/* * Copyright (c) 2019-2020 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/CLGEMMMatrixMultiplyNativeKernel.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 CLGEMMMatrixMultiplyNativeKernel using CLGEMMMatrixMultiplyNative = CLSynthetizeFunction; // Fixture for CLGEMMMatrixMultiplyNative template using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyNativeValidationFixture; // Fixture for CLGEMMMatrixMultiplyNative3D template using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiplyNative3DValidationFixture; namespace { // *INDENT-OFF* // clang-format off RelativeTolerance rel_tolerance_f32(0.001f); constexpr float abs_tolerance_f32(0.0001f); /** Alpha values to test - Precommit */ const auto a_values = framework::dataset::make("alpha", {1.0f, -0.75f} ); /** Beta values to test - Precommit */ const auto beta_values = framework::dataset::make("beta", {-0.75f, 0.0f} ); /** 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", 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), }); /** M0 values to test - Precommit */ const auto m0_values_precommit = framework::dataset::make("M0", { 4, 6 }); /** 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 }); /** H0 values to test - Precommit */ const auto h0_values_precommit = framework::dataset::make("H0", 1, 3); /** M0 values to test - Nightly */ const auto m0_values_nightly = framework::dataset::make("M0", 1, 8); /** N0 values to test - Nightly */ const auto n0_values_nightly = framework::dataset::make("N0", { 2, 3, 4, 8 }); /** K0 values to test - Nightly */ const auto k0_values_nightly = framework::dataset::make("K0", { 2, 3, 4, 8 }); /** Broadcast bias from vector to matrix */ const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", { false, true } ); /** Configuration test */ void 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, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info) { 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; GEMMKernelInfo kernel_info; kernel_info.m = M; kernel_info.n = N; kernel_info.k = K; 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 bias_shape(N, broadcast_bias? 1 : M, broadcast_bias? 1 : b_value); const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type), TensorInfo(rhs_shape, 1, data_type), kernel_info); // Create tensors CLTensor lhs = create_tensor(lhs_shape, data_type); CLTensor rhs = create_tensor(rhs_shape, data_type); CLTensor bias = create_tensor(bias_shape, data_type); CLTensor dst = create_tensor(dst_shape, data_type); ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function CLGEMMMatrixMultiplyNative gemm; gemm.configure(&lhs, &rhs, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, kernel_info); } /** 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, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info) { 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; GEMMKernelInfo kernel_info; kernel_info.m = M; kernel_info.n = N; kernel_info.k = K; 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 bias_shape(N, broadcast_bias? 1 : M, broadcast_bias? 1 : b_value); const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type), TensorInfo(rhs_shape, 1, data_type), kernel_info); // Create tensors CLTensor lhs = create_tensor(lhs_shape, data_type); CLTensor rhs = create_tensor(rhs_shape, data_type); CLTensor bias = create_tensor(bias_shape, data_type); CLTensor dst = create_tensor(dst_shape, data_type); ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function CLGEMMMatrixMultiplyNative gemm; gemm.configure(&lhs, &rhs, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, kernel_info); // Padding can be added along rhs and bias's X dimension return dst.info()->padding().empty() && lhs.info()->padding().empty() && bias.info()->padding().bottom == 0 && bias.info()->padding().top == 0; } } // namespace TEST_SUITE(CL) TEST_SUITE(GEMMMatrixMultiplyNative) TEST_SUITE(Float) TEST_SUITE(FP32) DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), framework::dataset::make("batch_size", 1)), m0_values_precommit), n0_values_precommit), k0_values_precommit), broadcast_bias_values), act_values), m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, act_value) { validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, DataType::F32, act_value); } /** Validate zero padding tests * * A series of validation tests to check that no padding is added as part of configuration for 4 different scenarios. * * 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 * - No blocks in both x and y dimensions, scalar store (N0==1) * - Special case: partial_n0 == 5 (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, 50, 256, }), framework::dataset::make("N", { 48, 29, 16, 122, 20, 21, })), framework::dataset::make("M0", { 4, 8, 7, 2, 1, 8, })), framework::dataset::make("N0", { 4, 4, 16, 3, 1, 8, })), m_value, n_value, m0_value, n0_value) { bool status = validate_zero_padding(m_value, n_value, 23, 1, m0_value, n0_value, 4, false, DataType::F32, ActivationLayerInfo()); ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS); } FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::ALL, 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("DataType", DataType::F32)), a_values), beta_values), broadcast_bias_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::DISABLED, 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), framework::dataset::make("DataType", DataType::F32)), a_values), beta_values), broadcast_bias_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::ALL, 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), framework::dataset::make("DataType", DataType::F32)), a_values), beta_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::DISABLED, 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), framework::dataset::make("DataType", DataType::F32)), a_values), beta_values), act_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } TEST_SUITE_END() // FP32 TEST_SUITE_END() // Float TEST_SUITE_END() // GEMMMatrixMulipltyNative TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute