Batch generation with GPT2

How to do batch generation with the GPT2 model?

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Batch generation is now possible for GPT2 in master by leveraging the functionality shown in this PR: .

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 generate().
All you need to add is:

  1. set tokenizer.padding_side = "left" (probably reset it back later)
  2. pass in attention_mask to generate()

Explanation: (see full example in the end)

  1. 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.
  2. 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).

Example: tokens=<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!

@patrickvonplaten @ttj I think this is a good question! Could we discuss on how to do batch inference with past_key_values?

Is it possible to have variable max_gen_length? depending on the length of the input sequence, for instance? (e.g. max_gen_length = len(tokenizer.tokenize(input_seq) + 20)?

It looks like you are looking for max_new_tokens?

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hi, I’m using the input parameter “past_key_values” to train a gpt model. So I wonder when doing batch generation in this way, if I pass “past_key_values” to model through the parameter “model_kwargs”, whether the generation method will work as expected?


Correct me if I’m wrong but for GPT2, during training mode, when padding_side='left', position_ids should be passed correctly or else the position_ids is created as if right padding is used … making it inconsistent, right ?

I’m referring to this line where the position_ids is created if it is not passed …
transformers/ at main · huggingface/transformers (

Thank you !

Hey @lqtrung :wave: The position_ids don’t need to be passed, as long as the right attention_mask is. prepare_inputs_for_generation (see here) takes care of that for you :smiley:

Hi @joaogante , thank you for the response.

I believe that the position_ids is properly prepared during generation as you said because the prepare_inputs_for_generation is called …

But my question is about during training where that function is not called and the gpt2 modeling script does not compute position_ids based on the attention mask (so it is not correct when ‘left’ padding is used …)

So I’m not sure about the recommended practice:

  1. Is ‘right’ padding always used during training … and ‘left’ padding is only used during batch generation ?
  2. Or the training and generation should have the same padding scheme and in this case the gpt2 modeling script should handle the position_ids better ?

@lqtrung what you described as option 1. (right padding during training, left padding during inference) is the way to go.

You can also always pass position_ids, but the settings above get you the correct results without passing them. A caveat here is that you never want GPT2 to generate after its pad token (note: GPT2 doesn’t have a pad token, but it is common to set pad token = eos token), even if you pass the correct position_ids. GPT2 was not trained for that case, and the results will be gibberish – right padding will often get you in this situation.

A good resource to reason about this is the illustrated GPT2 :slight_smile: