README.md

Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions (ICLR'23)

Code for paper Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions. In this work, we propose to let the model perform sampling during training and learn from those self-sampled correct or partially-correct solutions, which are automatically identified by comparing the final or intermediate execution states. An example is shown as below.

Updates

• 2023-03-08: Initial code release
• 2023-02-17: Camera-ready version updated on arxiv
• 2023-01-20: Paper is accepted at ICLR 2023

Environment Setup

Note: all of the following has only been tested on Linux machines, you may need to build your own tree-sitter parsers if a different platform is used.

(Recommended) Create a new conda environment

conda create -n trace-codegen python=3.8
conda activate trace-codegen

Clone the code and install the dependencies

git clone [email protected]:microsoft/TraceCodegen
cd TraceCodegen
pip install -r requirements.txt

(Optional) Set up wandb for experiment tracking. First following wandb documentation to login, then change the following lines in trainer.logger+ fields of the yaml config file you would like to run:

entity: <your-user/org-name>
project: <your-project-name>

(Optional) At any point, if you met with the Python import problem (e.g., ModuleNotFoundError), try doing this in the main (TraceCodegen) directory:

export PYTHONPATH=`pwd`

Data and Preprocessing

We conduct experiments on the MathQA-Python and GSM8k datasets. As they have different licenses and preprocessing pipelines, here we describe them separately. But first, let's make a data directory:

mkdir data

MathQA-Python

First follow this script for generation the MathQA-Python dataset from the original MathQA dataset. After that, make sure your data directory looks something like this:

data
|-- mathqa
|   |-- train-python.jsonl
|   |-- val-python.jsonl
|   |-- test-python.jsonl
|---...

We preprocess MathQA-Python by respliting the data with template-based deduplication (see detail in paper). To do this, run the preprocessing script with the following:

python resplit_mathqa_python.py

After this, your data directory should now look something like this:

data
|-- mathqa
|   |-- train-python.jsonl
|   |-- val-python.jsonl
|   |-- test-python.jsonl
|   |-- train_dedup.jsonl
|   |-- val_dedup.jsonl
|---...

Note that we only combine and resplit the orignal train and validation set, and the test set kept untouched.

GSM8k

As the solution to GSM8k questions are originally annotated as math formulas, we used a script to automatically extract MathQA-Python style programs as solutions. To replicate this, first download the data from the original GSM8k repo. After that, your data directory should look like this:

data
|-- gsmath
|   |-- train.jsonl
|   |-- test.jsonl
|   |-- ...
|---...

Now run the preprocessing script for GSM8k:

python preprocessing/preprocess_gsm8k.py

After this, your data directory should look like this:

data
|-- gsmath
|   |-- train.jsonl
|   |-- test.jsonl
|   |-- gsmath_train.jsonl
|   |-- gsmath_val.jsonl
|   |-- gsmath_test.jsonl
|   |-- ...
|---...

Model Training

Our training framework is built on top of PyTorch-Lightning (version 1.5.10). More specifically, you would only need to change the yaml configuration files if you would like to adjust the hyperparameters (e.g., batch size, gpus, dataset file, etc).

Note: To run model training, we recommend using GPUs that have at least 32GiB of memory, or decrease the training batch size accordingly. All our experiments are conducted on 8x V100-32GB GPUs.

Basic usage

python trainer.py fit --config <config_file_path>.yaml

Existing yaml config files can be found in training_configs, you can also find all the hyperparameter settings (e.g., batch size) in the Appendix of the paper.

Using different transformer models

If you would like to switch between GPT-Neo-125M and GPT-Neo-2.7B models, be sure to change the following fields in the yaml config file:

trainer:
  ...
  strategy: deepspeed_stage_2_offload # for 2.7B, or "ddp_find_unused_parameters_false" for 125M
...
model:
  class_path: ...
  init_args:
    transformer_model_name: &transformer EleutherAI/gpt-neo-2.7B # or EleutherAI/gpt-neo-125M
...
data:
  class_path: ...
  init_args:
    ...
    batch_size: 2 # [Optional] change this according to the GPU memory
    val_batch_size: 4 # [Optional] change this according to the GPU memory
...

Using different datasets

Since the MathQA-Python and GSM8k datasets are in the same format after preprocessing, you just need to change the file paths in the following fields of the yaml config file:

data:
    ...
    train_file_path: data/mathqa/train_dedup.jsonl # or "data/gsmath/gsmath_train.jsonl" for gsm8k
    val_file_path: data/mathqa/val_dedup.jsonl # or "data/gsmath/gsmath_val.jsonl" for gsm8k

Use fully- or partially-correct self-sampled solutions

To this end, you just need to use different yaml config files in training_configs:

training_configs/gpt_self_sampling.yaml # for using self-sampled fully-correct solutions only
training_configs/gpt_self_sampling_partial.yaml # for also using self-sampled partially-correct solutions

Using different learning objectives

• For the MLE baseline, just run with the config file of training_configs/gpt_mle.yaml
• For running MML, set the following in the yaml config file:

model:
    ...
    init_args:
        ...
        mle_lambda: 0.0
        mml_lambda: 1.0
    ...

• For running $\beta$-MML, keep the above and set beta_smoothing: <beta>
• For running MLE-Aug, set mle_lambda: 1.0 and mml_lambda: 0.0 in above.

All other hyperparameters

For all other hyperparameters, please read the rest of the fields in the yaml file and the corresponding __init__ function in the corresponding class, or refer to the pytorch-lightning documents.

Model Inference

For running model inference (e.g., on the test set), use the following command:

python trainer.py validate --config <your-config-file> --model.init_args.load_ckpt_file <saved_ckpt_file>

Citation

If you use the code in this repository, consider cite:

@inproceedings{ni2023selfsampling,
  title={Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions},
  author={Ni, Ansong and Inala, Jeevana Priya and Wang, Chenglong and Polozov, Alex and Meek, Christopher and Radev, Dragomir and Gao, Jianfeng},
  booktitle={The 2023 International Conference on Learning Representations}
}

For any questions, please open an issue. PRs are definitely welcomed, and please check the following section about contributing to this repo.

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

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