Go to DMLR homepageComPile: A Large IR Dataset from Production Sources
Abstract: Code is increasingly becoming a core data modality of modern machine learning research impacting not only the way we write code
with conversational agents like OpenAI's ChatGPT, Google's Bard, or Anthropic's Claude, the way we translate code from one language
into another, but also the compiler infrastructure underlying the language. While modeling approaches may vary and representations differ,
the targeted tasks often remain the same within the individual classes of models. Yet, relying solely on the ability of modern models to extract
information from unstructured code does not take advantage of 70 years of programming language and compiler development by not utilizing the structure inherent to programs in the data collection. This detracts from the performance of models working over a tokenized representation of input code and precludes the use of these models in the compiler itself. To work towards the first intermediate
representation (IR) based models, we fully utilize the LLVM compiler infrastructure, shared by a number of languages, to generate
a $1.4$T Llama 2 token dataset of LLVM IR. We generated this dataset from programming languages built on the shared LLVM
infrastructure, including Rust, Swift, Julia, and C/C++, by hooking into LLVM code generation either through the language's package
manager or the compiler directly to extract the dataset of intermediate representations from production grade programs.
Statistical analysis proves the utility of our dataset not only for large language model training, but also for the introspection into the code generation process itself as well as for training of machine-learned compiler components.
Certifications: Dataset Certification
Keywords: LLVM,Code,Compiler Representation,Large Language Models,LLMs,Machine Learning for Code,Performance
Assigned Action Editor: ~Sebastian_Schelter1
Submission Number: 46
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