Longformer-chinese

All work is based on Longformer(https://github.com/allenai/longformer)

Longformer-chinese 提供了：基于BERT的中文预训练模型、在分类任务上的实现

WHAT'S DIFFERENT

Longformer-chinese 基于BERT框架进行修改，在embedding层会与原版的稍有区别。加载时使用longformer.longformer：

from longformer.longformer import *
config = LongformerConfig.from_pretrained('schen/longformer-chinese-base-4096')
model = Longformer.from_pretrained('schen/longformer-chinese-base-4096', config=config)

使用schen/longformer-chinese-base-4096会自动从transformers下载预训练模型，也可以自行下载后替换成所在目录： https://huggingface.co/schen/longformer-chinese-base-4096

How to use

1. Download pretrained model

• longformer-base-4096
• longformer-large-4096

2. Install environment and code

   conda create --name longformer python=3.7
   conda activate longformer
   conda install cudatoolkit=10.0
   pip install git+https://github.com/allenai/longformer.git

3. Run the model

   import torch
   from longformer.longformer import Longformer, LongformerConfig
   from longformer.sliding_chunks import pad_to_window_size
   from transformers import RobertaTokenizer
   
   config = LongformerConfig.from_pretrained('longformer-base-4096/') 
   # choose the attention mode 'n2', 'tvm' or 'sliding_chunks'
   # 'n2': for regular n2 attantion
   # 'tvm': a custom CUDA kernel implementation of our sliding window attention
   # 'sliding_chunks': a PyTorch implementation of our sliding window attention
   config.attention_mode = 'sliding_chunks'
   
   model = Longformer.from_pretrained('longformer-base-4096/', config=config)
   tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
   tokenizer.model_max_length = model.config.max_position_embeddings
   
   SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000)  # long input document
   
   input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1
   
   # TVM code doesn't work on CPU. Uncomment this if `config.attention_mode = 'tvm'`
   # model = model.cuda(); input_ids = input_ids.cuda()
   
   # Attention mask values -- 0: no attention, 1: local attention, 2: global attention
   attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to local attention
   attention_mask[:, [1, 4, 21,]] =  2  # Set global attention based on the task. For example,
                                        # classification: the <s> token
                                        # QA: question tokens
   
   # padding seqlen to the nearest multiple of 512. Needed for the 'sliding_chunks' attention
   input_ids, attention_mask = pad_to_window_size(
           input_ids, attention_mask, config.attention_window[0], tokenizer.pad_token_id)
   
   output = model(input_ids, attention_mask=attention_mask)[0]

Model pretraining

This notebook demonstrates our procedure for training Longformer starting from the RoBERTa checkpoint. The same procedure can be followed to get a long-version of other existing pretrained models.

TriviaQA

• Training scripts: scripts/triviaqa.py
• Pretrained large model: here (replicates leaderboard results)
• Instructions: scripts/cheatsheet.txt

CUDA kernel

Our custom CUDA kernel is implemented in TVM. For now, the kernel only works on GPUs and Linux. We tested it on Ubuntu, Python 3.7, CUDA10, PyTorch >= 1.2.0. If it doesn't work for your environment, please create a new issue.

Compiling the kernel: We already include the compiled binaries of the CUDA kernel, so most users won't need to compile it, but if you are intersted, check scripts/cheatsheet.txt for instructions.

Known issues

Please check the repo issues for a list of known issues that we are planning to address soon. If your issue is not discussed, please create a new one.

Citing

If you use Longformer in your research, please cite Longformer: The Long-Document Transformer.

@article{Beltagy2020Longformer,
  title={Longformer: The Long-Document Transformer},
  author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
  journal={arXiv:2004.05150},
  year={2020},
}

Longformer is an open-source project developed by the Allen Institute for Artificial Intelligence (AI2). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.