Graphcore Application Examples

This repository contains a catalogue of application examples that have been optimised to run on Graphcore IPUs for both training and inference. Access reproducible code for a wide range of popular models covering NLP, Computer Vision, Speech, Multimodal, GNNs, AI for Simulation, Recommender Systems, and more. This includes a selection of models that achieve state of the art performance on IPUs, as well as code examples for self-learning.

Run models out-the-box on IPUs integrated with popular ML frameworks and libraries:

To see what's new, check out our Model Garden 🌷, where you can easily filter applications by domain and framework.

For more detailed benchmark information, visit our Performance Results page.

The code presented here requires you to use Poplar SDK 3.2.x, and has been tested using Ubuntu 20.04 and Python 3.8

Please install and enable the Poplar SDK following the instructions in the Getting Started guide for your IPU system.

Developer resources

• Documentation: Explore our software documentation, user guides, and technical notes
• Tutorials: Hands-on code tutorials, simple application and feature examples
• How-to Videos: Watch practical how-to videos and demos by Graphcore engineers
• Research Papers: Read publications from Graphcore's Research team and IPU innovators

Support

If you encounter a problem or want to suggest an improvement to our example applications please raise a GitHub issue, contact us at [email protected], or get in touch through the #help channel of the Graphcore Slack Community!

If you require POD128 or POD256 setup and configuration for our applications, please contact our engineering support.

Repository contents

1. Computer Vision
2. Natural Language Processing
3. Speech
4. Multimodal
5. Graph Neural Network
6. AI for Simulation
7. Recommender Systems
8. Reinforcement Learning
9. Sparsity
10. Probability
11. Miscellaneous
12. Archived

Computer Vision

Model	Domain	Type	Source
ResNet	Image classification	Training, Inference	TensorFlow 2, PyTorch, PyTorch Lightning
EfficientNet	Image classification	Training, Inference	PyTorch, PyTorch Lightning
MobileNetv3	Image classification	Training, Inference	PyTorch
ViT (Vision Transformer)	Image classification	Training	PyTorch, Hugging Face Optimum
DINO	Image classification	Training	PyTorch
Swin	Image classification	Training	PyTorch
MAE (Masked AutoEncoder)	Image classification	Training	PyTorch
Yolov4-P5	Object detection	Inference	PyTorch
EfficientDet	Object detection	Inference	TensorFlow 2
UNet (Medical)	Image segmentation	Training, Inference	TensorFlow 2
Neural Image Fields	Neural radiance fields	Training	TensorFlow 2

Natural Language Processing

Model	Domain	Type	Source
BERT	NLP	Training, Inference	PyTorch , TensorFlow 2, PopXL, PaddlePaddle, Hugging Face Optimum
Packed BERT	NLP	Training	PyTorch
BERT-large	NLP	Fine-tuning	Hugging Face Optimum
GPT2	NLP	Training	PyTorch, Hugging Face Optimum
GPTJ	NLP	Training	PopXL
GPT3-2.7B	NLP	Training	PopXL
GPT3-175B	NLP	Training	PopXL
RoBERTa	NLP	Training	Hugging Face Optimum
DeBERTa	NLP	Training	Hugging Face Optimum
HuBERT	NLP	Training	Hugging Face Optimum
BART Base	NLP	Training	Hugging Face Optimum
T5-small	NLP	Training	Hugging Face Optimum
Bloom	NLP	Inference	PopXL
Dolly	NLP	Inference	PopXL
MT5-small	NLP	Fine-tuning	Hugging Face Optimum
MT5-large	NLP	Fine-tuning	Hugging Face Optimum

Speech

Model	Domain	Type	Source
Fastpitch	TTS (TextToSpeech)	Training	PyTorch
Conformer	STT (SpeechToText)	Training, Inference	PyTorch
wav2vec2-base	STT (SpeechToText)	Fine-tuning, Inference	Hugging Face Optimum
wav2vec2-large	STT (SpeechToText)	Fine-tuning, Inference	Hugging Face Optimum
Whisper-tiny	STT (SpeechToText)	Inference	Hugging Face Optimum

Multimodal

Model	Domain	Type	Source
miniDALL-E	Multimodal	Training	PyTorch
CLIP	Multimodal	Training	PyTorch
LXMERT	Multimodal	Training	Hugging Face Optimum
Frozen in time	Multimodal	Training, Inference	PyTorch

Graph Neural Network

Model	Domain	Type	Source
MPNN (Message Passing Neural Networks)	GNN	Training, Inference	PyTorch Geometric , TensorFlow 2
Spektral GNN library with QM9	GNN	Training	TensorFlow 2
Cluster GCN	GNN	Training, Inference	PyTorch Geometric
TGN (Temporal Graph Networks)	GNN	Training	PyTorch
NBFNet	GNN	Training, Inference	PyTorch Geometric
SchNet	GNN	Training, Inference	PyTorch Geometric
GPS++ - OGB-LSC PCQM4Mv2 competition submission	GNN	Training, Inference	TensorFlow 2

AI for Simulation

Model	Domain	Type	Source
Approximate Bayesian Computation (ABC) COVID-19	Medical	Inference	TensorFlow 2

Benchmarking tools

To easily run the examples with tested and optimised configurations and to reproduce the performance shown on our Performance Results page, you can use the examples-utils benchmarking module, which comes with every example when you install its requirements. To use this simple, shared interface for almost any of the examples provided here, locate and look through the example's benchmarks.yml file and run:

python3 -m examples_utils benchmark --spec <path to benchmarks.yml file> --benchmark <name of benchmark>

Refer to the examples-utils benchmarking module README for more information.

PopVision® Tools

Visualise your code's inner workings with PopVision, a user-friendly, graphical interface to optimise your machine learning models.

Download the PopVision tools to analyse IPU performance and utilisation.

Utilities

The utils/ folder contains utility libraries and scripts that are used across the code examples. This includes:

• utils/examples_tests - Common Python helper functions for the examples repository unit tests
• utils/benchmarks - Common Python helper functions for running benchmarks on the IPU in different frameworks

License

Unless otherwise specified by a LICENSE file in a subdirectory, the LICENSE referenced at the top level applies to the files in this repository.

Changelog

March 2023

• Added the following models:
  • GNN: NBFNet (PyTorch Geometric), SchNet (PyTorch Geometric), Cluster-GCN (PyTorch Geometric), GIN (PyTorch Geometric), GPS++ - OGB-LSC PCQM4Mv2 competition submission (TensorFlow 2)
  • NLP : GPT3_175B (PopXL), Bloom (PopXL)
• Removed all PopART applications, as well as the following:
  • Miscellaneous: Monte-Carlo ray tracing
  • AI for simulation: DeepDriveMD
  • (Preview) Multimodel: ruDalle
  • Speech: FastSpeech2
  • Vision: ResNeXt inference
• Moved the contents of the Graphcore/tutorials repository into this repository (PopART tutorials have also been removed)

Dec 2022

• Added the following models:
  • GNN: TGN (PyTorch)
• Deprecating all PopART applications. Support will be removed in the next release.
• Removed all TensorFlow 1 applications.
• Ubuntu 18.04 no longer supported.

Sep 2022

• Added the following models:
  • Vision : MAE (PyTorch), G16 EfficientNet (PyTorch)
  • NLP : GPTJ (PopXL), GPT3-2.7B (PopXL)
  • Multimodal : Frozen in time (PyTorch), ruDalle (Preview) (PopXL)
• Deprecating all TensorFlow 1 applications. Support will be removed in the next release.

Aug 2022

• Changed the folder name of the following models:
  • NLP: from gpt to gpt2
  • Speech: from wenet-conformer to conformer

July 2022

• Major reorganisation of all the apps so that they are arranged as: problem domain / model / framework.
• Problem domains: Vision, NLP, Speech, GNN, Sparsity, AI for Simulation, Recommender systems, Reinforcement learning, Probability, Multimodal, and Miscellaneous.
• Added the following models:
  • Vision: Swin (PyTorch) , ViT (Hugging Face Optimum)
  • NLP: GPT2 Small/Medium/Large (PyTorch), BERT-Base/Large (PopXL), BERT-Base (PaddlePaddle), BERT-Base/Large (Hugging Face Optimum), GPT2 Small/Medium (Hugging Face Optimum), RoBERTa Base/Large (Hugging Face Optimum), DeBERTa (Hugging Face Optimum), HuBERT (Hugging Face Optimum), BART (Hugging Face Optimum), T5 small (Hugging Face Optimum)
  • Speech: Fastpitch (PyTorch), WeNet-Conformer-Medium (PyTorch) ,Wav2Vec2 (Hugging Face Optimum)
  • Multimodal: CLIP (PyTorch), LXMERT (Hugging Face Optimum)
  • AI for Simulation: et0 (TensorFlow 1)
• Removed Conformer-small/large (PyTorch)
• Archived Minigo (TensorFlow 1)

May 2022

• Added the following models:
  • Vision : ViT-pretraining (PyTorch), DINO (PyTorch), EfficientDet-inference (TensorFlow 2), Neural Image Fields (TensorFlow 2)
  • NLP : PackedBERT (PyTorch, PopART), BERT-Large (TensorFlow 2)
  • Speech : FastSpeech2-inference (TensorFlow 2), Conformer-Large (PyTorch)
  • GNN : Cluster GCN (TensorFlow 2)
  • AI for Simulation : DeepDriveMD (TensorFlow 2)

December 2021

• Added the following models:
  • Vision: miniDALL-E (PyTorch), Faster RCNN (PopART), UNet (TensorFlow 2), ResNet50 (TensorFlow 2)
  • NLP: BERT (TensorFlow 2)
  • Speech: FastSpeech2 (TensorFlow 2), Transformer Transducer (PopART), Conformer-Small (PyTorch)
  • GNN: TGN (TensorFlow 1), MPNN (TensorFlow 2)

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