/* * Copyright (c) 2018-2020 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. */ #include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h" #include "arm_compute/core/KernelDescriptors.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensorAllocator.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; // Create function for CLGEMMReshapeLHSMatrixKernel using CLGEMMReshapeLHSMatrix = CLSynthetizeFunction; // Create function for CLGEMMReshapeRHSMatrixKernel using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction; // Create function for CLGEMMMatrixMultiplyReshapedKernel using CLGEMMMatrixMultiplyReshaped = CLSynthetizeFunction; // Fixture for CLGEMMMatrixMultiplyReshaped template using CLGEMMMatrixMultiplyReshapedFixture = GEMMMatrixMultiplyReshapedValidationFixture; // Fixture for CLGEMMMatrixMultiplyReshaped mixed precision template using CLGEMMMatrixMultiplyReshapedMixedPrecisionFixture = GEMMMatrixMultiplyReshapedValidationFixture; // Fixture for CLGEMMMatrixMultiplyReshaped3D template using CLGEMMMatrixMultiplyReshaped3DFixture = GEMMMatrixMultiplyReshaped3DValidationFixture; // Fixture for CLGEMMMatrixMultiplyReshaped3D mixed precision template using CLGEMMMatrixMultiplyReshaped3DMixedPrecisionFixture = GEMMMatrixMultiplyReshaped3DValidationFixture; namespace { // *INDENT-OFF* // clang-format off RelativeTolerance rel_tolerance_f32(0.001f); constexpr float abs_tolerance_f32(0.0001f); RelativeTolerance rel_tolerance_f16_mixed_precision(0.001f); constexpr float abs_tolerance_f16_mixed_precision(0.01f); RelativeTolerance rel_tolerance_f16(0.001f); constexpr float abs_tolerance_f16(0.01f); /** M values to test */ const auto m_values = framework::dataset::make("M", 17); /** 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", 21); /** K values to test */ const auto k_values = framework::dataset::make("K", 13); /** Batch size values to test */ const auto b_values = framework::dataset::make("batch_size", 2, 3); /** Activation values to test */ const auto act_values = framework::dataset::make("Activation", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f), }); /** Alpha values to test - Precommit */ const auto a_values_precommit = framework::dataset::make("alpha", {-0.75f} ); /** Beta values to test - Precommit */ const auto beta_values_precommit = framework::dataset::make("beta", {-0.35f} ); /** 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 }); /** V0 values to test - Precommit */ const auto v0_values_precommit = framework::dataset::make("V0", 1, 3); /** H0 values to test - Precommit */ const auto h0_values_precommit = framework::dataset::make("H0", 1, 3); /** Alpha values to test - Nightly */ const auto a_values_nightly = framework::dataset::make("alpha", {1.0f} ); /** Beta values to test - Nightly */ const auto beta_values_nightly = framework::dataset::make("beta", {1.0f} ); /** 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", { 8 }); /** K0 values to test - Nightly */ const auto k0_values_nightly = framework::dataset::make("K0", { 4 }); /** N0 values to test with export to OpenCL image object - Nightly */ const auto n0_export_to_cl_image_values_nightly = framework::dataset::make("N0", { 4, 8, 16 }); /** K0 values to test with export to OpenCL image object - Nightly */ const auto k0_export_to_cl_image_values_nightly = framework::dataset::make("K0", { 4, 8, 16 }); /** V0 values to test - Nightly */ const auto v0_values_nightly = framework::dataset::make("V0", 1, 3); /** H0 values to test - Nightly */ const auto h0_values_nightly = framework::dataset::make("H0", 1, 3); /** Interleave values to test with LHS matrix */ const auto i_values_lhs = framework::dataset::make("interleave_lhs", { true, false }); /** Interleave values to test with RHS matrix */ const auto i_values_rhs = framework::dataset::make("interleave_rhs", { true, false }); /** Broadcast bias from vector to matrix */ const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", { false, true } ); /** LHS transposed values */ const auto lhs_transpose_values = framework::dataset::make("lhs_transpose", { false, true } ); /** Zero padding test */ bool validate_zero_padding(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, 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 = false; 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 lhs_shape_reshaped = compute_lhs_reshaped_shape(TensorInfo(lhs_shape, 1, dt_input0), lhs_info); 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_reshaped, 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 tensors CLTensor lhs_reshaped = create_tensor(lhs_shape_reshaped, dt_input0); CLTensor rhs_reshaped = create_tensor(rhs_shape_reshaped, dt_input1); CLTensor bias = create_tensor(bias_shape, dt_input2); CLTensor dst = create_tensor(dst_shape, dt_output); 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); // Validate zero-padding CLGEMMMatrixMultiplyReshaped gemm; gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info); // Padding can be added along rhs and bias's X/Y dimension return dst.info()->padding().empty() && lhs_reshaped.info()->padding().empty(); } } // namespace TEST_SUITE(CL) TEST_SUITE(GEMMMatrixMultiplyReshaped) /** Validate zero padding tests * * A series of validation tests to check the zero padding requirement * * Checks performed in order: * - No partial blocks in both x and y dimensions * - Partial blocks in x dimension * - Partial blocks in y dimension * - Partial blocks in both x and y dimensions * - Special case: partial_n0 == 9 (vstore1 should be invoked instead of vstore_partial_1) */ DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("M", { 24, 64, 101, 1, 103 }), framework::dataset::make("N", { 48, 29, 16, 121, 41 })), framework::dataset::make("M0", { 4, 8, 4, 2, 4 })), framework::dataset::make("N0", { 4, 4, 16, 2, 16 })), m_value, n_value, m0_value, n0_value) { constexpr DataType dt = DataType::F32; bool status = validate_zero_padding(m_value, n_value, 23, 1, m0_value, n0_value, 4, 1, false, false, false, 0, 0, ActivationLayerInfo(), dt, dt, dt, dt, 1.0f, 1.0f); ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS); } // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( framework::dataset::make("Input0Info", { TensorInfo(TensorShape(64U, 5U, 2U), 1, DataType::F32), // OK TensorInfo(TensorShape(64U, 5U, 2U), 1, DataType::F16), // OK TensorInfo(TensorShape(64U, 5U, 2U), 1, DataType::QASYMM8), // Data type not supported TensorInfo(TensorShape(10U, 5U, 2U), 1, DataType::F32), // Incorrect dimension bias TensorInfo(TensorShape(64U, 5U, 2U), 1, DataType::F32), // Mismatching shapes TensorInfo(TensorShape(64U, 5U, 2U), 1, DataType::F16), // OK, do not broadcast bias TensorInfo(TensorShape(64U, 5U, 2U), 1, DataType::F16), // OK, wider accummulation TensorInfo(TensorShape(64U, 5U, 2U), 1, DataType::F16), // OK, RHS 4,4,2 }), framework::dataset::make("Input1Info",{ TensorInfo(TensorShape(64U, 6U, 2U), 1, DataType::F32), TensorInfo(TensorShape(64U, 6U, 2U), 1, DataType::F16), TensorInfo(TensorShape(64U, 5U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(64U, 6U, 2U), 1, DataType::F32), TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(64U, 6U, 2U), 1, DataType::F16), TensorInfo(TensorShape(64U, 6U, 2U), 1, DataType::F16), TensorInfo(TensorShape(128U, 3U, 2U), 1, DataType::F16), })), framework::dataset::make("Input2Info", { TensorInfo(TensorShape(21U), 1, DataType::F32), TensorInfo(TensorShape(21U), 1, DataType::F16), TensorInfo(TensorShape(21U), 1, DataType::QASYMM8), TensorInfo(TensorShape(21U), 1, DataType::F32), TensorInfo(TensorShape(21U), 1, DataType::F32), TensorInfo(TensorShape(21U,17U), 1, DataType::F16), TensorInfo(TensorShape(21U,17U), 1, DataType::F16), TensorInfo(TensorShape(21U,17U,2U), 1, DataType::F16), })), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U,17U,2U), 1, DataType::F32), TensorInfo(TensorShape(21U,17U,2U), 1, DataType::F16), TensorInfo(TensorShape(21U,17U,2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(21U,17U,2U), 1, DataType::F32), TensorInfo(TensorShape(21U,17U,2U), 1, DataType::F32), TensorInfo(TensorShape(21U,17U,2U), 1, DataType::F16), TensorInfo(TensorShape(21U,17U,2U), 1, DataType::F16), TensorInfo(TensorShape(21U,17U,2U), 1, DataType::F16), })), framework::dataset::make("LHSMInfo",{ GEMMLHSMatrixInfo(4,4,1,false,true), GEMMLHSMatrixInfo(4,4,1,false,true), GEMMLHSMatrixInfo(4,4,1,false,true), GEMMLHSMatrixInfo(4,2,4,false,false), GEMMLHSMatrixInfo(4,2,4,false,false), GEMMLHSMatrixInfo(4,4,1,false,true), GEMMLHSMatrixInfo(4,4,1,false,true), GEMMLHSMatrixInfo(4,4,1,false,true), })), framework::dataset::make("RHSMInfo",{ GEMMRHSMatrixInfo(4,4,1,true,true,false), GEMMRHSMatrixInfo(4,4,1,true,true,false), GEMMRHSMatrixInfo(4,4,1,true,true,false), GEMMRHSMatrixInfo(2,2,1,true,false,false), GEMMRHSMatrixInfo(2,2,1,true,false,false), GEMMRHSMatrixInfo(4,4,1,true,true,false), GEMMRHSMatrixInfo(4,4,1,true,true,false), GEMMRHSMatrixInfo(4,4,2,true,false,false), })), framework::dataset::make("GEMMInfo",{ GEMMKernelInfo( 17 /**set_is_resizable(true), &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)) == expected, framework::LogLevel::ERRORS); } TEST_SUITE(Float) TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F32)), a_values_precommit), beta_values_precommit), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::DISABLED, combine(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_nightly), n0_values_nightly), k0_values_nightly), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F32)), a_values_nightly), beta_values_nightly), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F32)), a_values_precommit), beta_values_precommit), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::DISABLED, combine(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), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F32)), a_values_nightly), beta_values_nightly), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } TEST_SUITE(ExportToCLImage) DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( framework::dataset::make("Input0Info", { TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F32), // OK or incorrect if cl_khr_image2d_from_buffer not supported TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F32), // OK or incorrect if cl_khr_image2d_from_buffer not supported TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F32), // OK or incorrect if cl_khr_image2d_from_buffer not supported TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F32), // Incorrect k0 TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F32), // Incorrect n0 }), framework::dataset::make("Input1Info",{ TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F32), TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F32), TensorInfo(TensorShape(512U, 8U, 2U), 1, DataType::F32), TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F32), TensorInfo(TensorShape(128U, 32U, 2U), 1, DataType::F32), })), framework::dataset::make("Input2Info", { TensorInfo(TensorShape(64U), 1, DataType::F32), TensorInfo(TensorShape(64U), 1, DataType::F32), TensorInfo(TensorShape(64U), 1, DataType::F32), TensorInfo(TensorShape(64U), 1, DataType::F32), TensorInfo(TensorShape(64U), 1, DataType::F32), })), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F32), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F32), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F32), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F32), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F32), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F32), })), framework::dataset::make("LHSMInfo",{ GEMMLHSMatrixInfo(4, 4, 1, false, true), GEMMLHSMatrixInfo(4, 8, 1, false, true), GEMMLHSMatrixInfo(4, 4, 1, false, true), GEMMLHSMatrixInfo(4, 2, 1, false, false), GEMMLHSMatrixInfo(4, 4, 1, false, false), })), framework::dataset::make("RHSMInfo",{ GEMMRHSMatrixInfo(4, 4, 1, true, true, true), GEMMRHSMatrixInfo(4, 8, 1, true, true, true), GEMMRHSMatrixInfo(8, 4, 1, true, true, true), GEMMRHSMatrixInfo(4, 2, 1, true, false, true), GEMMRHSMatrixInfo(2, 4, 1, true, false, true), })), framework::dataset::make("GEMMInfo",{GEMMKernelInfo( 64 /**set_is_resizable(true), &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)) == (expected && image2d_from_buffer_supported(CLKernelLibrary::get().get_device())), framework::LogLevel::ERRORS); } FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F32)), a_values_precommit), beta_values_precommit), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) { 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(RunLarge, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::NIGHTLY, combine(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_nightly), n0_export_to_cl_image_values_nightly), k0_export_to_cl_image_values_nightly), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F32)), a_values_nightly), beta_values_nightly), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) { 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(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F32)), a_values_precommit), beta_values_precommit), lhs_transpose_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) { 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(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::NIGHTLY, combine(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_export_to_cl_image_values_nightly), k0_export_to_cl_image_values_nightly), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F32)), a_values_nightly), beta_values_nightly), lhs_transpose_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) { 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() // ExportToCLImage TEST_SUITE_END() // FP32 TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F16)), a_values_precommit), beta_values_precommit), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::DISABLED, combine(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_nightly), n0_values_nightly), k0_values_nightly), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F16)), a_values_nightly), beta_values_nightly), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F16)), a_values_precommit), beta_values_precommit), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::DISABLED, combine(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), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F16)), a_values_nightly), beta_values_nightly), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); } TEST_SUITE(ExportToCLImage) DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( framework::dataset::make("Input0Info", { TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), // OK or incorrect if cl_khr_image2d_from_buffer not supported TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), // OK or incorrect if cl_khr_image2d_from_buffer not supported TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), // OK or incorrect if cl_khr_image2d_from_buffer not supported TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), // Incorrect k0 TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), // Incorrect n0 }), framework::dataset::make("Input1Info",{ TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), TensorInfo(TensorShape(512U, 8U, 2U), 1, DataType::F16), TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), TensorInfo(TensorShape(128U, 32U, 2U), 1, DataType::F16), })), framework::dataset::make("Input2Info", { TensorInfo(TensorShape(64U), 1, DataType::F16), TensorInfo(TensorShape(64U), 1, DataType::F16), TensorInfo(TensorShape(64U), 1, DataType::F16), TensorInfo(TensorShape(64U), 1, DataType::F16), TensorInfo(TensorShape(64U), 1, DataType::F16), })), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F16), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F16), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F16), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F16), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F16), TensorInfo(TensorShape(64U, 64U, 2U), 1, DataType::F16), })), framework::dataset::make("LHSMInfo",{ GEMMLHSMatrixInfo(4, 4, 1, false, true), GEMMLHSMatrixInfo(4, 8, 1, false, true), GEMMLHSMatrixInfo(4, 4, 1, false, true), GEMMLHSMatrixInfo(4, 2, 1, false, false), GEMMLHSMatrixInfo(4, 4, 1, false, false), })), framework::dataset::make("RHSMInfo",{ GEMMRHSMatrixInfo(4, 4, 1, true, true, true), GEMMRHSMatrixInfo(4, 8, 1, true, true, true), GEMMRHSMatrixInfo(8, 4, 1, true, true, true), GEMMRHSMatrixInfo(4, 2, 1, true, false, true), GEMMRHSMatrixInfo(2, 4, 1, true, false, true), })), framework::dataset::make("GEMMInfo",{GEMMKernelInfo( 64 /**set_is_resizable(true), &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)) == (expected && image2d_from_buffer_supported(CLKernelLibrary::get().get_device())), framework::LogLevel::ERRORS); } FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F16)), a_values_precommit), beta_values_precommit), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) { 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(RunLarge, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::NIGHTLY, combine(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_nightly), n0_export_to_cl_image_values_nightly), k0_export_to_cl_image_values_nightly), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F16)), a_values_nightly), beta_values_nightly), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) { 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(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F16)), a_values_precommit), beta_values_precommit), lhs_transpose_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) { 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(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::NIGHTLY, combine(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_export_to_cl_image_values_nightly), k0_export_to_cl_image_values_nightly), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", true)), framework::dataset::make("DataType", DataType::F16)), a_values_nightly), beta_values_nightly), lhs_transpose_values), act_values)) { // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) { 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() // ExportToCLImage TEST_SUITE_END() // FP16 TEST_SUITE(MixedPrecision) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedMixedPrecisionFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F16)), a_values_precommit), beta_values_precommit), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16_mixed_precision, 0.f, abs_tolerance_f16_mixed_precision); } FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedMixedPrecisionFixture, framework::DatasetMode::DISABLED, combine(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_nightly), n0_values_nightly), k0_values_nightly), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F16)), a_values_nightly), beta_values_nightly), broadcast_bias_values), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16_mixed_precision, 0.f, abs_tolerance_f16_mixed_precision); } FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DMixedPrecisionFixture, framework::DatasetMode::ALL, combine(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), v0_values_precommit), h0_values_precommit), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F16)), a_values_precommit), beta_values_precommit), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16_mixed_precision, 0.f, abs_tolerance_f16_mixed_precision); } FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DMixedPrecisionFixture, framework::DatasetMode::DISABLED, combine(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), v0_values_nightly), h0_values_nightly), i_values_lhs), i_values_rhs), framework::dataset::make("export_to_cl_image_rhs", false)), framework::dataset::make("DataType", DataType::F16)), a_values_nightly), beta_values_nightly), lhs_transpose_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16_mixed_precision, 0.f, abs_tolerance_f16_mixed_precision); } TEST_SUITE_END() // MixedPrecision TEST_SUITE_END() // Float TEST_SUITE_END() // GEMMMatrixMultiplyReshaped TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute