Package: onnxt5
Summarization, translation, Q&A, text generation and more at blazing speed using a T5 version implemented in ONNX.This package is still in alpha stage, therefore some functionalities such as beam searches are still in development.InstallationONNX-T5 is available on PyPi.pipinstallonnxt5For the dev version you can run the following.gitclonehttps://github.com/abelriboulot/onnxt5cdonnxt5
pipinstall-e.UsageThe simplest way to get started for generation is to use the default pre-trained
version of T5 on ONNX included in the package.NOTE:Please note that the first time you call get_encoder_decoder_tokenizer, the models are
being downloaded which might take a minute or two.fromonnxt5importGenerativeT5fromonnxt5.apiimportget_encoder_decoder_tokenizerdecoder_sess,encoder_sess,tokenizer=get_encoder_decoder_tokenizer()generative_t5=GenerativeT5(encoder_sess,decoder_sess,tokenizer,onnx=True)prompt='translate English to French: I was a victim of a series of accidents.'output_text,output_logits=generative_t5(prompt,max_length=100,temperature=0.)# output_text: "J'ai été victime d'une série d'accidents."Other tasks just require to change the prefix in your prompt, for instance for summarization:prompt='summarize: <PARAGRAPH>'output_text,output_logits=generative_t5(prompt,max_length=100,temperature=0.)If you want to get the embeddings of text, you can run the followingfromonnxt5.apiimportget_encoder_decoder_tokenizer,run_embeddings_textdecoder_sess,encoder_sess,tokenizer=get_encoder_decoder_tokenizer()prompt='Listen, Billy Pilgrim has come unstuck in time.'encoder_embeddings,decoder_embeddings=run_embeddings_text(encoder_sess,decoder_sess,tokenizer,prompt)ONNXT5 also lets you export and use your own models. See theexamples\folder for more detailed examples.T5 works with tokens such assummarize:,translate English to German:, orquestion: ... context:. You can see a
list of the pretrained tasks and token in the appendix D of theoriginal paper.FunctionalitiesRun any of the T5 trained tasks in a line (translation, summarization, sentiment analysis, completion, generation)Export your own T5 models to ONNX easilyUtility functions to generate what you need quicklyUp to 4X speedup compared to PyTorch execution for smaller contextsBenchmarksThe outperformance varies heavily based on the length of the context. For contexts less than ~500 words,
ONNX outperforms greatly, going up to a 4X speedup compared to PyTorch. However, the longer the context, the smaller the
speedup of ONNX, with Pytorch being faster above 500 words.GPU Benchmark, Embedding TaskGPU Benchmark, Generation TaskContributingThe project is still in its infancy, so I would love your feedback, to know what problems you are trying to solve, hear issues you're encountering,
and discuss features that would help you. Therefore feel free to shoot me an e-mail (seemy profilefor the address!) or
join ourslack community.AcknowledgementsThis repo is based on the work of Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and
Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu from Google, as well as the implementation of T5 from the
huggingface team, the work of the Microsoft ONNX and onnxruntime teams, in particular Tianlei Wu, and the work of Thomas Wolf on generation of text.Original T5 Paper@article{2019t5,
  author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
  title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
  journal = {arXiv e-prints},
  year = {2019},
  archivePrefix = {arXiv},
  eprint = {1910.10683},
}Microsoft onnxruntime repoHuggingFace implementation of T5