Lsg Bart Large 4096

🚀 LSG model

This is an LSG model, which is adapted from BART-large for encoder - decoder tasks without additional pretraining. It can handle long sequences more efficiently than some other models, relying on Local + Sparse + Global attention (LSG).

Prerequisites

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

Related Links

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

Table of Contents

• Usage
• Parameters
• Sparse selection type
• Tasks

Model Features

This model uses the same number of parameters/layers and the same tokenizer as BART - large. It can handle long sequences faster and more efficiently than Longformer (LED) or BigBird (Pegasus) from the hub. The model requires sequences whose length is a multiple of the block size. It is "adaptive" and can automatically pad the sequences if needed (adaptive=True in config). However, it is recommended to truncate the inputs (truncation=True) and optionally to pad with a multiple of the block size (pad_to_multiple_of=...). It is implemented in PyTorch.

🚀 Quick Start

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-large-4096", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-large-4096")

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-large-4096", 
    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-large-4096", 
    trust_remote_code=True, 
    pass_global_tokens_to_decoder=True, # Pass encoder global tokens to decoder
)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-large-4096")

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)

Classification example

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ccdv/lsg-bart-large-4096", 
    trust_remote_code=True, 
    pass_global_tokens_to_decoder=True, # Pass encoder global tokens to decoder
)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-large-4096")

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)

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

Citation

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}
}