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--- |
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license: mit |
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language: |
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- en |
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--- |
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## Overview |
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`dataset_permissive{.json/.parquet}` is a curated collection of pairs of pytorch programs and equivalent triton code (generated by torch inductor) which can be used to train models to translate pytorch code to triton code. |
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The triton code was generated using **PyTorch 2.5.0** so for best results during evaluation / running the triton code we recommend using that version of pytorch. |
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## Dataset Creation |
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The dataset was created through the following process: |
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1. **Repository Collection**: PyTorch repositories were collected from GitHub using repositories (and associated hashes) from the [Stack v1](https://huggingface.co/datasets/bigcode/the-stack). |
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2. **PyTorch Module Extraction**: We extracted the pytorch code from the repositories, and seperated them into individual `torch.nn` modules with appropriate dependencies. |
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3. **Creating Unit Tests**: We created unit tests for each module to ensure that the code was working as expected. Code in which could not create unit tests for was removed. |
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4. **Extracting Triton Code**: We used torch.compile in order to produce triton code from the pytorch code. |
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5. **Transorming Triton Code**: We transformed the triton code into one which resembled the format seen in [KernelBench](https://github.com/ScalingIntelligence/KernelBench). |
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5. **Metadata Enrichment**: Each repository entry was enriched with metadata such as license information, star count, and commit SHA. |
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## Data Structure |
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Each entry in the dataset contains the following fields: |
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| Field | Description | |
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|-------|-------------| |
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| `repo_name` | The name of the repository in the format `username/repository` | |
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| `licenses` | List of licenses associated with the repository | |
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| `stars` | Number of GitHub stars the repository has | |
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| `sha` | The commit SHA hash used for version reference | |
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| `repo_link` | Direct link to the repository at the specific commit (GitHub URL) | |
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| *Additional fields* | The dataset may contain other repository-specific information | |
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## File Formats |
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The dataset is available in two formats: |
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1. **JSON**: `dataset_permissive.json` - A human-readable format that can be easily parsed by most programming languages. |
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2. **Parquet**: `dataset_permissive.parquet` - A columnar storage format optimized for analytics and big data processing. |
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## Usage Examples |
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### Loading the Dataset in Python |
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#### Using JSON: |
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```python |
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import json |
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# Load the JSON version |
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with open('dataset_permissive.json', 'r') as f: |
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repos = json.load(f) |
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# Example: Print the first 5 repository names |
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for repo in repos[:5]: |
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print(repo['repo_name']) |
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``` |
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#### Using Parquet: |
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```python |
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import pandas as pd |
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# Load the Parquet version |
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df = pd.read_parquet('dataset_permissive.parquet') |
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# Example: Get repositories with more than 1000 stars |
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popular_repos = df[df['stars'] > 1000] |
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print(f"Number of popular repositories: {len(popular_repos)}") |
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``` |
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## License Information |
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The `dataset_permissive` contains only repositories with permissive licenses, including but not limited to: |
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- MIT License |
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- Apache License 2.0 |
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- BSD Licenses (various) |
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- Mozilla Public License |
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- Unlicense |
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- zlib License |
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The dataset itself is provided for research and development purposes. Users should still verify the license of individual repositories before using their code in production or commercial settings. |
