ccdv/lsg-bart-base-16384

LSG model

Transformers >= 4.36.1
This model relies on a custom modeling file, you need to add trust_remote_code=True
See #13467

LSG ArXiv paper.
Github/conversion script is available at this link.

• Usage
• Parameters
• Sparse selection type
• Tasks

This model is adapted from BART-base for encoder-decoder tasks without additional pretraining. It uses the same number of parameters/layers and the same tokenizer.

This model can handle long sequences but faster and more efficiently than Longformer (LED) or BigBird (Pegasus) from the hub and relies on Local + Sparse + Global attention (LSG).

The model requires sequences whose length is a multiple of the block size. The model is "adaptive" and automatically pads the sequences if needed (adaptive=True in config). It is however recommended, thanks to the tokenizer, to truncate the inputs (truncation=True) and optionally to pad with a multiple of the block size (pad_to_multiple_of=...). \

Implemented in PyTorch.

Usage

The model relies on a custom modeling file, you need to add trust_remote_code=True to use it.

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("ccdv/lsg-bart-base-16384", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-16384")

Parameters

You can change various parameters like :

• the number of global tokens (num_global_tokens=1)
• local block size (block_size=128)
• sparse block size (sparse_block_size=128)
• sparsity factor (sparsity_factor=2)
• mask_first_token (mask first token since it is redundant with the first global token)
• see config.json file

Default parameters work well in practice. If you are short on memory, reduce block sizes, increase sparsity factor and remove dropout in the attention score matrix.

from transformers import AutoModel

model = AutoModel.from_pretrained("ccdv/lsg-bart-base-16384", 
    trust_remote_code=True, 
    num_global_tokens=16,
    block_size=64,
    sparse_block_size=64,
    attention_probs_dropout_prob=0.0
    sparsity_factor=4,
    sparsity_type="none",
    mask_first_token=True
)

Sparse selection type

There are 6 different sparse selection patterns. The best type is task dependent.
If sparse_block_size=0 or sparsity_type="none", only local attention is considered.
Note that for sequences with length < 2*block_size, the type has no effect.

• sparsity_type="bos_pooling" (new)
  • weighted average pooling using the BOS token
  • Works best in general, especially with a rather large sparsity_factor (8, 16, 32)
  • Additional parameters:
    • None
• sparsity_type="norm", select highest norm tokens
  • Works best for a small sparsity_factor (2 to 4)
  • Additional parameters:
    • None
• sparsity_type="pooling", use average pooling to merge tokens
  • Works best for a small sparsity_factor (2 to 4)
  • Additional parameters:
    • None
• sparsity_type="lsh", use the LSH algorithm to cluster similar tokens
  • Works best for a large sparsity_factor (4+)
  • LSH relies on random projections, thus inference may differ slightly with different seeds
  • Additional parameters:
    • lsg_num_pre_rounds=1, pre merge tokens n times before computing centroids
• sparsity_type="stride", use a striding mecanism per head
  • Each head will use different tokens strided by sparsify_factor
  • Not recommended if sparsify_factor > num_heads
• sparsity_type="block_stride", use a striding mecanism per head
  • Each head will use block of tokens strided by sparsify_factor
  • Not recommended if sparsify_factor > num_heads

Tasks

Seq2Seq example for summarization:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-16384", 
    trust_remote_code=True, 
    pass_global_tokens_to_decoder=True, # Pass encoder global tokens to decoder
)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-16384")

SENTENCE = "This is a test sequence to test the model. " * 300
token_ids = tokenizer(
    SENTENCE, 
    return_tensors="pt", 
    padding="max_length", # Optional but recommended
    truncation=True # Optional but recommended
    )
output = model(**token_ids)

Classification example:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ccdv/lsg-bart-base-16384", 
    trust_remote_code=True, 
    pass_global_tokens_to_decoder=True, # Pass encoder global tokens to decoder
)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-16384")

SENTENCE = "This is a test sequence to test the model. " * 300
token_ids = tokenizer(
    SENTENCE, 
    return_tensors="pt", 
    #pad_to_multiple_of=... # Optional
    truncation=True
    )
output = model(**token_ids)

> SequenceClassifierOutput(loss=None, logits=tensor([[-0.3051, -0.1762]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)

BART

@article{DBLP:journals/corr/abs-1910-13461,
  author    = {Mike Lewis and
               Yinhan Liu and
               Naman Goyal and
               Marjan Ghazvininejad and
               Abdelrahman Mohamed and
               Omer Levy and
               Veselin Stoyanov and
               Luke Zettlemoyer},
  title     = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
               Generation, Translation, and Comprehension},
  journal   = {CoRR},
  volume    = {abs/1910.13461},
  year      = {2019},
  url       = {http://arxiv.org/abs/1910.13461},
  eprinttype = {arXiv},
  eprint    = {1910.13461},
  timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}