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license: apache-2.0
language:
- en
tags:
- code-generation
- decompilation
- cuda
- ptx
- llm
---
# Dataset for "Enhancing LLM to Decompile Optimized PTX to Readable CUDA for Tensor Programs" (ASE 2025)
## Dataset Description
This dataset accompanies the paper:
> Sun X, Tang F, Zhang Y, et al. Enhancing LLM to Decompile Optimized PTX to Readable CUDA for Tensor Programs[C]//2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2025: 2235-2247.
It contains pairs of input PTX kernels and output CUDA kernels generated by two auto-schedulers: **Ansor** and **Welder**. The dataset is designed to train and evaluate large language models (LLMs) for the task of decompiling optimized PTX back to human-readable CUDA code.
## Dataset Structure
The dataset is split into four Parquet files:
- `train_ansor.parquet`
- `train_welder.parquet`
- `test_ansor.parquet`
- `test_welder.parquet`
Each file contains samples with the following fields:
| Field | Type | Description |
|----------|--------|-----------------------------------------------------------------------------|
| `file` | string | Source JSON filename (without extension). For Ansor, it encodes parameters. |
| `name` | string or int | Sample identifier (string for Ansor, integer for Welder). |
| `kernel` | string | (Ansor only) Kernel function name. |
| `input` | string | Input PTX kernel. |
| `output` | string | Output CUDA kernel. |
> **Note**: The `kernel` field is present only in Ansor samples; Welder samples do not have this field.
## Data Splits
| Split | Number of Samples |
|----------------|-------------------|
| train_ansor | 371,472 |
| train_welder | 383,790 |
| test_ansor | 1,905 |
| test_welder | 1,793 |
## Usage Example
```python
from datasets import load_dataset
dataset = load_dataset("your-username/ptxdec-dataset")
train_ansor = dataset["train_ansor"]
print(train_ansor[0]) |