Graph Sage OGBN Example with Scheduler #9877
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…ng, that file with an added scheduler, and the scheduler file with Perforated Backpropagation
RorryB
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This PR is to include an example of how Perforated Backpropagation can be used to improve pytorch_geometric models. It adds on the [previous PR](#9877) which added a scheduler, both of which improve upon the test accuracy of the original ogbn_train.py example. Perforated Backpropagation empowers each neuron in a PyTorch network with Dendrite nodes. These nodes are outside the network, but able to inform the neurons inside the network to make better decisions by leveraging a covariance based learning rule to reduce residual error. The PyTorch add-on combines modern neuroscience with modern software to create neural networks that are smarter, smaller, and more accurate. Run docker from torch_geometric directory docker run --gpus all -i --shm-size=8g -v .:/pai -w /pai -t nvcr.io/nvidia/pyg:24.11-py3 /bin/bash Within Docker pip install -e . pip install safetensors pip install perforatedai Ran original with: CUDA_VISIBLE_DEVICES=0 python examples/ogbn_train.py --dataset ogbn-products --batch_size 128 --model sage Results: Test Accuracy: 77.06% Run PAI with: PAIEMAIL=YourEmail PAITOKEN=YourToken CUDA_VISIBLE_DEVICES=0 python ogbn_train_perforatedai.py --dataset ogbn-products --batch_size 128 --saveName ogbnPAI --model sage Results: Test Accuracy: 78.05% Update: Example updated to include base usage with default SGFormer. Original: CUDA_VISIBLE_DEVICES=0 python ogbn_train.py --dataset ogbn-products --batch_size 128 Results: Test Accuracy: 80.86% PAI: PAIEMAIL=YourEmail PAITOKEN=YourToken CUDA_VISIBLE_DEVICES=0 python ogbn_train_perforatedai.py --dataset ogbn-products --batch_size 128 --saveName ogbnPAI Results: Test Accuracy: 81.27% --------- Co-authored-by: Akihiro Nitta <nitta@akihironitta.com>
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This PR is to update the ogbn example to have a scheduler, improving performance from a test accuracy of 75.52% to 77.19%