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View Code? Open in Web Editor NEWDeepcoder paper implementation
License: MIT License
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Train nn on gcloud gpu
generate the data files
can't find the file gen-examples in deepcoder/scripts
Improve performance
Current implementation is too slow beyond just python.
T=3, 100 programs
their implementation:
dfs - 500us
sort-and-add - 1ms
mine:
dfs - 38s
sort-and-add - 2640s
dfs slowdown: 76000x
sort-and-add slowdown: 2640000x
Want to get the slowdown to ~100x range (just python)
possible reasons:
- not caching partial results
- unnecessary object creations
Generate baseline results
- refactor dfs to take in multiple input/output examples
- consider # nodes explored instead of wall time
Not able to run
Hi,
You have changed options in gen_program.py but your readme is not updated. Can you please update it.
I am getting following errro-
gen-programs.py: error: unrecognized arguments: --max_nb_prog 100000 --out deepcoder/dataset/programs_small.txt
License ?
Hello, under what license is this project released under ? I would like to study it to learn from it. Thank you.
_pickle.PicklingError: Can't pickle <function <lambda> at 0x7fb178651c10>: attribute lookup <lambda> on deepcoder.dsl.impl failed
I got this error running: ./deepcoder/scripts/gen-datasets.sh
File "/Users/davidlaxer/anaconda3/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <function <lambda> at 0x7fb178651c10>: attribute lookup <lambda> on deepcoder.dsl.impl failed
I am on OS X Monterey 12.1 Beta, in a tensorflow-metal virtual environment.
% python --version
Python 3.8.5
% ./deepcoder/scripts/gen-datasets.sh
+ set -e
+ PROG_LENS=(1 2 3 4 5)
+ NB_TRAINS=(1000000 1000000 5000000 5000000 0)
+ NB_TESTS=(100 100 500 100 100)
+ NB_INPUTS=3
+ for idx in '${!PROG_LENS[*]}'
+ PROG_LEN=1
+ NB_TRAIN=1000000
+ NB_TEST=100
+ echo 'Generating programs T=1 #train=1000000 #test=100'
Generating programs T=1 #train=1000000 #test=100
+ OUT_PREFIX=T=1
+ TRAIN_PROG=T=1_train_programs.txt
+ TEST_PROG=T=1_test_programs.txt
+ TRAIN_OUT=T=1_train.json
+ TEST_OUT=T=1_test.json
+ python -m deepcoder.scripts.gen-programs --nb_inputs 3 --nb_train 1000000 --nb_test 100 --prog_len 1 --train_out T=1_train_programs.txt --test_out T=1_test_programs.txt --enforce_disjoint
Traceback (most recent call last):
File "/Users/davidlaxer/anaconda3/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/Users/davidlaxer/anaconda3/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/Users/davidlaxer/deepcoder/deepcoder/scripts/gen-programs.py", line 97, in <module>
main()
File "/Users/davidlaxer/deepcoder/deepcoder/scripts/gen-programs.py", line 48, in main
programs = enumerate_programs(input_type_combinations, args.prog_len,
File "/Users/davidlaxer/deepcoder/deepcoder/search.py", line 195, in enumerate_programs
worker.start()
File "/Users/davidlaxer/anaconda3/lib/python3.8/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Users/davidlaxer/anaconda3/lib/python3.8/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/Users/davidlaxer/anaconda3/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Users/davidlaxer/anaconda3/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/Users/davidlaxer/anaconda3/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/Users/davidlaxer/anaconda3/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/Users/davidlaxer/anaconda3/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <function <lambda> at 0x7fb178651c10>: attribute lookup <lambda> on deepcoder.dsl.impl failed
Extending DSL
Any thoughts on extending the DSL to incorporate:
i. user defined function(s) from the existing DSL functions and lambdas?
ii. recursion?
Thanks in advance!
Jupyter notebook of experiments
- 2d embedding visualization
- neural network prediction of example program
- training summary (not provided in paper)
- search speedups (dfs + sort-and-add):
- train on T = 3, test on T = 3, P = 500
- train on T = 4, test on T = 5, P = 100
- generalization: T_train = [1...4], T_test = [1,...,5]
Test programs are semantically disjoint from train programs where T_train <= T_test.
Each training set is composed of programs of length T (not <= T).
Code cleanup
- Fix semantics of Variable
- iterate_inputs typemap is called with both a dict of type -> Variable list and a dict of type -> int index list.
- Google style guide conformance
- Fix uses of _ (typemap vs type_map)
- Fix uses of abbreviations (p vs program)
- Audit direct from module imports
Implement sort-and-add
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