Hugot: Huggingface 🤗 pipelines for golang

What

The goal of this library is to provide an easy, scalable, and hassle-free way to run huggingface transformer pipelines in golang applications. It is built on the following principles:

Fidelity to the original Huggingface python implementations: the aim is to accurately replicate huggingface inference implementations for the implemented pipelines, so that models trained and tested in python can be seamlessly deployed in a golang application
Hassle-free and performant production use: we exclusively support onnx exports of huggingface models. Pytorch transformer models that don't have an onnx version can be easily exported to onnx via huggingface optimum, and used with the library
Run on your hardware: this library is for those who want to run transformer models tightly coupled with their go applications, without the performance drawbacks of having to hit a rest API, or the hassle of setting up and maintaining e.g. a python RPC service that talks to go.

Why

Developing and fine-tuning transformer models with the huggingface python library is a great experience, but if your production stack is golang-based being able to reliably deploy and scale the resulting pytorch models can be challenging and require quite some setup. This library aims to allow you to just lift-and-shift your python model and use the same huggingface pipelines you use for development for inference in a go application.

For whom

For the golang developer or ML engineer who wants to run transformer piplines on their own hardware, tightly coupled with their own application.

What is already there

Currently we have implementations for the following transfomer pipelines:

featureExtraction
textClassification (single label classification only)
tokenClassification

Implementations for additional pipelines will follow. We also very gladly accept PRs to expand the set of pipelines! See here for the missing pipelines that can be implemented, and the contributing section below if you want to lend a hand.

Hugot can be used both as a library and as a command-line application. See below for usage instructions.

Limitations

Apart from the fact that only the aforementioned pipelines are currently implemented, the current limitations are: - the library and cli are only tested on amd64-linux - only CPU inference is supported

Pipelines are also tested on specifically NLP use cases. In particular, we use the following models for testing:

feature extraction: all-MiniLM-L6-v2
text classification: distilbert-base-uncased-finetuned-sst-2-english
token classification: distilbert-NER

If you encounter any further issues or want further features, please open an issue.

Installation and usage

Hugot can be used in two ways: as a library in your go application, or as a command-line binary.

Use it as a library

To use Hugot as a library in your application, you will need the following dependencies on your system:

the tokenizers.a file obtained from building the tokenizer go library (which is itself a fork of https://github.com/daulet/tokenizers). This file should be at /usr/lib/tokenizers.a so that hugot can load it.
the onnxruntime.go file obtained from the onnxruntime project. This is dynamically linked by hugot and used by the onnxruntime inference libraryonnxruntime_go. This file should be at /usr/lib/onnxruntime.so or /usr/lib64/onnxruntime.so

You can get the libtokenizers.a in two ways. Assuming you have rust installed, you can compile the tokenizers library and get the required libtokenizers.a:

git clone https://github.com/Knights-Analytics/tokenizers -b main && \
    cd tokenizers && \
    cargo build --release
mv target/release/libtokenizers.a /usr/lib/libtokenizers.a

Alternatively, you can just download libtokenizers.a from the release section of the repo.

For onnxruntime, it suffices to download it, untar it, and place it in the right location:

curl -LO https://github.com/microsoft/onnxruntime/releases/download/v${ONNXRUNTIME_VERSION}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz && \
   tar -xzf onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz && \
   mv ./onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}/lib/libonnxruntime.so.${ONNXRUNTIME_VERSION} /usr/lib/onnxruntime.so

See also the dockerfile used for building & testing.

Once these pieces are in place, the library can be used as follows:

import (
	"github.com/knights-analytics/hugot"
	"github.com/knights-analytics/hugot/pipelines"
)

func check(err error) {
    if err != nil {
        panic(err.Error())
    }
}
// start a new session. This looks for the onnxruntime.so library in its default path, e.g. /usr/lib/onnxruntime.so
session, err := hugot.NewSession()
// if your onnxruntime.so is somewhere else, you can explicitly set it by using WithOnnxLibraryPath
// session, err := hugot.NewSession(WithOnnxLibraryPath("/path/to/onnxruntime.so"))
check(err)
// A successfully created hugot session needs to be destroyed when you're done
defer func(session *hugot.Session) {
    err := session.Destroy()
    check(err)
}(session)
// Let's download an onnx sentiment test classification model in the current directory
// note: if you compile your library with build flag NODOWNLOAD, this will exclude the downloader.
// Useful in case you just want the core engine (because you already have the models) and want to
// drop the dependency on huggingfaceModelDownloader.
modelPath, err := session.DownloadModel("KnightsAnalytics/distilbert-base-uncased-finetuned-sst-2-english", "./")
check(err)
// we now create a text classification pipeline. It requires the path to the onnx model folder we just downloaded,
// and a pipeline name
sentimentPipeline, err := session.NewTextClassificationPipeline(modelPath, "testPipeline")
check(err)
// we can now use the pipeline for prediction on a batch of strings
batch := []string{"This movie is disgustingly good !", "The director tried too much"}
batchResult, err := sentimentPipeline.RunPipeline(batch)
check(err)
// and do whatever we want with it :)
s, err := json.Marshal(batchResult)
check(err)
fmt.Println(string(s))
// {"ClassificationOutputs":[[{"Label":"POSITIVE","Score":0.9998536}],[{"Label":"NEGATIVE","Score":0.99752176}]]}

See also hugot_test.go for further examples.

Use it as a cli: Huggingface 🤗 pipelines from the command line

With hugot you don't need python, pytorch, or even go to run huggingface transformers. Simply install the hugot cli (alpha):

curl https://raw.githubusercontent.com/knights-analytics/hugot/main/scripts/install-hugot-cli.sh | bash

This will install the hugot binary at $HOME/.local/bin/hugot, and the corresponding onnxruntime.so library at $HOME/lib/hugot/onnxruntime.so. The if $HOME/.local/bin is on your $PATH, you can do:

hugot run --model=KnightsAnalytics/distilbert-base-uncased-finetuned-sst-2-english --input=/path/to/input.jsonl --output=/path/to/folder/output --type=textClassification

Hugot will load the model, process the input, and write the results in the output folder. Note that the hugot cli currently expects the input in a specific format: json lines with an "input" key containing the string to process. Example:

{"input": "The director tried too much"}
{"input": "The film was excellent"}

Will produce a file called result_0.jsonl in the output folder with contents:

{"input":"The director tried too much","output":[{"Label":"NEGATIVE","Score":0.99752176}]}
{"input":"The film was excellent","output":[{"Label":"POSITIVE","Score":0.99986285}]}

Note that if --input is not provided, hugot will read from stdin, and if --output is not provided, it will write to stdout. This allows to chain things like:

echo '{"input":"The director tried too much"}' | hugot run --model=/path/to/model --type=textClassification | jq

To be able to run transformers fully from the command line.

Note that the --model parameter can be: 1. the full path to a model to load 2. the name of a huggingface model. Hugot will first try to look for the model at $HOME/hugot, or will try to download the model from huggingface.

Contributing

Development environment

The easiest way to contribute to hugot is by developing inside a docker container that has the tokenizer and onnxruntime libraries. From the source folder, it should be as easy as:

make start-dev-container

which will download the test models, build the test container, and launch it (see compose-dev), mounting the source code at /home/testuser/repositories/hugot. Then you can attach to the container with e.g. vscode remote extension as testuser. The vscode attached container configuration file can be set to:

{
    "remoteUser": "testuser",
    "workspaceFolder": "/home/testuser/repositories/hugot",
    "extensions": [
		"bierner.markdown-preview-github-styles",
		"golang.go",
		"ms-azuretools.vscode-docker"
	],
    "remoteEnv": {"GOPATH": "/home/testuser/go"}
}

Once you're done, you can tear the container down with:

make stop-dev-container

Alternatively, you can use your IDE devcontainer support, and point it to the Dockerfile.

If you prefer to develop on bare metal, you will need to download the tokenizers.a to /usr/lib/tokenizers.a and onnxruntime.so to /usr/lib/onnxruntime.so.

Run the tests

The full test suite can be ran as follows. From the source folder:

make clean run-tests

This will build a test image and run all tests in a container. A testTarget folder will appear in the source directory with the test results.

Contribution process

create or find an issue for your contribution
fork and develop
add tests and make sure the full test suite passes and test coverage does not dip below 80%
create a MR linking to the relevant issue

Thank you for contributing to hugot!

zeushammer/hugot