runtime/test/generated/models/rnn.model.cpp

// Generated from rnn.mod.py
// DO NOT EDIT
// clang-format off
#include "TestGenerated.h"

namespace generated_tests::rnn {

void CreateModel(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {2, 8});
  OperandType type1(Type::TENSOR_FLOAT32, {16, 8});
  OperandType type2(Type::TENSOR_FLOAT32, {16, 16});
  OperandType type3(Type::TENSOR_FLOAT32, {16});
  OperandType type4(Type::TENSOR_FLOAT32, {2, 16});
  OperandType type5(Type::INT32, {});
  // Phase 1, operands
  auto input = model->addOperand(&type0);
  auto weights = model->addOperand(&type1);
  auto recurrent_weights = model->addOperand(&type2);
  auto bias = model->addOperand(&type3);
  auto hidden_state_in = model->addOperand(&type4);
  auto activation_param = model->addOperand(&type5);
  auto hidden_state_out = model->addOperand(&type4);
  auto output = model->addOperand(&type4);
  // Phase 2, operations
  static int32_t activation_param_init[] = {1};
  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input, weights, recurrent_weights, bias, hidden_state_in},
    {hidden_state_out, output});
  assert(model->isValid());
}

bool is_ignored(int i) {
  static std::set<int> ignore = {0};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::rnn
namespace generated_tests::rnn {

void CreateModel_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {2, 8});
  OperandType type1(Type::TENSOR_FLOAT32, {16, 8});
  OperandType type2(Type::TENSOR_FLOAT32, {16, 16});
  OperandType type3(Type::TENSOR_FLOAT32, {16});
  OperandType type4(Type::TENSOR_FLOAT32, {2, 16});
  OperandType type5(Type::INT32, {});
  OperandType type6(Type::TENSOR_FLOAT32, {0, 0});
  // Phase 1, operands
  auto input = model->addOperand(&type0);
  auto weights = model->addOperand(&type1);
  auto recurrent_weights = model->addOperand(&type2);
  auto bias = model->addOperand(&type3);
  auto hidden_state_in = model->addOperand(&type4);
  auto activation_param = model->addOperand(&type5);
  auto hidden_state_out = model->addOperand(&type6);
  auto output = model->addOperand(&type6);
  // Phase 2, operations
  static int32_t activation_param_init[] = {1};
  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input, weights, recurrent_weights, bias, hidden_state_in},
    {hidden_state_out, output});
  assert(model->isValid());
}

bool is_ignored_dynamic_output_shape(int i) {
  static std::set<int> ignore = {0};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::rnn