140 lines
9.5 KiB
C++
140 lines
9.5 KiB
C++
/*
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* Copyright (c) 2018-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/runtime/CL/functions/CLRNNLayer.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/PaddingCalculator.h"
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#include "tests/datasets/RNNLayerDataset.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/fixtures/RNNLayerFixture.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace
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{
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RelativeTolerance<float> tolerance_f32(0.001f); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F32 */
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RelativeTolerance<half> rel_tolerance_f16(half(0.2)); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F16 */
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constexpr float abs_tolerance_f16(0.02f); /**< Absolute tolerance value for comparing reference's output against implementation's output for DataType:F16 */
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} // namespace
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TEST_SUITE(CL)
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TEST_SUITE(RNNLayer)
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8), // Wrong data type
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size
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TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong weights size
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TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong recurrent weights size
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TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong bias size
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TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong output size
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TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong hidden output size
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}),
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framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
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})),
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framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
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})),
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framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U), 1, DataType::F32),
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TensorInfo(TensorShape(30U), 1, DataType::F32),
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TensorInfo(TensorShape(11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U), 1, DataType::F32),
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})),
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framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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})),
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framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32),
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})),
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framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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})),
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framework::dataset::make("Expected", { false, false, false, false, false, false, false })),
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input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected)
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{
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ARM_COMPUTE_EXPECT(bool(CLRNNLayer::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);
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}
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// clang-format on
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// *INDENT-ON*
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template <typename T>
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using CLRNNLayerFixture = RNNLayerValidationFixture<CLTensor, CLAccessor, CLRNNLayer, T>;
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TEST_SUITE(FP32)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLRNNLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32)))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_f32);
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}
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TEST_SUITE_END() // FP32
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TEST_SUITE(FP16)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLRNNLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F16)))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16);
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}
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TEST_SUITE_END() // FP16
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TEST_SUITE_END() // RNNLayer
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TEST_SUITE_END() // CL
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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