| | --- |
| | 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 |
