Compute Library
 22.05
LSTMLayer.cpp
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2  * Copyright (c) 2018-2021 Arm Limited.
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24 #include "arm_compute/runtime/NEON/functions/NELSTMLayer.h"
25 #include "tests/NEON/Accessor.h"
26 #include "tests/PaddingCalculator.h"
27 #include "tests/datasets/LSTMLayerDataset.h"
28 #include "tests/framework/Asserts.h"
29 #include "tests/framework/Macros.h"
30 #include "tests/framework/datasets/Datasets.h"
31 #include "tests/validation/Validation.h"
32 #include "tests/validation/fixtures/LSTMLayerFixture.h"
33 
34 namespace arm_compute
35 {
36 namespace test
37 {
38 namespace validation
39 {
40 namespace
41 {
42 RelativeTolerance<float> tolerance_f32(0.00001f);
43 RelativeTolerance<half> tolerance_f16(half(0.1));
44 } // namespace
45 
46 TEST_SUITE(NEON)
47 TEST_SUITE(LSTMLayer)
48 
49 // *INDENT-OFF*
50 // clang-format off
51 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip(zip(
52  framework::dataset::make("InputInfo", { TensorInfo(TensorShape(8U, 2U), 1, DataType::U8), // Wrong data type
53  TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size
54  TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong input weights size
55  TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong recurrent weights size
56  TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong cell bias size
57  TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong cell state size
58  TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong output size
59  TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong scratch size
60  }),
61  framework::dataset::make("InputWeightsInfo", { TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
62  TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
63  TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
64  TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
65  TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
66  TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
67  TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
68  TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
69  })),
70  framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
71  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
72  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
73  TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
74  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
75  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
76  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
77  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
78  })),
79  framework::dataset::make("CellBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32),
80  TensorInfo(TensorShape(16U), 1, DataType::F32),
81  TensorInfo(TensorShape(16U), 1, DataType::F32),
82  TensorInfo(TensorShape(16U), 1, DataType::F32),
83  TensorInfo(TensorShape(30U), 1, DataType::F32),
84  TensorInfo(TensorShape(16U), 1, DataType::F32),
85  TensorInfo(TensorShape(16U), 1, DataType::F32),
86  TensorInfo(TensorShape(16U), 1, DataType::F32),
87  })),
88  framework::dataset::make("ProjectionBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32),
89  TensorInfo(TensorShape(16U), 1, DataType::F32),
90  TensorInfo(TensorShape(16U), 1, DataType::F32),
91  TensorInfo(TensorShape(16U), 1, DataType::F32),
92  TensorInfo(TensorShape(16U), 1, DataType::F32),
93  TensorInfo(TensorShape(16U), 1, DataType::F32),
94  TensorInfo(TensorShape(16U), 1, DataType::F32),
95  TensorInfo(TensorShape(16U), 1, DataType::F32),
96  })),
97  framework::dataset::make("CellStateInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
98  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
99  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
100  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
101  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
102  TensorInfo(TensorShape(11U), 1, DataType::F32),
103  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
104  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
105  })),
106  framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
107  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
108  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
109  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
110  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
111  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
112  TensorInfo(TensorShape(11U, 2U), 1, DataType::F32),
113  TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
114  })),
115  framework::dataset::make("ScratchInfo", { TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
116  TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
117  TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
118  TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
119  TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
120  TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
121  TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
122  TensorInfo(TensorShape(12U, 2U), 1, DataType::F32),
123  })),
124  framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
125  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
126  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
127  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
128  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
129  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
130  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
131  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
132  })),
133  framework::dataset::make("Expected", { false, false, false, false, false, false, false, false })),
134  input_info, input_weights_info, recurrent_weights_info, cell_bias_info, projection_bias_info, cell_state_info, output_info, scratch_info, info, expected)
135 {
136  LSTMParams<ITensorInfo> lstm_params_info;
137  auto cell_bias_clone = cell_bias_info.clone();
138  lstm_params_info.set_peephole_params(cell_bias_clone.get(), cell_bias_clone.get())
139  .set_projection_params(&recurrent_weights_info, &projection_bias_info)
140  .set_cifg_params(&input_weights_info, &recurrent_weights_info, cell_bias_clone.get(), cell_bias_clone.get());
141 
142  ARM_COMPUTE_EXPECT(bool(NELSTMLayer::validate(&input_info.clone()->set_is_resizable(false), &input_weights_info.clone()->set_is_resizable(false), &input_weights_info.clone()->set_is_resizable(false),
143  &input_weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false),
144  &recurrent_weights_info.clone()->set_is_resizable(false), &cell_bias_info.clone()->set_is_resizable(false), &cell_bias_info.clone()->set_is_resizable(false),
145  &cell_bias_info.clone()->set_is_resizable(false),
146  &output_info.clone()->set_is_resizable(false), &cell_state_info.clone()->set_is_resizable(false),
147  &scratch_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &cell_state_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false),
148  lstm_params_info, info, 0.05, 0.9)) == expected, framework::LogLevel::ERRORS);
149 }
150 // clang-format on
151 // *INDENT-ON*
152 
153 template <typename T>
154 using NELSTMLayerFixture = LSTMLayerValidationFixture<Tensor, Accessor, NELSTMLayer, LSTMParams<ITensor>, T>;
155 
156 TEST_SUITE(FP32)
157 FIXTURE_DATA_TEST_CASE(RunSmall, NELSTMLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType",
158  DataType::F32)),
159  framework::dataset::make("ProjectionOpt", { true, false })),
160  framework::dataset::make("PeepholeOpt", { true, false })),
161  framework::dataset::make("UseLayerNorm", { true, false })))
162 {
163  // Validate output
164  validate(Accessor(_target), _reference, tolerance_f32);
165  validate(Accessor(_target_scratch), _reference_scratch, tolerance_f32);
166 }
167 TEST_SUITE_END() // FP32
168 
169 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
170 TEST_SUITE(FP16)
171 FIXTURE_DATA_TEST_CASE(RunSmall, NELSTMLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType",
172  DataType::F16)),
173  framework::dataset::make("ProjectionOpt", { true, false })),
174  framework::dataset::make("PeepholeOpt", { true, false })),
175  framework::dataset::make("UseLayerNorm", { true, false })))
176 {
177  // Validate output
178  validate(Accessor(_target), _reference, tolerance_f16);
179  validate(Accessor(_target_scratch), _reference_scratch, tolerance_f16);
180 }
181 TEST_SUITE_END() // FP16
182 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
183 TEST_SUITE_END() // LSTMLayer
184 TEST_SUITE_END() // Neon
185 } // namespace validation
186 } // namespace test
187 } // namespace arm_compute
Accessor.h
tolerance_f32
RelativeTolerance< float > tolerance_f32(0.001f)
F32 Tolerance value for comparing reference&#39;s output against implementation&#39;s output for floating poi...
arm_compute::NELSTMLayer::validate
static Status validate(const ITensorInfo *input, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias, const ITensorInfo *output_state_in, const ITensorInfo *cell_state_in, const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output, const LSTMParams< ITensorInfo > &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold=0.f, float projection_threshold=0.f)
Static function to check if given info will lead to a valid configuration of NELSTMLayer.
Definition: NELSTMLayer.cpp:375
arm_compute::ActivationLayerInfo::ActivationFunction::RELU
Rectifier ( )
PaddingCalculator.h
arm_compute::Format::U8
1 channel, 1 U8 per channel
NELSTMLayer.h
arm_compute::half
half_float::half half
16-bit floating point type
Definition: Types.h:48
arm_compute::Format::F32
1 channel, 1 F32 per channel
arm_compute::test::validation::ARM_COMPUTE_EXPECT
ARM_COMPUTE_EXPECT(has_error==expected, framework::LogLevel::ERRORS)
arm_compute::test::validation::output_info
output_info
Definition: FFT.cpp:145
arm_compute::test::framework::dataset::make
std::enable_if< is_container< T >::value, ContainerDataset< T > >::type make(std::string name, T &&values)
Helper function to create a ContainerDataset.
Definition: ContainerDataset.h:160
arm_compute
Copyright (c) 2017-2022 Arm Limited.
Definition: introduction.dox:24
arm_compute::Format::F16
1 channel, 1 F16 per channel
arm_compute::test::validation::DATA_TEST_CASE
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), }), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f/32768.f, 0)), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f/32768.f, 0)), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f/32768.f, 0)), })), framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SQRT), })), framework::dataset::make("Expected", { false, true, true, true, false, false, true, true, false })), input_info, output_info, act_info, expected)
Definition: ActivationLayer.cpp:99
arm_compute::test::Accessor
Accessor implementation for Tensor objects.
Definition: Accessor.h:35
arm_compute::test::framework::DatasetMode
DatasetMode
Possible dataset modes.
Definition: DatasetModes.h:40
Datasets.h
arm_compute::test::validation::TEST_SUITE_END
TEST_SUITE_END() FIXTURE_DATA_TEST_CASE(RunSmall
[CLActivationLayer Test snippet]
Definition: DequantizationLayer.cpp:111
arm_compute::test::validation::input_info
input_info
Definition: FFT.cpp:145
arm_compute::test::validation::NELSTMLayerFixture
LSTMLayerValidationFixture< Tensor, Accessor, NELSTMLayer, LSTMParams< ITensor >, T > NELSTMLayerFixture
Definition: LSTMLayer.cpp:154
arm_compute::test::validation::validate
validate(CLAccessor(output_state), expected_output)
arm_compute::test::validation::FIXTURE_DATA_TEST_CASE
FIXTURE_DATA_TEST_CASE(RunSmall, CLAbsLayerFixture< half >, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)))
Definition: AbsLayer.cpp:50
arm_compute::test::validation::expected
expected
Definition: BatchNormalizationLayer.cpp:166
arm_compute::test::validation::info
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
arm_compute::test::framework::DatasetMode::ALL
Validation.h
arm_compute::test::framework::LogLevel::ERRORS
tolerance_f16
RelativeTolerance< half_float::half > tolerance_f16(half(0.2))
F16 Tolerance value for comparing reference&#39;s output against implementation&#39;s output for floating poi...
arm_compute::test::validation::zip
zip(zip(framework::dataset::make("Weights", { TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U, 1U), 1, DataType::F32), }), framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32), TensorInfo(TensorShape(2U), 1, DataType::F16), TensorInfo(TensorShape(5U), 1, DataType::F32), })), framework::dataset::make("Expected", { true, false, false}))
arm_compute::test::validation::TEST_SUITE
TEST_SUITE(QASYMM8_to_F32) FIXTURE_DATA_TEST_CASE(RunSmall
arm_compute::DataType
DataType
Available data types.
Definition: Types.h:79
arm_compute::test::validation::combine
combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)))
Definition: AbsLayer.cpp:65
Asserts.h