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README.md
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license:
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# ComPile: A Large IR Dataset from Production Sources
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## About
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Utilizing the LLVM compiler infrastructur shared by a number of languages, ComPile is a
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LLVM IR. The dataset is generated from programming languages built on the shared LLVM infrastructure, including Rust,
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Swift, Julia, and C/C++, by hooking into LLVM code generation either through the language's package manager or the
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compiler directly to extract the dataset of intermediate representations from production grade programs using our
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[dataset collection utility for the LLVM compilation infrastructure](https://
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For an in-depth look at the statistical properties of dataset, please have a look at our [arXiv preprint](https://arxiv.org/abs/2309.15432).
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license: cc-by-4.0
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---
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# ComPile: A Large IR Dataset from Production Sources
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## About
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Utilizing the LLVM compiler infrastructur shared by a number of languages, ComPile is a large dataset of
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LLVM IR. The dataset is generated from programming languages built on the shared LLVM infrastructure, including Rust,
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Swift, Julia, and C/C++, by hooking into LLVM code generation either through the language's package manager or the
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compiler directly to extract the dataset of intermediate representations from production grade programs using our
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[dataset collection utility for the LLVM compilation infrastructure](https://doi.org/10.5281/zenodo.10155761).
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For an in-depth look at the statistical properties of dataset, please have a look at our [arXiv preprint](https://arxiv.org/abs/2309.15432).
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## Usage
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Using ComPile is relatively simple with HuggingFace's `datasets` library. To load the dataset, you can simply
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run the following in a Python interpreter or within a Python script:
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```python
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from datasets import load_dataset
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ds = load_dataset('llvm-ml/ComPile', split='train')
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```
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While this will just work, the download will take quite a while as `datasets` by default will download
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all 550GB+ within the dataset and cache it locally. Note that the data will be placed in the directory
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specified by the environment variable `HF_DATASETS_CACHE`, which defaults to `~/.cache/huggingface`.
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You can also load the dataset in a streaming format, where no data is saved locally:
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```python
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ds = load_dataset('llvm-ml/ComPile', split='train', streaming=True)
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```
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This makes experimentation much easier as no upfront large time investment is required, but is
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significantly slower than loading in the dataset from the local disk. For experimentation that
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requires more performance but might not require the whole dataset, you can also specify a portion
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of the dataset to download. For example, the following code will only download the first 10%
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of the dataset:
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```python
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ds = load_dataset('llvm-ml/ComPile', split='train[:10%]')
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```
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Once the dataset has been loaded, the individual module files can be accessed by iterating through
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the dataset or accessing specific indices:
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```python
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# We can iterate through the dataset
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next(iter(ds))
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# We can also access modules at specific indices
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ds[0]
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```
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Filtering and map operations can also be efficiently applied using primitives available within the
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HuggingFace `datasets` library. More documentation is available [here](https://huggingface.co/docs/datasets/index).
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## Dataset Format
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Each row in the dataset consists of an individual LLVM-IR Module along with some metadata. There are
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six columns associated with each row:
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1. `content` - This column contains the raw bitcode that composes the module. This can be written to a `.bc`
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file and manipulated using the standard llvm utilities or passed in directly through stdin if using something
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like Python's `subprocess`.
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2. `license_expression` - This column contains the SPDX expression describing the license of the project that the
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module came from.
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3. `license_source` - This column describes the way the `license_expression` was determined. This might indicate
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an individual package ecosystem (eg `spack`), license detection (eg `go_license_detector`), or might also indicate
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manual curation (`manual`).
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4. `license_files` - This column contains an array of license files. These file names map to licenses included in
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`/licenses/licenses-0.parquet`.
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5. `package_source` - This column contains information on the package that the module was sourced from. This is
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typically a link to a tar archive or git repository from which the project was built, but might also contain a
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mapping to a specific package ecosystem that provides the source, such as Spack.
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6. `language` - This column indicates the source language that the module was compiled from.
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## Licensing
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The individual modules within the dataset are subject to the licenses of the projects that they come from. License
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information is available in each row, including the SPDX license expression, the license files, and also a link to
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the package source where license information can be further validated.
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The curation of these modules is licensed under a CC-BY-4.0 license.
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