README.md

---
license: cc-by-nc-sa-4.0
task_categories:
  - graph-ml
  - tabular-regression
tags:
  - inference-runtime-prediction
  - onnx
  - computational-graph
  - graph-neural-network
  - pytorch-geometric
  - gpu-profiling
  - nvidia-t4
pretty_name: "NNIRP Dataset: How You Split Is What You Get"
size_categories:
  - 100K<n<1M
---

# NNIRP Dataset: How You Split Is What You Get

A dataset and evaluation protocol for predicting inference runtime of neural network models from their ONNX computational graphs. Contains 103,070 profiling samples from 190 source configurations spanning 6 architecture families, organized into 156 clusters across 28 sub-families.

## Dataset Summary

Each sample includes three data layers:

| Layer | Format | Size | Description |
|---|---|---|---|
| **Profiling** | `.json` | ~130 MB | Runtime, VRAM, and RAM statistics measured on an NVIDIA T4 GPU |
| **PyG Features** | `.pt.zst` | ~500 MB | PyTorch Geometric graph encodings with node, edge, and graph-level features |
| **ONNX Graphs** | `.onnx` | ~148 GB | Lightweight ONNX computational graphs (topology only, no trained weights) |

## Architecture Families

| Family | Sub-families | Clusters | Source Configs |
|---|---|---|---|
| attention_decoder | 5 | — | — |
| attention_encoder | 11 | — | — |
| attention_encoder_decoder | 4 | — | — |
| convolutional | 2 | — | — |
| detection | 4 | — | — |
| recurrent | 2 | — | — |
| **Total** | **28** | **156** | **190** |

## Dataset Structure

Data is organized as **one tar.gz archive per source configuration per data layer**:

```
NNIRP-dataset/
├── manifests/
│   ├── splits.json                    # Canonical train/val/test split
│   ├── clusters.json                  # Cluster taxonomy (ID → cluster → sub-family → family)
│   └── hf_model_type_case_ids.json    # HuggingFace model_type → source config ID
├── profiling/
│   ├── 792.tar.gz                     # Profiling JSONs for source config 792
│   ├── 793.tar.gz
│   └── ...                            # 190 archives
├── pyg-features/
│   ├── 792.tar.gz                     # PyG .pt.zst files for source config 792
│   └── ...                            # 190 archives
└── onnx-graphs/
    ├── 792.tar.gz                     # ONNX .onnx files for source config 792
    └── ...                            # 190 archives
```

Each archive is named by its **source configuration ID** (integer). Source configurations are grouped into a three-level hierarchy (family → sub-family → cluster). Extracting an archive yields the sample files directly (flat, no nested directories). The `manifests/clusters.json` file provides the complete mapping from source configuration IDs to clusters, sub-families, and families.

## Data Splits

The canonical **cluster-atomic** split ensures no cluster straddles two splits. All validation and test clusters satisfy a bigram coverage threshold (≥0.80) against the training pool.

| Split | Source Configs | Clusters |
|---|---|---|
| Train | 117 | 103 |
| Val | 38 | 22 |
| Test | 35 | 31 |

Split assignments are defined in `manifests/splits.json`.

## PyG Feature Schema

Each `.pt.zst` file is a zstandard-compressed PyTorch Geometric `Data` object:

| Field | Shape | Description |
|---|---|---|
| `x` | `[N, 14]` | Node features: FLOPs, input/output/weight bytes, rank, dims, counts (most log2(1+x)-transformed; rank and port counts are raw) |
| `op_type_id` | `[N]` | Operator type vocabulary index (88 ONNX operators + `<UNK>` at index 0) |
| `edge_index` | `[2, E]` | Directed dataflow edges (COO format) |
| `edge_attr` | `[E, 18]` | Edge features: port indices, tensor shape, rank, bytes, dtype one-hot |
| `u` | `[1, 5]` | Graph-level features: log2(nodes, edges, total FLOPs, total bytes, batch size) |
| `y` | `[1, 2]` | Regression targets: log2(runtime_ms), log2(peak_vram_mb) |

## Profiling JSON Schema

Each `.json` file contains summary statistics (count, mean, median, variance, min, max) for:
- `Runtime (ms)` — inference latency
- `Peak VRAM (MB)` — GPU memory usage
- `Peak RAM (MB)` — system memory usage
- `Peak Disk Usage (MB)`, `Disk Read (MB)`, `Disk Write (MB)`

All measurements are from an NVIDIA T4 GPU with CUDA, using PyTorch eager-mode inference.

## Loading Examples

### Extract and load PyG features for one source configuration

```python
import tarfile, io, json, zstandard, torch

# Extract a single source config's PyG features
with tarfile.open("pyg-features/900.tar.gz", "r:gz") as tar:
    tar.extractall("pyg-features/900/")

# Load one sample
def load_pyg_sample(path: str):
    dctx = zstandard.ZstdDecompressor()
    with open(path, "rb") as f:
        raw = dctx.decompress(f.read())
    return torch.load(io.BytesIO(raw), weights_only=False)

data = load_pyg_sample("pyg-features/900/apple--aimv2-large-patch14-224-lit_im224_b4_fp32.pt.zst")
print(data.x.shape)           # [N, 14] node features
print(data.edge_index.shape)  # [2, E] edges
print(data.y)                 # log2(runtime_ms)
```

### Extract and load profiling data

```python
import tarfile, json

with tarfile.open("profiling/900.tar.gz", "r:gz") as tar:
    tar.extractall("profiling/900/")

with open("profiling/900/apple--aimv2-large-patch14-224-lit_im224_b4_fp32.json") as f:
    prof = json.load(f)
print(f"Runtime: {prof['Runtime (ms)']['mean']:.2f} ms")
print(f"VRAM: {prof['Peak VRAM (MB)']['mean']:.0f} MB")
```

### Load split and taxonomy

```python
import json

with open("manifests/splits.json") as f:
    splits = json.load(f)
train_ids = splits["train"]  # list of source config IDs

with open("manifests/clusters.json") as f:
    clusters = json.load(f)
# Map source config ID → family
for cluster_name, info in clusters["clusters"].items():
    family = clusters["subfamily_to_family"][info["sub_family"]]
    for config_id in info["cases"]:
        print(f"  Config {config_id}: {cluster_name} ({family})")
```

### Download a single source configuration via the HF Hub

```python
from huggingface_hub import hf_hub_download

# Download one archive
path = hf_hub_download(
    repo_id="nnirp/NNIRP-dataset",
    filename="pyg-features/900.tar.gz",
    repo_type="dataset",
)
```

## Data Collection

Data was collected through a three-stage automated pipeline:
1. **ONNX export** — neural network models are exported using a lightweight procedure that captures computational graph topology without trained weights
2. **GPU profiling** — inference runtime, peak VRAM, and peak RAM are measured on an NVIDIA T4 GPU across multiple repetitions
3. **Feature encoding** — ONNX graphs are converted to PyTorch Geometric `Data` objects with structured node, edge, and graph-level features

**Parametric source configurations** sweep hyperparameters (layer count, hidden dimension, batch size, precision) to generate dense scaling curves. **HuggingFace source configurations** profile model variants grouped by `transformers` model type. All models use randomly initialized weights; exported artifacts retain only graph topology and shape metadata.

## Limitations

- All profiling was performed on a single GPU type (NVIDIA T4); predictions may not generalize to other hardware without re-profiling
- ONNX export coverage is incomplete for some operators and dynamic control flow patterns
- Runtime measurements reflect PyTorch eager-mode inference; optimized inference engines may show different characteristics
- Parametric source configurations account for ~22% of source configurations but ~89% of samples

## License

CC-BY-NC-SA 4.0

## Citation

```bibtex
@inproceedings{nnirp2026,
  title={How You Split Is What You Get: A Dataset and Evaluation Protocol for Neural Network Inference Runtime Prediction},
  author={Anonymous},
  booktitle={NeurIPS 2026 Evaluations and Datasets Track},
  year={2026}
}
```