1 // clang-format off 2 // Generated file (from: rnn_state_relaxed.mod.py). Do not edit 3 void CreateModel(Model *model) { 4 OperandType type0(Type::TENSOR_FLOAT32, {2, 8}); 5 OperandType type1(Type::TENSOR_FLOAT32, {16, 8}); 6 OperandType type2(Type::TENSOR_FLOAT32, {16, 16}); 7 OperandType type3(Type::TENSOR_FLOAT32, {16}); 8 OperandType type4(Type::TENSOR_FLOAT32, {2, 16}); 9 OperandType type5(Type::INT32, {}); 10 // Phase 1, operands 11 auto input = model->addOperand(&type0); 12 auto weights = model->addOperand(&type1); 13 auto recurrent_weights = model->addOperand(&type2); 14 auto bias = model->addOperand(&type3); 15 auto hidden_state_in = model->addOperand(&type4); 16 auto activation_param = model->addOperand(&type5); 17 auto hidden_state_out = model->addOperand(&type4); 18 auto output = model->addOperand(&type4); 19 // Phase 2, operations 20 static int32_t activation_param_init[] = {1}; 21 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output}); 23 // Phase 3, inputs and outputs 24 model->identifyInputsAndOutputs( 25 {input, weights, recurrent_weights, bias, hidden_state_in}, 26 {hidden_state_out, output}); 27 // Phase 4: set relaxed execution 28 model->relaxComputationFloat32toFloat16(true); 29 assert(model->isValid()); 30 } 31 32 inline bool is_ignored(int i) { 33 static std::set<int> ignore = {0}; 34 return ignore.find(i) != ignore.end(); 35 } 36 37 void CreateModel_dynamic_output_shape(Model *model) { 38 OperandType type0(Type::TENSOR_FLOAT32, {2, 8}); 39 OperandType type1(Type::TENSOR_FLOAT32, {16, 8}); 40 OperandType type2(Type::TENSOR_FLOAT32, {16, 16}); 41 OperandType type3(Type::TENSOR_FLOAT32, {16}); 42 OperandType type4(Type::TENSOR_FLOAT32, {2, 16}); 43 OperandType type5(Type::INT32, {}); 44 OperandType type6(Type::TENSOR_FLOAT32, {0, 0}); 45 // Phase 1, operands 46 auto input = model->addOperand(&type0); 47 auto weights = model->addOperand(&type1); 48 auto recurrent_weights = model->addOperand(&type2); 49 auto bias = model->addOperand(&type3); 50 auto hidden_state_in = model->addOperand(&type4); 51 auto activation_param = model->addOperand(&type5); 52 auto hidden_state_out = model->addOperand(&type6); 53 auto output = model->addOperand(&type6); 54 // Phase 2, operations 55 static int32_t activation_param_init[] = {1}; 56 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); 57 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output}); 58 // Phase 3, inputs and outputs 59 model->identifyInputsAndOutputs( 60 {input, weights, recurrent_weights, bias, hidden_state_in}, 61 {hidden_state_out, output}); 62 // Phase 4: set relaxed execution 63 model->relaxComputationFloat32toFloat16(true); 64 assert(model->isValid()); 65 } 66 67 inline bool is_ignored_dynamic_output_shape(int i) { 68 static std::set<int> ignore = {0}; 69 return ignore.find(i) != ignore.end(); 70 } 71 72
