transformers/docs/source/en/model_doc/longt5.md

This model was published in HF papers on 2021-12-15 and contributed to Hugging Face Transformers on 2022-06-13.

LongT5

Overview

The LongT5 model was proposed in LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. It's an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.

The abstract from the paper is the following:

Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training (PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call {\em Transient Global} (TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs. We are able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on question answering tasks.

This model was contributed by stancld. The original code can be found here.

Usage tips

• [LongT5ForConditionalGeneration] is an extension of [T5ForConditionalGeneration] exchanging the traditional encoder self-attention layer with efficient either local attention or transient-global (tglobal) attention.
• Unlike the T5 model, LongT5 does not use a task prefix. Furthermore, it uses a different pre-training objective inspired by the pre-training of [PegasusForConditionalGeneration].
• LongT5 model is designed to work efficiently and very well on long-range sequence-to-sequence tasks where the input sequence exceeds commonly used 512 tokens. It is capable of handling input sequences of a length up to 16,384 tokens.
• For Local Attention, the sparse sliding-window local attention operation allows a given token to attend only r tokens to the left and right of it (with r=127 by default). Local Attention does not introduce any new parameters to the model. The complexity of the mechanism is linear in input sequence length l: O(l*r).
• Transient Global Attention is an extension of the Local Attention. It, furthermore, allows each input token to interact with all other tokens in the layer. This is achieved via splitting an input sequence into blocks of a fixed length k (with a default k=16). Then, a global token for such a block is obtained via summing and normalizing the embeddings of every token in the block. Thanks to this, the attention allows each token to attend to both nearby tokens like in Local attention, and also every global token like in the case of standard global attention (transient represents the fact the global tokens are constructed dynamically within each attention operation). As a consequence, TGlobal attention introduces a few new parameters -- global relative position biases and a layer normalization for global token's embedding. The complexity of this mechanism is O(l(r + l/k)).
• An example showing how to evaluate a fine-tuned LongT5 model on the pubmed dataset is below.

import evaluate
from datasets import load_dataset

from transformers import AutoTokenizer, LongT5ForConditionalGeneration


dataset = load_dataset("scientific_papers", "pubmed", split="validation")
model = (
    LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps", device_map="auto")
    .to("auto")
    .half()
)
tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")


def generate_answers(batch):
    inputs_dict = tokenizer(
        batch["article"], max_length=16384, padding="max_length", truncation=True, return_tensors="pt"
    )
    input_ids = inputs_dict.input_ids.to(model.device)
    attention_mask = inputs_dict.attention_mask.to(model.device)
    output_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=512, num_beams=2)
    batch["predicted_abstract"] = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    return batch


result = dataset.map(generate_answer, batched=True, batch_size=2)
rouge = evaluate.load("rouge")
rouge.compute(predictions=result["predicted_abstract"], references=result["abstract"])

Resources

• Translation task guide
• Summarization task guide

LongT5Config

autodoc LongT5Config

LongT5Model

autodoc LongT5Model - forward

LongT5ForConditionalGeneration

autodoc LongT5ForConditionalGeneration - forward

LongT5EncoderModel

autodoc LongT5EncoderModel - forward