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

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)

1. 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