pNLP-Mixer - Unofficial PyTorch Implementation

pNLP-Mixer: an Efficient all-MLP Architecture for Language

Implementation of pNLP-Mixer in PyTorch and PyTorch Lightning.

pNLP-Mixer is the first successful application of the MLP-Mixer architecture in NLP. With a novel embedding-free projection layer, pNLP-Mixer shows performance comparable to transformer-based models (e.g. mBERT, RoBERTa) with significantly smaller parameter count and no expensive pretraining procedures.

Fig. 1 of pNLP-Mixer: an Efficient all-MLP Architecture for Language

Requirements

Python >= 3.6.10
PyTorch >= 1.8.0
PyTorch Lightning >= 1.4.3
All other requirements are listed in the requirements.txt file.

Configurations

Please check configuration examples and also comments in the cfg directory.

Commands

Caching Vocab Hashes

python projection.py -v VOCAB_FILE -c CFG_PATH -g NGRAM_SIZE -o OUTPUT_FILE

VOCAB_FILE: path to the vocab file that contains
CFG_PATH: path to the configurations file
NGRAM_SIZE: size of n-grams used during hashing
OUTPUT_FILE: path where the resulting .npy file will be stored

Training / Testing

python run.py -c CFG_PATH -n MODEL_NAME -m MODE -p CKPT_PATH

CFG_PATH: path to the configurations file
MODEL_NAME: model name to be used for pytorch lightning logging
MODE: train or test (default: train)
CKPT_PATH: (optional) checkpoint path to resume training from or to use for testing

Results

The checkpoints used for evaluation are available here.

MTOP

Model Size	Reported	Ours
pNLP-Mixer X-Small	76.9%	79.3%
pNLP-Mixer Base	80.8%	79.4%
pNLP-Mixer X-Large	82.3%	82.1%

MultiATIS

Model Size	Reported	Ours
pNLP-Mixer X-Small	90.0%	91.3%
pNLP-Mixer Base	92.1%	92.8%
pNLP-Mixer X-Large	91.3%	92.9%

* Note that the paper reports the performance on the MultiATIS dataset using a 8-bit quantized model, whereas our performance was measured using a 32-bit float model.

IMDB

Model Size	Reported	Ours
pNLP-Mixer X-Small	81.9%	81.5%
pNLP-Mixer Base	78.6%	82.2%
pNLP-Mixer X-Large	82.9%	82.9%

Paper

@article{fusco2022pnlp,
  title={pNLP-Mixer: an Efficient all-MLP Architecture for Language},
  author={Fusco, Francesco and Pascual, Damian and Staar, Peter},
  journal={arXiv preprint arXiv:2202.04350},
  year={2022}
}

Contributors

Tony Woo @ MINDsLab Inc. (shwoo@mindslab.ai)

Special thanks to:

Hyoung-Kyu Song @ MINDsLab Inc.
Kang-wook Kim @ MINDsLab Inc.

TODO

8-bit quantization

License

akontra/pnlp-mixer

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