1. NLCodec

A set of (low level) Natural Language Encoder-Decoders (codecs), that are useful in preprocessing stage of NLP pipeline. These codecs include encoding of sequences into one of the following:

  1. Character

  2. Word

  3. BPE based subwords

  4. Class (for multiclass classification)

  5. Byte: Character is a Unicode codepoint (which can be higher than 255) where as bytes are [0-255]; a proxy over utf-8 scheme

It provides python (so embed into your app) and CLI APIs (use it as stand alone tool).

There are many BPE implementations available already, but this one provides differs:

  1. Pure python implementation that is easy to modify anything to try new ideas. (other implementations require c++ expertise to modify the core)

  2. BPE model is a simple text that can be inspected with less or cut. It includes info on which pieces were put together and what frequencies etc.

  3. Reasonably faster than the other pure python implementations — speed in python comes with the cost of extra memory due to indexing.

  4. PySpark backend for extracting term frequencies from large datasets

2. Installation

Please run only one of these

# Install from pypi (recommended)
$ pip install nlcodec

# Clone repo for development mode
git clone https://github.com/isi-nlp/nlcodec
cd nlcodec
pip install --editable .

pip installer registers these CLI tools in your PATH:

  • nlcodec  — CLI for learn, encode, decode. Same as python -m nlcodec + nlcodec-learn  — CLI for learn BPE, with PySpark backend. Same as python -m nlcodec.learn

  • nlcodec-db — CLI for bitextdb. Same as python -m nlcodec.bitextdb

  • nlcodec-freq — CLI for extracting word and char frequencies from corpus using spark backend.

3. CLI Usage

$ python -m nlcodec -h
usage: __main__.py [-h] [-i INP] [-o OUT] -m MODEL [-idx] [-vs VOCAB_SIZE]
                   [-l {char,word,bpe,class}] [-mf MIN_FREQ]

positional arguments:
                        "task" or sub-command.
                            "learn" - learns vocabulary. use --level and vocab_size for type and size
                            "encode" - encodes a dataset
                            "decode" - decodes an already encoded dataset
                            "estimate" - estimates quality attributes of an encoding

optional arguments:
  -h, --help            show this help message and exit
  -i INP, --inp INP     Input file path (default: <_io.TextIOWrapper
                        name='<stdin>' mode='r' encoding='UTF-8'>)
  -o OUT, --out OUT     Output file path. Not valid for "learn" or "estimate"
                        task (default: <_io.TextIOWrapper name='<stdout>'
                        mode='w' encoding='UTF-8'>)
  -m MODEL, --model MODEL
                        Path to model aka vocabulary file (default: None)
  -idx, --indices       Indices instead of strings. Valid for task=encode and
                        task=decode (default: None)

args for task=learn:
  -vs VOCAB_SIZE, --vocab_size VOCAB_SIZE
                        Vocabulary size. Valid only for task=learn. This is
                        required for "bpe", but optional for "word" and "char"
                        models, specifying it will trim the vocabulary at
                        given top most frequent types. (default: -1)
  -l {char,word,bpe}, --level {char,word,bpe}
                        Encoding Level; Valid only for task=learn (default:
  -mf MIN_FREQ, --min_freq MIN_FREQ
                        Minimum frequency of types for considering inclusion
                        in vocabulary. Types fewer than this frequency will be
                        ignored. For --level=word, freq is type freq and
                        default is 2.for --level=char or --level=bpe,
                        characters fewer than this value will be excluded.
                        default=20 (default: None)


# learn
head -2000 somefile.tok | nlcodec learn -l bpe -m bpe.model --vocab_size 2000

# encode  with text pieces
head  somefile.tok  | nlcodec encode -m bpe.model

# encode with indexes
head  somefile.tok  | nlcodec encode -m bpe.model -idx

# decode -- undo encoding
head  somefile.tok  | nlcodec decode -m bpe.model
head  somefile.tok  | nlcodec decode -m bpe.model -idx

# estimate quality
head  somefile.tok  | nlcodec estimate -m bpe.model

4. Python API

4.1. Using a vocabulary

from nlcodec import  load_scheme
path = 'path/to/vocab.model'
vocab = load_scheme(path)
line = 'this is a sample sentence'

# encode a line of text into list of ids

# parallel encode a bunch of lines using multiple cpus
vocab.encode_parallel(seqs=[line], n_cpus=2)

# encode a line of text into pieces

# decode

4.2. Creating a vocabulary

from nlcodec import learn_vocab
inp = ['line 1', 'line 2']
level = 'bpe' # other options = char, word
model = 'path/to/vocab.model'
learn_vocab(inp, level, model, vocab_size=8000, min_freq=1, char_coverage=0.9995)

5. BPE Subword sub optimal splits for regularization

from nlcodec import load_scheme, BPEScheme
path = 'path/to/bpe-vocab.model'
bpe: BPEScheme = load_scheme(path)
some_type = bpe.table[1000] # select some bpe piece type

# get stochastic split
some_type.get_stochastic_split(split_ratio=0.5, name=False)
# get all possible permutations

6. Scaling for Big data(sets) with PySpark

For larger datasets, you may take advantage of PySpark.

Please install PySpark using pip install pyspark
python -m nlcodec.learn  # nlcodec-learn
 .... (same as "python -m nlcodec learn" sub command but with these extra otions: )
  -spark SPARK_MASTER, --spark-master SPARK_MASTER
                        Spark master (default: local[*])
  -dm DRIVER_MEM, --driver-mem DRIVER_MEM
                        Spark driver memory (default: 4g)
  -dd, --dedup          Deduplicate the sentences: use only unique sequences
                        (default: True)
  -ndd, --no-dedup      Do not deduplicate. (default: False)

$ python -m nlcodec.learn -i train.eng.tok train.kan.tok -l bpe -vs 8000  -m ~/tmp/bpe.8k.model

# Tip: This also created, two intermediate files
# these can be reused again with "nlcodec learn -tfs -i <path>"

To compute term-frequencies on a separate step:

$ python -m nlcodec.term_freq -h
usage: term_freq.py [-h] [-i INP [INP ...]] [-wf WORD_FREQS] [-cf CHAR_FREQS]
                    [-dd] [-ndd]

optional arguments:
  -h, --help            show this help message and exit
  -i INP [INP ...], --inp INP [INP ...]
                        Input file paths (default: None)
  -wf WORD_FREQS, --word_freqs WORD_FREQS
                        Output file path for word frequencies (default: None)
  -cf CHAR_FREQS, --char_freqs CHAR_FREQS
                        Output file path for character frequencies (default:
  -dd, --dedup          Deduplicate the sentences: use only unique sequences
                        (default: True)
  -ndd, --no-dedup      Do not deduplicate. (default: False)

And, then learn vocabulary from extracted frequencies. Example:

# word vocab of 32K
python -m nlcodec learn -i words.tsv -tfs -l word -vs 32000 -m word.model

# Character vocab of 99.95% coverage
python -m nlcodec learn -i chars.tsv -tfs -l char  -mf 1 -cv 0.9995 -m char.model

# BPE vocab of 8K
python -m nlcodec learn -i words.tsv -tfs -l bpe -vs 8000 -m bpe.model

# BPE vocab until minimum merge frequency is 100; set -vs=64000  as some large number
python -m nlcodec learn -i words.tsv -tfs -l bpe -vs 64000 -m bpe.model -cv 0.99995 -mce 100

6.1. Python API example

from typing import List
from nlcodec import learn_vocab, term_freq
from pathlib import Path
import logging as log

def train(model_type: str, vocab_size: int, model_path: str, files: List[str],
          char_coverage: float = 0, dedup=True, spark=None):
    :param model_type: word, char, bpe
    :param vocab_size: vocabulary size
    :param model_path: where to store vocabulary model
    :param files: text for creating vcabulary
    :param char_coverage: character coverage (0, 1]. value <= 0 => default coverage
    :param: spark: an instance of spark.sql.SparkSession (optional)

    kwargs = dict(char_coverage=char_coverage) if char_coverage > 0 else {}
    stats_file = Path(model_path + '.termfreqs')
    if not stats_file.exists():
        log.info("Extracting term frequencies... ")
        paths = [f if isinstance(f, Path) else Path(f) for f in files]
        wfs, chfs, n_lines = term_freq.word_counts(paths=paths, dedup=dedup, spark=spark)
        log.info(f"Lines = {n_lines:,}, Word Types: {len(wfs):,} Char Types:{len(chfs):,}")
        stats = chfs if model_type == 'char' else wfs
        log.info(f"Writing frequencies to {stats_file}")
        with stats_file.open('w') as out:
            term_freq.write_stats(stats=stats, out=out, line_count=n_lines)
        kwargs['term_freqs'] = True
    inp = stats_file.read_text().splitlines()
    learn_vocab(inp=inp, level=model_type, model=model_path, vocab_size=vocab_size, **kwargs)

In the above example, if you already have spark.sql.SparkSession instance, set it to spark argument. By default, a local SparkSession will be created. and shutdown.

To control the default spark backend, set these environment variables before calling the above code.

import os

7. Database

nlcodec.db.core provides Db and MultipartDb which are great for optimized storage of unequal length sequences (which are prevalent in NLP/NMT).

  1. MultipartDb divides large datasets into parts; so we can load and unload parts to avoid OOM or paging to slower disks.

  2. It works nicely with pyspark partitions

  3. Dynamically figures out the right integer size (1, 2, 4, 8 bytes) to reduce memory

Please look at test_db.py and test_db_batch.py for example use cases. Here are some:

from nlcodec.db.core import Db, MultipartDb
from nlcodec import spark
import numpy as np
import random
from pathlib import Path
import shutil

def get_data(n_recs, vocab_size):
    for i in range(n_recs):
        _len = np.random.randint(4, 40)
        x = np.random.randint(0, vocab_size, _len)
        y = np.random.randint(0, vocab_size, _len + random.randint(0, 5))
        yield x, y

def test_db():
    recs = list(get_data(n_recs=20_000, vocab_size=32_000))
    db = Db.create(recs=recs, field_names=['x', 'y'])
    assert len(db) == len(recs)
    for (x, y), rec in zip(recs, db):
        assert np.array_equal(rec.x, x)
        assert np.array_equal(rec.y, y)

def test_large_db():
    n_parts = 20
    total = 1_000_000  # lines  can go upto 500M
    vocab_size = 64_000

    path = Path('tmp.test.multipart.largedb')
        db = MultipartDb.create(path=path, recs=get_data(total, vocab_size), has_id=False,
                                field_names=['x', 'y'], part_size=total // n_parts,
        size = sum(f.stat().st_size for f in path.glob('**/*') if f.is_file())
        print(f'{len(db)} rows; {size:,} bytes')

def test_spark_write():
        import pyspark
    except ImportError:
        log.warning("pyspark not found; skipping this test")
    total = 100_000
    vocab = 32_000
    n_parts = 20
    recs = get_data(n_recs=total, vocab_size=vocab)
    path = Path('tmp.multipart.spark.db')
        with spark.session() as session:
            rdd = session.sparkContext.parallelize(enumerate(recs))
            db = spark.rdd_as_db(rdd=rdd, db_path=path, field_names=['x', 'y'],
                                 max_parts=n_parts * 10,  overwrite=True)
        assert len(db) == total

8. Batch

nlcodec.db.batch offers Batch and BatchIterable that are useful for interacting with DBs. They are designed for NMT usecase for now, but (I believe, it) can be easily adapted to others.

def test_multipart_db_batch():
    path = Path('tmp.test.multidb.batch')

    if not path.exists():
        n_recs = 100_000
        recs = list(get_data(n_recs=n_recs, vocab_size=32_000))
        MultipartDb.create(path=path, recs=recs, field_names=['x', 'y'], part_size=n_recs//10)
        batch_size = 2000
        bs = BatchIterable(data_path=path, batch_size=batch_size, batch_meta=batch_meta,
        count = 0
        for b in bs:
            count += 1
            assert b.y_toks <= batch_size

9. nlcodec-db BitextDb

nlcodec-db is a CLI utility for encoding parallel text files using nlocdec and storing them in MultipartDb. This works on top of pyspark.

$ python -m nlcodec.bitextdb -h
usage: bitextdb.py [-h] -sm PATH [-tm PATH] -st PATH -tt PATH -db PATH [-np N]
                   [-sn SRC_COL] [-tn TGT_COL] [-sl SRC_LEN] [-tl TGT_LEN]
                   [--truncate | --no-truncate] [-spark SPARK_MASTER]
                   [-dm DRIVER_MEM]

Encodes bitext nlcdec and stores them in db, using pyspark backend.

optional arguments:
  -h, --help            show this help message and exit
  -sm PATH, --src-model PATH
                        source model (default: None)
  -tm PATH, --tgt-model PATH
                        target model; default=same as src-model (default:
  -st PATH, --src-text PATH
                        source text (default: None)
  -tt PATH, --tgt-text PATH
                        target text (default: None)
  -db PATH, --db PATH   Path to store db (default: None)
  -np N, --num-parts N  A value greater than 0 forces the db to have these
                        many parts (default: 0)
  -sn SRC_COL, --src-col SRC_COL
                        source column name (default: x)
  -tn TGT_COL, --tgt-col TGT_COL
                        target column name (default: y)
  -sl SRC_LEN, --src-len SRC_LEN
                        source length max (default: 256)
  -tl TGT_LEN, --tgt-len TGT_LEN
                        target length max (default: 256)
  --truncate            truncate longer sentences (default: True)
  --no-truncate         drop longer sentences (default: True)
  -spark SPARK_MASTER, --spark_master SPARK_MASTER
                        Use Spark master (default: local[*])
  -dm DRIVER_MEM, --driver_mem DRIVER_MEM
                        Memory for spark driver (default: 4g)

$ nlcodec-db -db ~/tmp/nldb-02 -sm ~/tmp/bpe.8k.model \
  -st train.kan.tok -tt train.eng.tok --num-parts 20


Author: Thamme Gowda

This work was done at USC Information Sciences Institute as part the general machine translation research.