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Tool for suggesting lemma names in Coq verification projects

License: MIT License

Python 99.38% Makefile 0.07% Shell 0.56%

coq deep-learning machine-learning mathcomp name-generation neural-networks pytorch serapi

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brando90 sailfish009 desperatesonic juanerolon

roosterize's Issues

IndexError: list index out of range

I get this when I try to get suggestions for the file wmso.v in the RegLang project:

$ python -m roosterize.main suggest_lemmas  --project=../reglang  --serapi-options="-Q theories,RegLang"  --model-dir=./models/roosterize-ta  --output=./output
>>>>> Extracting lemmas ...
[   371W]roosterize.data.DataMiner: /home/palmskog/src/coq/misc/roosterize/output/raw-data already exists, will overwrite the files.
100%|...| 12/12 [01:04<00:00,  6.03s/it]
100%|...| 12/12 [00:14<00:00,  1.24s/it]
>>>>> Initializing model ...
[ 80674W]roosterize.ml.naming.OpenNMTMultiSourceForNaming: Cuda is not available
>>>>> Processing data ...
>>>>> Applying model ...
/home/palmskog/opt/miniconda3/envs/pytorch/lib/python3.7/site-packages/seutil/project/../../torchtext/data/field.py:359: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  var = torch.tensor(arr, dtype=self.dtype, device=device)
Traceback (most recent call last):
  File "/home/palmskog/opt/miniconda3/envs/pytorch/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/palmskog/opt/miniconda3/envs/pytorch/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/palmskog/src/coq/misc/roosterize/roosterize/main.py", line 250, in <module>
    CliUtils.main(sys.argv[1:], globals(), normalize_options)
  File "/home/palmskog/opt/miniconda3/envs/pytorch/lib/python3.7/site-packages/seutil/CliUtils.py", line 117, in main
    actions[action](**options)
  File "/home/palmskog/src/coq/misc/roosterize/roosterize/main.py", line 141, in suggest_lemmas
    model.eval(output_dir/"eval-processed-data", model_dir, output_dir/"eval-result")
  File "/home/palmskog/src/coq/misc/roosterize/roosterize/ml/naming/NamingModelBase.py", line 100, in eval
    predictions_results: List[List[Tuple[str, float]]] = self.eval_impl(processed_data_dir, model_dir, self.BEAM_SEARCH_SIZE, self.K)
  File "/home/palmskog/src/coq/misc/roosterize/roosterize/ml/naming/OpenNMTMultiSourceForNaming.py", line 835, in eval_impl
    attn_debug=opt.attn_debug
  File "/home/palmskog/src/coq/misc/roosterize/roosterize/ml/onmt/MultiSourceTranslator.py", line 194, in translate
    translations = xlation_builder.from_batch(batch_data)
  File "/home/palmskog/src/coq/misc/roosterize/roosterize/ml/onmt/MultiSourceTranslationBuilder.py", line 90, in from_batch
    for n in range(self.n_best)]
  File "/home/palmskog/src/coq/misc/roosterize/roosterize/ml/onmt/MultiSourceTranslationBuilder.py", line 90, in <listcomp>
    for n in range(self.n_best)]
  File "/home/palmskog/src/coq/misc/roosterize/roosterize/ml/onmt/MultiSourceTranslationBuilder.py", line 31, in _build_target_tokens
    tokens.append(src_vocab.itos[tok - len(vocab)])
IndexError: list index out of range

question about kernel tree

hello! I am really interested in the "tree chopping" methods mentioned in your paper. However I know very little about the internal data structures of Coq, I wonder is there any documentation easy to read about this?
for example, when I use coq_serapi to input :
forall xyz:nat, xyz<3
I got output:
(Prod (Name (Id xyz)) (Ind (((Mutind (MPfile (DirPath ((Id Datatypes) (Id Init) (Id Coq)))) (DirPath ()) (Id nat)) 0) (Instance ()))) (App (Const ((Constant (MPfile (DirPath ((Id Peano) (Id Init) (Id Coq)))) (DirPath ()) (Id lt)) (Instance ()))) ((Rel 1) (App (Construct ((((Mutind (MPfile (DirPath ((Id Datatypes) (Id Init) (Id Coq)))) (DirPath ()) (Id nat)) 0) 2) (Instance ()))) ((App (Construct ((((Mutind (MPfile (DirPath ((Id Datatypes) (Id Init) (Id Coq)))) (DirPath ()) (Id nat)) 0) 2) (Instance ()))) ((App (Construct ((((Mutind (MPfile (DirPath ((Id Datatypes) (Id Init) (Id Coq)))) (DirPath ()) (Id nat)) 0) 2) (Instance ()))) ((Construct ((((Mutind (MPfile (DirPath ((Id Datatypes) (Id Init) (Id Coq)))) (DirPath ()) (Id nat)) 0) 1) (Instance ()))))))))))))
This looks so terrifying! I really wonder about the meanings of Mutind, MPfile and Rel blablabla....
So I can better understand what parts of the kernel tree is important for information extraction.
looking forward to your reply, thanks a lot!

Feature: tell Roosterize to build Coq projects in batch mode

Right now, when Roosterize suggests lemma names for a complete project, that project has to be built first (usually using make in it root directory). However, it would be more intuitive to pass a build command to Roosterize than build the project separately.

For example, one could introduce a buildcmd command-line option, used as follows:

python -m roosterize.main suggest_lemmas \
 --project=../StructTact \
 --serapi-options="-Q theories,StructTact" \
 --model-dir=./models/roosterize-ta \
 --output=./output \
 --buildcmd "./configure && make -j2"

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