| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - computational-graphs |
| - tensor-compiler |
| - deep-learning |
| size_categories: |
| - 100M<n<1B |
| pretty_name: Dataset |
| --- |
| |
| # GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler Research |
|
|
| GraphNet is a large-scale dataset of deep learning computation graphs, built as a standard benchmark for tensor compiler optimization. |
|
|
| ## Dataset Description |
|
|
| GraphNet contains **4,066** computational graph samples from various models, organized into 4 distinct configurations based on graph characteristics: |
|
|
| - **full_graph** (10 samples): Complete model computation graphs |
| - **typical_graph** (33 samples): Representative subgraphs capturing common patterns |
| - **fusible_graph** (1,935 samples): Fusible operator subgraphs |
| - **sole_op_graph** (2,088 samples): Individual operator graphs |
| |
| ### Data Sources |
| |
| The computational graphs are extracted from popular frameworks and model repositories, such as timm, transformers, mmseg, mmpose, cosyvoice, nemo, ultralytics, etc. |
| |
| ## Dataset Structure |
| |
| Each configuration contains a Parquet file with the following schema: |
| |
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `uuid` | string | Unique identifier for the graph sample | |
| | `repo_name` | string | Source repository name | |
| | `relative_model_path` | string | Path to model within repository | |
| | `sample_type` | string | Graph type (full_graph, typical_graph, etc.) | |
| | `is_subgraph` | boolean | Whether this is extracted from a larger graph | |
| | `num_ops` | integer | Number of operations in the graph | |
| | `graph_hash` | string | Unique hash of the graph structure | |
| | `framework` | string | Framework used (torch) | |
| | `dynamic` | boolean | Whether graph has dynamic shapes | |
| | `source` | string | Original model source | |
| | `heuristic_tag` | string | Domain tag (audio, vision, nlp, etc.) | |
| | `dimension_generalization_passes` | JSON | Applied dimension generalization passes | |
| | `data_type_generalization_passes` | JSON | Applied datatype generalization passes | |
| |
| ## Usage |
| |
| ### Load with `datasets` library |
| |
| ```python |
| from datasets import load_dataset |
| |
| # Load specific configurations using data_dir |
| full_graph = load_dataset("PaddlePaddle/GraphNet", data_dir="full_graph") |
| typical_graph = load_dataset("PaddlePaddle/GraphNet", data_dir="typical_graph") |
| fusible_graph = load_dataset("PaddlePaddle/GraphNet", data_dir="fusible_graph") |
| sole_op_graph = load_dataset("PaddlePaddle/GraphNet", data_dir="sole_op_graph") |
| |
| # Access data |
| print(f"Full graphs: {len(full_graph['train'])} samples") |
| print(f"First sample: {full_graph['train'][0]}") |
| ``` |
| |
| ### Load all data at once |
| |
| ```python |
| from datasets import load_dataset |
| |
| # Load all samples together |
| dataset = load_dataset("PaddlePaddle/GraphNet") |
| print(f"Total samples: {len(dataset['train'])}") |
| ``` |
| |
| |
| ## Dataset Statistics |
| |
| - **Total Samples**: 4,066 |
| - **Sample Types**: 4 configurations |
| - **Domains**: Audio, Vision, NLP, Multi-modal |
| |
| ## Citation |
| |
| If you use this dataset in your research, please cite: |
| |
| ```bibtex |
| @dataset{graphnet2026, |
| title={GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler Research}, |
| author={PaddlePaddle}, |
| year={2026}, |
| publisher={Hugging Face}, |
| howpublished={\url{https://huggingface.co/datasets/PaddlePaddle/GraphNet}} |
| } |
| ``` |
| |
| ## License |
| |
| Apache License 2.0 - See LICENSE file for details |
