How to do batch generation with the GPT2 model?
Batch generation is now possible for GPT2 in master by leveraging the functionality shown in this PR: https://github.com/huggingface/transformers/pull/7552?notification_referrer_id=MDE4Ok5vdGlmaWNhdGlvblRocmVhZDEyMTMzNzA0MDA6MjM0MjM2MTk%3D#event-3876130796 .
For more info on how to prepare a GPT2 for batch generation, you can checkout this test:
Hi I am the author of the PR.
You can now do batch generation by calling the same
All you need to add is:
tokenizer.padding_side = "left"(probably reset it back later)
- pass in
Explanation: (see full example in the end)
- We need
tokenizer.padding_side = "left"because we will use the logits of the right-most token to predict the next token, so the padding should be on the left.
- This what this PR added. Here is a summary:
GPT-2 uses absolute positional embedding (
position_ids), before this change, no
position_ids is passed in to the model, and the model automatically generates the embeddings from 0 to n, even if there is padding (e.g. when input is a batch).
<pad> <pad> a b c -> position_ids=
0 1 2 3 4, what we expect is
x x 0 1 2 (
x means don’t case)
This PR adds positional embedding in
prepare_inputs_for_generation(), which is called in
generate(), by calculating them using
attention_mask, and that’s why you need to pass it in.
You can find a full example in PR.
Hi, there. Thanks for your work to support batch inference in GPT2. However, I still have one confusion, which may need your help. Thanks in advance!
If I wanna pass the “past_key_values”, how should I process the position_ids and attention mask? Supposing the length of my past_key_values is 2, the padded input is just like your example: , , a, b, c. Should I change the attention mask from 0, 0, 1, 1, 1 to 1, 1, 0, 0, 1, 1, 1, where the first double “1” refers. to the past_key_values.
Thanks a lot!