Skip to content

Latest commit

 

History

History
157 lines (137 loc) · 12.3 KB

USAGE.md

File metadata and controls

157 lines (137 loc) · 12.3 KB
Raw

How to use the scripts

Generate Benchmark (Simple preprocessing)

  • utils/fetch_aiger.py : Fetch the benchmark from the hwmcc, and simplly preprocess it (processed data should be in benchmark_folder/ folder)
    • usage: cd utils && python fetch_aiger.py
    • note:
      • change aag_dir (dump folder), csv_file (running info and aiger location prefix) and aag_list[i] (aiger files location) in the script to fetch different benchmarks. Modify fetch_aig_from_csv to ajust the preprocessing methods (only unsat? only sat? Only hard cases?). Modify simplify to determine whether convert aig to aag
      • Comment out aag_list = [aag for aag in aag_list if aag.split('/')[-1] in hard_aag_list] if we don't want to filter out the some cases
      • Choose what filter method you want to use in fetch_aig_from_csv()

Build Dataset (Cex -> Graph)

  • build_dataset.py : Build the dataset from the benchmark
    • usage: python build_dataset.py --model-checker <model-checker> --simplification-level <simplification-level> --benchmark <benchmark> --ground_truth_folder_prefix <ground_truth_folder_prefix> --subset_range <subset_range>
    • parameters:
      • --model-checker : The model checker to use (e.g. abc, ic3ref)
      • --simplification-level : The simplification level to use (e.g. thorough, deep, moderate, slight , naive)
      • --benchmark : The benchmark to use in benchmark_folder (e.g. hwmcc2007_all, hwmcc2020_all, hwmcc2020_all_only_unsat, hwmcc2020_small)
      • --ground_truth_folder_prefix : The prefix of the ground truth folder (e.g. /data/guangyuh/coding_env/AIG2INV/AIG2INV_main/ground_truth/hwmcc20_abc_7200_result, /data/guangyuh/coding_env/AIG2INV/AIG2INV_main/ground_truth/hwmcc20_abc_7200_result, /data/guangyuh/coding_env/AIG2INV/AIG2INV_main/clause-learning/data-collect/hwmcc07-7200-result/output/tip/, /data/guangyuh/coding_env/AIG2INV/AIG2INV_main/clause-learning/data-collect/hwmcc07-7200-result/output/tip/)
      • --subset_range : The range of data in benchmark to generate (e.g. 1, 23)
    • note:
      • There exists a list named AigCaseBlackList, which can exclude some cases from the benchmark (e.g. using utils/check_proc.sh to find those hard-extraction cases, and add them to the list. echo "$count" or echo "$aag_files_and_count" after sourcing)
      • Delete the existing dataset_xxxx/ folder before running the script

Train the Model

select which model to use (currently only neurograph is available)

  • train_neurograph/train.py: Train the model

    • usage: python train.py --task-name <task-name> --local_rank <local_rank> --dim <dim> --n_rounds <n_rounds> --epochs <epochs> --inf_dev <inf_dev> --gen_log <gen_log> --log-dir <log-dir> --model-dir <model-dir> --data-dir <data-dir> --restore <restore> --train-file <train-file> --val-file <val-file> --mode <mode> --gpu-id <gpu-id> --batch-size <batch-size> --pos-weight <pos-weight> --lr <lr>
    • parameters:
      • --task-name : The task name (e.g. neuropdr)
      • --local_rank : The local rank for dpp (e.g. -1)
      • --dim : The dimension of variable and clause embeddings (e.g. 128)
      • --n_rounds : The number of rounds of message passing (e.g. 26)
      • --epochs : The number of epochs (e.g. 10)
      • --inf_dev : The device to use (e.g. gpu)
      • --gen_log : The log file to use (e.g. log/data_maker.log)
      • --log-dir : The log folder dir (e.g. log/)
      • --model-dir : The model folder dir (e.g. neurograph_model/)
      • --data-dir : The data folder dir (e.g. dataset/)
      • --restore : The model to continue training (e.g. neuropdr_2021-01-06_07:56:51_last.pth.tar)
      • --train-file : The train file dir (e.g. dataset_hwmcc2020_small_abc_slight_1)
      • --val-file : The validation file dir (e.g. dataset_hwmcc2020_small_abc_slight_1)
      • --mode : The mode to use (e.g. train, debug)
      • --gpu-id : The gpu id to use (e.g. 0,1)
      • --batch-size : The batch size to use (e.g. 2)
      • --pos-weight : The positive weight in BCEWithLogitsLoss (e.g. 1.0)
      • --lr : The learning rate to use (e.g. 0.00001)

  • train_gcn/train.py parser.add_argument('--dataset', type=str, default=None, help='dataset name') # no need if load pickle parser.add_argument('--load-pickle', type=str, default=None, help='load pickle file name') parser.add_argument('--dump-pickle-name', type=str, default=None, help='dump pickle file name') # no need if load pickle parser.add_argument('--model-name', type=str, default=None, help='model name to save')

    • usage: python train.py --dataset <dataset> (optional: --load-pickle, --model-name, --dump-pickle-name <dump-pickle-name> --dual , etc.)
    • parameters:
      • --dataset : The dataset to use (e.g. /data/guangyuh/coding_env/AIG2INV/AIG2INV_main/dataset_hwmcc2007_tip_ic3ref_no_simplification_0-22/bad_cube_cex2graph/expr_to_build_graph/)
      • --load-pickle : The pickle file to load (e.g. hwmcc07_tip_ic3ref_no_simplification_0-22.pickle)
      • --dump-pickle-name : The pickle file to dump (e.g. hwmcc07_tip_ic3ref_no_simplification_0-22.pickle)
      • --model-name : The model name to save (e.g. hwmcc07_ic3ref_tip_no_simplification_0-22.pt)
      • --dual : Use dual GNN or not (One for pre-defined feature, one for unsuperivsed embedding)
    • note:
      • Adjust DATASET_SPLIT if you just want to test the code
      • Adjust h = self.dropout(h) in train_gcn/GNN_Model.py and WEIGHT_DECAY in train_gcn/train.py if you want to prevent overfitting
      • Define how to split the dataset by yourself (using train_test_split())
      • Uncomment prof.step() in training loop if you want to profile the code
      • Check threshold_finder = ThresholdFinder(val_dataloader, model, device), confirm which dataloader you want to use in testing
      • Check Validation loop in training loop, uncomment if you need it (e.g. early stop or hyperparameter fine tuning)
      • Check node embedding method that you want to employ in graph_list = employ_graph_embedding(graph_list,args)
      • Comment out default args if you want to input them in command line
      • Comment out G = G.to_undirected() if you want to use directed graph
      • Uncomment graph_list_struc_feat = employ_graph_embedding(graph_list,args) if you want to use additional node embedding
      • If using dual GNN, choose the GNN model start with dual (), other will fail. (e.g.DualGCNModle,DualGraphSAGEModel,etc.)

Validate the Model

  • main.py : Validate the model
    • usage: python main.py --threshold <threshold> --selected-built-dataset <selected-built-dataset> --NN-model <NN-model> --gpu-id <gpu-id> (optional: --compare_with_abc, --re-predict, etc.)
    • parameters:
      • --threshold : The threshold to use (e.g. 0.5)
      • --selected-built-dataset : The built dataset with graph generated to use (e.g. dataset_hwmcc2020_small_abc_slight_1)
      • --NN-model : The NN model to use (e.g. neuropdr_2023-01-06_07:56:51_last.pth.tar)
      • --gpu-id : The gpu id to use (e.g. 0,1)
      • --compare_with_abc : Compare the result with abc (optional)
      • --re-predict : Re-predict the data (optional)
      • --compare_with_ic3ref_basic_generalization: Compare the result with ic3ref basic generalization (optional)
      • --compare_with_nnic3_basic_generalization': Compare the result with nnic3 basic generalization (optional)
      • --aig-case-name: Test the single aiger (optional)
    • note:
      • delete the duplicate data in case4comp/ folder before runing the script (if this script has been run before with the same arguments)

Analyze the Result

  • result_analyzer : analyze the result
    • usage: python result_analyzer.py --log-file <log-file>
    • parameters:
      • --log-file : The log file to use (e.g. compare_with_ic3ref.csv)

Debug

Debug the collect.py

  • data2dataset/cex2smt2/collect.py: Convert aig+inv to graph
    • usage: python collect.py --aag <aag> --generalize <generalize> --cnf <cnf> --generate_smt2 <generate_smt2> --inv-correctness-check <inv-correctness-check> --run-mode <run-mode> --model-checker <model-checker> --thorough-simplification <thorough-simplification> --deep-simplification <deep-simplification> --moderate-simplification <moderate-simplification> --slight-simplification <slight-simplification> --naive-simplification <naive-simplification> --ground-truth-folder-prefix <ground-truth-folder-prefix> --dump-folder-prefix <dump-folder-prefix>
    • parameters:
      • --aag : The aag file to use (e.g. tip2_2.aag)
      • --generalize : Generalize the predesessor (e.g. True)
      • --cnf : The cnf file to use (e.g. tip2_2.cnf)
      • --generate_smt2 : Generate smt2 file (e.g. True)
      • --inv-correctness-check : Check the correctness of the invariant (e.g. True)
      • --run-mode : Normal or debug. Debug model will exit after inv correctness check (e.g. debug)
      • --model-checker : The model checker to use (e.g. ic3ref)
      • --thorough-simplification : Use sympy in tr simplification + aig operator simplification during tr construction + z3 simplification + counterexample cube simplification (e.g. False)
      • --deep-simplification : Use sympy in tr simplification + aig operator simplification during tr construction + z3 simplification (e.g. False)
      • --moderate-simplification : Aig operator simplification during tr construction + z3 simplification (e.g. False)
      • --slight-simplification : Z3 simplification + ternary simulation (e.g. False)
      • --naive-simplification : Only use sympy to simplify the counterexample cube (e.g. False)
      • --ground-truth-folder-prefix : The prefix of the ground truth folder (e.g. /data/guangyuh/coding_env/AIG2INV/AIG2INV_main/clause-learning/data-collect/hwmcc07-7200-result/output/tip/)
      • --dump-folder-prefix : The prefix of the dump folder (e.g. /data/guangyuh/coding_env/AIG2INV/AIG2INV_main/dataset)
    • note:
      • _check_tr_correctness_after_simplification() should be called if the simplification is turned on or debug

Others

Visualize the Result/Clean Log

  • tool_box.py: Visualize the result/clean log
    • usage: python tool_box.py --clean_trivial_log <clean_trivial_log> --calculate_pickle_number <calculate_pickle_number> --json2mermaid <json2mermaid> --file_path <file_path>
    • parameters:
      • --clean_trivial_log : Clean trivial log (e.g. True)
      • --calculate_pickle_number : Calculate the number of pickle files (e.g. True)
      • --json2mermaid : Convert json to mermaid (e.g. True)
      • --file_path : The file path to use (e.g. dataset_hwmcc2020_small_abc_slight_1)

Symbolic Regression

  • data2dataset/cex2smt2/symbolic_reg_model.py: Symbolic regression model
    • usage: python symbolic_reg_model.py --model-file <model-file> --validate <validate> --model <model>
    • parameters:
      • --model-file : The path to the model file (e.g. symbolic_reg_model_2021-01-06_07:56:51_last.pth.tar)
      • --validate : Determin whether to validate the model (e.g. True)
      • --model : Determin which model to use (e.g. 1)

AutoRegressor

  • data2dataset/cex2smt2/pytorch_auto_regression.py: Auto-pytorch regression model
  • data2dataset/cex2smt2/sklearn_auto_regression.py: Auto-sklearn regression model

SAT Model Uniform Distribution Sampling

  • data2dataset/cex2smt2/ModelSampler.py: SAT model uniform distribution sampling
    • usage: python ModelSampler.py --smt2-file <smt2-file>
    • parameters:
      • --smt2-file : The path to the smt2 file (e.g. tip2_2.smt2)
  • data2dataset/cex2smt2/ModelSample_real_ud : Using crytominisat to perform real uniform distribution sampling
    • note: This script is under development. It is not used in the paper.

Find complex case (not used in the paper)

  • utils/check_proc.sh: check the process (normally used to find complex case if build_data.py has been run for a long time)
    • usage: source ./check_proc.sh && echo "$count" or source ./check_proc.sh && echo "$aag_files_and_count"