1 // clang-format off
2 // Generated file (from: lstm2_state2.mod.py). Do not edit
CreateModel(Model * model)3 void CreateModel(Model *model) {
4   OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
5   OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
6   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
7   OperandType type3(Type::TENSOR_FLOAT32, {0});
8   OperandType type4(Type::TENSOR_FLOAT32, {4});
9   OperandType type5(Type::TENSOR_FLOAT32, {0, 0});
10   OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
11   OperandType type7(Type::INT32, {});
12   OperandType type8(Type::FLOAT32, {});
13   OperandType type9(Type::TENSOR_FLOAT32, {1, 12});
14   // Phase 1, operands
15   auto input = model->addOperand(&type0);
16   auto input_to_input_weights = model->addOperand(&type1);
17   auto input_to_forget_weights = model->addOperand(&type1);
18   auto input_to_cell_weights = model->addOperand(&type1);
19   auto input_to_output_weights = model->addOperand(&type1);
20   auto recurrent_to_intput_weights = model->addOperand(&type2);
21   auto recurrent_to_forget_weights = model->addOperand(&type2);
22   auto recurrent_to_cell_weights = model->addOperand(&type2);
23   auto recurrent_to_output_weights = model->addOperand(&type2);
24   auto cell_to_input_weights = model->addOperand(&type3);
25   auto cell_to_forget_weights = model->addOperand(&type4);
26   auto cell_to_output_weights = model->addOperand(&type4);
27   auto input_gate_bias = model->addOperand(&type4);
28   auto forget_gate_bias = model->addOperand(&type4);
29   auto cell_gate_bias = model->addOperand(&type4);
30   auto output_gate_bias = model->addOperand(&type4);
31   auto projection_weights = model->addOperand(&type5);
32   auto projection_bias = model->addOperand(&type3);
33   auto output_state_in = model->addOperand(&type6);
34   auto cell_state_in = model->addOperand(&type6);
35   auto activation_param = model->addOperand(&type7);
36   auto cell_clip_param = model->addOperand(&type8);
37   auto proj_clip_param = model->addOperand(&type8);
38   auto scratch_buffer = model->addOperand(&type9);
39   auto output_state_out = model->addOperand(&type6);
40   auto cell_state_out = model->addOperand(&type6);
41   auto output = model->addOperand(&type6);
42   // Phase 2, operations
43   static int32_t activation_param_init[] = {4};
44   model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
45   static float cell_clip_param_init[] = {0.0f};
46   model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1);
47   static float proj_clip_param_init[] = {0.0f};
48   model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1);
49   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});
50   // Phase 3, inputs and outputs
51   model->identifyInputsAndOutputs(
52     {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},
53     {scratch_buffer, output_state_out, cell_state_out, output});
54   assert(model->isValid());
55 }
56 
is_ignored(int i)57 inline bool is_ignored(int i) {
58   static std::set<int> ignore = {0, 1, 2};
59   return ignore.find(i) != ignore.end();
60 }
61 
CreateModel_dynamic_output_shape(Model * model)62 void CreateModel_dynamic_output_shape(Model *model) {
63   OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
64   OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
65   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
66   OperandType type3(Type::TENSOR_FLOAT32, {0});
67   OperandType type4(Type::TENSOR_FLOAT32, {4});
68   OperandType type5(Type::TENSOR_FLOAT32, {0, 0});
69   OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
70   OperandType type7(Type::INT32, {});
71   OperandType type8(Type::FLOAT32, {});
72   // Phase 1, operands
73   auto input = model->addOperand(&type0);
74   auto input_to_input_weights = model->addOperand(&type1);
75   auto input_to_forget_weights = model->addOperand(&type1);
76   auto input_to_cell_weights = model->addOperand(&type1);
77   auto input_to_output_weights = model->addOperand(&type1);
78   auto recurrent_to_intput_weights = model->addOperand(&type2);
79   auto recurrent_to_forget_weights = model->addOperand(&type2);
80   auto recurrent_to_cell_weights = model->addOperand(&type2);
81   auto recurrent_to_output_weights = model->addOperand(&type2);
82   auto cell_to_input_weights = model->addOperand(&type3);
83   auto cell_to_forget_weights = model->addOperand(&type4);
84   auto cell_to_output_weights = model->addOperand(&type4);
85   auto input_gate_bias = model->addOperand(&type4);
86   auto forget_gate_bias = model->addOperand(&type4);
87   auto cell_gate_bias = model->addOperand(&type4);
88   auto output_gate_bias = model->addOperand(&type4);
89   auto projection_weights = model->addOperand(&type5);
90   auto projection_bias = model->addOperand(&type3);
91   auto output_state_in = model->addOperand(&type6);
92   auto cell_state_in = model->addOperand(&type6);
93   auto activation_param = model->addOperand(&type7);
94   auto cell_clip_param = model->addOperand(&type8);
95   auto proj_clip_param = model->addOperand(&type8);
96   auto scratch_buffer = model->addOperand(&type5);
97   auto output_state_out = model->addOperand(&type5);
98   auto cell_state_out = model->addOperand(&type5);
99   auto output = model->addOperand(&type5);
100   // Phase 2, operations
101   static int32_t activation_param_init[] = {4};
102   model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
103   static float cell_clip_param_init[] = {0.0f};
104   model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1);
105   static float proj_clip_param_init[] = {0.0f};
106   model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1);
107   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});
108   // Phase 3, inputs and outputs
109   model->identifyInputsAndOutputs(
110     {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},
111     {scratch_buffer, output_state_out, cell_state_out, output});
112   assert(model->isValid());
113 }
114 
is_ignored_dynamic_output_shape(int i)115 inline bool is_ignored_dynamic_output_shape(int i) {
116   static std::set<int> ignore = {0, 1, 2};
117   return ignore.find(i) != ignore.end();
118 }
119 
120