| /* |
| * 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<CLGEMMMatrixMultiplyNativeKernel>; |
| |
| // Fixture for CLGEMMMatrixMultiplyNative |
| template <typename T> |
| using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyNativeValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>; |
| |
| // Fixture for CLGEMMMatrixMultiplyNative3D |
| template <typename T> |
| using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiplyNative3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>; |
| |
| namespace |
| { |
| // *INDENT-OFF* |
| // clang-format off |
| RelativeTolerance<float> 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 } ); |
| |
| /** 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( |
| // 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)), |
| // 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())); |
| |
| /** 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<CLTensor>(lhs_shape, data_type); |
| CLTensor rhs = create_tensor<CLTensor>(rhs_shape, data_type); |
| CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type); |
| CLTensor dst = create_tensor<CLTensor>(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<CLTensor>(lhs_shape, data_type); |
| CLTensor rhs = create_tensor<CLTensor>(rhs_shape, data_type); |
| CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type); |
| CLTensor dst = create_tensor<CLTensor>(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(RunSmallBoundaryHandlingPartialInXPartialInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| combine(combine( |
| framework::dataset::make("M", 3), |
| framework::dataset::make("N", 1)), |
| boundary_handling_cases)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingPartialInXFullInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| combine(combine( |
| framework::dataset::make("M", 64), |
| framework::dataset::make("N", 51)), |
| boundary_handling_cases)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingFullInXFullInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| combine(combine( |
| framework::dataset::make("M", 64), |
| framework::dataset::make("N", 32)), |
| boundary_handling_cases)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingFullInXPartialInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| combine(combine( |
| framework::dataset::make("M", 37), |
| framework::dataset::make("N", 32)), |
| boundary_handling_cases)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture<float>, 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<float>, 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<float>, 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<float>, 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 |