README_contribute.md

Contributing to GraphNet

To guarantee the dataset’s overall quality, reproducibility, and cross-compiler compatibility, we define the following construction constraints:

1. Computation graphs must be executable in imperative (eager) mode.
2. Computation graphs and their corresponding Python code must support serialization and deserialization.
3. The full graph can be decomposed into two disjoint subgraphs.
4. Operator names within each computation graph must be statically parseable.
5. If custom operators are used, their implementation code must be fully accessible.

Graph Extraction & Validation

GraphNet provides automated tools for graph extraction and validation.

Demo: Extract & Validate ResNet‑18

git clone https://github.com/PaddlePaddle/GraphNet.git
cd GraphNet

# Set your workspace directory
export GRAPH_NET_EXTRACT_WORKSPACE=/home/yourname/graphnet_workspace/

# Extract the ResNet‑18 computation graph
python graph_net/test/vision_model_test.py

# Validate the extracted graph (e.g.,/home/yourname/graphnet_workspace/resnet18/)
python -m graph_net.torch.validate \
  --model-path $GRAPH_NET_EXTRACT_WORKSPACE/resnet18/

Illustration – Extraction Workflow

• Source code of custom_op is required only when corresponding operator is used in the module, and no specific format is required.

Step 1: graph_net.torch.extract

Wrap the model with the extractor — that’s all you need:

import graph_net

# Instantiate the model (e.g., a torchvision model)
model = ...  

# Extract your own model
model = graph_net.torch.extract(name="model_name", dynamic=True)(model)

After running, the extracted graph will be saved to: $GRAPH_NET_EXTRACT_WORKSPACE/model_name/.

For more details, see docstring of graph_net.torch.extract defined in graph_net/torch/extractor.py.

Step 2: graph_net.torch.validate

To verify that the extracted model meets requirements, we use graph_net.torch.validate in the CI tool and also ask contributors to self-check in advance:

python -m graph_net.torch.validate \
  --model-path $GRAPH_NET_EXTRACT_WORKSPACE/model_name

All construction constraints will be examined automatically. After passing validation, a unique graph_hash.txt will be generated and later checked in the CI procedure to avoid redundancy.

📁 Repository Structure

This repository is organized as follows:

Directory	Description
graph_net/	Core module for graph extraction, validation, and benchmarking
paddle_samples/	Computation graph samples extracted from PaddlePaddle
samples/	Computation graph samples extracted from PyTorch
docs/	Technical documents and contributor guides

Below is the structure of the graph_net/:

graph_net/
 ├─ config/    # Config files, params
 ├─ paddle/    # PaddlePaddle graph extraction & validation
 ├─ torch/     # PyTorch graph extraction & validation
 ├─ test/      # Unit tests and example scripts
 └─ *.py       # Benchmark & analysis scripts