1 /*
2 * Copyright (c) 2018-2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "arm_compute/runtime/NEON/functions/NERNNLayer.h"
25 #include "tests/NEON/Accessor.h"
26 #include "tests/PaddingCalculator.h"
27 #include "tests/datasets/RNNLayerDataset.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/RNNLayerFixture.h"
33
34 namespace arm_compute
35 {
36 namespace test
37 {
38 namespace validation
39 {
40 namespace
41 {
42 RelativeTolerance<float> tolerance_f32(0.001f); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F32 */
43 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
44 RelativeTolerance<half> tolerance_f16(half(0.1)); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F16 */
45 constexpr float abs_tolerance_f16(0.02f); /**< Absolute tolerance value for comparing reference's output against implementation's output for DataType:F16 */
46 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
47 } // namespace
48
49 TEST_SUITE(NEON)
TEST_SUITE(RNNLayer)50 TEST_SUITE(RNNLayer)
51
52 // *INDENT-OFF*
53 // clang-format off
54 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
55 framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8), // Wrong data type
56 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size
57 TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong weights size
58 TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong recurrent weights size
59 TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong bias size
60 TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong output size
61 TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong hidden output size
62 TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
63 }),
64 framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
65 TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
66 TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
67 TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
68 TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
69 TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
70 TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
71 TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
72 })),
73 framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
74 TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
75 TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
76 TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
77 TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
78 TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
79 TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
80 TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
81 })),
82 framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32),
83 TensorInfo(TensorShape(11U), 1, DataType::F32),
84 TensorInfo(TensorShape(11U), 1, DataType::F32),
85 TensorInfo(TensorShape(11U), 1, DataType::F32),
86 TensorInfo(TensorShape(30U), 1, DataType::F32),
87 TensorInfo(TensorShape(11U), 1, DataType::F32),
88 TensorInfo(TensorShape(11U), 1, DataType::F32),
89 TensorInfo(TensorShape(32U), 1, DataType::F32),
90 })),
91 framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
92 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
93 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
94 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
95 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
96 TensorInfo(TensorShape(11U), 1, DataType::F32),
97 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
98 TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
99 })),
100 framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
101 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
102 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
103 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
104 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
105 TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
106 TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32),
107 TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
108 })),
109 framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
110 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
111 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
112 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
113 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
114 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
115 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
116 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
117 })),
118 framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })),
119 input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected)
120 {
121 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);
122 }
123 // clang-format on
124 // *INDENT-ON*
125
126 template <typename T>
127 using NERNNLayerFixture = RNNLayerValidationFixture<Tensor, Accessor, NERNNLayer, T>;
128
129 TEST_SUITE(FP32)
130 FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32)))
131 {
132 // Validate output
133 validate(Accessor(_target), _reference, tolerance_f32);
134 }
135 TEST_SUITE_END() // FP32
136
137 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)138 TEST_SUITE(FP16)
139 FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F16)))
140 {
141 // Validate output
142 validate(Accessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
143 }
144 TEST_SUITE_END() // FP16
145 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
146 TEST_SUITE_END() // RNNLayer
147 TEST_SUITE_END() // Neon
148 } // namespace validation
149 } // namespace test
150 } // namespace arm_compute
151