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System Info

- `transformers` version: 4.18.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.3
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.5.1 (False)
- Tensorflow version (GPU?): 2.4.0 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: N/A
- Using distributed or parallel set-up in script?: N/A

Who can help?

@LysandreJik @SaulLu

Information

• [ ] The official example scripts
• [ ] My own modified scripts

Tasks

• [ ] An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• [ ] My own task or dataset (give details below)

Reproduction

I'm interested in finding out the max sequence length that a model can be run with. After some code browsing, my current understanding that this is a property stored in the tokenizer model_max_length.

I wrote a simple script to load a tokenzier for a pretrained model and print the model max length. This is the important part:

    # initialize the tokenizer to be able to print model_max_length
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    logger.info(f"Model max length {tokenizer.model_max_length}")

I used this to print max seq length for models such as BERT, RoBERTa, etc. All with expected results. For DeBERTa, I get confusing results.

If I run my script with DeBERTA-v3 as follows:

python check_model_max_len.py --model_name microsoft/deberta-v3-large --output_dir ./tmp --cache_dir ./tmp/cache

I get Model max length 1000000000000000019884624838656

If I understand correctly, this is a large integer used for models that can support "infinite" size lengths.

If I run my script with --model_name microsoft/deberta-v2-xlarge, I get Model max length 512

I don't understand if this is a bug or a feature :) My understanding is that the main difference between DeBERTa V2 and V3 is the use of ELECTRA style discriminator during MLM pretraining in V3. I don't understand why this difference would lead to a difference in supported max sequence lengths between the two models.

I also don't understand why some properties are hardcoded in the python files, e.g.,

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

I would expect these to be in the config files for the corresponding models.

Expected behavior

I would expect the max supported lengths for DeBERTa-V2 and DeBERTa-V3 models to be the same. Unless, I'm missing something. Thanks for your help!

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It's likely an error! Do you want to open a discussion on the model repo directly? https://huggingface.co/microsoft/deberta-v3-base/discussions/new

i get the same result 1000000000000000019884624838656

I'm seeing the same for the 125m and 350m OPT tokenizers (haven't checked the larger ones):

>>> AutoTokenizer.from_pretrained("facebook/opt-350m")
PreTrainedTokenizer(name_or_path='facebook/opt-350m', vocab_size=50265, model_max_len=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), 'pad_token': AddedToken("<pad>", rstrip=False, lstrip=False, single_word=False, normalized=True)})
>>> AutoTokenizer.from_pretrained("facebook/opt-125m")
PreTrainedTokenizer(name_or_path='facebook/opt-125m', vocab_size=50265, model_max_len=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), 'pad_token': AddedToken("<pad>", rstrip=False, lstrip=False, single_word=False, normalized=True)})

Is this definitely a bug?

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deberta v3 uses relative position embeddings which means it isn't limited to the typical 512 token limit.

As taken from section A.5 in their paper:

With relative position bias, we choose to truncate the maximum relative distance to k as in equation 3. Thus in each layer, each token can attend directly to at most (2k - 1) tokens and itself. By stacking Transformer layers, each token in the l-th layer can attend to at most (2k-1)*l tokens implicitly. Taking DeBERTa_large as an example, where k = 512, L = 24, in theory, the maximum sequence length that can be handled is 24,528.

That being said, it will start to slow down a ton once the sequence length gets bigger than 512

Yes, I thought this might be the case, however, the same is true for deberta v2 if I remember correctly and the answer for that is different. What I was asking in the original post is why the the difference between v2 and v3. Thanks for clarifying part of the question/answer.

I meant to add to my last post: The max length of 1000000000000000019884624838656 is typically an error when the max length is not specified in the tokenizer config file.

There was a discussion about it here: https://huggingface.co/google/muril-base-cased/discussions/1 And the solution was to modify the tokenizer config file: https://huggingface.co/google/muril-base-cased/discussions/2

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This is still an issue with the config file and/or config file parser.

@bcdarwin

What is the issue?