Pretraining Without Attention(BiGS)

Bidirectional Language Modeling with State Space Model

BiGS is the first model to achieve BERT-level transfer learning on the GLUE benchmark with subquadratic complexity in length (or without attention). It is also the first bidirectional state space model in NLP.

Pytorch models port from JAX

Torch Masked Language Model

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import torch
from BiGS.modeling_bigs import BiGSForMaskedLM
model = BiGSForMaskedLM.from_pretrained('JunxiongWang/BiGS_128')

Torch Sequence Classification Model

import torch
from BiGS.modeling_bigs import BiGSForSequenceClassification
model = BiGSForSequenceClassification.from_pretrained('JunxiongWang/BiGS_128')

For GLUE task, please see GLUE_torch.md and GLUE_torch_freeze.md. If you don't want to use MNLI checkpoints to finetune MRPC, RTE, STS-B, please run GLUE_torch_freeze.md. Notice that torch version has slight worse results compared with Jax version.

Official JAX Implementation

Paper | |

This repository contains BiGS's jax model definitions, pretrained models weights, training and fintuning code for our paper exploring using state space models for pretraining. You can find more details in our paper.

Pretraining Without Attention
Junxiong Wang, Jing Nathan Yan, Albert Gu, Alexander M.Rush
Cornell University, Cornell Tech, DeepMind

Transformers have been essential to pretraining success in NLP. While other architectures have been used, downstream accuracy is either significantly worse, or requires attention layers to match standard benchmarks such as GLUE. This work explores pretraining without attention by using recent advances in sequence routing based on state-space models (SSMs). Our proposed model, Bidirectional Gated SSM (BiGS), combines SSM layers with a multiplicative gating architecture that has been effective in simplified sequence modeling architectures. The model learns static layers that do not consider pair-wise interactions. Even so, BiGS is able to match BERT pretraining accuracy on GLUE and can be extended to long-form pretraining of 4096 tokens without approximation. Analysis shows that while the models have similar accuracy, the approach has significantly different inductive biases than BERT in terms of interactions and syntactic representations.

This repo contains:

• 🪐 JAX implementation of BiGS and its variants,
• 🛸 Pre-trained BiGS Models of various lengths,
• 💥 Training scripts to train BiGS from scratch,
• 💫 Fine-tuning scripts for GLUE tasks

Setup

You can run our models on both GPUs and TPUs.

For TPUs,

pip install -r requirements-tpu.txt

For GPUs,

pip install -r requirements-gpu.txt

Download Models

Pretrained Models

Sentence Length	Trained Tokens	Link
128	~11B	BiGS-11B-128
128	~29B	BiGS-29B-128
128	~97B	BiGS-97B-128
512	~108B	BiGS-108B-512
1024	~110B	BiGS-110B-1024
4096	~110B	BiGS-110B-4096

MNLI Checkpoints

Sentence Length	Trained Tokens	Model
128	~11B	BiGS-11B-128MNLI
128	~29B	BiGS-29B-128MNLI
128	~97B	BiGS-97B-128MNLI
512	~108B	BiGS-108B-512MNLI

Example Usage

Load Masked Language Model

import jax
from jax import numpy as jnp
from transformers import BertTokenizer
from BiGS.modeling_flax_bigs import FlaxBiGSForMaskedLM

tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
model = FlaxBiGSForMaskedLM.from_pretrained('JunxiongWang/BiGS_128')

text = "The goal of life is [MASK]."
encoded_input = tokenizer(text, return_tensors='np', padding='max_length', max_length=128)
output = model(**encoded_input)
tokenizer.convert_ids_to_tokens(jnp.flip(jnp.argsort(jax.nn.softmax(output.logits[encoded_input['input_ids']==103]))[0])[:10])
# output: ['happiness', 'love', 'peace', 'perfection', 'life', 'enlightenment', 'god', 'survival', 'freedom', 'good']
jnp.flip(jnp.sort(jax.nn.softmax(output.logits[encoded_input['input_ids']==103]))[0])[:10]
# probability: [0.16052087, 0.04306792, 0.03651363, 0.03468223, 0.02927081, 0.02549769, 0.02385132, 0.02261189, 0.01672831, 0.01619471]

text = "Paris is the [MASK] of France."
encoded_input = tokenizer(text, return_tensors='np', padding='max_length', max_length=128)
output = model(**encoded_input)
tokenizer.convert_ids_to_tokens(jnp.flip(jnp.argsort(jax.nn.softmax(output.logits[encoded_input['input_ids']==103]))[0])[:8])
# output: ['capital', 'centre', 'center', 'city', 'capitol', 'prefecture', 'headquarters', 'president', 'metropolis', 'heart']
jnp.flip(jnp.sort(jax.nn.softmax(output.logits[encoded_input['input_ids']==103]))[0])[:10]
# probability: [0.9981787 , 0.00034076, 0.00026992, 0.00026926, 0.00017787, 0.00004816, 0.00004256, 0.00003716, 0.00003634, 0.00002893]

Load Sequence Classification Model

from BiGS.modeling_flax_bigs import FlaxBiGSForSequenceClassification
model = FlaxBiGSForSequenceClassification.from_pretrained('JunxiongWang/BiGS_512')

Load Question Answering Model

from BiGS.modeling_flax_bigs import FlaxBiGSForQuestionAnswering
model = FlaxBiGSForQuestionAnswering.from_pretrained('JunxiongWang/BiGS_512')

Load Multiple Choice Classification Model

from BiGS.modeling_flax_bigs import FlaxBiGSForMultipleChoice
model = FlaxBiGSForMultipleChoice.from_pretrained('JunxiongWang/BiGS_512')

Pretraining

See pretrain.md

Finetuning

GLUE

See GLUE.md and GLUE_freeze.md. If you don't want to use MNLI checkpoints to finetune MRPC, RTE, STS-B, please run GLUE_freeze.md.

Reference

If you find this project inspiring or use our repository, kindly please cite

@article{wang2022pretraining,
  title={Pretraining Without Attention},
  author={Wang, Junxiong and Yan, Jing Nathan and Gu, Albert and Rush, Alexander M},
  journal={arXiv preprint arXiv:2212.10544},
  year={2022}
}