/* * Copyright (c) 2019-2022 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "arm_compute/core/KernelDescriptors.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/experimental/PostOps.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensorAllocator.h" #include "src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h" #include "src/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h" #include "tests/CL/CLAccessor.h" #include "tests/CL/Helper.h" #include "tests/PaddingCalculator.h" #include "tests/datasets/ShapeDatasets.h" #include "tests/framework/Asserts.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" #include "tests/validation/fixtures/GEMMFixture.h" namespace arm_compute { namespace test { namespace validation { using namespace arm_compute::misc::shape_calculator; using namespace arm_compute::opencl::kernels; // Create function for ClGemmReshapeRhsMatrixKernel using CLGEMMReshapeRHSMatrix = CLSynthetizeOperator; // Create function for ClGemmMatrixMultiplyReshapedOnlyRhsKernel using CLGEMMMatrixMultiplyReshapedOnlyRHS = CLSynthetizeOperator; // Fixture for CLGEMMMatrixMultiplyReshapedOnlyRHS template using CLGEMMMatrixMultiplyReshapedOnlyRHSFixture = GEMMMatrixMultiplyReshapedOnlyRHSValidationFixture; // Fixture for CLGEMMMatrixMultiplyReshapedOnlyRHS3D template using CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture = GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture; // Fixture for CLGEMMMatrixMultiplyReshapedOnlyRHS with post ops template using CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture = GEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsValidationFixture; namespace { // *INDENT-OFF* // clang-format off RelativeTolerance rel_tolerance_f32(0.001f); constexpr float abs_tolerance_f32(0.0001f); RelativeTolerance rel_tolerance_f16(0.001f); constexpr float abs_tolerance_f16(0.01f); /** Alpha values to test */ const auto a_values = framework::dataset::make("alpha", {-0.75f} ); /** Beta values to test */ const auto beta_values = framework::dataset::make("beta", {-0.35f} ); /** M values to test */ const auto m_values = framework::dataset::make("M", 37); /** M_W values to test */ const auto m_w_values = framework::dataset::make("M_W", 5); /** M_H values to test */ const auto m_h_values = framework::dataset::make("M_H", 7); /** N values to test */ const auto n_values = framework::dataset::make("N", 51); /** K values to test */ const auto k_values = framework::dataset::make("K", 23); /** Batch size values to test */ const auto b_values = framework::dataset::make("batch_size", 2); /** Activation values to test */ const auto act_values = framework::dataset::make("Activation", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 10.f), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ELU), }); /** M0 values to test - precommit */ const auto m0_values_precommit = framework::dataset::make("M0", { 4 }); /** N0 values to test - precommit*/ const auto n0_values_precommit = framework::dataset::make("N0", { 4 }); /** K0 values to test - precommit*/ const auto k0_values_precommit = framework::dataset::make("K0", { 4 }); /** M0 values to test - nightly */ const auto m0_values_nightly = framework::dataset::make("M0", { 8 }); /** N0 values to test - nightly */ const auto n0_values_nightly = framework::dataset::make("N0", { 16 }); /** K0 values to test - nightly */ const auto k0_values_nightly = framework::dataset::make("K0", { 16 }); /** H0 values to test */ const auto h0_values = framework::dataset::make("H0", 1, 3); /** Interleave values to test with RHS matrix */ const auto i_values_rhs = framework::dataset::make("interleave_rhs", { true, false }); /** Transpose values to test with RHS matrix */ const auto t_values_rhs = framework::dataset::make("transpose_rhs", { true, false }); /** Broadcast bias from vector to matrix */ const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", { false, true } ); /** Boundary handling cases for testing partial/non-partial (full) block dimensions, resulting from different combinations * of M, M0, N and N0 values. * M0 and N0 are kept constant, while the different test cases need to vary M and N. * * Eg. M = 64 and N = 33 result in a block dimension that has no partial blocks (all full blocks) in Y dimension and * parital blocks in X dimension. */ const auto boundary_handling_cases = combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( // Large k to force potential out-of-bound reads on input0 framework::dataset::make("K", 315), // Batch size == 1 to force potential out-of-bound reads on input0 framework::dataset::make("batch_size", 1)), framework::dataset::make("M0", 4)), framework::dataset::make("N0", 4)), framework::dataset::make("K0", 4)), framework::dataset::make("H0", 3)), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", {true, false})), // Only need to test F32 as F16 shares identical boundary handling logics framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("alpha", -0.75f )), framework::dataset::make("beta", -0.35f )), broadcast_bias_values), framework::dataset::make("Activation", ActivationLayerInfo())); /** Post Ops */ using PostOpArgBroadcast = CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture::PostOpArgBroadcast; experimental::PostOpList post_ops_1() { experimental::PostOpList post_ops{}; post_ops.push_back_op>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); post_ops.push_back_op>( std::make_tuple(true, true, false), // If broadcast in dims 0, 1 and 2 0, ConvertPolicy::SATURATE); post_ops.push_back_op>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); return post_ops; } experimental::PostOpList post_ops_2() { experimental::PostOpList post_ops{}; post_ops.push_back_op>( std::make_tuple(false, true, true), // If broadcast in dims 0, 1 and 2 1, ConvertPolicy::SATURATE); post_ops.push_back_op>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); return post_ops; } experimental::PostOpList post_ops_3() { experimental::PostOpList post_ops{}; post_ops.push_back_op>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); post_ops.push_back_op>( std::make_tuple(false, false, true), // If broadcast in dims 0, 1 and 2 1, ConvertPolicy::SATURATE); return post_ops; } // To test that the output of the main op is the first parameter in prelu post op experimental::PostOpList post_ops_4() { experimental::PostOpList post_ops{}; post_ops.push_back_op>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); post_ops.push_back_op>( std::make_tuple(false, false, true), // If true, broadcast in corresponding dim: 0, 1 or 2 0, ConvertPolicy::SATURATE); post_ops.push_back_op>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); return post_ops; } // To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param experimental::PostOpList post_ops_5() { experimental::PostOpList post_ops{}; post_ops.push_back_op>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); post_ops.push_back_op>( std::make_tuple(false, false, false), // If true, broadcast in corresponding dim: 0, 1 or 2 1, ConvertPolicy::SATURATE); post_ops.push_back_op>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); return post_ops; } /** Different Post Op Lists */ const auto post_op_lists = framework::dataset::make("post_op_lists", { post_ops_1(), post_ops_2(), post_ops_3(), post_ops_4(), post_ops_5() } ); bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList& post_ops) { const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true); const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false); // Create TensorInfo for post op arguments TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type); TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type); TensorInfo input2_info(TensorShape(n), 1, data_type); TensorInfo output_info(TensorShape(n, m, batch), 1, data_type); const TensorInfo reshaped_input1_info = input1_info.clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(input1_info, rhs_info)); GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */, false /**< reinterpret the input as 3D */, true /**< Flag used to broadcast the bias addition */, false /**< wider accumm */, false /**< has pad y */, ActivationLayerInfo::ActivationFunction::IDENTITY, 1 /**< Multiplication factor for the width of the 1xW transposed block */, 1 /**< Multiplication factor for the height of the 4x4 interleaved block */, lhs_info, rhs_info, 0 /**< Offset to be added to each element of the matrix A */, 0 /**< Offset to be added to each element of the matrix B */, post_ops); return bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(&input0_info.clone()->set_is_resizable(true), &reshaped_input1_info.clone()->set_is_resizable(true), &input2_info.clone()->set_is_resizable(true), &output_info.clone()->set_is_resizable(true),1.f,1.f, lhs_info, rhs_info, gemm_info)); } /** Configuration test */ bool validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, unsigned int h0_value, bool i_value_rhs, bool t_value_rhs, bool export_to_cl_image, bool broadcast_bias, bool input_as_3d, unsigned int depth_output_gemm3d, const ActivationLayerInfo &act_info, DataType dt_input0, DataType dt_input1, DataType dt_input2, DataType dt_output, float alpha, float beta) { const unsigned int M = m_value; const unsigned int N = n_value; const unsigned int K = k_value; GEMMLHSMatrixInfo lhs_info; lhs_info.m0 = m0_value; lhs_info.k0 = k0_value; GEMMRHSMatrixInfo rhs_info; rhs_info.n0 = n0_value; rhs_info.k0 = k0_value; rhs_info.h0 = h0_value; rhs_info.interleave = i_value_rhs; rhs_info.transpose = t_value_rhs; rhs_info.export_to_cl_image = export_to_cl_image; GEMMKernelInfo kernel_info; kernel_info.m = M; kernel_info.n = N; kernel_info.k = K; kernel_info.depth_output_gemm3d = depth_output_gemm3d; kernel_info.reinterpret_input_as_3d = input_as_3d; kernel_info.broadcast_bias = broadcast_bias; kernel_info.activation_info = act_info; const TensorShape lhs_shape(K, M, b_value); const TensorShape rhs_shape(N, K, b_value); const TensorShape rhs_shape_reshaped = compute_rhs_reshaped_shape(TensorInfo(rhs_shape, 1, dt_input1), rhs_info); const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, dt_input0), TensorInfo(rhs_shape_reshaped, 1, dt_input1), kernel_info); const TensorShape bias_shape(N, M, // Correct calculation should be: broadcast_bias? 1 : M, it's wrong here on purpose just for validation test broadcast_bias? 1 : b_value); // Create tensor info TensorInfo lhs = TensorInfo(lhs_shape, 1, dt_input0); TensorInfo rhs_reshaped = TensorInfo(rhs_shape_reshaped, 1, dt_input1); TensorInfo bias = TensorInfo(bias_shape, 1, dt_input2); TensorInfo dst = TensorInfo(dst_shape, 1, dt_output); // Create and configure function CLGEMMMatrixMultiplyReshapedOnlyRHS gemm; return bool(gemm.validate(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info)); } } // namespace TEST_SUITE(CL) TEST_SUITE(GEMMMatrixMultiplyReshapedOnlyRHS) /** Validate tests * * A series of validation tests on configurations which according to the API specification * the function should fail against. * * Checks performed in order: * - Mismachting data type: input1, input2 and output need to have same data type as input0. Support data type: F32/F16. * - Unsupported M0: MO can only be 1,2,3,4,5,6,7,8 * - Unsupported N0: NO can only be 2,3,4,8,16 * - Unsupported K0: KO can only be 2,3,4,8,16 * - Unsupported bias addition: bias broadcast mode is 0 if the input or output has to be reinterpreted as 3D * - Incorrect bias diemension when bias broadcast mode is 1 and beta is not 0.0f, should be (n, 1), not (n, m) * - Incorrect input0 dimension when input is reinterpreted as 3D: input0->dimension(1) * input0->dimension(2) != m * - Correct support for creating an OpenCL image object from buffer * - Incorrect support for creating an OpenCL image object from buffer. N0 is 2 but it can only be 4,8 and 16 * - Correct F16 support for creating an OpenCL image object from buffer. */ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( framework::dataset::make("batch_size", { 1, 1, 1, 1, 1, 1, 2, 1, 1, 1 }), framework::dataset::make("M0", { 4, 9, 4, 4, 4, 4, 4, 4, 4, 4 })), framework::dataset::make("N0", { 4, 4, 18, 4, 4, 4, 4, 8, 2, 8 })), framework::dataset::make("K0", { 4, 4, 4, 1, 4, 4, 4, 4, 4, 4 })), framework::dataset::make("broadcast_bias", { false, false, false, false, false, true, true, false, false, false })), framework::dataset::make("input_as_3d", { 0, 0, 0, 0, 1, 0, 1, 0, 0, 0 })), framework::dataset::make("depth_output_gemm3d", { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 })), framework::dataset::make("export_to_cl_image", { false, false, false, false, false, false, false, true, true, true })), framework::dataset::make("data_type_input0", { DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F16})), framework::dataset::make("data_type_input1", { DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F16})), framework::dataset::make("data_type_input2", { DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F16})), framework::dataset::make("data_type_output", { DataType::F16, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F32, DataType::F16})), framework::dataset::make("Beta", { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 1.0f, 0.0f , 1.0f})), framework::dataset::make("Expected", { false, false, false, false, false, false, false, true, false, true })), b_value, m0_value, n0_value, k0_value, broadcast_bias, input_as_3d, depth_output_gemm3d, export_to_cl_image, dt_input0, dt_intpu1, dt_input2, dt_output, beta, expected) { bool expected_value = expected; // Change expected to false if the target platform does not support the OpenCL cl_khr_image2d_from_buffer extension if(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device()) && export_to_cl_image) { expected_value = false; } bool status = validate_configuration(37, 51, 23, b_value, m0_value, n0_value, k0_value, 1, false, false, export_to_cl_image, broadcast_bias, input_as_3d, depth_output_gemm3d, ActivationLayerInfo(), dt_input0, dt_intpu1, dt_input2, dt_output, 1.0f, beta); ARM_COMPUTE_EXPECT(status == expected_value, framework::LogLevel::ERRORS); } TEST_SUITE(ValidateFusedPostOpsConfigs) TEST_SUITE(Invalid) TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL) { const auto data_type = DataType::F32; const unsigned int m = 17; const unsigned int n = 1; const unsigned int k = 13; const unsigned int batch = 2; TensorShape post_op_arg0_shape(n, m, batch); TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); auto post_op_arg1_info = post_op_arg_info.clone(); // Unsupported sequence of post ops experimental::PostOpList post_ops{}; post_ops.push_back_op>( &post_op_arg_info, 1, ConvertPolicy::SATURATE); post_ops.push_back_op>( post_op_arg1_info.get(), 0, ConvertPolicy::SATURATE); ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); } TEST_CASE(OutputWidened, framework::DatasetMode::ALL) { // Invalid broadcast: post op tensors "widen" the output tensor const auto data_type = DataType::F32; const unsigned int m = 17; const unsigned int n = 1; const unsigned int k = 1; const unsigned int batch = 1; TensorShape post_op_arg_shape(n, m, batch + 4); // output's batch dimension is "widened", which is not allowed TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); experimental::PostOpList post_ops{}; post_ops.push_back_op>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); } TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL) { // Invalid broadcast: post op tensors broadcast in the first dimension (X) only const auto data_type = DataType::F32; const unsigned int m = 22; const unsigned int n = 16; const unsigned int k = 15; const unsigned int batch = 3; TensorShape post_op_arg_shape(1, m, batch); TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); experimental::PostOpList post_ops{}; post_ops.push_back_op>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); } TEST_SUITE_END() // Invalid TEST_SUITE(Valid) TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL) { const auto data_type = DataType::F32; const unsigned int m = 22; const unsigned int n = 16; const unsigned int k = 15; const unsigned int batch = 3; experimental::PostOpList post_ops{}; ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); } TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL) { const auto data_type = DataType::F32; const unsigned int m = 22; const unsigned int n = 16; const unsigned int k = 15; const unsigned int batch = 3; TensorShape post_op_arg_shape(n, 1, batch); TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); experimental::PostOpList post_ops{}; post_ops.push_back_op>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); } TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL) { const auto data_type = DataType::F32; const unsigned int m = 22; const unsigned int n = 16; const unsigned int k = 15; const unsigned int batch = 3; TensorShape post_op_arg_shape(1, 1, batch); TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); experimental::PostOpList post_ops{}; post_ops.push_back_op>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); } TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL) { const auto data_type = DataType::F32; const unsigned int m = 22; const unsigned int n = 16; const unsigned int k = 15; const unsigned int batch = 3; TensorShape post_op_arg_shape(1, 1, 1); TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); experimental::PostOpList post_ops{}; post_ops.push_back_op>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); } TEST_SUITE_END() // Valid TEST_SUITE_END() // ValidateFusedPostOps TEST_SUITE(Float) TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunPrecommitBoundaryHandlingPartialInXPartialInY, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture, framework::DatasetMode::PRECOMMIT, combine(combine( framework::dataset::make("M", 3), framework::dataset::make("N", 1)), boundary_handling_cases)) { // Validate output if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunPrecommitBoundaryHandlingPartialInXFullInY, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture, framework::DatasetMode::PRECOMMIT, combine(combine( framework::dataset::make("M", 64), framework::dataset::make("N", 43)), boundary_handling_cases)) { // Validate output if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunPrecommitBoundaryHandlingFullInXFullInY, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture, framework::DatasetMode::PRECOMMIT, combine(combine( framework::dataset::make("M", 64), framework::dataset::make("N", 32)), boundary_handling_cases)) { // Validate output if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunPrecommitBoundaryHandlingFullInXPartialInY, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture, framework::DatasetMode::PRECOMMIT, combine(combine( framework::dataset::make("M", 37), framework::dataset::make("N", 32)), boundary_handling_cases)) { // Validate output if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), b_values), m0_values_precommit), n0_values_precommit), k0_values_precommit), h0_values), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", {false, true})), framework::dataset::make("DataType", DataType::F32)), a_values), beta_values), broadcast_bias_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunNightly, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), b_values), m0_values_nightly), n0_values_nightly), k0_values_nightly), h0_values), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", {false, true})), framework::dataset::make("DataType", DataType::F32)), a_values), beta_values), broadcast_bias_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunPrecommit3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values, m_h_values), n_values), k_values), b_values), m0_values_precommit), n0_values_precommit), k0_values_precommit), h0_values), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", {false, true})), framework::dataset::make("has_pad_y", {false, true})), framework::dataset::make("DataType", DataType::F32)), a_values), beta_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunNightly3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values, m_h_values), n_values), k_values), b_values), m0_values_nightly), n0_values_nightly), k0_values_nightly), h0_values), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", {false, true})), framework::dataset::make("has_pad_y", {false, true})), framework::dataset::make("DataType", DataType::F32)), a_values), beta_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } TEST_SUITE(FusedPostOps) FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), b_values), m0_values_precommit), n0_values_precommit), k0_values_precommit), framework::dataset::make("H0", {1})), framework::dataset::make("interleave_rhs", { true })), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", {false, true})), framework::dataset::make("DataType", DataType::F32)), a_values), beta_values), framework::dataset::make("broadcast_bias", { false } )), act_values), post_op_lists) ) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } TEST_SUITE_END() // FusedPostOps TEST_SUITE_END() // FP32 TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), b_values), m0_values_precommit), n0_values_precommit), k0_values_precommit), h0_values), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F16)), a_values), beta_values), broadcast_bias_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunNightly, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), b_values), m0_values_nightly), n0_values_nightly), k0_values_nightly), h0_values), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F16)), a_values), beta_values), broadcast_bias_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunPrecommit3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values, m_h_values), n_values), k_values), b_values), m0_values_precommit), n0_values_precommit), k0_values_precommit), h0_values), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("has_pad_y", {false, true})), framework::dataset::make("DataType", DataType::F16)), a_values), beta_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } FIXTURE_DATA_TEST_CASE(RunNightly3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values, m_h_values), n_values), k_values), b_values), m0_values_nightly), n0_values_nightly), k0_values_nightly), h0_values), i_values_rhs), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("has_pad_y", {false, true})), framework::dataset::make("DataType", DataType::F16)), a_values), beta_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } TEST_SUITE(FusedPostOps) FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), b_values), m0_values_precommit), n0_values_precommit), k0_values_precommit), framework::dataset::make("H0", {1})), framework::dataset::make("interleave_rhs", { true })), t_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F16)), a_values), beta_values), framework::dataset::make("broadcast_bias", { false } )), act_values), post_op_lists) ) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(validate_result) { validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } else { ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); framework::ARM_COMPUTE_PRINT_INFO(); } } TEST_SUITE_END() // FusedPostOps TEST_SUITE_END() // FP16 TEST_SUITE_END() // Float TEST_SUITE_END() // GEMMMatrixMulipltyReshapedOnlyRHS TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute