1 // clang-format off 2 // Generated file (from: lstm3_state.mod.py). Do not edit 3 void CreateModel(Model *model) { 4 OperandType type0(Type::TENSOR_FLOAT32, {2, 5}); 5 OperandType type1(Type::TENSOR_FLOAT32, {20, 5}); 6 OperandType type10(Type::TENSOR_FLOAT32, {2, 80}); 7 OperandType type2(Type::TENSOR_FLOAT32, {20, 16}); 8 OperandType type3(Type::TENSOR_FLOAT32, {20}); 9 OperandType type4(Type::TENSOR_FLOAT32, {16, 20}); 10 OperandType type5(Type::TENSOR_FLOAT32, {0}); 11 OperandType type6(Type::TENSOR_FLOAT32, {2, 16}); 12 OperandType type7(Type::TENSOR_FLOAT32, {2, 20}); 13 OperandType type8(Type::INT32, {}); 14 OperandType type9(Type::FLOAT32, {}); 15 // Phase 1, operands 16 auto input = model->addOperand(&type0); 17 auto input_to_input_weights = model->addOperand(&type1); 18 auto input_to_forget_weights = model->addOperand(&type1); 19 auto input_to_cell_weights = model->addOperand(&type1); 20 auto input_to_output_weights = model->addOperand(&type1); 21 auto recurrent_to_intput_weights = model->addOperand(&type2); 22 auto recurrent_to_forget_weights = model->addOperand(&type2); 23 auto recurrent_to_cell_weights = model->addOperand(&type2); 24 auto recurrent_to_output_weights = model->addOperand(&type2); 25 auto cell_to_input_weights = model->addOperand(&type3); 26 auto cell_to_forget_weights = model->addOperand(&type3); 27 auto cell_to_output_weights = model->addOperand(&type3); 28 auto input_gate_bias = model->addOperand(&type3); 29 auto forget_gate_bias = model->addOperand(&type3); 30 auto cell_gate_bias = model->addOperand(&type3); 31 auto output_gate_bias = model->addOperand(&type3); 32 auto projection_weights = model->addOperand(&type4); 33 auto projection_bias = model->addOperand(&type5); 34 auto output_state_in = model->addOperand(&type6); 35 auto cell_state_in = model->addOperand(&type7); 36 auto activation_param = model->addOperand(&type8); 37 auto cell_clip_param = model->addOperand(&type9); 38 auto proj_clip_param = model->addOperand(&type9); 39 auto scratch_buffer = model->addOperand(&type10); 40 auto output_state_out = model->addOperand(&type6); 41 auto cell_state_out = model->addOperand(&type7); 42 auto output = model->addOperand(&type6); 43 // Phase 2, operations 44 static int32_t activation_param_init[] = {4}; 45 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); 46 static float cell_clip_param_init[] = {0.0f}; 47 model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); 48 static float proj_clip_param_init[] = {0.0f}; 49 model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); 50 model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); 51 // Phase 3, inputs and outputs 52 model->identifyInputsAndOutputs( 53 {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, 54 {scratch_buffer, output_state_out, cell_state_out, output}); 55 assert(model->isValid()); 56 } 57 58 inline bool is_ignored(int i) { 59 static std::set<int> ignore = {0}; 60 return ignore.find(i) != ignore.end(); 61 } 62 63 void CreateModel_dynamic_output_shape(Model *model) { 64 OperandType type0(Type::TENSOR_FLOAT32, {2, 5}); 65 OperandType type1(Type::TENSOR_FLOAT32, {20, 5}); 66 OperandType type11(Type::TENSOR_FLOAT32, {0, 0}); 67 OperandType type2(Type::TENSOR_FLOAT32, {20, 16}); 68 OperandType type3(Type::TENSOR_FLOAT32, {20}); 69 OperandType type4(Type::TENSOR_FLOAT32, {16, 20}); 70 OperandType type5(Type::TENSOR_FLOAT32, {0}); 71 OperandType type6(Type::TENSOR_FLOAT32, {2, 16}); 72 OperandType type7(Type::TENSOR_FLOAT32, {2, 20}); 73 OperandType type8(Type::INT32, {}); 74 OperandType type9(Type::FLOAT32, {}); 75 // Phase 1, operands 76 auto input = model->addOperand(&type0); 77 auto input_to_input_weights = model->addOperand(&type1); 78 auto input_to_forget_weights = model->addOperand(&type1); 79 auto input_to_cell_weights = model->addOperand(&type1); 80 auto input_to_output_weights = model->addOperand(&type1); 81 auto recurrent_to_intput_weights = model->addOperand(&type2); 82 auto recurrent_to_forget_weights = model->addOperand(&type2); 83 auto recurrent_to_cell_weights = model->addOperand(&type2); 84 auto recurrent_to_output_weights = model->addOperand(&type2); 85 auto cell_to_input_weights = model->addOperand(&type3); 86 auto cell_to_forget_weights = model->addOperand(&type3); 87 auto cell_to_output_weights = model->addOperand(&type3); 88 auto input_gate_bias = model->addOperand(&type3); 89 auto forget_gate_bias = model->addOperand(&type3); 90 auto cell_gate_bias = model->addOperand(&type3); 91 auto output_gate_bias = model->addOperand(&type3); 92 auto projection_weights = model->addOperand(&type4); 93 auto projection_bias = model->addOperand(&type5); 94 auto output_state_in = model->addOperand(&type6); 95 auto cell_state_in = model->addOperand(&type7); 96 auto activation_param = model->addOperand(&type8); 97 auto cell_clip_param = model->addOperand(&type9); 98 auto proj_clip_param = model->addOperand(&type9); 99 auto scratch_buffer = model->addOperand(&type11); 100 auto output_state_out = model->addOperand(&type11); 101 auto cell_state_out = model->addOperand(&type11); 102 auto output = model->addOperand(&type11); 103 // Phase 2, operations 104 static int32_t activation_param_init[] = {4}; 105 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); 106 static float cell_clip_param_init[] = {0.0f}; 107 model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); 108 static float proj_clip_param_init[] = {0.0f}; 109 model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); 110 model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); 111 // Phase 3, inputs and outputs 112 model->identifyInputsAndOutputs( 113 {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, 114 {scratch_buffer, output_state_out, cell_state_out, output}); 115 assert(model->isValid()); 116 } 117 118 inline bool is_ignored_dynamic_output_shape(int i) { 119 static std::set<int> ignore = {0}; 120 return ignore.find(i) != ignore.end(); 121 } 122 123
