Generate: handle text conditioning with multimodal encoder-decoder models #22748
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cc @younesbelkada @NielsRogge FYI -- this PR consolidates your recent changes regarding text conditioning on multimodal models. The next models should be easier to add :) |
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The documentation is not available anymore as the PR was closed or merged. |
sgugger
approved these changes
Apr 13, 2023
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Thanks a lot @gante! 🙏 |
novice03
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Jun 23, 2023
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What does this PR do?
Consolidates
decoder_input_idspreparation changes in a single place, for all future multimodal encoder-decoder models on PT and TF.In a nutshell, this PR generalizes the following use cases:
decoder_input_ids, but it is missing the BOS token (some tokenizers, like the T5 tokenizer, do not prepend a BOS token). In this case, a BOS token is prepended.input_ids, but the encoder has noinput_idsinput. In this case,input_idsis handled just likedecoder_input_ids.Slow tests were run on T5, Pix2Struct, BLIP, and BLIP2.