1 // clang-format off 2 // Generated file (from: lstm3_relaxed.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 // Phase 4: set relaxed execution 56 model->relaxComputationFloat32toFloat16(true); 57 assert(model->isValid()); 58 } 59 60 inline bool is_ignored(int i) { 61 static std::set<int> ignore = {0}; 62 return ignore.find(i) != ignore.end(); 63 } 64 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 } 121 122 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
