Compute Library
 22.11
GEMMLowp.cpp
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24 #include "arm_compute/core/Types.h"
31 #include "tests/NEON/Accessor.h"
32 #include "tests/NEON/Helper.h"
34 #include "tests/datasets/GEMMLowpFusedOffsetOutputDataset.h"
35 #include "tests/datasets/LargeGEMMLowpDataset.h"
36 #include "tests/datasets/ShapeDatasets.h"
37 #include "tests/datasets/SmallGEMMLowpDataset.h"
39 #include "tests/framework/Macros.h"
42 #include "tests/validation/fixtures/GEMMLowpFixture.h"
43 
44 namespace arm_compute
45 {
46 namespace test
47 {
48 namespace validation
49 {
50 TEST_SUITE(NEON)
51 TEST_SUITE(GEMMLowp)
52 TEST_SUITE(MatrixMultiplyCore)
53 using NEGEMMLowpMatrixMultiplyCoreFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore>;
54 using NEGEMMLowpBatchedMatMulFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore, false, false, true>;
55 
56 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallGEMMLowpDataset(), datasets::LargeGEMMLowpDataset()),
57  shape_a, shape_b, shape_c, a_offset, b_offset)
58 {
59  // Create tensors
60  Tensor a = create_tensor<Tensor>(shape_a, DataType::QASYMM8);
61  Tensor b = create_tensor<Tensor>(shape_b, DataType::QASYMM8);
62  Tensor c = create_tensor<Tensor>(shape_c, DataType::S32);
63 
64  a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
65  b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
66 
68  ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
69  ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
70 
71  // Create and configure function
72  NEGEMMLowpMatrixMultiplyCore gemmlowp_mm;
73  gemmlowp_mm.configure(&a, &b, nullptr, &c);
74 
75  // Validate padding is zero
76  validate(a.info()->padding(), PaddingSize());
77  validate(b.info()->padding(), PaddingSize());
78  validate(c.info()->padding(), PaddingSize());
79 }
80 
81 // *INDENT-OFF*
82 // clang-format off
84  framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Input not a multiple of 4
85  TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Mismatching data type
86  TensorInfo(TensorShape(20U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions
87  TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions
89  }),
95  })),
101  })),
102  framework::dataset::make("Expected", { true, false, false, false, true })),
103  a_info, b_info, output_info, expected)
104 {
105  // Lock tensors
106  Status status = NEGEMMLowpMatrixMultiplyCore::validate(&a_info.clone()->set_is_resizable(false),
107  &b_info.clone()->set_is_resizable(false),
108  nullptr,
109  &output_info.clone()->set_is_resizable(false));
111 }
112 // clang-format on
113 // *INDENT-ON*
114 
115 /** Test case for memory injection in @ref cpu::CpuGemmLowpMatrixMultiplyCore.
116  *
117  * Configure the operator once and inject memory at run-time in multiple executions.
118  *
119  * Checks performed in order:
120  * - Both runs compute the same output
121  */
122 TEST_CASE(MemoryInjection, framework::DatasetMode::ALL)
123 {
124  auto gemm = std::make_unique<cpu::CpuGemmLowpMatrixMultiplyCore>();
125  auto a_info = TensorInfo(TensorShape(32U, 72U), 1, DataType::QASYMM8);
126  auto b_info = TensorInfo(TensorShape(17U, 32U), 1, DataType::QASYMM8);
127  auto dst_info = TensorInfo(TensorShape(17U, 72U), 1, DataType::S32);
128  a_info.set_quantization_info(QuantizationInfo(1.0f / 255, -9));
129  b_info.set_quantization_info(QuantizationInfo(1.0f / 255, 1));
130  const auto gemm_info = GEMMInfo{};
131  gemm->configure(&a_info, &b_info, nullptr, &dst_info, gemm_info);
132 
133  // telhs are newly created every call of this lambda function
134  auto a = create_tensor<Tensor>(a_info);
135  auto b = create_tensor<Tensor>(b_info);
136  auto dst = create_tensor<Tensor>(dst_info);
137  a.allocator()->allocate();
138  b.allocator()->allocate();
139  dst.allocator()->allocate();
140 
141  ITensorPack run_pack =
142  {
143  { TensorType::ACL_SRC_0, &a },
144  { TensorType::ACL_SRC_1, &b },
145  { TensorType::ACL_DST, &dst }
146  };
147  ITensorPack prep_pack =
148  {
149  { TensorType::ACL_SRC_1, &b },
150  };
151 
152  auto mg = MemoryGroup{};
153  auto ws = manage_workspace<Tensor>(gemm->workspace(), mg, run_pack, prep_pack);
154 
155  auto run_conv = [&]() -> Tensor
156  {
157  auto dst = create_tensor<Tensor>(dst_info);
158  dst.allocator()->allocate();
159  run_pack.add_tensor(TensorType::ACL_DST, &dst);
160 
161  library->fill_tensor_value(Accessor(a), static_cast<uint8_t>(1));
162  library->fill_tensor_value(Accessor(b), static_cast<uint8_t>(2));
163  // This operator is configured once and captured by this lambda.
164  gemm->prepare(prep_pack);
165  gemm->run(run_pack);
166  return dst;
167  };
168  auto result_0 = run_conv();
169  auto result_1 = run_conv();
170  for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i)
171  {
172  ARM_COMPUTE_EXPECT(((uint8_t *)result_0.buffer())[i] == ((uint8_t *)result_1.buffer())[i], framework::LogLevel::ERRORS);
173  }
174 }
175 
176 /** Test case for memory injection in @ref NEGEMMLowpMatrixMultiplyCore.
177  *
178  * Make sure @ref NEGEMMLowpMatrixMultiplyCore still works through injecting the memory at configure time using the old API.
179  *
180  * Checks performed in order:
181  * - Both runs compute the same output
182  */
183 TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL)
184 {
185  auto gemm = std::make_unique<NEGEMMLowpMatrixMultiplyCore>();
186  auto a_info = TensorInfo(TensorShape(32U, 72U), 1, DataType::QASYMM8);
187  auto b_info = TensorInfo(TensorShape(17U, 32U), 1, DataType::QASYMM8);
188  auto dst_info = TensorInfo(TensorShape(17U, 72U), 1, DataType::S32);
189  a_info.set_quantization_info(QuantizationInfo(1.0f / 255, -9));
190  b_info.set_quantization_info(QuantizationInfo(1.0f / 255, 1));
191  const auto gemm_info = GEMMInfo{};
192  auto run_conv = [&]()
193  {
194  auto a = create_tensor<Tensor>(a_info);
195  auto b = create_tensor<Tensor>(b_info);
196  auto dst = create_tensor<Tensor>(dst_info);
197  gemm->configure(&a, &b, nullptr, &dst, gemm_info);
198  a.allocator()->allocate();
199  b.allocator()->allocate();
200  dst.allocator()->allocate();
201  library->fill_tensor_value(Accessor(a), static_cast<uint8_t>(1));
202  library->fill_tensor_value(Accessor(b), static_cast<uint8_t>(2));
203  gemm->run();
204  return dst;
205  };
206  auto result_0 = run_conv();
207  auto result_1 = run_conv();
208  for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i)
209  {
210  ARM_COMPUTE_EXPECT(((uint8_t *)result_0.buffer())[i] == ((uint8_t *)result_1.buffer())[i], framework::LogLevel::ERRORS);
211  }
212 }
213 
214 TEST_SUITE(BatchedMatMul)
215 FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpBatchedMatMulFixture, framework::DatasetMode::ALL, datasets::SmallGEMMLowpBatchedMatMulDataset())
216 {
217  validate(Accessor(_target), _reference);
218 }
219 TEST_SUITE_END() // BatchedMatMul
220 
222 {
223  // Validate output
224  validate(Accessor(_target), _reference);
225 }
226 
228 {
229  // Validate output
230  validate(Accessor(_target), _reference);
231 }
232 
233 using NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture = GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore>;
234 TEST_SUITE(FusedOffsetOutput)
235 FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::ALL, combine(datasets::SmallGEMMLowpFusedOffsetOutputUint8Dataset(),
236  framework::dataset::make("DataType", { DataType::QASYMM8 })))
237 {
238  // Validate output
239  validate(Accessor(_target), _reference);
240 }
241 
244 {
245  // Validate output
246  validate(Accessor(_target), _reference);
247 }
248 TEST_SUITE_END() // FusedOffsetOutput
249 TEST_SUITE_END() // MatrixMultiplyCore
250 TEST_SUITE_END() // GEMMLowp
251 TEST_SUITE_END() // NEON
252 } // namespace validation
253 } // namespace test
254 } // namespace arm_compute
Shape of a tensor.
Definition: TensorShape.h:39
SimpleTensor< float > b
Definition: DFT.cpp:157
ARM_COMPUTE_EXPECT(has_error==expected, framework::LogLevel::ERRORS)
std::enable_if< is_container< T >::value, ContainerDataset< T > >::type make(std::string name, T &&values)
Helper function to create a ContainerDataset.
Status class.
Definition: Error.h:52
Copyright (c) 2017-2022 Arm Limited.
TensorAllocator * allocator()
Return a pointer to the tensor&#39;s allocator.
Definition: Tensor.cpp:48
ITensorInfo * info() const override
Interface to be implemented by the child class to return the tensor&#39;s metadata.
Definition: Tensor.cpp:33
1 channel, 1 S32 per channel
virtual bool is_resizable() const =0
Flag indicating whether the size of the tensor can be changed.
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
GEMMLowpMatrixMultiplyCoreValidationFixture< Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore > NEGEMMLowpMatrixMultiplyCoreFixture
Definition: GEMMLowp.cpp:53
std::unique_ptr< AssetsLibrary > library
Definition: main.cpp:76
TEST_SUITE_END() FIXTURE_DATA_TEST_CASE(RunSmall
[CLActivationLayer Test snippet]
quantized, asymmetric fixed-point 8-bit number unsigned
void allocate() override
Allocate size specified by TensorInfo of CPU memory.
GEMMLowpMatrixMultiplyCoreValidationFixture< Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore, false, false, true > NEGEMMLowpBatchedMatMulFixture
Definition: GEMMLowp.cpp:54
Basic implementation of the tensor interface.
Definition: Tensor.h:37
validate(CLAccessor(output_state), expected_output)
virtual PaddingSize padding() const =0
Padding of tensor.
virtual ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info)=0
Set the quantization settings (scale and offset) of the tensor.
UniqueGemmCommon< Top, Tret > gemm(const GemmArgs &args, const OutputStage &os)
BorderSize PaddingSize
Container for 2D padding size.
Definition: Types.h:397
GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture< Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore > NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture
Definition: GEMMLowp.cpp:233
FIXTURE_DATA_TEST_CASE(RunSmall, CLAbsLayerFixture< half >, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)))
Definition: AbsLayer.cpp:50
Tensor packing service.
Definition: ITensorPack.h:39
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:43
static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info=GEMMInfo())
Static function to check if given info will lead to a valid configuration of NEGEMMLowpMatrixMultiply...
GEMM information class.
Definition: Types.h:2339
JoinDataset< T, U > concat(T &&dataset1, U &&dataset2)
Helper function to create a JoinDataset.
Definition: JoinDataset.h:160
TEST_CASE(FusedActivation, framework::DatasetMode::ALL)
Validate fused activation expecting the following behaviours:
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
Function to run Gemm on quantized types.
combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)))
Definition: AbsLayer.cpp:65