ArmNN
 25.11
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ExpOperator.hpp File Reference
Include dependency graph for ExpOperator.hpp:
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Functions

TosaSerializationBasicBlock * ConvertExpOperator (const Layer *layer, const std::vector< const TensorInfo * > &inputs, const std::vector< const TensorInfo * > &outputs, const ElementwiseUnaryDescriptor *unaryDescriptor)

Function Documentation

◆ ConvertExpOperator()

TosaSerializationBasicBlock * ConvertExpOperator ( const Layer * layer,
const std::vector< const TensorInfo * > & inputs,
const std::vector< const TensorInfo * > & outputs,
const ElementwiseUnaryDescriptor * unaryDescriptor )

Definition at line 13 of file ExpOperator.cpp.

17{
18 if (unaryDescriptor->m_Operation != UnaryOperation::Exp)
19 {
20 throw armnn::Exception("ConvertExpOperator: Unsupported elementwise unary operation in descriptor.");
21 }
22
23 std::string inputName = std::string("input_");
24 std::string outputName = std::string("output0_");
25 std::string blockName = std::string("Op_EXP_block_") + GetUniqueTosaMappingID();
26
27 // If a layer is present then the block will be used for execution, so input and output names need to be determined
28 // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter.
29 if(layer != nullptr)
30 {
31 inputName = GenerateUniqueInputName(layer->GetInputSlot(0));
32 outputName = GenerateUniqueOutputName(*layer);
33 }
34
35 std::vector<TosaSerializationTensor*> tensors;
36 std::vector<TosaSerializationOperator*> operators;
37
38 float input_scale = inputs[0]->GetQuantizationScale();
39 float output_scale = outputs[0]->GetQuantizationScale();
40 int32_t input_zp = inputs[0]->GetQuantizationOffset();
41 int32_t output_zp = outputs[0]->GetQuantizationOffset();
42 DataType inputDType = inputs[0]->GetDataType();
43 if (inputDType == DataType::QAsymmS8 ||
44 inputDType == DataType::QSymmS8)
45 {
46 auto exp_func = [](float x) -> float { return std::exp(x); };
47 TosaTableAttribute attribute(
48 getTosaConst8bitTable(input_scale, input_zp, output_scale, output_zp, exp_func));
49 operators.push_back(new TosaSerializationOperator(tosa::Op_TABLE,
50 Attribute_TableAttribute,
51 &attribute,
52 {inputName},
53 {outputName}));
54 }
55 else if (inputDType == DataType::QSymmS16)
56 {
57 throw Exception("ConvertExpOperator() unsupported int 16 not implemented yet.");
58 // The following generates the table, tosa attribute and operator for int16 exponential.
59 // However, running the int16 EXP EndToEnd test causes incorrect output values.
60 // At the time of writing the EXP operator there is no requirment for int16 support.
61 // Points to enable int16 in the future:
62 // - TOSA specifies EXP int16 input must have int32 output
63 // - We potentially need a rescale after the int32 EXP output to convert back to int16.
64 /*
65 auto exp_func = [](float x) -> float { return std::exp(x); };
66 TosaTableAttribute attribute(
67 getTosaConst16bitTable<float>(input_scale, input_zp, output_scale, output_zp, exp_func));
68 operators.push_back(new TosaSerializationOperator(tosa::Op_TABLE,
69 Attribute_TableAttribute,
70 &attribute,
71 {inputName},
72 {outputName}));
73 */
74 }
75 else if (inputDType == DataType::Signed32 ||
76 inputDType == DataType::Signed64)
77 {
78 throw Exception(
79 "ConvertExpOperator() unsupported int 32. Only int 8 and int 16 quantized types are supported.");
80 }
81 // Floating point EXP operator
82 else
83 {
84 operators.push_back(new TosaSerializationOperator(tosa::Op_EXP,
85 Attribute_NONE,
86 nullptr,
87 {inputName},
88 {outputName}));
89 }
90
91 // Only add input tensor if connected layer is an input layer.
92 // As intermediate or constant tensors will be created separately.
93 // There also can't be duplicate tensor.
94 if(inputName.find("input_") != std::string::npos)
95 {
96 std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
97 DType inputDType0 = ArmNNToDType(inputDType);
98 tensors.push_back(new TosaSerializationTensor(inputName, inputShape0, inputDType0, {}));
99 }
100
101 std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
102
103 // Re-enable below line for int16 EXP support which requires int32 output in TOSA and remove second line.
104 // DType outputDType0 =
105 // (inputDType == DataType::QSymmS16) ? DType::DType_INT32 : ArmNNToDType(outputs[0]->GetDataType());
106 DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
107
108 tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
109
110 // operatorInputNames/operatorOutputNames ends up being the same as
111 // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings
112 return new TosaSerializationBasicBlock(blockName, // name
113 mainName, // region name
114 operators, // operators
115 tensors, // tensors
116 {inputName}, // inputs
117 {outputName}); // outputs
118}
std::string GenerateUniqueOutputName(const Layer &layer, uint32_t layerSlot=0)
const std::string mainName
DType ArmNNToDType(const DataType &type)
std::string GenerateUniqueInputName(const armnn::InputSlot &slot)
std::string GetUniqueTosaMappingID()
std::vector< int32_t > GetTosaTensorShape(const TensorShape &shape)
std::vector< int16_t > getTosaConst8bitTable(float input_scale, int32_t input_zp, float output_scale, int32_t output_zp, std::function< float(float)> func)
Base class for all ArmNN exceptions so that users can filter to just those.
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition Layer.hpp:337
DataType
Definition Types.hpp:49
UnaryOperation m_Operation
Specifies the elementwiseUnary operation to execute.

References ArmNNToDType(), GenerateUniqueInputName(), GenerateUniqueOutputName(), Layer::GetInputSlot(), getTosaConst8bitTable(), GetTosaTensorShape(), GetUniqueTosaMappingID(), ElementwiseUnaryDescriptor::m_Operation, and mainName.

Referenced by GetTosaMapping().