| /* |
| * Copyright (c) 2018-2021, 2024 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/runtime/NEON/functions/NERNNLayer.h" |
| #include "tests/NEON/Accessor.h" |
| #include "tests/PaddingCalculator.h" |
| #include "tests/datasets/RNNLayerDataset.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/RNNLayerFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| RelativeTolerance<float> tolerance_f32(0.001f); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F32 */ |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| RelativeTolerance<half> tolerance_f16(half(0.1)); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F16 */ |
| constexpr float abs_tolerance_f16(0.02f); /**< Absolute tolerance value for comparing reference's output against implementation's output for DataType:F16 */ |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| } // namespace |
| |
| TEST_SUITE(NEON) |
| TEST_SUITE(RNNLayer) |
| |
| // *INDENT-OFF* |
| // clang-format off |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8), // Wrong data type |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong weights size |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong recurrent weights size |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong bias size |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong output size |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong hidden output size |
| TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
| }), |
| framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
| })), |
| framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
| })), |
| framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U), 1, DataType::F32), |
| TensorInfo(TensorShape(30U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U), 1, DataType::F32), |
| TensorInfo(TensorShape(32U), 1, DataType::F32), |
| })), |
| framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
| })), |
| framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32), |
| TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
| })), |
| framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| })), |
| framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })), |
| input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected) |
| { |
| ARM_COMPUTE_EXPECT(bool(NERNNLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &hidden_output_info.clone()->set_is_resizable(false), info)) == expected, framework::LogLevel::ERRORS); |
| } |
| // clang-format on |
| // *INDENT-ON* |
| |
| template <typename T> |
| using NERNNLayerFixture = RNNLayerValidationFixture<Tensor, Accessor, NERNNLayer, T>; |
| |
| TEST_SUITE(FP32) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_f32); |
| } |
| TEST_SUITE_END() // FP32 |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| TEST_SUITE(FP16) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F16))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_f16, 0.02f, abs_tolerance_f16); |
| } |
| TEST_SUITE_END() // FP16 |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| TEST_SUITE_END() // RNNLayer |
| TEST_SUITE_END() // Neon |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |