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‎ICLR2023/README.md‎

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This is the code and data for the paper: Language Models can teach themselves to code better
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https://arxiv.org/abs/2207.14502
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LICENSE
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MIT License - as already specified in the ../LICENSE file of PythonProgrammingPuzzles repo
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GPU USAGE
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GPU usage was large , especially for the 2.7B sized model which is ~20X the 125M.
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Data generation takes the most GPU usage and took about 2500 GPU hours for 2.7B (on v100)
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Finetuning on the 1M generated data took about 40 GPU hours for 2.7B (on v100) per epoch of finetuning - 10 epochs = 400 GPU hours
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Solving the 228 problem testset with 100 attempts using the finetuned 2.7B model took about 4 hours (on v100)
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We mostly used v100, but we used whatever was available, so T4 and A100 sometimes if they were free.
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Tried everything at 125M first - debug there and make it work perfect - then roll out the 1.3 and 2.7 jobs
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DATASETS
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In data directory are the datasets used. We feel the most interesting dataset is data/Codex_PAPER_1M_iter_0.txt
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which is generated by Codex and gave the best results when finetuned on. All the datasets are part of our public release.
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SETUP
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src/requirements.txt is what we install on our cluster machines - the cluster comes with NVidia drivers and matching pytorch
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./requirements.txt is what I personally have installed on my local machine and tested this runs - but it has lots of stuff you don't need
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So try src/requirements.txt only - and if that doesn't work - then /requirements.txt has all versions of everything installed on my machine
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Getting a deepspeed 0.6.1 matching a pytorch matching a nvidia driver install was tricky for me on some machines, torch 1.10 and 1.11 both work
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GENERATING/FINETUNING -> run "cd src, ./babysit.sh GPU_INDEX_TO_USE" -> GPU_INDEX_TO_USE=0 typically
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In src/babysit.sh is the script that generates data, and finetunes on that data in a loop, finetuning the GPT-Neo 125M/1.3B/2.7B models
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In src/babysit.sh TEST_LOCAL=1 controls running locally on machine's GPUs which is great for fast testing, or =0 is launching on the cluster which is slow but has lots of GPUs
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Realistically you have to train on a cluster - data generation takes a long time so having lots of machines all generating data is the feasible approach.
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But given enough time - this will run locally on 1 GPU. 1 year for 2.7B, or 2 weeks for 125M.
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We found generating 75k samples after deduping worked for iteration_0 - finetune on that data.
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Then using that fine_tuned model in iter_1 generating data happens more quickly - the finetuned model solves many more problems
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Repeating that process works well.
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On 125M we looked at just training on only 125M generated data from iter_0 versus iter_1 versus iter_2 - generating 600K for each iteration.
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It seemed finetuning on iter_2 data was best on the testset 26.9/228 solved vs iter_1=26.1/228 vs iter_0=22.2/228
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With 1M samples from 125M generated data sampled across all the iterations 0,1,2 we got 26.75/228
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We understand why it's faster to generate iter_2 data on a finetuned model - it solves more problems.
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But why are the generated puzzles&solutions better for training the model on?
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We will explore that more in the future - and try iterating a lot farther than 3 iterations - although our preliminary experiments on 125M show it tops out at 3 iterations
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FINETUNING ONLY -> run "cd src, ./fine_tune1.sh GPU_INDEX_TO_USE" -> GPU_INDEX_TO_USE=0 typically
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# ./fine_tune1.sh GPU MODEL_TO_TRAIN EXPERIMENT_NAME_DIRECTORY TRAIN_DATA EPOCHS
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This allows the repeated finetuning on a specific dataset.
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Use this to do a temperature grid search, or try different variations of parameters on a specific dataset.
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Detailed instructions for reproducing experiments:
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# Generating Codex data
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python gen.py -n=32 -max_tokens=4096 -model_path=openai/code-davinci-002 -model_path_solve=openai/code-cushman-001 -out=../data/codex/iter_0 -seed=2022
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# Measuring codex accuracy via API calls
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./solve2.sh
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python solve.py -prefix=../data/train_prefix.txt -attempts=1 -model_path=openai/code-cushman-001 -gpu=0 -fixed_temp=0.8 -out=../data/codex -puzzles=../data/test_228.json -seed=2022 -batch_size=64
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# Producing verified Codex_PAPER_1M_iter_0.txt from the puzzle/solution old style data generated by Codex
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python preprocess.py -path=../data/codex/old_verified -f_name=Codex_PAPER_1M_iter_0.txt -max_sols_per_puzzle=8 -old_style_json=True -max_examples=1000000 -include_failures=False -seed=2022
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cp ../data/codex/old/Codex_PAPER_1M_iter_0.txt ../data/Codex_PAPER_1M_iter_0.txt
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# Producing unverified Codex_unverified_PAPER_1M_iter_0.txt from the puzzle/solution old style data generated by Codex
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python preprocess.py -path=../data/codex/old_unverified -f_name=Codex_unverified_PAPER_1M_iter_0.txt -max_sols_per_puzzle=8 -old_style_json=True -max_examples=1000000 -include_failures=True -seed=2022
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cp ../data/codex/old_unverified/Codex_unverified_PAPER_1M_iter_0.txt ../data/Codex_unverified_PAPER_1M_iter_0.txt
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# Producing 125M_PAPER_25K_iter_0.txt from the puzzle/solution new style data
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python preprocess.py ../data/125M_PAPER/iter_0 125M_PAPER_25K_iter_0.txt 8 False 25000 False -seed=2022
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cp ../data/125M_PAPER/iter_0/125M_PAPER_25K_iter_0.txt ../data/125M_PAPER_25K_iter_0.txt
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# Producing 125M_PAPER_1M_iter_1.txt from the puzzle/solution new style data
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python preprocess.py ../data/125M_PAPER/iter_1 125M_PAPER_1M_iter_1.txt 8 False 1000000 False -seed=2022
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cp ../data/125M_PAPER/iter_1/125M_PAPER_1M_iter_1.txt ../data/125M_PAPER_1M_iter_1.txt
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# Producing 125M_PAPER_1M_iter_2.txt from the puzzle/solution new style data13B
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python preprocess.py ../data/125M_PAPER/iter_2 125M_PAPER_1M_iter_2.txt 8 False 1000000 False -seed=2022
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cp ../data/125M_PAPER/iter_2/125M_PAPER_1M_iter_2.txt ../data/125M_PAPER_1M_iter_2.txt
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# Producing 13B_PAPER_25K_iter_0.txt from the puzzle/solution new style data
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python preprocess.py ../data/13B_PAPER/iter_0 13B_PAPER_25K_iter_0.txt 8 False 25000 False -seed=2022
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cp ../data/13B_PAPER/iter_0/13B_PAPER_25K_iter_0.txt ../data/13B_PAPER_25K_iter_0.txt
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# Producing 13B_PAPER_1M_iter_1.txt from the puzzle/solution new style data
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python preprocess.py ../data/13B_PAPER/iter_1 13B_PAPER_1M_iter_1.txt 8 False 1000000 False -seed=2022
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cp ../data/13B_PAPER/iter_1/13B_PAPER_1M_iter_1.txt ../data/13B_PAPER_1M_iter_1.txt
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# Producing 13B_PAPER_1M_iter_2.txt from the puzzle/solution new style data
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python preprocess.py ../data/13B_PAPER/iter_2 13B_PAPER_1M_iter_2.txt 8 False 1000000 False -seed=2022
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cp ../data/13B_PAPER/iter_2/13B_PAPER_1M_iter_2.txt ../data/13B_PAPER_1M_iter_2.txt
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# Producing 27B_PAPER_25K_iter_0.txt from the puzzle/solution new style data
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python preprocess.py ../data/27B_PAPER/iter_0 27B_PAPER_25K_iter_0.txt 8 False 25000 False -seed=2022
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cp ../data/27B_PAPER/iter_0/27B_PAPER_25K_iter_0.txt ../data/27B_PAPER_25K_iter_0.txt
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# Producing 27B_PAPER_1M_iter_1.txt from the puzzle/solution new style data
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python preprocess.py ../data/27B_PAPER/iter_1 27B_PAPER_1M_iter_1.txt 8 False 1000000 False -seed=2022
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cp ../data/27B_PAPER/iter_1/27B_PAPER_1M_iter_1.txt ../data/27B_PAPER_1M_iter_1.txt
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# Producing 27B_PAPER_1M_iter_2.txt from the puzzle/solution new style data
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python preprocess.py ../data/27B_PAPER/iter_2 27B_PAPER_1M_iter_2.txt 8 False 1000000 False -seed=2022
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cp ../data/27B_PAPER/iter_2/27B_PAPER_1M_iter_2.txt ../data/27B_PAPER_1M_iter_2.txt
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# Data files produced by babysit.sh - generating data from gpt-neo-* and Codex
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# At the time of experiments running, Codex wasn't finetunable, so only iteration 0 data was available
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Codex_PAPER_1M_iter_0.txt
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125M_PAPER_25K_iter_0.txt
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13B_PAPER_25K_iter_0.txt
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27B_PAPER_25K_iter_0.txt
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125M_PAPER_1M_iter_1.txt
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13B_PAPER_1M_iter_1.txt
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27B_PAPER_1M_iter_1.txt
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125M_PAPER_1M_iter_2.txt
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13B_PAPER_1M_iter_2.txt
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27B_PAPER_1M_iter_2.txt
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# Figure 5 - 3 diagrams - showing the 3 GPT models trained on verified codex vs unverified codex vs baseline
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# 5a GPT-NEO 125M
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./fine_tune1.sh 0 125M ft1_Codex_PAPER_1M_iter_0 Codex_PAPER_1M_iter_0.txt
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./fine_tune1.sh 0 125M ft1_Codex_unverified_PAPER_1M_iter_0 Codex_unverified_PAPER_1M_iter_0.txt
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./solve1.sh 0 125M 10 228
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# 5b GPT-NEO 13B
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./fine_tune1.sh 0 13B ft1_Codex_PAPER_1M_iter_0 Codex_PAPER_1M_iter_0.txt
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./fine_tune1.sh 0 13B ft1_Codex_unverified_PAPER_1M_iter_0 Codex_unverified_PAPER_1M_iter_0.txt
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./solve1.sh 0 13B 10 228 5
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# 5c GPT-NEO 27B
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./fine_tune1.sh 0 27B ft1_Codex_PAPER_1M_iter_0 Codex_PAPER_1M_iter_0.txt
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./fine_tune1.sh 0 27B ft1_Codex_unverified_PAPER_1M_iter_0 Codex_unverified_PAPER_1M_iter_0.txt
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./solve1.sh 0 13B 10 228 5
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# Figure 6 - 3 diagrams - showing test228 Pass@ for the 3 GPT models trained on data from 4 generators (codex and 3 GPT-Neo) and baseline
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# 6a - GPT-NEO 125M trained on 4 different datasets and baseline
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# ./fine_tune1.sh 0 125M ft1_Codex_PAPER_1M_iter_0 Codex_PAPER_1M_iter_0.txt (dupe of 5a)
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./fine_tune1.sh 0 125M ft1_125M_PAPER_1M_iter_2 125M_PAPER_1M_iter_2.txt
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./fine_tune1.sh 0 125M ft1_13B_PAPER_1M_iter_2 13B_PAPER_1M_iter_2.txt
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./fine_tune1.sh 0 125M ft1_27B_PAPER_1M_iter_2 27B_PAPER_1M_iter_2.txt
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# 6b - GPT-NEO 13B trained on 4 different datasets and baseline
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# ./fine_tune1.sh 0 13B ft1_Codex_PAPER_1M_iter_0 Codex_PAPER_1M_iter_0.txt (dupe of 5b)
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./fine_tune1.sh 0 13B ft1_125M_PAPER_1M_iter_2 125M_PAPER_1M_iter_2.txt
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./fine_tune1.sh 0 13B ft1_13B_PAPER_1M_iter_2 13B_PAPER_1M_iter_2.txt
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./fine_tune1.sh 0 13B ft1_27B_PAPER_1M_iter_2 27B_PAPER_1M_iter_2.txt
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# 6c - GPT-NEO 27B trained on 4 different datasets and baseline
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# ./fine_tune1.sh 0 27B ft1_Codex_PAPER_1M_iter_0 Codex_PAPER_1M_iter_0.txt (dupe of 5c)
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./fine_tune1.sh 0 27B ft1_125M_PAPER_1M_iter_2 125M_PAPER_1M_iter_2.txt
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./fine_tune1.sh 0 27B ft1_13B_PAPER_1M_iter_2 13B_PAPER_1M_iter_2.txt
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./fine_tune1.sh 0 27B ft1_27B_PAPER_1M_iter_2 27B_PAPER_1M_iter_2.txt
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# Launch on torch2020 - edit solve.yaml for correct parameters of model and epoch
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./tst_human_eval_base.sh 0 125M 1024
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./tst_human_eval_ft1.sh 0 125M 1024
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./tst_human_eval_ft5.sh 0 125M 1024
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./tst_human_eval_ft10.sh 0 125M 1024

‎ICLR2023/requirements.txt‎

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adal==1.2.7
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aiohttp==3.8.1
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aiosignal==1.2.0
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amlt==8.0.9
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applicationinsights==0.11.10
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asn1crypto==0.24.0
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astor==0.8.1
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async-timeout==4.0.1
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attrs==17.4.0
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Automat==0.6.0
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azure-common==1.1.27
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azure-core==1.17.0
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azure-data-tables==12.0.0b6
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azure-graphrbac==0.61.1
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azure-identity==1.4.1
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azure-mgmt-authorization==0.61.0
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azure-mgmt-containerregistry==2.8.0
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azure-mgmt-keyvault==2.2.0
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azure-mgmt-resource==13.0.0
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azure-mgmt-storage==11.2.0
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azure-storage-blob==2.1.0
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azure-storage-common==2.1.0
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azure-storage-file==2.1.0
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azureml-automl-core==1.26.0
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azureml-contrib-k8s==0.1.16
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azureml-contrib-pipeline-steps==1.26.0
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azureml-core==1.26.0
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azureml-dataprep==2.13.2
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azureml-dataprep-native==32.0.0
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azureml-dataprep-rslex==1.11.2
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azureml-dataset-runtime==1.26.0
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azureml-k8s-mt==1.0.4
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azureml-pipeline-core==1.26.0
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azureml-pipeline-steps==1.26.0
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azureml-telemetry==1.26.0
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azureml-train-automl-client==1.26.0
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azureml-train-core==1.26.0
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azureml-train-restclients-hyperdrive==1.26.0
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backcall==0.2.0
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backports.tempfile==1.0
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backports.weakref==1.0.post1
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beautifulsoup4==4.9.3
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bitstring==3.1.9
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black==21.8b0
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blinker==1.4
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blis==0.7.4
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blobxfer==1.10.0
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cachetools==4.2.2
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catalogue==2.0.6
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certifi==2018.1.18
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cffi==1.14.6
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chardet==3.0.4
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charset-normalizer==2.0.7
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click==7.1.2
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click-completion @ git+https://github.com/temporaer/click-completion.git@41b21868cac0781d25b37da624bae2fd1f36be88
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click-option-group==0.5.3
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click-plugins==1.1.1
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cloud-init==20.2
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cloudpickle==1.6.0
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colorama==0.3.7
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colorlog==6.4.1
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command-not-found==0.3
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configobj==5.0.6
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configparser==5.0.2
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constantly==15.1.0
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contextlib2==21.6.0
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cryptography==3.4.8
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cycler==0.10.0
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cymem==2.0.5
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datasets==1.15.1
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debugpy==1.4.3
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decorator==5.0.9
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deepspeed==0.5.1
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dill==0.3.4
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distro==1.6.0
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distro-info===0.18ubuntu0.18.04.1
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docker==5.0.1
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docker-pycreds==0.4.0
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dotnetcore2==2.1.21
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ecdsa==0.17.0
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entrypoints==0.3
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et-xmlfile==1.1.0
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fail2ban==0.10.2
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fastai==2.5.2
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fastcore==1.3.26
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fastdownload==0.0.5
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fastprogress==1.0.0
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filelock==3.0.12
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Flask==2.0.1
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Flask-Cors==3.0.10
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Flask-Executor==0.9.4
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Flask-FontAwesome==0.1.5
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frozenlist==1.2.0
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fsspec==2021.11.0
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gitdb==4.0.7
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GitPython==3.1.18
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httplib2==0.9.2
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huggingface-hub==0.1.2
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humanize==3.11.0
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hyperlink==17.3.1
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idna==2.6
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incremental==16.10.1
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ipdb==0.13.9
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ipykernel==6.4.1
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ipython==7.27.0
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ipython-genutils==0.2.0
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isodate==0.6.0
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itsdangerous==2.0.1
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jedi==0.18.0
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Jinja2==3.0.1
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jmespath==0.10.0
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joblib==1.0.1
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jsonpatch==1.16
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jsonpickle==2.0.0
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jsonpointer==1.10
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jsonschema==2.6.0
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jupyter-client==7.0.5
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jupyter-core==4.8.1
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keyring==10.6.0
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keyrings.alt==3.0
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kiwisolver==1.3.2
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language-selector==0.1
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libtmux==0.10.1
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Mako==1.1.5
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MarkupSafe==2.0.1
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marshmallow==3.10.0
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matplotlib==3.4.3
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matplotlib-inline==0.1.3
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mlb-core==0.0.4
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msal==1.14.0
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msal-extensions==0.2.2
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msrest==0.6.19
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msrestazure==0.6.4
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multidict==5.2.0
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multiprocess==0.70.12.2
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murmurhash==1.0.5
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mypy-extensions==0.4.3
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ndg-httpsclient==0.5.1
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nest-asyncio==1.5.1
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netifaces==0.10.4
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ninja==1.10.2
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ntlm-auth==1.5.0
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numpy==1.21.2
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oauthlib==3.1.1
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openai==0.13.0
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openpyxl==3.0.9
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orderedset==2.0.3
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packaging==21.0
149+
PAM==0.4.2
150+
pandas==1.3.2
151+
pandas-stubs==1.2.0.45
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parso==0.8.2
153+
passpy==1.0.2
154+
pathspec==0.9.0
155+
pathtools==0.1.2
156+
pathy==0.6.0
157+
Pebble==4.6.3
158+
petname==2.6
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pexpect==4.8.0
160+
pickleshare==0.7.5
161+
Pillow==8.3.2
162+
platformdirs==2.3.0
163+
portalocker==1.7.1
164+
preshed==3.0.5
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promise==2.3
166+
prompt-toolkit==3.0.20
167+
protobuf==3.17.3
168+
psb2==1.0.0
169+
psutil==5.8.0
170+
ptyprocess==0.7.0
171+
pyarrow==1.0.1
172+
pyasn1==0.4.2
173+
pyasn1-modules==0.2.1
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pycparser==2.20
175+
pycrypto==2.6.1
176+
pydantic==1.8.2
177+
Pygments==2.10.0
178+
PyGObject==3.26.1
179+
PyJWT==1.5.3
180+
pyOpenSSL==17.5.0
181+
pyparsing==2.4.7
182+
pyperclip==1.8.2
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pyserial==3.4
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python-apt==1.6.5+ubuntu0.3
185+
python-dateutil==2.8.2
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python-debian==0.1.32
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python-gnupg==0.4.7
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pytz==2021.1
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pyxdg==0.25
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PyYAML==5.4.1
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pyzmq==22.3.0
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regex==2021.8.28
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requests==2.25.1
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requests-ntlm==1.1.0
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requests-oauthlib==1.3.0
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requests-unixsocket==0.1.5
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ruamel.yaml==0.17.16
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ruamel.yaml.clib==0.2.6
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sacremoses==0.0.45
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scikit-learn==0.24.2
201+
scipy==1.7.1
202+
SecretStorage==2.3.1
203+
sentry-sdk==1.3.1
204+
service-identity==16.0.0
205+
shellingham==1.4.0
206+
shortuuid==1.0.1
207+
six==1.16.0
208+
sklearn==0.0
209+
smart-open==5.2.1
210+
smmap==4.0.0
211+
soupsieve==2.2.1
212+
spacy==3.1.2
213+
spacy-legacy==3.0.8
214+
srsly==2.4.1
215+
ssh-import-id==5.7
216+
sshpubkeys==3.3.1
217+
strictfire==0.4.1
218+
subprocess32==3.5.4
219+
systemd-python==234
220+
tabulate==0.8.9
221+
tensorboardX==1.8
222+
termcolor==1.1.0
223+
thinc==8.0.10
224+
threadpoolctl==2.2.0
225+
tokenizers==0.10.3
226+
toml==0.10.2
227+
tomli==1.2.1
228+
torch==1.9.0
229+
torchvision==0.10.0
230+
tornado==6.1
231+
tqdm==4.62.2
232+
traitlets==5.1.0
233+
transformers==4.10.0
234+
Twisted==17.9.0
235+
typer==0.3.2
236+
typing-extensions==3.10.0.2
237+
ufw==0.36
238+
unattended-upgrades==0.1
239+
urllib3==1.26.6
240+
virtualenv==15.1.0
241+
WALinuxAgent==2.2.45
242+
wasabi==0.8.2
243+
wcwidth==0.2.5
244+
websocket-client==1.2.1
245+
Werkzeug==2.0.1
246+
xdg==5.1.1
247+
xxhash==2.0.2
248+
yarl==1.7.2
249+
zope.interface==4.3.2

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