());
             const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min());

             std::mt19937                           generator(library->seed() + _hash);
             std::uniform_real_distribution<float>  distribution_float(-5.0f, 3.0f);
             std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max);

             const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]
             const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]

             const int32_t offset_lhs = distribution_t(generator);
             const int32_t offset_rhs = distribution_t(generator);

             lhs_q_info = QuantizationInfo(scale_lhs, offset_lhs);
             rhs_q_info = QuantizationInfo(scale_rhs, offset_rhs);

             const int m = shape_a.y();
             const int n = shape_b.x();
             const int k = shape_a.x();

             const float bias_fraction = enable_bias ? 0.5f : 0.f;

             QuantizationHint q_hint = suggest_matmul_dst_q_info_and_bias(lhs_q_info, rhs_q_info, m, n, k, data_type, bias_fraction);
             dst_q_info              = q_hint.q_info;
             _min_bias               = q_hint.bias_min;
             _max_bias               = q_hint.bias_max;
         }

         if(pretranspose_a)
         {
             permute(shape_a, PermutationVector(1U, 0U));
         }

         if(pretranspose_b)
         {
             permute(shape_b, PermutationVector(1U, 0U));
         }

         // Skip configurations unsupported by the device.
         _device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
         if(!_device_supports_export_to_cl_image && export_rhs_to_cl_image)
         {
             ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
             framework::ARM_COMPUTE_PRINT_INFO();
             return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
         }

         _device_supports_mmul = arm_matrix_multiply_supported(CLKernelLibrary::get().get_device());
         if(!_device_supports_mmul && use_mmul)
         {
             ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped");
             framework::ARM_COMPUTE_PRINT_INFO();
             return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
         }

         _target    = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info);
         _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info);
     }

 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:
             {
                 arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(lo), float(hi) };
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::F32:
             {
                 std::uniform_real_distribution<float> distribution(lo, hi);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
                 library->fill_tensor_uniform(tensor, i);
         }
     }

     template <typename U>
     void fill_bias_s32(U &&tensor, int i, int32_t min, int32_t max)
     {
         std::uniform_int_distribution<int32_t> distribution(min, max);
         library->fill(tensor, distribution, i);
     }

     template <typename U, typename D>
     void fill_constant(U &&tensor, D value)
     {
         library->fill_tensor_value(tensor, value);
     }

     CLTensor compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0,
                             bool export_rhs_to_cl_image, DataType data_type, const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info)
     {
         CLSynthetizeOperator<KernelType> matMul{};
         MatMulKernelInfo                 matmul_info;
         matmul_info.adj_lhs                = pretranspose_a;
         matmul_info.adj_rhs                = pretranspose_b;
         matmul_info.m0                     = M0;
         matmul_info.n0                     = N0;
         matmul_info.k0                     = K0;
         matmul_info.export_rhs_to_cl_image = export_rhs_to_cl_image;

         bool is_quantized = is_data_type_quantized(data_type);

         // Create tensors
         CLTensor a    = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info);
         CLTensor b    = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info);
         CLTensor bias = create_tensor<CLTensor>(output_shape[0], (is_quantized) ? DataType::S32 : data_type, 1, dst_q_info);
         CLTensor dst  = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info);

         matMul.configure(a.info(), b.info(), (_enable_bias) ? bias.info() : nullptr, dst.info(), matmul_info);
         ARM_COMPUTE_ASSERT(a.info()->is_resizable());
         ARM_COMPUTE_ASSERT(b.info()->is_resizable());
         ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

         // Allocate tensors
         a.allocator()->allocate();
         b.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

         // Fill tensors
         fill(CLAccessor(a), _hash + 1);
         fill(CLAccessor(b), _hash + 2);

         // Compute matMul kernel
         ITensorPack tensors_pack({ { ACL_SRC_0, &a },
             { ACL_SRC_1, &b },
             { ACL_DST, &dst }
         });

         if(_enable_bias)
         {
             // Allocate, fill and add bias to TensorPack obj
             bias.allocator()->allocate();
             if(is_quantized)
             {
                 fill_bias_s32(CLAccessor(bias), _hash + 3, _min_bias, _max_bias);
             }
             else
             {
                 fill(CLAccessor(bias), _hash + 3);
             }
             tensors_pack.add_tensor(ACL_SRC_2, &bias);
         }

         matMul.run(tensors_pack);

         return dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, DataType data_type,
                                       const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info)
     {
         // We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D
         // This is necessary unless we choose to extend gemm reference for 5D+ tensors
         TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimZ);
         TensorShape shape_a_collapsed      = shape_a.collapsed_from(Window::DimZ);
         TensorShape shape_b_collapsed      = shape_b.collapsed_from(Window::DimZ);

         // Create reference
         SimpleTensor<T> a{ shape_a_collapsed, data_type, 1, lhs_q_info };
         SimpleTensor<T> b{ shape_b_collapsed, data_type, 1, rhs_q_info };
         SimpleTensor<T> c{ output_shape_collapsed, data_type, 1, dst_q_info };

         // Fill reference
         fill(a, _hash + 1);
         fill(b, _hash + 2);

         /* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M),
            therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K)
            in order to be able to call reference implementation that works with (B x M x K) input.
            Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */

         // Define transposed shapes
         TensorShape a_transposed_shape(a.shape());
         a_transposed_shape.set(0, a.shape().y());
         a_transposed_shape.set(1, a.shape().x());

         TensorShape b_transposed_shape(b.shape());
         b_transposed_shape.set(0, b.shape().y());
         b_transposed_shape.set(1, b.shape().x());

         // Define transposed tensors
         SimpleTensor<T> a_transposed{ a_transposed_shape, data_type };
         SimpleTensor<T> b_transposed{ b_transposed_shape, data_type };

         // pretranspose a if necessary
         if(pretranspose_a)
         {
             a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U));
         }

         // pretranspose b if necessary
         if(pretranspose_b)
         {
             b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
         }

         // Use transposed tensors if boolean enabled else use original tensors
         SimpleTensor<T> result = gemm_reference<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c);

         // We reshape the gemm output back if the tensor is high dimensional
         if(output_shape_collapsed != output_shape)
         {
             result = reference::reshape_layer(result, output_shape);
         }

         return result;
     }

     template <typename U = T>
     typename std::enable_if < std::is_same<U, float>::value || std::is_same<U, half>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
     {
         // Fill bias, then copy first dimension into subsequent dimensions to mimic broadcast
         // of bias tensor from shape [dst.dimension(0)] to [dst.tensor_shape()] in target kernel
         if(_enable_bias)
         {
             fill(c, _hash + 3);
             const int n          = c.shape().x();
             const int other_dims = c.shape().collapsed_from(1)[1];
             for(int i = 1; i < other_dims; ++i) // For all data, copy first n elements into remaining batches
             {
                 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<U>(a, b, c, 1.0f, (_enable_bias) ? 1.0f : 0.f);
     }

     template <typename U = T>
     typename std::enable_if < std::is_same<U, int8_t>::value || std::is_same<U, uint8_t>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
     {
         const UniformQuantizationInfo aq = a.quantization_info().uniform();
         const UniformQuantizationInfo bq = b.quantization_info().uniform();
         const UniformQuantizationInfo cq = c.quantization_info().uniform();

         const SimpleTensor<int32_t> result = reference::gemmlowp_matrix_multiply_core<int32_t, U, U>(a, b, c.shape(), -aq.offset, -bq.offset);

         std::vector<int32_t> gemmlowp_multipliers{ 1 };
         std::vector<int32_t> gemmlowp_shifts{ 1 };
         const int            gemmlowp_offset = cq.offset;
         const float          scale           = aq.scale * bq.scale / cq.scale;

         quantization::calculate_quantized_multiplier(scale, &gemmlowp_multipliers[0], &gemmlowp_shifts[0]);
         constexpr int32_t gemmlowp_min_bound = std::numeric_limits<int32_t>::min();
         constexpr int32_t gemmlowp_max_bound = std::numeric_limits<int32_t>::max();

         SimpleTensor<int> bias{ c.shape(), DataType::S32 };
         if(_enable_bias)
         {
             // Identical to float implementation, fill and copy values of bias first dimension
             fill_bias_s32(bias, _hash + 3, _min_bias, _max_bias);
             const int          n          = bias.shape().x();
             const int          other_dims = bias.shape().collapsed_from(1)[1];
             const unsigned int dt_size    = sizeof(int32_t);
             for(int i = 1; i < other_dims; ++i)
             {
                 memcpy(bias.data() + i * n, bias.data(), n * dt_size);
             }
         }
         else
         {
             fill_constant(bias, static_cast<int32_t>(0)); // effectively disable bias
         }

         const SimpleTensor<U> final_result = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, U>(result, bias,
                                                                                                                gemmlowp_multipliers, gemmlowp_shifts, gemmlowp_offset, gemmlowp_min_bound, gemmlowp_max_bound);

         return final_result;
     }

     CLTensor        _target{};
     SimpleTensor<T> _reference{};
     bool            _enable_bias{ false };
     bool            _device_supports_export_to_cl_image{ true };
     bool            _device_supports_mmul{ true };
     int32_t         _min_bias{ 0 };
     int32_t         _max_bias{ 0 };
     int32_t         _hash{ 0 };
 };

 template <typename T, typename KernelType, bool use_mmul = false>
 class MatMulKernelValidationFixture : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>
 {
 public:
     void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
     {
         MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type,
                                                                              false /* enable bias */);
     }
 };

 template <typename T, typename KernelType, bool use_mmul = false>
 class MatMulKernelWithBiasValidation : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>
 {
 public:
     void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
     {
         MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type,
                                                                              true /* enable bias */);
     }
 };
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif // ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H
	/*
	* 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.
	*/
	#ifndef ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H
	#define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H

	#include "arm_compute/core/KernelDescriptors.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"

	#include "tests/CL/CLAccessor.h"
	#include "tests/CL/Helper.h"
	#include "tests/framework/Asserts.h" // Required for ARM_COMPUTE_ASSERT
	#include "tests/framework/Fixture.h"
	#include "tests/validation/Helpers.h"
	#include "tests/validation/Validation.h"
	#include "tests/validation/reference/GEMM.h"
	#include "tests/validation/reference/GEMMLowp.h"
	#include "tests/validation/reference/Permute.h"
	#include "tests/validation/reference/ReshapeLayer.h"
	#include <cmath>
	#include <random>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	using namespace arm_compute::opencl::kernels;

	template <typename T, typename KernelType, bool use_mmul = false>
	class MatMulKernelGenericValidationFixture : public framework::Fixture
	{
	public:
	void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type,
	bool enable_bias)
	{
	// This hash is used by random generators. There may be hash collisions but
	// this is intentional as it's a very easy way to make the the current
	// random generation process almost different for many test configurations,
	// which were using the same set of values before.
	_hash = M0 + N0 + K0 + shape_a[0] + shape_a[1] + shape_b[0] + shape_b[1] + enable_bias + export_rhs_to_cl_image;

	// Flag to create a bias
	_enable_bias = enable_bias;

	// For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
	QuantizationInfo lhs_q_info;
	QuantizationInfo rhs_q_info;
	QuantizationInfo dst_q_info;

	if(is_data_type_quantized(data_type))
	{
	const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max());
	const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min());

	std::mt19937 generator(library->seed() + _hash);
	std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f);
	std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max);

	const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]
	const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]

	const int32_t offset_lhs = distribution_t(generator);
	const int32_t offset_rhs = distribution_t(generator);

	lhs_q_info = QuantizationInfo(scale_lhs, offset_lhs);
	rhs_q_info = QuantizationInfo(scale_rhs, offset_rhs);

	const int m = shape_a.y();
	const int n = shape_b.x();
	const int k = shape_a.x();

	const float bias_fraction = enable_bias ? 0.5f : 0.f;

	QuantizationHint q_hint = suggest_matmul_dst_q_info_and_bias(lhs_q_info, rhs_q_info, m, n, k, data_type, bias_fraction);
	dst_q_info = q_hint.q_info;
	_min_bias = q_hint.bias_min;
	_max_bias = q_hint.bias_max;
	}

	if(pretranspose_a)
	{
	permute(shape_a, PermutationVector(1U, 0U));
	}

	if(pretranspose_b)
	{
	permute(shape_b, PermutationVector(1U, 0U));
	}

	// Skip configurations unsupported by the device.
	_device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
	if(!_device_supports_export_to_cl_image && export_rhs_to_cl_image)
	{
	ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
	framework::ARM_COMPUTE_PRINT_INFO();
	return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
	}

	_device_supports_mmul = arm_matrix_multiply_supported(CLKernelLibrary::get().get_device());
	if(!_device_supports_mmul && use_mmul)
	{
	ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped");
	framework::ARM_COMPUTE_PRINT_INFO();
	return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
	}

	_target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info);
	_reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info);
	}

	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:
	{
	arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(lo), float(hi) };
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::F32:
	{
	std::uniform_real_distribution<float> distribution(lo, hi);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	library->fill_tensor_uniform(tensor, i);
	}
	}

	template <typename U>
	void fill_bias_s32(U &&tensor, int i, int32_t min, int32_t max)
	{
	std::uniform_int_distribution<int32_t> distribution(min, max);
	library->fill(tensor, distribution, i);
	}

	template <typename U, typename D>
	void fill_constant(U &&tensor, D value)
	{
	library->fill_tensor_value(tensor, value);
	}

	CLTensor compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0,
	bool export_rhs_to_cl_image, DataType data_type, const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info)
	{
	CLSynthetizeOperator<KernelType> matMul{};
	MatMulKernelInfo matmul_info;
	matmul_info.adj_lhs = pretranspose_a;
	matmul_info.adj_rhs = pretranspose_b;
	matmul_info.m0 = M0;
	matmul_info.n0 = N0;
	matmul_info.k0 = K0;
	matmul_info.export_rhs_to_cl_image = export_rhs_to_cl_image;

	bool is_quantized = is_data_type_quantized(data_type);

	// Create tensors
	CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info);
	CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info);
	CLTensor bias = create_tensor<CLTensor>(output_shape[0], (is_quantized) ? DataType::S32 : data_type, 1, dst_q_info);
	CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info);

	matMul.configure(a.info(), b.info(), (_enable_bias) ? bias.info() : nullptr, dst.info(), matmul_info);
	ARM_COMPUTE_ASSERT(a.info()->is_resizable());
	ARM_COMPUTE_ASSERT(b.info()->is_resizable());
	ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

	// Allocate tensors
	a.allocator()->allocate();
	b.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

	// Fill tensors
	fill(CLAccessor(a), _hash + 1);
	fill(CLAccessor(b), _hash + 2);

	// Compute matMul kernel
	ITensorPack tensors_pack({ { ACL_SRC_0, &a },
	{ ACL_SRC_1, &b },
	{ ACL_DST, &dst }
	});

	if(_enable_bias)
	{
	// Allocate, fill and add bias to TensorPack obj
	bias.allocator()->allocate();
	if(is_quantized)
	{
	fill_bias_s32(CLAccessor(bias), _hash + 3, _min_bias, _max_bias);
	}
	else
	{
	fill(CLAccessor(bias), _hash + 3);
	}
	tensors_pack.add_tensor(ACL_SRC_2, &bias);
	}

	matMul.run(tensors_pack);

	return dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, DataType data_type,
	const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info)
	{
	// We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D
	// This is necessary unless we choose to extend gemm reference for 5D+ tensors
	TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimZ);
	TensorShape shape_a_collapsed = shape_a.collapsed_from(Window::DimZ);
	TensorShape shape_b_collapsed = shape_b.collapsed_from(Window::DimZ);

	// Create reference
	SimpleTensor<T> a{ shape_a_collapsed, data_type, 1, lhs_q_info };
	SimpleTensor<T> b{ shape_b_collapsed, data_type, 1, rhs_q_info };
	SimpleTensor<T> c{ output_shape_collapsed, data_type, 1, dst_q_info };

	// Fill reference
	fill(a, _hash + 1);
	fill(b, _hash + 2);

	/* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M),
	therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K)
	in order to be able to call reference implementation that works with (B x M x K) input.
	Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */

	// Define transposed shapes
	TensorShape a_transposed_shape(a.shape());
	a_transposed_shape.set(0, a.shape().y());
	a_transposed_shape.set(1, a.shape().x());

	TensorShape b_transposed_shape(b.shape());
	b_transposed_shape.set(0, b.shape().y());
	b_transposed_shape.set(1, b.shape().x());

	// Define transposed tensors
	SimpleTensor<T> a_transposed{ a_transposed_shape, data_type };
	SimpleTensor<T> b_transposed{ b_transposed_shape, data_type };

	// pretranspose a if necessary
	if(pretranspose_a)
	{
	a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U));
	}

	// pretranspose b if necessary
	if(pretranspose_b)
	{
	b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
	}

	// Use transposed tensors if boolean enabled else use original tensors
	SimpleTensor<T> result = gemm_reference<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c);

	// We reshape the gemm output back if the tensor is high dimensional
	if(output_shape_collapsed != output_shape)
	{
	result = reference::reshape_layer(result, output_shape);
	}

	return result;
	}

	template <typename U = T>
	typename std::enable_if < std::is_same<U, float>::value \|\| std::is_same<U, half>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
	{
	// Fill bias, then copy first dimension into subsequent dimensions to mimic broadcast
	// of bias tensor from shape [dst.dimension(0)] to [dst.tensor_shape()] in target kernel
	if(_enable_bias)
	{
	fill(c, _hash + 3);
	const int n = c.shape().x();
	const int other_dims = c.shape().collapsed_from(1)[1];
	for(int i = 1; i < other_dims; ++i) // For all data, copy first n elements into remaining batches
	{
	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<U>(a, b, c, 1.0f, (_enable_bias) ? 1.0f : 0.f);
	}

	template <typename U = T>
	typename std::enable_if < std::is_same<U, int8_t>::value \|\| std::is_same<U, uint8_t>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
	{
	const UniformQuantizationInfo aq = a.quantization_info().uniform();
	const UniformQuantizationInfo bq = b.quantization_info().uniform();
	const UniformQuantizationInfo cq = c.quantization_info().uniform();

	const SimpleTensor<int32_t> result = reference::gemmlowp_matrix_multiply_core<int32_t, U, U>(a, b, c.shape(), -aq.offset, -bq.offset);

	std::vector<int32_t> gemmlowp_multipliers{ 1 };
	std::vector<int32_t> gemmlowp_shifts{ 1 };
	const int gemmlowp_offset = cq.offset;
	const float scale = aq.scale * bq.scale / cq.scale;

	quantization::calculate_quantized_multiplier(scale, &gemmlowp_multipliers[0], &gemmlowp_shifts[0]);
	constexpr int32_t gemmlowp_min_bound = std::numeric_limits<int32_t>::min();
	constexpr int32_t gemmlowp_max_bound = std::numeric_limits<int32_t>::max();

	SimpleTensor<int> bias{ c.shape(), DataType::S32 };
	if(_enable_bias)
	{
	// Identical to float implementation, fill and copy values of bias first dimension
	fill_bias_s32(bias, _hash + 3, _min_bias, _max_bias);
	const int n = bias.shape().x();
	const int other_dims = bias.shape().collapsed_from(1)[1];
	const unsigned int dt_size = sizeof(int32_t);
	for(int i = 1; i < other_dims; ++i)
	{
	memcpy(bias.data() + i * n, bias.data(), n * dt_size);
	}
	}
	else
	{
	fill_constant(bias, static_cast<int32_t>(0)); // effectively disable bias
	}

	const SimpleTensor<U> final_result = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, U>(result, bias,
	gemmlowp_multipliers, gemmlowp_shifts, gemmlowp_offset, gemmlowp_min_bound, gemmlowp_max_bound);

	return final_result;
	}

	CLTensor _target{};
	SimpleTensor<T> _reference{};
	bool _enable_bias{ false };
	bool _device_supports_export_to_cl_image{ true };
	bool _device_supports_mmul{ true };
	int32_t _min_bias{ 0 };
	int32_t _max_bias{ 0 };
	int32_t _hash{ 0 };
	};

	template <typename T, typename KernelType, bool use_mmul = false>
	class MatMulKernelValidationFixture : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>
	{
	public:
	void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
	{
	MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type,
	false /* enable bias */);
	}
	};

	template <typename T, typename KernelType, bool use_mmul = false>
	class MatMulKernelWithBiasValidation : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>
	{
	public:
	void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
	{
	MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type,
	true /* enable bias */);
	}
	};
	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif // ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H