TFtftransformers

Converting Hugginface tokenizers to Tensorflow tokenizers. The main reason is to be able to bundle the tokenizer and model into one Reusable SavedModel, inspired by the Tensorflow Official Guide on tokenizers

Source Code: https://github.com/Hugging-Face-Supporter/tftokenizers

Models we know works:

"bert-base-cased"
"bert-base-uncased"
"bert-base-multilingual-cased"
"bert-base-multilingual-uncased"
# Distilled
"distilbert-base-cased"
"distilbert-base-multilingual-cased"
"microsoft/MiniLM-L12-H384-uncased"
# Non-english
"KB/bert-base-swedish-cased"
"bert-base-chinese"

Examples

This is an example of how one can use Huggingface model and tokenizers bundled together as a Reusable SavedModel and yields the same result as using the model and tokenizer from Huggingface 🤗

import tensorflow as tf
from transformers import TFAutoModel
from tftokenizers import TFModel, TFAutoTokenizer

# Load base models from Huggingface
model_name = "bert-base-cased"
model = TFAutoModel.from_pretrained(model_name)

# Load converted TF tokenizer
tokenizer = TFAutoTokenizer.from_pretrained(model_name)

# Create a TF Reusable SavedModel
custom_model = TFModel(model=model, tokenizer=tokenizer)

# Tokenizer and model can handle `tf.Tensors` or regular strings
tf_string = tf.constant(["Hello from Tensorflow"])
s1 = "SponGE bob SQuarePants is an avenger"
s2 = "Huggingface to Tensorflow tokenizers"
s3 = "Hello, world!"

output = custom_model(tf_string)
output = custom_model([s1, s2, s3])

# We can now pass input as tensors
output = custom_model(
    inputs=tf.constant([s1, s2, s3], dtype=tf.string, name="inputs"),
)

# Save tokenizer
saved_name = "reusable_bert_tf"
tf.saved_model.save(custom_model, saved_name)

# Load tokenizer
reloaded_model = tf.saved_model.load(saved_name)
output = reloaded_model([s1, s2, s3])
print(output)

Setup

git clone https://github.com/Hugging-Face-Supporter/tftokenizers.git
cd tftokenizers
poetry install
poetry shell

Run

To convert a Huggingface tokenizer to Tensorflow, first choose one from the models or tokenizers from the Huggingface hub to download.

NOTE

Currently only BERT models work with the converter.

Download

First download tokenizers from the hub by name. Either run the bash script do download multiple tokenizers or download a single tokenizer with the python script.

The idea is to eventually only to automatically download and convert

python tftokenizers/download.py -n bert-base-uncased
bash scripts/download_tokenizers.sh

Convert

Convert downloaded tokenizer from Huggingface format to Tensorflow

python tftokenizers/convert.py

Before Commit

make build

FAQ

How to know what tokenizer is used?

TL;DR

from transformers import AutoTokenizer

name = "bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(name)

# IF the tokenizer is fast:
print(tokenizer.is_fast)
# Base tokenizer model
print(type(tokenizer.backend_tokenizer.model))
# Check if it is a SentencePiece tokenizer
# Should be `vocab.txt` or `vocab.json` if not SentencePiece tokenizer
# SencePiece if "vocab_file":
#   "sentencepiece.bpe.model"
print(tokenizer.vocab_files_names)

# Else
# Find if the model is a SentencePiece model with
print(vars(tokenizer).get("spm_file", None))
# print(vars(tokenizer).get("sp_model", None))

What tokenizers are used by what models?

`sentencepiece.bpe.model` for "BPE" and
`spiece.model` for Unigram

'source_spm': 'source.spm',
'target_spm': 'target.spm',
'vocab': 'vocab.json'

will be a single file or a list of files
...

How to implement the tokenizers from Huggingface to Tensorflow?

You will need to download the Huggingface tokenizer of your choice, determine the type of the tokenizer (is_fast, tokenizer type and vocab_file_names). Then map the tokenizer used to the Tensorflow supported equivalent:

https://github.com/tensorflow/text/issues/422

BPE and Unigram:

• All BPE implementations for Tensorflow is backed by SentencePiece
• SentencePiece in TensorFlow
• Official Answer 1
• Official Answer 2
• How to load a SentencePiece model
• Input will need to be Tensors
• How to load model from vocab

WordPiece:

• BertTokenizer or
• WordPiece or
• FastWordPiece

https://github.com/tensorflow/text/issues/116 https://github.com/tensorflow/text/issues/414

What other ways are there to convert a tokenizer?

import tensorflow as tf
import tensorflow_text as text
from transformers import AutoTokenizer, TFAutoModel
from transformers.utils.logging import set_verbosity_error

from tftokenizers.file import (
    get_filename_from_path,
    get_vocab_from_path,
    load_json
)
from tftokenizers.model import TFModel
from tftokenizers.tokenizer import TFAutoTokenizer, TFTokenizerBase

set_verbosity_error()
tf.get_logger().setLevel("ERROR")

pretrained_model_name = "bert-base-cased"


# a) by model_name
tf_tokenizer = TFAutoTokenizer.from_pretrained(pretrained_model_name)

# b) bundled with the model, similar to TFHub
model = TFAutoModel.from_pretrained(pretrained_model_name)
custom_model = TFModel(model=model, tokenizer=tf_tokenizer)

# c) from source, using the saved files of a transformers tokenizer
# Make sure you run download.py or the download script first
PATH = "saved_tokenizers/bert-base-uncased"
vocab = get_vocab_from_path(PATH)
vocab_path = get_filename_from_path(PATH, "vocab")

config = load_json(f"{PATH}/tokenizer_config.json")
tokenizer_spec = load_json(f"{PATH}/tokenizer.json")
special_tokens_map = load_json(f"{PATH}/special_tokens_map.json")

tokenizer_base_params = dict(lower_case=True, token_out_type=tf.int64)
tokenizer_base = text.BertTokenizer(vocab_path, **tokenizer_base_params)
custom_tokenizer = TFTokenizerBase(
    vocab_path=vocab_path,
    tokenizer_base=tokenizer_base,
    hf_spec=tokenizer_spec,
    config=config,
)

How to save Huggingface Tokenizer files locally?

python tftokenizers/download.py -n KB/bert-base-swedish-cased

bash scrips/download_tokenizers.sh

WIP

• Convert a BERT tokenizer from Huggingface to Tensorflow
• Make a TF Reusabel SavedModel with Tokenizer and Model in the same class. Emulate how the TF Hub example for BERT works.
• Find methods for identifying the base tokenizer model and map those settings and special tokens to new tokenizers
• Extend the tokenizers to more tokenizer types and identify them from a huggingface model name
• Document how others can use the library and document the different stages in the process
• Improve the conversion pipeline (s.a. Download and export files if not passed in or available locally)
• model_max_length should be regulated. However, some newer models have the max_lenght for tokenizers at 1000_000_000
• Support more tokenizers, starting with SentencePiece
• Identify tokenizer conversion limitations
• Support encoding of two sentences at a time Ref
• Allow the tokenizers to be used for Masking (MLM) Ref