OpenVINO Tokenizers

OpenVINO Tokenizers adds text processing operations to OpenVINO.

Features

• Perform tokenization and detokenization without third-party dependencies
• Convert a HuggingFace tokenizer into OpenVINO model tokenizer and detokenizer
• Combine OpenVINO models into a single model
• Add greedy decoding pipeline to text generation model

Installation

(Recommended) Create and activate virtual env:

python3 -m venv venv
source venv/bin/activate
 # or
conda create --name openvino_tokenizer 
conda activate openvino_tokenizer

Minimal Installation

Use minimal installation when you have a converted OpenVINO tokenizer:

pip install openvino-tokenizers
 # or
conda install -c conda-forge openvino openvino-tokenizers

Convert Tokenizers Installation

If you want to convert HuggingFace tokenizers into OpenVINO tokenizers:

pip install openvino-tokenizers[transformers]
 # or
conda install -c conda-forge openvino openvino-tokenizers && pip install transformers[sentencepiece] tiktoken

Build and install from source after OpenVINO installation

source path/to/installed/openvino/setupvars.sh
git clone https://github.com/openvinotoolkit/openvino_contrib.git
cd openvino_contrib/modules/custom_operations/
pip install .[transformers]

Build and install for development

source path/to/installed/openvino/setupvars.sh
git clone https://github.com/openvinotoolkit/openvino_contrib.git
cd openvino_contrib/modules/custom_operations/
pip install -e .[all]
# verify installation by running tests
cd user_ie_extensions/tokenizer/python/tests/
pytest .

C++ Installation

You can use converted tokenizers in C++ pipelines with prebuild binaries.

1. Download OpenVINO archive distribution for your OS from here and extract the archive.
2. Download OpenVINO Tokenizers prebuild libraries from here. To ensure compatibility first three numbers of OpenVINO Tokenizers version should match OpenVINO version and OS.
3. Extract OpenVINO Tokenizers archive into OpenVINO installation directory:
   • Windows: <openvino_dir>\runtime\bin\intel64\Release\
   • MacOS_x86: <openvino_dir>/runtime/lib/intel64/Release/
   • MacOS_arm64: <openvino_dir>/runtime/lib/arm64/Release/
   • Linux_x86: <openvino_dir>/runtime/lib/intel64/
   • Linux_arm64: <openvino_dir>/runtime/lib/aarch64/

After that you can add binary extension in the code with:

• core.add_extension("user_ov_extensions.dll") for Windows
• core.add_extension("libuser_ov_extensions.dylib") for MacOS
• core.add_extension("libuser_ov_extensions.so") for Linux

and read/compile converted (de)tokenizers models.

Usage

:warning: OpenVINO Tokenizers can be inferred on a CPU device only.

Convert HuggingFace tokenizer

OpenVINO Tokenizers ships with CLI tool that can convert tokenizers from Huggingface Hub or Huggingface tokenizers saved on disk:

convert_tokenizer codellama/CodeLlama-7b-hf --with-detokenizer -o output_dir

There is also convert_tokenizer function that can convert tokenizer python object.

import numpy as np
from transformers import AutoTokenizer
from openvino import compile_model, save_model
from openvino_tokenizers import convert_tokenizer

hf_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
ov_tokenizer = convert_tokenizer(hf_tokenizer)

compiled_tokenzier = compile_model(ov_tokenizer)
text_input = ["Test string"]

hf_output = hf_tokenizer(text_input, return_tensors="np")
ov_output = compiled_tokenzier(text_input)

for output_name in hf_output:
    print(f"OpenVINO {output_name} = {ov_output[output_name]}")
    print(f"HuggingFace {output_name} = {hf_output[output_name]}")
# OpenVINO input_ids = [[ 101 3231 5164  102]]
# HuggingFace input_ids = [[ 101 3231 5164  102]]
# OpenVINO token_type_ids = [[0 0 0 0]]
# HuggingFace token_type_ids = [[0 0 0 0]]
# OpenVINO attention_mask = [[1 1 1 1]]
# HuggingFace attention_mask = [[1 1 1 1]]

# save tokenizer for later use
save_model(ov_tokenizer, "openvino_tokenizer.xml")

loaded_tokenizer = compile_model("openvino_tokenizer.xml")
loaded_ov_output = loaded_tokenizer(text_input)
for output_name in hf_output:
    assert np.all(loaded_ov_output[output_name] == ov_output[output_name])

Connect Tokenizer to a Model

To infer and convert the original model, install torch or torch-cpu to the virtual environment.

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from openvino import compile_model, convert_model
from openvino_tokenizers import convert_tokenizer, connect_models

checkpoint = "mrm8488/bert-tiny-finetuned-sms-spam-detection"
hf_tokenizer = AutoTokenizer.from_pretrained(checkpoint)
hf_model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

text_input = ["Free money!!!"]
hf_input = hf_tokenizer(text_input, return_tensors="pt")
hf_output = hf_model(**hf_input)

ov_tokenizer = convert_tokenizer(hf_tokenizer)
ov_model = convert_model(hf_model, example_input=hf_input.data)
combined_model = connect_models(ov_tokenizer, ov_model)
compiled_combined_model = compile_model(combined_model)

openvino_output = compiled_combined_model(text_input)

print(f"OpenVINO logits: {openvino_output['logits']}")
# OpenVINO logits: [[ 1.2007061 -1.4698029]]
print(f"HuggingFace logits {hf_output.logits}")
# HuggingFace logits tensor([[ 1.2007, -1.4698]], grad_fn=<AddmmBackward0>)

Use Extension With Converted (De)Tokenizer or Model With (De)Tokenizer

Import openvino_tokenizers will add all tokenizer-related operations to OpenVINO, after which you can work with saved tokenizers and detokenizers.

import numpy as np
import openvino_tokenizers
from openvino import Core

core = Core()

# detokenizer from codellama sentencepiece model
compiled_detokenizer = core.compile_model("detokenizer.xml")

token_ids = np.random.randint(100, 1000, size=(3, 5))
openvino_output = compiled_detokenizer(token_ids)

print(openvino_output["string_output"])
# ['sc�ouition�', 'intvenord hasient', 'g shouldwer M more']

Text generation pipeline

import numpy as np
from openvino import compile_model, convert_model
from transformers import AutoModelForCausalLM, AutoTokenizer
from openvino_tokenizers import add_greedy_decoding, convert_tokenizer

# Use different repo for the tokenizer because the original repo doesn't have .model file
# Sentencepiece(Unigram) tokenizer supported only with .model file
tokenizer_checkpoint = "microsoft/Llama2-7b-WhoIsHarryPotter"
model_checkpoint = "nickypro/tinyllama-15M"
hf_tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)
hf_model = AutoModelForCausalLM.from_pretrained(model_checkpoint, use_cache=False)

# convert hf tokenizer
text_input = ["Quick brown fox was"]
ov_tokenizer, ov_detokenizer = convert_tokenizer(hf_tokenizer, with_detokenizer=True)
compiled_tokenizer = compile_model(ov_tokenizer)

# transform input text into tokens
ov_input = compiled_tokenizer(text_input)
hf_input = hf_tokenizer(text_input, return_tensors="pt")

# convert Pytorch model to OpenVINO IR and add greedy decoding pipeline to it
ov_model = convert_model(hf_model, example_input=hf_input.data)
ov_model_with_greedy_decoding = add_greedy_decoding(ov_model)
compiled_model = compile_model(ov_model_with_greedy_decoding)

# generate new tokens
new_tokens_size = 10
prompt_size = ov_input["input_ids"].shape[-1]
input_dict = {
  output.any_name: np.hstack([tensor, np.zeros(shape=(1, new_tokens_size), dtype=np.int_)])
  for output, tensor in ov_input.items()
}
for idx in range(prompt_size, prompt_size + new_tokens_size):
  output = compiled_model(input_dict)["token_ids"]
  input_dict["input_ids"][:, idx] = output[:, idx - 1]
  input_dict["attention_mask"][:, idx] = 1
ov_token_ids = input_dict["input_ids"]

hf_token_ids = hf_model.generate(
  **hf_input,
  min_new_tokens=new_tokens_size,
  max_new_tokens=new_tokens_size,
  temperature=0,  # greedy decoding
)

# decode model output
compiled_detokenizer = compile_model(ov_detokenizer)
ov_output = compiled_detokenizer(ov_token_ids)["string_output"]
hf_output = hf_tokenizer.batch_decode(hf_token_ids, skip_special_tokens=True)
print(f"OpenVINO output string: `{ov_output}`")
# OpenVINO output string: `['Quick brown fox was walking through the forest. He was looking for something']`
print(f"HuggingFace output string: `{hf_output}`")
# HuggingFace output string: `['Quick brown fox was walking through the forest. He was looking for something']`

Supported Tokenizer Types

Test Results

This report is autogenerated and includes tokenizers and detokenizers tests. The Output Matched, % column shows the percent of test strings for which the results of OpenVINO and Hugingface Tokenizers are the same. To update the report run pytest tokenizers_test.py --update_readme in modules/custom_operations/user_ie_extensions/tokenizer/python/tests directory.

Output Match by Tokenizer Type

Output Match by Model

Recreating Tokenizers From Tests

In some tokenizers, you need to select certain settings so that their output is closer to the Huggingface tokenizers:

• THUDM/chatglm2-6b detokenizer always skips special tokens. Use skip_special_tokens=True during conversion
• THUDM/chatglm3-6b detokenizer don't skips special tokens. Use skip_special_tokens=False during conversion
• All tested tiktoken based detokenizers leave extra spaces. Use clean_up_tokenization_spaces=False during conversion