1 // Generated file (from: lstm_state.mod.py). Do not edit
CreateModel(Model * model)2 void CreateModel(Model *model) {
3 OperandType type8(Type::FLOAT32, {});
4 OperandType type7(Type::INT32, {});
5 OperandType type5(Type::TENSOR_FLOAT32, {0,0});
6 OperandType type3(Type::TENSOR_FLOAT32, {0});
7 OperandType type9(Type::TENSOR_FLOAT32, {1, 16});
8 OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
9 OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
10 OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
11 OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
12 OperandType type4(Type::TENSOR_FLOAT32, {4});
13 // Phase 1, operands
14 auto input = model->addOperand(&type0);
15 auto input_to_input_weights = model->addOperand(&type1);
16 auto input_to_forget_weights = model->addOperand(&type1);
17 auto input_to_cell_weights = model->addOperand(&type1);
18 auto input_to_output_weights = model->addOperand(&type1);
19 auto recurrent_to_intput_weights = model->addOperand(&type2);
20 auto recurrent_to_forget_weights = model->addOperand(&type2);
21 auto recurrent_to_cell_weights = model->addOperand(&type2);
22 auto recurrent_to_output_weights = model->addOperand(&type2);
23 auto cell_to_input_weights = model->addOperand(&type3);
24 auto cell_to_forget_weights = model->addOperand(&type3);
25 auto cell_to_output_weights = model->addOperand(&type3);
26 auto input_gate_bias = model->addOperand(&type4);
27 auto forget_gate_bias = model->addOperand(&type4);
28 auto cell_gate_bias = model->addOperand(&type4);
29 auto output_gate_bias = model->addOperand(&type4);
30 auto projection_weights = model->addOperand(&type5);
31 auto projection_bias = model->addOperand(&type3);
32 auto output_state_in = model->addOperand(&type6);
33 auto cell_state_in = model->addOperand(&type6);
34 auto activation_param = model->addOperand(&type7);
35 auto cell_clip_param = model->addOperand(&type8);
36 auto proj_clip_param = model->addOperand(&type8);
37 auto scratch_buffer = model->addOperand(&type9);
38 auto output_state_out = model->addOperand(&type6);
39 auto cell_state_out = model->addOperand(&type6);
40 auto output = model->addOperand(&type6);
41 // Phase 2, operations
42 static int32_t activation_param_init[] = {4};
43 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
44 static float cell_clip_param_init[] = {0.0f};
45 model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1);
46 static float proj_clip_param_init[] = {0.0f};
47 model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1);
48 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});
49 // Phase 3, inputs and outputs
50 model->identifyInputsAndOutputs(
51 {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},
52 {scratch_buffer, output_state_out, cell_state_out, output});
53 assert(model->isValid());
54 }
55
is_ignored(int i)56 bool is_ignored(int i) {
57 static std::set<int> ignore = {0};
58 return ignore.find(i) != ignore.end();
59 }
60