Compute Library
 22.05
SoftmaxLayer.cpp
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24 #include "arm_compute/core/Types.h"
30 #include "tests/NEON/Accessor.h"
32 #include "tests/datasets/ShapeDatasets.h"
34 #include "tests/framework/Macros.h"
37 #include "tests/validation/fixtures/SoftmaxLayerFixture.h"
38 namespace arm_compute
39 {
40 namespace test
41 {
42 namespace validation
43 {
44 namespace
45 {
46 /** Tolerance for float operations */
47 constexpr AbsoluteTolerance<float> tolerance_f32(0.000001f);
48 RelativeTolerance<half> tolerance_f16(half(0.2));
49 
50 /** Tolerance for quantized operations */
51 constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1);
52 constexpr AbsoluteTolerance<int8_t> tolerance_qasymm8_signed(1);
53 
54 /** CNN data types */
55 const auto CNNDataTypes = framework::dataset::make("DataType",
56 {
57 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
59 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
61 });
62 } // namespace
63 
64 TEST_SUITE(NEON)
65 TEST_SUITE(SoftmaxLayer)
66 // *INDENT-OFF*
67 // clang-format off
68 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
69  framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Mismatching data types
70  TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Mismatching shapes
71  TensorInfo(TensorShape(27U, 13U), 1, DataType::QASYMM8, // Invalid output quantization info
72  QuantizationInfo(1.f/256, 12)),
75  QuantizationInfo(1.f/256, 12)),
77  TensorInfo(TensorShape(32U, 13U), 1, DataType::QASYMM8, //Invalid axis high
78  QuantizationInfo(1.f/256, 12)),
79  TensorInfo(TensorShape(32U, 13U), 1, DataType::QASYMM8, //Invalid axis low
80  QuantizationInfo(1.f/256, 12)),
81  }),
85  QuantizationInfo(1.f/256, 12)),
88  QuantizationInfo(1.f/256, 0)),
91  QuantizationInfo(1.f/256, 0)),
93  QuantizationInfo(1.f/256, 0)),
94  })),
95  framework::dataset::make("beta", { 1.0,
96  2.0,
97  1.0,
98  2.0,
99  1.0,
100  1.0,
101  2.0,
102  1.0,
103  })),
104  framework::dataset::make("axis", { 0,
105  0,
106  0,
107  1,
108  0,
109  -1,
110  2,
111  -3,
112  })),
113  framework::dataset::make("Expected", { false, false, false, true, true, true, false, false })),
114  input_info, output_info, beta, axis, expected)
115 {
116  ARM_COMPUTE_EXPECT(bool(NESoftmaxLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), beta, axis)) == expected, framework::LogLevel::ERRORS);
117 }
118 // clang-format on
119 // *INDENT-ON*
120 
121 template <typename T>
122 using NESoftmaxLayerFixture = SoftmaxValidationFixture<Tensor, Accessor, NESoftmaxLayer, T>;
123 
124 DATA_TEST_CASE(KernelSelection_max_logits, framework::DatasetMode::ALL, concat(
125  combine(framework::dataset::make("CpuExt", std::string("NEON")),
130  })),
131  combine(framework::dataset::make("CpuExt", std::string("SVE")),
136  }))),
138 {
139  using namespace cpu::kernels;
140 
142  cpu_isa.neon = (cpu_ext == "NEON");
143  cpu_isa.sve = (cpu_ext == "SVE");
145 
146  const auto *selected_impl = CpuLogits1DMaxKernel::get_implementation(DataTypeISASelectorData{ data_type, cpu_isa }, cpu::KernelSelectionType::Preferred);
147 
149 
150  std::string expected = lower_string(cpu_ext) + "_" + cpu_impl_dt(data_type) + "_logits_1d_max";
151  std::string actual = selected_impl->name;
152 
154 }
155 
157  combine(framework::dataset::make("CpuExt", std::string("NEON")),
162  })),
163  combine(framework::dataset::make("CpuExt", std::string("SVE")),
166  }))),
167  combine(framework::dataset::make("CpuExt", std::string("SVE2")),
170  }))),
172 {
173  using namespace cpu::kernels;
174 
176  cpu_isa.neon = (cpu_ext == "NEON");
177  cpu_isa.sve = (cpu_ext == "SVE");
178  cpu_isa.sve2 = (cpu_ext == "SVE2");
180 
181  const auto *selected_impl = CpuLogits1DSoftmaxKernel<false>::get_implementation(DataTypeISASelectorData{ data_type, cpu_isa }, cpu::KernelSelectionType::Preferred);
182 
184 
185  std::string expected = lower_string(cpu_ext) + "_" + cpu_impl_dt(data_type) + "_softmax_logits_1d";
186  std::string actual = selected_impl->name;
187 
189 }
190 
191 TEST_SUITE(Float)
192 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
193 TEST_SUITE(FP16)
196  framework::dataset::make("Beta", { 1.0f, 2.0f })),
197  framework::dataset::make("Axis", { 0, 1 })))
198 {
199  // Validate output
200  validate(Accessor(_target), _reference, tolerance_f16);
201 }
204  framework::dataset::make("Beta", { 1.0f, 2.0f })),
205  framework::dataset::make("Axis", { 0, 2, -1 })))
206 {
207  // Validate output
208  validate(Accessor(_target), _reference, tolerance_f16);
209 }
212  framework::dataset::make("Beta", { 1.0f, 2.0f })),
213  framework::dataset::make("Axis", { 0 })))
214 {
215  // Validate output
216  validate(Accessor(_target), _reference, tolerance_f16);
217 }
218 TEST_SUITE_END() //FP16
219 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
220 
221 TEST_SUITE(FP32)
222 FIXTURE_DATA_TEST_CASE(RunSmall2D, NESoftmaxLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SoftmaxLayerSmallShapes(),
223  framework::dataset::make("DataType", DataType::F32)),
224  framework::dataset::make("Beta", { 1.0f, 2.0f })),
225  framework::dataset::make("Axis", { 0, -1 })))
226 {
227  // Validate output
228  validate(Accessor(_target), _reference, tolerance_f32);
229 }
232  framework::dataset::make("Beta", { 1.0f, 2.0f })),
233  framework::dataset::make("Axis", { 0, -2, 3 })))
234 {
235  // Validate output
236  validate(Accessor(_target), _reference, tolerance_f32);
237 }
240  framework::dataset::make("Beta", { 1.0f, 2.0f })),
241  framework::dataset::make("Axis", { 0 })))
242 {
243  // Validate output
244  validate(Accessor(_target), _reference, tolerance_f32);
245 }
246 TEST_SUITE_END() //FP32
247 TEST_SUITE_END() //Float
248 
249 template <typename T>
250 using NESoftmaxLayerQuantizedFixture = SoftmaxValidationQuantizedFixture<Tensor, Accessor, NESoftmaxLayer, T>;
251 
252 TEST_SUITE(Quantized)
254 FIXTURE_DATA_TEST_CASE(RunSmall2D, NESoftmaxLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SoftmaxLayerSmallShapes(),
255  framework::dataset::make("DataType", DataType::QASYMM8)),
256  combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }),
257  framework::dataset::make("Beta", { 1.0f, 2.f }))),
258  framework::dataset::make("Axis", { 0, -1 })))
259 {
260  // Validate output
261  validate(Accessor(_target), _reference, tolerance_qasymm8);
262 }
263 FIXTURE_DATA_TEST_CASE(RunSmall4D, NESoftmaxLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::Small4DShapes(),
265  combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }),
266  framework::dataset::make("Beta", { 1.0f, 2.f }))),
267  framework::dataset::make("Axis", { 0, 1, -2 })))
268 {
269  // Validate output
270  validate(Accessor(_target), _reference, tolerance_qasymm8);
271 }
272 FIXTURE_DATA_TEST_CASE(RunLarge, NESoftmaxLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SoftmaxLayerLargeShapes(),
274  combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }),
275  framework::dataset::make("Beta", { 1.0f, 2.0f }))),
276  framework::dataset::make("Axis", { 0 })))
277 {
278  // Validate output
279  validate(Accessor(_target), _reference, tolerance_qasymm8);
280 }
281 TEST_SUITE_END() //QASYMM8
282 
284 FIXTURE_DATA_TEST_CASE(RunSmall2D, NESoftmaxLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SoftmaxLayerSmallShapes(),
285  framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
286  combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }),
287  framework::dataset::make("Beta", { 1.0f, 2.f }))),
288  framework::dataset::make("Axis", { 0, -1 })))
289 {
290  // Validate output
291  validate(Accessor(_target), _reference, tolerance_qasymm8_signed);
292 }
295  combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }),
296  framework::dataset::make("Beta", { 1.0f, 2.f }))),
297  framework::dataset::make("Axis", { 0, 1, -1 })))
298 {
299  // Validate output
300  validate(Accessor(_target), _reference, tolerance_qasymm8_signed);
301 }
302 TEST_SUITE_END() //QASYMM8_SIGNED
303 
304 TEST_SUITE_END() //Quantized
305 
306 TEST_SUITE_END() //SoftmaxLayer
307 TEST_SUITE_END() //NEON
308 } // namespace validation
309 } // namespace test
310 } // namespace arm_compute
Retrieve the best implementation available for the given Cpu ISA, ignoring the build flags...
Shape of a tensor.
Definition: TensorShape.h:39
RelativeTolerance< float > tolerance_f32(0.001f)
F32 Tolerance value for comparing reference&#39;s output against implementation&#39;s output for floating poi...
const CpuCastKernel::CastKernel * selected_impl
Definition: Cast.cpp:205
half_float::half half
16-bit floating point type
Definition: Types.h:48
1 channel, 1 F32 per channel
ARM_COMPUTE_EXPECT(has_error==expected, framework::LogLevel::ERRORS)
constexpr AbsoluteTolerance< int8_t > tolerance_qasymm8_signed
Definition: Scale.cpp:518
std::enable_if< is_container< T >::value, ContainerDataset< T > >::type make(std::string name, T &&values)
Helper function to create a ContainerDataset.
std::string lower_string(const std::string &val)
Lower a given string.
Definition: Utils.cpp:351
Copyright (c) 2017-2022 Arm Limited.
cpuinfo::CpuIsaInfo cpu_isa
Definition: Cast.cpp:207
std::string cpu_impl_dt(const DataType &data_type)
Returns the suffix string of CPU kernel implementation names based on the given data type...
Definition: Utils.h:1245
1 channel, 1 F16 per channel
CPU ISA (Instruction Set Architecture) information.
Definition: CpuIsaInfo.h:37
Quantization information.
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)
Accessor implementation for Tensor objects.
Definition: Accessor.h:35
DatasetMode
Possible dataset modes.
Definition: DatasetModes.h:40
TEST_SUITE_END() FIXTURE_DATA_TEST_CASE(RunSmall
[CLActivationLayer Test snippet]
quantized, asymmetric fixed-point 8-bit number unsigned
validate(CLAccessor(output_state), expected_output)
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_ERROR_ON_NULLPTR(selected_impl)
static Status validate(const ITensorInfo *input, const ITensorInfo *output, float beta=1.0f, int32_t axis=0)
Static function to check if given info will lead to a valid configuration of NESoftmaxLayer.
ARM_COMPUTE_EXPECT_EQUAL(expected, actual, framework::LogLevel::ERRORS)
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:43
JoinDataset< T, U > concat(T &&dataset1, U &&dataset2)
Helper function to create a JoinDataset.
Definition: JoinDataset.h:160
quantized, asymmetric fixed-point 8-bit number signed
SoftmaxValidationFixture< Tensor, Accessor, NESoftmaxLayer, T > NESoftmaxLayerFixture
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...
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}))
TEST_SUITE(QASYMM8_to_F32) FIXTURE_DATA_TEST_CASE(RunSmall
DataType
Available data types.
Definition: Types.h:79
combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)))
Definition: AbsLayer.cpp:65