Duplicate from GPUMODE/KernelBook

Co-authored-by: Sahan Paliskara <sahancpal@users.noreply.huggingface.co>

.gitattributes

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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LICENSE

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+MIT License
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+language:
+- en
+---
+## Overview
+
+`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.
+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.
+
+## Dataset Creation
+
+The dataset was created through the following process:
+
+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).
+2. **PyTorch Module Extraction**: We extracted the pytorch code from the repositories, and seperated them into individual `torch.nn` modules with appropriate dependencies.
+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.
+4. **Extracting Triton Code**: We used torch.compile in order to produce triton code from the pytorch code.
+5. **Transorming Triton Code**: We transformed the triton code into one which resembled the format seen in [KernelBench](https://github.com/ScalingIntelligence/KernelBench).
+5. **Metadata Enrichment**: Each repository entry was enriched with metadata such as license information, star count, and commit SHA.
+
+The scripts to do this yourself can be found [here](https://github.com/pytorch-labs/popcorn-kernels/tree/main/github_pytorch_index)
+
+## Data Structure
+
+Each entry in the dataset contains the following fields:
+
+| Field | Description |
+|-------|-------------|
+| `repo_name` | The name of the repository in the format `username/repository` |
+| `licenses` | List of licenses associated with the repository |
+| `stars` | Number of GitHub stars the repository has |
+| `sha` | The commit SHA hash used for version reference |
+| `repo_link` | Direct link to the repository at the specific commit (GitHub URL) |
+| *Additional fields* | The dataset may contain other repository-specific information |
+
+## File Formats
+
+The dataset is available in two formats:
+
+1. **JSON**: `dataset_permissive.json` - A human-readable format that can be easily parsed by most programming languages.
+2. **Parquet**: `dataset_permissive.parquet` - A columnar storage format optimized for analytics and big data processing.
+
+## Usage Examples
+
+### Loading the Dataset in Python
+
+#### Using JSON:
+```python
+import json
+
+# Load the JSON version
+with open('dataset_permissive.json', 'r') as f:
+    repos = json.load(f)
+
+# Example: Print the first 5 repository names
+for repo in repos[:5]:
+    print(repo['repo_name'])
+```
+
+#### Using Parquet:
+```python
+import pandas as pd
+
+# Load the Parquet version
+df = pd.read_parquet('dataset_permissive.parquet')
+
+# Example: Get repositories with more than 1000 stars
+popular_repos = df[df['stars'] > 1000]
+print(f"Number of popular repositories: {len(popular_repos)}")
+```
+
+## License Information
+
+The `dataset_permissive` contains only repositories with permissive licenses, including but not limited to:
+
+- MIT License
+- Apache License 2.0
+- BSD Licenses (various)
+- Mozilla Public License
+- Unlicense
+- zlib License
+
+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.
+
+## Citation
+
+```
+@software{kernelbook2025,
+    title={KernelBook},
+    author={Paliskara, Sahan and Saroufim, Mark},
+    year={2025},
+    month={5},
+    url={https://huggingface.co/datasets/GPUMODE/KernelBook},
+}
+```

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