Awesome

DataMUX

PyTorch implementation for the paper:

DataMUX: Data Multiplexing for Neural Networks
Vishvak Murahari, Carlos E. Jimenez, Runzhe Yang, Karthik Narasimhan

models

This repository contains code for reproducing results. We provide pretrained model weights and associated configs to run inference or train these models from scratch. If you find this work useful in your research, please cite:

@inproceedings{
murahari2022datamux,
title={Data{MUX}: Data Multiplexing for Neural Networks},
author={Vishvak Murahari and Carlos E Jimenez and Runzhe Yang and Karthik R Narasimhan},
booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},
year={2022},
url={https://openreview.net/forum?id=UdgtTVTdswg}
}

Setup and Dependencies
Usage
Reference
License

Setup and Dependencies

Our code is implemented in PyTorch. To setup, do the following:

Install Python 3.6
Get the source:

git clone https://github.com/princeton-nlp/DataMUX.git datamux

Install requirements into the datamux virtual environment, using Anaconda:

conda env create -f env.yaml

Usage

Overview

For sentence-level classification tasks, refer to run_glue.py and run_glue.sh. For token-level classification tasks, refer to run_ner.py and run_ner.sh.

Pre-trained checkpoints

We release all the pretrained checkpoints on the Hugging Face model hub. We list the checkpoints below. For number of instances, use 2, 5, 10, 20 or 40.

Task	Model name on hub	Full path
Retrieval Warmup	datamux-retrieval-<num_instances>	princeton-nlp/datamux-retrieval-<num_instances>
MNLI	datamux-mnli-<num_instances>	princeton-nlp/datamux-mnli-<num_instances>
QNLI	datamux-qnli-<num_instances>	princeton-nlp/datamux-qnli-<num_instances>
QQP	datamux-qqp-<num_instances>	princeton-nlp/datamux-qqp-<num_instances>
SST2	datamux-sst2-<num_instances>	princeton-nlp/datamux-sst2-<num_instances>
NER	datamux-ner-<num_instances>	princeton-nlp/datamux-ner-<num_instances>

Settings

The bash scripts run_ner.sh and run_glue.sh take the following arguments:

Argument	Flag	Explanation	Argument Choices
NUM_INSTANCES	-N --num_instances	Number of multiplexing instances	2,5,10,20,40
DEMUXING	-d --demuxing	Demultiplexing architecture	"index", "mlp"
MUXING	-m --muxing	Multiplexing architecture	"gaussian_hadamard", "binary_hadamard", "random_ortho"
SETTING	-s --setting	Training setting	"baseline", "finetuning", "retrieval_pretraining"
TASK_NAME	--task	Task name during finetuning	"mnli", "qnli", "sst2", "qqp" for `run_glue.py` or "ner" for `run_ner.py`
LEARNING_RATE	--lr	Learning rate for optimization	Any float but we use either 2e-5 or 5e-5
BATCH_SIZE	--batch_size	Batch size (after multiplexing); note that the effective batch size is BATCH_SIZE * NUM_INSTANCES	Any integer. If left unset, will be set automatically based on value of N
CONFIG_NAME	--config_name	Config path for backbone Transformer Model	Any config file in `configs` directory
MODEL_PATH	--model_path	Model path if either continuing to train from a checkpoint or initialize from retrieval task pretrained checkpoint	Path to local checkpoint or path to model on the hub
LEARN_MUXING	--learn_muxing	Whether to learn instance embeddings in multiplexing
DO_TRAIN	--do_train	Pass flag to do training
DO_EVAL	--do_eval	Pass flag to do eval

Below we list exemplar commands for different training settings:

Retrieval pretraining

This commands runs retrieval pretraining for N=2

sh run_glue.sh \
   -N 2 \
   -d index \
   -m gaussian_hadamard \
   -s retrieval_pretraining \
   --config_name configs/ablations/base_model/roberta.json \
   --lr 5e-5 \
   --do_train \
   --do_eval

Finetuning

This command finetunes from a retrieval pretrained checkpoint with N=2

sh run_glue.sh \
   -N 2 \
   -d index \
   -m gaussian_hadamard \
   -s finetuning \
   --config_name configs/ablations/base_model/roberta.json \
   --lr 5e-5 \
   --task mnli \
   --model_path princeton-nlp/datamux-retrieval-2 \
   --do_train \
   --do_eval

Similar, to run token-level classification tasks like NER, change run_glue.sh to run_ner.sh

sh run_ner.sh \
   -N 2 \
   -d index \
   -m gaussian_hadamard \
   -s finetuning \
   --config_name configs/ablations/base_model/roberta.json \
   --lr 5e-5 \
   --task ner \
   --model_path princeton-nlp/datamux-retrieval-2 \
   --do_train \
   --do_eval

Baselines

For the non-multiplexed baselines, run the following commnands

sh run_glue.sh \
-N 1 \
-s baseline \
--config_name configs/ablations/base_model/roberta.json \
--lr 2e-5 \
--task mnli

Vision

For reproducing results on the vision tasks for MLPs and CNNs, please use this notebook

Reference

@inproceedings{
murahari2022datamux,
title={Data{MUX}: Data Multiplexing for Neural Networks},
author={Vishvak Murahari and Carlos E Jimenez and Runzhe Yang and Karthik R Narasimhan},
booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},
year={2022},
url={https://openreview.net/forum?id=UdgtTVTdswg}
}

License

Check LICENSE.md