| /* |
| * 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/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 "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" |
| #include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h" |
| #include "src/core/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; |
| |
| // Create function for CLGEMMReshapeLHSMatrixKernel |
| using CLGEMMReshapeLHSMatrix = CLSynthetizeFunction<CLGEMMReshapeLHSMatrixKernel>; |
| |
| // Create function for CLGEMMReshapeRHSMatrixKernel |
| using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction<CLGEMMReshapeRHSMatrixKernel>; |
| |
| // Create function for CLGEMMMatrixMultiplyKernel |
| using CLGEMMMatrixMultiplyReshaped = CLSynthetizeFunction<CLGEMMMatrixMultiplyKernel>; |
| |
| // Fixture for GEMMMatrixMultiplyInterleavedTransposedValidationFixture |
| template <typename T> |
| using CLGEMMMatrixMultiplyReshapedFixture = |
| GEMMMatrixMultiplyInterleavedTransposedValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>; |
| |
| // Fixture for GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture |
| template <typename T> |
| using CLGEMMMatrixMultiplyReshaped3DFixture = |
| GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>; |
| |
| namespace |
| { |
| // *INDENT-OFF* |
| // clang-format off |
| RelativeTolerance<float> rel_tolerance_f32(0.001f); |
| constexpr float abs_tolerance_f32(0.0001f); |
| |
| RelativeTolerance<half> rel_tolerance_f16(half(0.2)); |
| constexpr float tolerance_num_f16 = 0.02f; |
| |
| /** Alpha values to test */ |
| const auto alpha_values = framework::dataset::make("alpha", {1.0f, -0.75f} ); |
| |
| /** Beta values to test */ |
| const auto beta_values = framework::dataset::make("beta", {-0.35f, 0.0f} ); |
| |
| /** M, N combinations to test |
| * 1: Special 1x1 case |
| * 2: Special multples of processor size in both dimensions |
| * 3: Non multiples of processor size in both dimensions |
| */ |
| const auto m_n_values = zip( |
| framework::dataset::make("M", {1, 16, 37}), |
| framework::dataset::make("N", {1, 16, 51}) |
| ); |
| |
| /** 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); |
| |
| /** 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); |
| |
| /** 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 */ |
| const auto v0_values = framework::dataset::make("V0", 2); |
| |
| /** H0 values to test */ |
| const auto h0_values = framework::dataset::make("H0", 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}); |
| } // namespace |
| |
| TEST_SUITE(CL) |
| TEST_SUITE(GEMMMatrixMultiplyInterleavedTransposed) |
| TEST_CASE(Negative, framework::DatasetMode::ALL) |
| { |
| // The following tests are already integrated in the GEMMMatrixMultiply validation because |
| // in common with this validation |
| // - Unsupported QASYMM8 data type |
| // - Unsupported SIZE_T data type |
| // - Mixed precision with F32 |
| // - Max number of dimensions LHS matrix |
| // - Max number of dimensions RHS matrix |
| |
| // Invalid LHS dimensions |
| { |
| // The correct shape should be: lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32); |
| const auto lhs = TensorInfo(TensorShape(256U, 2U, 1U, 1U), 1, DataType::F32); |
| const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32); |
| const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| constexpr float alpha = 1.3f; |
| constexpr float beta = 0.7f; |
| const bool is_interleaved_transposed = true; |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false); |
| const GPUTarget gpu_target = GPUTarget::MIDGARD; |
| const bool fp_mixed_precision = false; |
| const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision); |
| ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS); |
| } |
| |
| // Invalid RHS dimensions |
| { |
| const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32); |
| // The correct shape should be rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32); |
| const auto rhs = TensorInfo(TensorShape(104U, 4U, 1U, 1U), 1, DataType::F32); |
| const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| constexpr float alpha = 1.3f; |
| constexpr float beta = 0.7f; |
| const bool is_interleaved_transposed = true; |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false); |
| const GPUTarget gpu_target = GPUTarget::MIDGARD; |
| const bool fp_mixed_precision = false; |
| const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision); |
| ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS); |
| } |
| |
| // Broadcast bias |
| { |
| const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32); |
| const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32); |
| // The correct shape should be bias = TensorInfo(TensorShape(24U, 1U, 1U, 1U), 1, DataType::F32); |
| const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| constexpr float alpha = 1.3f; |
| constexpr float beta = 0.7f; |
| const bool is_interleaved_transposed = true; |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, true); |
| const GPUTarget gpu_target = GPUTarget::MIDGARD; |
| const bool fp_mixed_precision = false; |
| const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision); |
| ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS); |
| } |
| |
| // Invalid dimensions for the bias |
| { |
| const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32); |
| const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32); |
| // The correct shape should be bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| const auto bias = TensorInfo(TensorShape(25U, 16U, 1U, 1U), 1, DataType::F32); |
| const auto out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| constexpr float alpha = 1.3f; |
| constexpr float beta = 0.7f; |
| const bool is_interleaved_transposed = true; |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false); |
| const GPUTarget gpu_target = GPUTarget::MIDGARD; |
| const bool fp_mixed_precision = false; |
| const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision); |
| ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS); |
| } |
| |
| // Invalid dimensions for the output |
| { |
| const auto lhs = TensorInfo(TensorShape(256U, 1U, 1U, 1U), 1, DataType::F32); |
| const auto rhs = TensorInfo(TensorShape(104U, 3U, 1U, 1U), 1, DataType::F32); |
| const auto bias = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| // The correct shape should be out = TensorInfo(TensorShape(24U, 16U, 1U, 1U), 1, DataType::F32); |
| const auto out = TensorInfo(TensorShape(24U, 13U, 1U, 1U), 1, DataType::F32); |
| constexpr float alpha = 1.3f; |
| constexpr float beta = 0.7f; |
| const bool is_interleaved_transposed = true; |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false); |
| const GPUTarget gpu_target = GPUTarget::MIDGARD; |
| const bool fp_mixed_precision = false; |
| const auto status = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision); |
| ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS); |
| } |
| } |
| |
| TEST_SUITE(Float) |
| TEST_SUITE(FP32) |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture<float>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| m_n_values, |
| k_values), |
| b_values), |
| alpha_values), |
| beta_values), |
| v0_values), |
| h0_values), |
| 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<float>, framework::DatasetMode::ALL, |
| 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), |
| alpha_values), |
| beta_values), |
| v0_values), |
| h0_values), |
| 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<half>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| m_n_values, |
| k_values), |
| b_values), |
| alpha_values), |
| beta_values), |
| v0_values), |
| h0_values), |
| 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<half>, framework::DatasetMode::ALL, |
| 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), |
| alpha_values), |
| beta_values), |
| v0_values), |
| h0_values), |
| 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 |