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1 // clang-format off
2 // Generated file (from: lstm3_state2_relaxed.mod.py). Do not edit
CreateModel(Model * model)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   // Phase 4: set relaxed execution
56   model->relaxComputationFloat32toFloat16(true);
57   assert(model->isValid());
58 }
59 
is_ignored(int i)60 inline bool is_ignored(int i) {
61   static std::set<int> ignore = {0};
62   return ignore.find(i) != ignore.end();
63 }
64 
CreateModel_dynamic_output_shape(Model * model)65 void CreateModel_dynamic_output_shape(Model *model) {
66   OperandType type0(Type::TENSOR_FLOAT32, {2, 5});
67   OperandType type1(Type::TENSOR_FLOAT32, {20, 5});
68   OperandType type11(Type::TENSOR_FLOAT32, {0, 0});
69   OperandType type2(Type::TENSOR_FLOAT32, {20, 16});
70   OperandType type3(Type::TENSOR_FLOAT32, {20});
71   OperandType type4(Type::TENSOR_FLOAT32, {16, 20});
72   OperandType type5(Type::TENSOR_FLOAT32, {0});
73   OperandType type6(Type::TENSOR_FLOAT32, {2, 16});
74   OperandType type7(Type::TENSOR_FLOAT32, {2, 20});
75   OperandType type8(Type::INT32, {});
76   OperandType type9(Type::FLOAT32, {});
77   // Phase 1, operands
78   auto input = model->addOperand(&type0);
79   auto input_to_input_weights = model->addOperand(&type1);
80   auto input_to_forget_weights = model->addOperand(&type1);
81   auto input_to_cell_weights = model->addOperand(&type1);
82   auto input_to_output_weights = model->addOperand(&type1);
83   auto recurrent_to_intput_weights = model->addOperand(&type2);
84   auto recurrent_to_forget_weights = model->addOperand(&type2);
85   auto recurrent_to_cell_weights = model->addOperand(&type2);
86   auto recurrent_to_output_weights = model->addOperand(&type2);
87   auto cell_to_input_weights = model->addOperand(&type3);
88   auto cell_to_forget_weights = model->addOperand(&type3);
89   auto cell_to_output_weights = model->addOperand(&type3);
90   auto input_gate_bias = model->addOperand(&type3);
91   auto forget_gate_bias = model->addOperand(&type3);
92   auto cell_gate_bias = model->addOperand(&type3);
93   auto output_gate_bias = model->addOperand(&type3);
94   auto projection_weights = model->addOperand(&type4);
95   auto projection_bias = model->addOperand(&type5);
96   auto output_state_in = model->addOperand(&type6);
97   auto cell_state_in = model->addOperand(&type7);
98   auto activation_param = model->addOperand(&type8);
99   auto cell_clip_param = model->addOperand(&type9);
100   auto proj_clip_param = model->addOperand(&type9);
101   auto scratch_buffer = model->addOperand(&type11);
102   auto output_state_out = model->addOperand(&type11);
103   auto cell_state_out = model->addOperand(&type11);
104   auto output = model->addOperand(&type11);
105   // Phase 2, operations
106   static int32_t activation_param_init[] = {4};
107   model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
108   static float cell_clip_param_init[] = {0.0f};
109   model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1);
110   static float proj_clip_param_init[] = {0.0f};
111   model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1);
112   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});
113   // Phase 3, inputs and outputs
114   model->identifyInputsAndOutputs(
115     {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},
116     {scratch_buffer, output_state_out, cell_state_out, output});
117   // Phase 4: set relaxed execution
118   model->relaxComputationFloat32toFloat16(true);
119   assert(model->isValid());
120 }
121 
is_ignored_dynamic_output_shape(int i)122 inline bool is_ignored_dynamic_output_shape(int i) {
123   static std::set<int> ignore = {0};
124   return ignore.find(i) != ignore.end();
125 }
126 
127