/* * Copyright (c) 2019 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/CLGEMMMatrixMultiplyKernel.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 CLGEMMMatrixMultiplyKernel using CLGEMMMatrixMultiplyReshaped = CLSynthetizeFunction; // Fixture for GEMMMatrixMultiplyInterleavedTransposedValidationFixture template using CLGEMMMatrixMultiplyReshapedFixture = GEMMMatrixMultiplyInterleavedTransposedValidationFixture; // Fixture for GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture template using CLGEMMMatrixMultiplyReshaped3DFixture = GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture; namespace { // *INDENT-OFF* // clang-format off RelativeTolerance rel_tolerance_f32(0.001f); constexpr float abs_tolerance_f32(0.0001f); RelativeTolerance rel_tolerance_f16(half(0.2)); constexpr float tolerance_num_f16 = 0.02f; /** Alpha values to test - Precommit */ const auto alpha_values = framework::dataset::make("alpha", {1.0f, -0.75f} ); /** Beta values to test - Precommit */ const auto beta_values = framework::dataset::make("beta", {-0.35f, 0.0f} ); /** M values to test - Precommit */ const auto m_values_precommit = framework::dataset::make("M", 37); /** N values to test - Precommit */ const auto n_values_precommit = framework::dataset::make("N", 51); /** K values to test - Precommit */ const auto k_values_precommit = framework::dataset::make("K", 23); /** M values to test - Nightly */ const auto m_values_nightly = framework::dataset::make("M", {421, 1}); /** N values to test - Nightly */ const auto n_values_nightly = framework::dataset::make("N", 323); /** K values to test - Nightly */ const auto k_values_nightly = framework::dataset::make("K", 207); /** M_W values to test - Precommit */ const auto m_w_values_precommit = framework::dataset::make("M_W", 5); /** M_H values to test - Precommit */ const auto m_h_values_precommit = framework::dataset::make("M_H", 7); /** M_W values to test - Nightly */ const auto m_w_values_nightly = framework::dataset::make("M_W", 13); /** M_H values to test - Nightly */ const auto m_h_values_nightly = framework::dataset::make("M_H", 27); /** Batch size values to test */ const auto b_values = framework::dataset::make("batch_size", 1, 3); /** Activation values to test */ const auto act_values = framework::dataset::make("Activation", { ActivationLayerInfo(), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f), }); /** V0 values to test - Precommit */ const auto v0_values_precommit = framework::dataset::make("V0", 2); /** H0 values to test - Precommit */ const auto h0_values_precommit = framework::dataset::make("H0", 4); /** V0 values to test - Nightly */ const auto v0_values_nightly = framework::dataset::make("V0", {2, 4}); /** H0 values to test - Nightly */ const auto h0_values_nightly = framework::dataset::make("H0", { 2, 4 }); /** Broadcast bias from vector to matrix */ const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", {false, true} ); /** GPU architectures values to test */ const auto gpu_arch_values = framework::dataset::make("GPUArch", { GPUTarget::MIDGARD, GPUTarget::BIFROST }); /** Data types values to test in the configuration */ const auto data_type_values = framework::dataset::make("DataType", { DataType::F32, DataType::F16 }); /** M values to test */ const auto fp16_mixed_precision_values = framework::dataset::make("fp16_mixed_precision", {true, false}); /** Configuration test */ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int v0_value, unsigned int h0_value, bool broadcast_bias, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch_value) { GEMMLHSMatrixInfo lhs_info; lhs_info.m0 = 4; lhs_info.k0 = 4; lhs_info.v0 = v0_value; lhs_info.interleave = true; lhs_info.transpose = true; GEMMRHSMatrixInfo rhs_info; rhs_info.n0 = data_type == DataType::F32? 4 : 8; rhs_info.k0 = 1; rhs_info.h0 = h0_value; rhs_info.interleave = false; rhs_info.transpose = false; GEMMReshapeInfo reshape_info(m_value, n_value, k_value, rhs_info.h0, lhs_info.v0, 0, false, broadcast_bias); const TensorShape lhs_shape(k_value, m_value, b_value); const TensorShape lhs_shape_reshaped = compute_lhs_reshaped_shape(TensorInfo(lhs_shape, 1, data_type), lhs_info, false); const TensorShape rhs_shape(n_value, k_value, b_value); const TensorShape rhs_shape_reshaped = compute_rhs_reshaped_shape(TensorInfo(rhs_shape, 1, data_type), rhs_info); const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape_reshaped, 1, data_type), TensorInfo(rhs_shape_reshaped, 1, data_type), reshape_info); const TensorShape bias_shape(n_value, broadcast_bias? 1 : m_value, broadcast_bias? 1 : b_value); // Create tensors CLTensor lhs_reshaped = create_tensor(lhs_shape_reshaped, data_type); CLTensor rhs_reshaped = create_tensor(rhs_shape_reshaped, data_type); CLTensor bias = create_tensor(bias_shape, data_type); CLTensor dst = create_tensor(dst_shape, data_type); 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); // Create and configure function CLGEMMMatrixMultiplyReshaped gemm; gemm.configure(gpu_arch_value, &lhs_reshaped, &rhs_reshaped, &bias, &dst, 1.0f, 2.0f, true, reshape_info, fp16_mixed_precision, act_info); } } // namespace TEST_SUITE(CL) TEST_SUITE(GEMMMatrixMultiplyInterleavedTransposed) TEST_SUITE(Float) TEST_SUITE(FP32) DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values_precommit, n_values_precommit), k_values_precommit), framework::dataset::make("batch_size", 1)), v0_values_precommit), h0_values_precommit), broadcast_bias_values), framework::dataset::make("fp16_mixed_precision", false)), act_values), data_type_values), gpu_arch_values), m_value, n_value, k_value, b_value, v0_value, h0_value, broadcast_bias, fp16_mixed_precision_value, act_value, data_type_value, gpu_arch_value) { validate_configuration(m_value, n_value, k_value, b_value, v0_value, h0_value, broadcast_bias, fp16_mixed_precision_value, act_value, data_type_value, gpu_arch_value); } FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values_precommit, n_values_precommit), k_values_precommit), b_values), alpha_values), beta_values), v0_values_precommit), h0_values_precommit), broadcast_bias_values), framework::dataset::make("fp16_mixed_precision", false)), act_values), framework::dataset::make("DataType", DataType::F32)), gpu_arch_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values_nightly, n_values_nightly), k_values_nightly), b_values), alpha_values), beta_values), v0_values_nightly), h0_values_nightly), broadcast_bias_values), framework::dataset::make("fp16_mixed_precision", false)), act_values), framework::dataset::make("DataType", DataType::F32)), gpu_arch_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( m_w_values_precommit, m_h_values_precommit), n_values_precommit), k_values_precommit), b_values), alpha_values), beta_values), v0_values_precommit), h0_values_precommit), broadcast_bias_values), framework::dataset::make("fp16_mixed_precision", false)), act_values), framework::dataset::make("DataType", DataType::F32)), gpu_arch_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values_nightly, m_h_values_nightly), n_values_nightly), k_values_nightly), b_values), alpha_values), beta_values), v0_values_nightly), h0_values_nightly), broadcast_bias_values), framework::dataset::make("fp16_mixed_precision", false)), act_values), framework::dataset::make("DataType", DataType::F32)), gpu_arch_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } 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( m_values_precommit, n_values_precommit), k_values_precommit), b_values), alpha_values), beta_values), v0_values_precommit), h0_values_precommit), broadcast_bias_values), fp16_mixed_precision_values), act_values), framework::dataset::make("DataType", DataType::F16)), gpu_arch_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values_nightly, n_values_nightly), k_values_nightly), b_values), alpha_values), beta_values), v0_values_nightly), h0_values_nightly), broadcast_bias_values), fp16_mixed_precision_values), act_values), framework::dataset::make("DataType", DataType::F16)), gpu_arch_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); } FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values_precommit, m_h_values_precommit), n_values_precommit), k_values_precommit), b_values), alpha_values), beta_values), v0_values_precommit), h0_values_precommit), broadcast_bias_values), fp16_mixed_precision_values), act_values), framework::dataset::make("DataType", DataType::F16)), gpu_arch_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); } FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values_nightly, m_h_values_nightly), n_values_nightly), k_values_nightly), b_values), alpha_values), beta_values), v0_values_nightly), h0_values_nightly), broadcast_bias_values), fp16_mixed_precision_values), act_values), framework::dataset::make("DataType", DataType::F16)), gpu_arch_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); } TEST_SUITE_END() // FP16 TEST_SUITE_END() // Float TEST_SUITE_END() // GEMMMatrixMulipltyInterleavedTransposed TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute