zanussbaum / flax-sentence-embeddings

Shared code for training sentence embeddings with Flax / JAX

flax-sentence-embeddings

This repository will be used to share code for the Flax / JAX community event to train sentence embeddings on 1B+ training pairs.

You can add your code by creating a pull request.

Dataloading

Dowload data

You can download the data using this basic python script at the root of the project. Download should be completed in about 20 minutes given your connection speed. Total size on disk is arround 25G.

python dataset/download_data.py --dataset_list=datasets_list.tsv --data_path=PATH_TO_STORE_DATASETS

Dataloading

First implementation of the dataloader takes as input a single jsonl.gz file. It creates a pointer on the file such that samples are loaded one by one. The implementation is based on torch standard Dataloader and Dataset classes. The class supports num_worker>0 such that data loading is done in a background process on the CPU, i.e. the data is loaded and tokenized in parallel to training the network. This avoid to create a bottleneck from I/O and tokenization. The implementation currently return {'anchor': '...,' 'positive': '...'}

from dataset.dataset import IterableCorpusDataset

corpus_dataset = IterableCorpusDataset(
  file_path=os.path.join(PATH_TO_STORE_DATASETS, 'stackexchange_duplicate_questions_title_title.json.gz'), 
  batch_size=2,
  num_workers=2, 
  transform=None)

corpus_dataset_itr = iter(corpus_dataset)
next(corpus_dataset_itr)

# {'anchor': 'Can anyone explain all these Developer Options?',
#  'positive': 'what is the advantage of using the GPU rendering options in Android?'}

def collate(batch_input_str):
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    batch = {'anchor': tokenizer.batch_encode_plus([b['anchor'] for b in batch_input_str], pad_to_max_length=True),
             'positive': tokenizer.batch_encode_plus([b['positive'] for b in batch_input_str], pad_to_max_length=True)}
    return batch

corpus_dataloader = DataLoader(
  corpus_dataset,
  batch_size=2,
  num_workers=2,
  collate_fn=collate,
  pin_memory=False,
  drop_last=True,
  shuffle=False)

print(next(iter(corpus_dataloader)))

# {'anchor': {'input_ids': [[101, 4531, 2019, 2523, 2090, 2048, 4725, 1997, 2966, 8830, 1998, 1037, 7142, 8023, 102, 0, 0, 0], [101, 1039, 1001, 10463, 5164, 1061, 2100, 2100, 24335, 26876, 11927, 4779, 4779, 2102, 2000, 3058, 7292, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}, 'positive': {'input_ids': [[101, 1045, 2031, 2182, 2007, 2033, 1010, 2048, 4725, 1997, 8830, 1025, 1037, 3115, 2729, 4118, 1010, 1998, 1037, 17009, 8830, 1012, 2367, 3633, 4374, 2367, 4118, 1010, 2049, 2035, 18154, 11095, 1012, 1045, 2572, 2667, 2000, 2424, 1996, 2523, 1997, 1996, 17009, 8830, 1998, 1037, 1005, 2092, 2108, 3556, 1005, 2029, 2003, 1037, 15973, 3643, 1012, 2054, 2003, 1996, 2190, 2126, 2000, 2424, 2151, 8924, 1029, 1041, 1012, 1043, 1012, 8833, 6553, 26237, 2944, 1029, 102], [101, 1045, 2572, 2667, 2000, 10463, 1037, 5164, 3058, 2046, 1037, 4289, 2005, 29296, 3058, 7292, 1012, 1996, 4289, 2003, 2066, 1024, 1000, 2297, 2692, 20958, 2620, 17134, 19317, 19317, 1000, 1045, 2228, 2023, 1041, 16211, 4570, 2000, 1061, 2100, 2100, 24335, 26876, 11927, 4779, 4779, 2102, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}}

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Installation

Poetry

A Poetry toml is provided to manage dependencies in a virtualenv. Check https://python-poetry.org/

Once you've installed poetry, you can connect to virtual env and update dependencies:

poetry shell
poetry update
poetry install

requirements.txt

Someone on your platform should generate it once with following command.

poetry export -f requirements.txt --output requirements.txt

Rust compiler for hugginface tokenizers

• Hugginface tokenizers require a Rust compiler so install one.

custom libs

• If you want a specific version of any library, edit the pyproject.toml, add it and/or replace "*" by it.

Running Tests

Call this in the project folder to execute unit tests.

python -m unittest discover -s tests

If you would like to see the coverage, run

python coverage run -m unittest discover
coverage html

Then open the folder called htmlcov and open the HTML file you want to see the coverage for. For more information, check out the coverage documentation