hf-trim 3.0.1

A tool to reduce the size of Hugging Face models via vocabulary trimming.

hf-trim

A package to reduce the size of 🤗 Hugging Face models via vocabulary trimming.

The library currently supports the following models (and their pretrained versions available on the Hugging Face Models hub);

1. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation
2. mBART: Multilingual Denoising Pre-training for Neural Machine Translation
3. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
4. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

"Why would I need to trim the vocabulary on a model?" 🤔

To put it simply, vocabulary trimming is a way to reduce a language model's memory footprint while retaining most of its performance.

Read more here.

Citation

If you use this software, please cite it as given below;

@software{Srivastava_hf-trim,
author = {Srivastava, Aditya},
license = {MPL-2.0},
title = {{hf-trim}}
url = {https://github.com/IamAdiSri/hf-trim}
}

Installation

You can also run the following command to install from PyPI;

$ pip install hf-trim

You can install from source;

$ git clone https://github.com/IamAdiSri/hf-trim
$ cd hf-trim
$ pip install .

Usage

Simple Example

from transformers import MT5Config, MT5Tokenizer, MT5ForConditionalGeneration
from hftrim.TokenizerTrimmer import TokenizerTrimmer
from hftrim.ModelTrimmers import MT5Trimmer

data = [
        " UN Chief Says There Is No Military Solution in Syria", 
        "Şeful ONU declară că nu există o soluţie militară în Siria"
]

# load pretrained config, tokenizer and model
config = MT5Config.from_pretrained("google/mt5-small")
tokenizer = MT5Tokenizer.from_pretrained("google/mt5-small")
model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")

# trim tokenizer
tt = TokenizerTrimmer(tokenizer)
tt.make_vocab(data)
tt.make_tokenizer()

# trim model
mt = MT5Trimmer(model, config, tt.trimmed_tokenizer)
mt.make_weights(tt.trimmed_vocab_ids)
mt.make_model()

You can directly use the trimmed model with mt.trimmed_model and the trimmed tokenizer with tt.trimmed_tokenizer.

Saving and Loading

# save with
tt.trimmed_tokenizer.save_pretrained('trimT5')
mt.trimmed_model.save_pretrained('trimT5')

# load with
config = MT5Config.from_pretrained("trimT5")
tokenizer = MT5Tokenizer.from_pretrained("trimT5")
model = MT5ForConditionalGeneration.from_pretrained("trimT5")

Limitations

• Fast tokenizers are currently unsupported.
• Tensorflow and Flax models are currently unsupported.

Roadmap

• Add support for MarianMT models.
• Add support for FSMT models.

Issues

Feel free to open an issue if you run into bugs, have any queries or want to request support for an architecture.

Contributing

Contributions are welcome, especially those adding functionality for new or currently unsupported models.