1 // clang-format off
2 // Generated file (from: unidirectional_sequence_lstm_norm_peephole_projection.mod.py). Do not edit
CreateModel(Model * model)3 void CreateModel(Model *model) {
4   OperandType type0(Type::TENSOR_FLOAT32, {3, 2, 5});
5   OperandType type1(Type::TENSOR_FLOAT32, {4, 5});
6   OperandType type10(Type::BOOL, {});
7   OperandType type11(Type::TENSOR_FLOAT32, {3, 2, 3});
8   OperandType type2(Type::TENSOR_FLOAT32, {4, 3});
9   OperandType type3(Type::TENSOR_FLOAT32, {4});
10   OperandType type4(Type::TENSOR_FLOAT32, {3, 4});
11   OperandType type5(Type::TENSOR_FLOAT32, {0});
12   OperandType type6(Type::TENSOR_FLOAT32, {2, 3});
13   OperandType type7(Type::TENSOR_FLOAT32, {2, 4});
14   OperandType type8(Type::INT32, {});
15   OperandType type9(Type::FLOAT32, {});
16   // Phase 1, operands
17   auto input = model->addOperand(&type0);
18   auto input_to_input_weights = model->addOperand(&type1);
19   auto input_to_forget_weights = model->addOperand(&type1);
20   auto input_to_cell_weights = model->addOperand(&type1);
21   auto input_to_output_weights = model->addOperand(&type1);
22   auto recurrent_to_intput_weights = model->addOperand(&type2);
23   auto recurrent_to_forget_weights = model->addOperand(&type2);
24   auto recurrent_to_cell_weights = model->addOperand(&type2);
25   auto recurrent_to_output_weights = model->addOperand(&type2);
26   auto cell_to_input_weights = model->addOperand(&type3);
27   auto cell_to_forget_weights = model->addOperand(&type3);
28   auto cell_to_output_weights = model->addOperand(&type3);
29   auto input_gate_bias = model->addOperand(&type3);
30   auto forget_gate_bias = model->addOperand(&type3);
31   auto cell_gate_bias = model->addOperand(&type3);
32   auto output_gate_bias = model->addOperand(&type3);
33   auto projection_weights = model->addOperand(&type4);
34   auto projection_bias = model->addOperand(&type5);
35   auto output_state_in = model->addOperand(&type6);
36   auto cell_state_in = model->addOperand(&type7);
37   auto activation_param = model->addOperand(&type8);
38   auto cell_clip_param = model->addOperand(&type9);
39   auto proj_clip_param = model->addOperand(&type9);
40   auto time_major_param = model->addOperand(&type10);
41   auto input_layer_norm_weights = model->addOperand(&type3);
42   auto forget_layer_norm_weights = model->addOperand(&type3);
43   auto cell_layer_norm_weights = model->addOperand(&type3);
44   auto output_layer_norm_weights = model->addOperand(&type3);
45   auto output = model->addOperand(&type11);
46   // Phase 2, operations
47   static int32_t activation_param_init[] = {4};
48   model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
49   static float cell_clip_param_init[] = {0.0f};
50   model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1);
51   static float proj_clip_param_init[] = {0.0f};
52   model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1);
53   static bool8 time_major_param_init[] = {true};
54   model->setOperandValue(time_major_param, time_major_param_init, sizeof(bool8) * 1);
55   model->addOperation(ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_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, time_major_param, input_layer_norm_weights, forget_layer_norm_weights, cell_layer_norm_weights, output_layer_norm_weights}, {output});
56   // Phase 3, inputs and outputs
57   model->identifyInputsAndOutputs(
58     {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, input_layer_norm_weights, forget_layer_norm_weights, cell_layer_norm_weights, output_layer_norm_weights},
59     {output});
60   assert(model->isValid());
61 }
62 
is_ignored(int i)63 inline bool is_ignored(int i) {
64   static std::set<int> ignore = {};
65   return ignore.find(i) != ignore.end();
66 }
67 
CreateModel_dynamic_output_shape(Model * model)68 void CreateModel_dynamic_output_shape(Model *model) {
69   OperandType type0(Type::TENSOR_FLOAT32, {3, 2, 5});
70   OperandType type1(Type::TENSOR_FLOAT32, {4, 5});
71   OperandType type10(Type::BOOL, {});
72   OperandType type12(Type::TENSOR_FLOAT32, {0, 0, 0});
73   OperandType type2(Type::TENSOR_FLOAT32, {4, 3});
74   OperandType type3(Type::TENSOR_FLOAT32, {4});
75   OperandType type4(Type::TENSOR_FLOAT32, {3, 4});
76   OperandType type5(Type::TENSOR_FLOAT32, {0});
77   OperandType type6(Type::TENSOR_FLOAT32, {2, 3});
78   OperandType type7(Type::TENSOR_FLOAT32, {2, 4});
79   OperandType type8(Type::INT32, {});
80   OperandType type9(Type::FLOAT32, {});
81   // Phase 1, operands
82   auto input = model->addOperand(&type0);
83   auto input_to_input_weights = model->addOperand(&type1);
84   auto input_to_forget_weights = model->addOperand(&type1);
85   auto input_to_cell_weights = model->addOperand(&type1);
86   auto input_to_output_weights = model->addOperand(&type1);
87   auto recurrent_to_intput_weights = model->addOperand(&type2);
88   auto recurrent_to_forget_weights = model->addOperand(&type2);
89   auto recurrent_to_cell_weights = model->addOperand(&type2);
90   auto recurrent_to_output_weights = model->addOperand(&type2);
91   auto cell_to_input_weights = model->addOperand(&type3);
92   auto cell_to_forget_weights = model->addOperand(&type3);
93   auto cell_to_output_weights = model->addOperand(&type3);
94   auto input_gate_bias = model->addOperand(&type3);
95   auto forget_gate_bias = model->addOperand(&type3);
96   auto cell_gate_bias = model->addOperand(&type3);
97   auto output_gate_bias = model->addOperand(&type3);
98   auto projection_weights = model->addOperand(&type4);
99   auto projection_bias = model->addOperand(&type5);
100   auto output_state_in = model->addOperand(&type6);
101   auto cell_state_in = model->addOperand(&type7);
102   auto activation_param = model->addOperand(&type8);
103   auto cell_clip_param = model->addOperand(&type9);
104   auto proj_clip_param = model->addOperand(&type9);
105   auto time_major_param = model->addOperand(&type10);
106   auto input_layer_norm_weights = model->addOperand(&type3);
107   auto forget_layer_norm_weights = model->addOperand(&type3);
108   auto cell_layer_norm_weights = model->addOperand(&type3);
109   auto output_layer_norm_weights = model->addOperand(&type3);
110   auto output = model->addOperand(&type12);
111   // Phase 2, operations
112   static int32_t activation_param_init[] = {4};
113   model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
114   static float cell_clip_param_init[] = {0.0f};
115   model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1);
116   static float proj_clip_param_init[] = {0.0f};
117   model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1);
118   static bool8 time_major_param_init[] = {true};
119   model->setOperandValue(time_major_param, time_major_param_init, sizeof(bool8) * 1);
120   model->addOperation(ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_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, time_major_param, input_layer_norm_weights, forget_layer_norm_weights, cell_layer_norm_weights, output_layer_norm_weights}, {output});
121   // Phase 3, inputs and outputs
122   model->identifyInputsAndOutputs(
123     {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, input_layer_norm_weights, forget_layer_norm_weights, cell_layer_norm_weights, output_layer_norm_weights},
124     {output});
125   assert(model->isValid());
126 }
127 
is_ignored_dynamic_output_shape(int i)128 inline bool is_ignored_dynamic_output_shape(int i) {
129   static std::set<int> ignore = {};
130   return ignore.find(i) != ignore.end();
131 }
132 
133