slimt

slimt (slɪm tiː) is an inference frontend for tiny models trained as part of the Bergamot project.

bergamot-translator builds on top of marian-dev and uses the inference code-path from marian-dev. While marian is a a capable neural network library with focus on machine translation, all the bells and whistles that come with it are not necessary to run inference on client-machines (e.g: autograd, multiple sequence-to-sequence architecture support, beam-search). For some use cases like an input-method engine doing translation (see lemonade) - single-thread operation existing along with other processes on the system suffices. This is the motivation for this transplant repository. There's not much novel here except easiness to wield. This repository is simply just the tiny part of marian. Code is reused where possible.

This effort is inspired by contemporary efforts like ggerganov/ggml and karpathy/llama2. tiny models roughly follow the transformer architecture, with Simpler Simple Recurrent Units (SSRU) in the decoder. The same models are used in Mozilla Firefox's offline translation addon.

Both tiny and base models have 6 encoder-layers and 2 decoder-layers, and for most existing pairs a vocabulary size of 32000 (with tied embeddings). The following table briefly summarizes some architectural differences between tiny and base models:

Variant	emb	ffn	params	f32	i8
base	512	2048	39.0M	149MB	38MB
tiny	256	1536	15.7M	61MB	17MB

The i8 models, quantized to 8-bit and as small as 17MB is used to provide translation for Mozilla Firefox's offline translation addon, among other things.

More information on the models are described in the following papers:

• From Research to Production and Back: Ludicrously Fast Neural Machine Translation
• Edinburgh’s Submissions to the 2020 Machine Translation Efficiency Task

The large-list of dependencies from bergamot-translator have currently been reduced to:

• For int8_t matrix-multiply intgemm (x86_64) or ruy (aarch64) or xsimd via gemmology.
• For vocabulary - sentencepiece.
• For sentence-splitting using regular-expressions PCRE2.
• For sgemm - Whatever BLAS provider is found via CMake (openblas, intel-oneapimkl, cblas). Feel free to provide hints.
• CLI11 (only a dependency for cmdline)

Source code is made public where basic functionality (text-translation) works for English-German tiny models. Parity in features and speed with marian and bergamot-translator (where relevant) is a work-in-progress. Eventual support for base models are planned. Contributions are welcome and appreciated.

Getting started

Clone with submodules.

git clone --recursive https://github.com/jerinphilip/slimt.git

Configure and build. slimt is still experimenting with CMake and dependencies. The following, being prepared towards linux distribution should work at the moment:

# Configure to use xsimd via gemmology
ARGS=(
    # Use gemmology
    -DWITH_GEMMOLOGY=ON               

    # On x86_64 machines use the following to enable a faster matrix
    # multiplication backend using SIMD. All of these can co-exist and dispatch
    # on best detecting CPU at runtime.
    -DUSE_AVX512=ON -DUSE_AVX2=ON -DUSE_SSSE3=ON -DUSE_SSE2=ON

    # Uncomment below line, comment x86_64 above and use for aarch64, armv7+neon)
    # -DUSE_NEON=ON 

    # Use sentencepiece installed via system.
    -DUSE_BUILTIN_SENTENCEPIECE=OFF        

    # Exports slimtConfig.cmake (cmake) and slimt.pc.in (pkg-config)
    -DSLIMT_PACKAGE=ON 

    # Customize installation prefix if need be.
    -DCMAKE_INSTALL_PREFIX=/usr/local
)

cmake -B build -S $PWD -DCMAKE_BUILD_TYPE=Release "${ARGS[@]}"
cmake --build build --target all

# Require sudo since /usr/local is writable usually only by root.
sudo cmake --build build --target install 

The above run expects the packages sentencepiece, xsimd and a BLAS provider to come from the system's package manager. Examples of this in distributions include:

# Debian based systems
sudo apt-get install -y libxsimd-dev libsentencepiece-dev libopenblas-dev

# ArchLinux
pacman -S openblas xsimd
yay -S sentencepiece-git

Successful build generate two executables slimt-cli and slimt-test for command-line usage and testing respectively.

build/bin/slimt-cli                           \
    --root <path/to/folder>                   \
    --model </relative/path/to/model>         \
    --vocabulary </relative/path/to/vocab>    \
    --shortlist </relative/path/to/shortlist>

build/slimt-test <test-name>

This is still very much a work in progress, towards being able to make lemonade available in distributions. Help is much appreciated here, please get in touch if you can help here.

Python

Python bindings to the C++ code are available. Python bindings provide a layer to download models and use-them via command line entrypoint slimt (the core slimt library only has the inference code).

python3 -m venv env
source env/bin/activate
python3 -m pip install wheel
python3 setup.py bdist_wheel
python3 -m pip install dist/<wheel-name>.whl

# Download en-de-tiny and de-en-tiny models.
slimt download -m en-de-tiny
slimt download -m de-en-tiny

Find an example of the built wheel running on colab below:

You may pass customizing cmake-variables via CMAKE_ARGS environment variable.

CMAKE_ARGS='-D...' python3 setup.py bdist_wheel