model.generate with prefix_allowed_tokens_fn throws RuntimeError: probability tensor contains either `inf`, `nan` or element < 0

[iamjanvijay]

Environment info

• transformers version: 4.15.0
• Platform: Linux-5.4.0-90-generic-x86_64-with-debian-bullseye-sid
• Python version: 3.7.12
• PyTorch version (GPU?): 1.10.0+cu102 (True)
• Tensorflow version (GPU?): 2.7.0 (True)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using GPU in script?: No
• Using distributed or parallel set-up in script?: No

Who can help

@patrickvonplaten @narsil

Information

Model I am using T5ForConditionalGeneration:

The problem arises when using my own modified scripts: Script to reproduce error is mentioned below.

The tasks I am working on is my own task or dataset: The task requires conditional generation from T5, in such a way, that the output vocabulary is restricted to a small set.

To reproduce

1. Run the following script to reproduce the behaviour.

from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config

lm_model = 't5-small'
model = T5ForConditionalGeneration.from_pretrained(lm_model)
tokenizer = T5Tokenizer.from_pretrained(lm_model)

def restrict_decode_vocab(batch_idx, prefix_beam):
    if len(prefix_beam)==3:
        restricted_vocab = tokenizer(' ', return_tensors="pt")['input_ids'].tolist()
    else:
        restricted_vocab = tokenizer('<extra_id_0> cute dog <extra_id_1> the <pad>', return_tensors="pt")['input_ids'].tolist()
    return restricted_vocab

source = ['The <extra_id_0> walks in <extra_id_1> park .']
source_encoding = tokenizer(source[:], padding='longest', return_tensors="pt")
input_ids, attention_mask = source_encoding['input_ids'], source_encoding['attention_mask']
decoded_beams = model.generate(input_ids=input_ids, attention_mask=attention_mask, do_sample=True, num_beams=2, prefix_allowed_tokens_fn=restrict_decode_vocab, min_length=4, max_length=4, remove_invalid_values=True)
print(decoded_beams)

2. Above script produces the following stack trace.

/home/jsingh319/uploaded_venvs/venv-koala-torch-1.10-python-3.7.12/lib/python3.7/site-packages/transformers/generation_utils.py:2259: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  next_indices = next_tokens // vocab_size
Traceback (most recent call last):
  File "reproduce_error.py", line 17, in <module>
    decoded_beams = model.generate(input_ids=input_ids, attention_mask=attention_mask, do_sample=True, num_beams=2, prefix_allowed_tokens_fn=restrict_decode_vocab, min_length=4, max_length=4, remove_invalid_values=True)
  File "/home/jsingh319/uploaded_venvs/venv-koala-torch-1.10-python-3.7.12/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
    return func(*args, **kwargs)
  File "/home/jsingh319/uploaded_venvs/venv-koala-torch-1.10-python-3.7.12/lib/python3.7/site-packages/transformers/generation_utils.py", line 1220, in generate
    **model_kwargs,
  File "/home/jsingh319/uploaded_venvs/venv-koala-torch-1.10-python-3.7.12/lib/python3.7/site-packages/transformers/generation_utils.py", line 2253, in beam_sample
    next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)
RuntimeError: probability tensor contains either `inf`, `nan` or element < 0

Expected behavior

No error.

Possible solution

The call function for class "InfNanRemoveLogitsProcessor" should include the following statement before returning "scores".

scores[scores == float("-inf")] = torch.finfo(scores.dtype).min

[Narsil]

@patrickvonplaten Pinging you to get your input on this.

It seems -inf are explicitely set by prefix_allowed_tokens_fn and remove_invalid_values doesn't remove float(-inf) specifically.

However the script does seems to fail currently.

I added a PR containing the "fix" to accelerate things along, but given everything is ingrained in tests and other logits processors actively use float(-inf) I am not sure this is the desired behavior.

Other options I consider viable:

• Stop using float(-inf) directly and use torch.finfo(scores.dtype).min instead (we don't introduce infinities anymore so should solve it)
• Change float(-inf) only before using torch.multinomialt.

[patrickvonplaten]

I don't think that this is related in any way to the InfNanRemoveLogitsProcessor processor. IMO, the reason for the error here is that in the 3rd generation step, all values of next_token_scores are set to -inf (I think) due to the prefix_allowed_tokens_fn that you've added. This is not a bug IMO with transformers, but with the prefix_allowed_tokens_fn function as it should not set all values to -inf.

A tip from my side @iamjanvijay would be to do the following. Create the PrefixConstrainedLogitsProcessor object with your function and just play around with it locally (what happens at generation step 3) I think you'll see then that it sets all values to -inf at some point which it shouldn't do

[iamjanvijay]

@patrickvonplaten @Narsil Thanks for your response. I was trying to check why this is happening. I found that if the restricted_vocab at any generation step only includes "\</s>" (end-of-sentence token) this error occurs. In other cases, the script doesn't encounter such an error. I'll try to look if all the elements at that generation step are set to -inf.

[Narsil]

I'll close my PR in the meantime. We can reopen it if needed, but I tend to agree with @patrickvonplaten that having everything float(-inf) can be considered a bug already.

[mindojune]

@patrickvonplaten @Narsil Thanks for your response. I was trying to check why this is happening. I found that if the restricted_vocab at any generation step only includes "" (end-of-sentence token) this error occurs. In other cases, the script doesn't encounter such an error. I'll try to look if all the elements at that generation step are set to -inf.

Have you found what was causing the issue by any chance @iamjanvijay? I'm encountering the same issue while I'm using the generate function with BART, but I'm not using any prefix_allowed_tokens, and this error usually happens when I've been training the model for a while. Like @iamjanvijay said, I suspect something to do with cases where some tokens are masked or filtered, but I haven't really figured out where/why it's happening. I'd appreciate any pointers.

[patrickvonplaten]

@mindojune - could you maybe open a new issue as it's not related to prefix_allowed_tokens_fn ?

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[hongyuntw]

@mindojune Hi, I am facing the same problem as you and this error usually happens after I have trained the model for a while. And I am also using BART. Do you have any idea why this is happening or how to fix this error? Thank you a lot.

[patrickvonplaten]

Hey @hongyuntw, the reason is that BART forces the second token to be this id https://huggingface.co/facebook/bart-large/blob/main/config.json#L27 . However if you use additionally something like prefix_allowed_tokens_fn which might also not allow this id: https://huggingface.co/facebook/bart-large/blob/main/config.json#L27 => then all ids are set to -inf in which case the model cannot generate anything anymore. To solve this I would probably set this config: https://huggingface.co/facebook/bart-large/blob/main/config.json#L27 to None

[paulbricman]

Was running into similar issues when using prefix_allowed_tokens_fn in tandem with beam-search multinomial sampling, and realized the top_k and top_p args were sometimes preventing all the allowed tokens from being used, as they were outside those two tops. no_repeat_ngram_size can have a similar effect.

Consider removing top_k and top_p if only allowing certain tokens is more important.

[chentiao]

i also have this problem,but i dont know how to fix

[Dhawgupta]

Same I am also running into this issue, has there been any resolution for this ?

[chentiao]

i try another way to avoid this problem,i was apply minigpt-4 in 3090,when i use v0 version weight,the problem happen,for result it ,i try many pytorch version ,but it dosent work.finally,i use a new model version v1.1,this problem also away.so,i think the problem is relative with model.for minigpt4,model decoding is relateted with fschat here

[Chanwhistle]

Did somebody solved this problem?

[amyeroberts]

cc @gante for reference

[gante]

@Chanwhistle have a look at this comment

If you believe this comment does not apply to you, then a reproducer of the issue will be needed 🤗