CTranslate2 is an optimized inference engine for OpenNMT-py and OpenNMT-tf models supporting both CPU and GPU execution. This project is geared towards efficient serving of standard translation models but is also a place for experimentation around model compression and inference acceleration.
Table of contents
- Key features
- Quickstart
- Installation
- Converting models
- Translating
- Building
- Testing
- Benchmarks
- Frequently asked questions
- Fast execution
The execution aims to be faster than a general purpose deep learning framework: it is up to 4x faster than OpenNMT-py on translation tasks. - Model quantization
Support INT16 quantization on CPU and INT8 quantization on CPU and GPU. - Parallel translation
Translations can be run efficiently in parallel without duplicating the model data in memory. - Dynamic memory usage
The memory usage changes dynamically depending on the request size while still meeting performance requirements thanks to caching allocators on both CPU and GPU. - Automatic instruction set dispatch
When using Intel MKL, the dispatch to the optimal instruction set is done at runtime. - Ligthweight on disk
Models can be quantized below 100MB with minimal accuracy loss. A full featured Docker image supporting GPU and CPU requires less than 1GB. - Easy to use translation APIs
The project exposes translation APIs in Python and C++ to cover most integration needs.
Some of these features are difficult to achieve with standard deep learning frameworks and are the motivation for this project.
The translation API supports several decoding options:
- decoding with greedy or beam search
- random sampling from the output distribution
- translating with a known target prefix
- returning alternatives at a specific location in the target
- constraining the decoding length
- returning multiple translation hypotheses
- returning attention vectors
- approximating the generation using a pre-compiled vocabulary map
The steps below assume a Linux OS and a Python installation.
1. Install the Python package:
pip install --upgrade pip
pip install ctranslate2
2. Convert a model trained with OpenNMT-py or OpenNMT-tf, for example the pretrained Transformer model (choose one of the two models):
a. OpenNMT-py
pip install OpenNMT-py
wget https://s3.amazonaws.com/opennmt-models/transformer-ende-wmt-pyOnmt.tar.gz
tar xf transformer-ende-wmt-pyOnmt.tar.gz
ct2-opennmt-py-converter --model_path averaged-10-epoch.pt --model_spec TransformerBase \
--output_dir ende_ctranslate2
b. OpenNMT-tf
pip install OpenNMT-tf
wget https://s3.amazonaws.com/opennmt-models/averaged-ende-export500k-v2.tar.gz
tar xf averaged-ende-export500k-v2.tar.gz
ct2-opennmt-tf-converter --model_path averaged-ende-export500k-v2 --model_spec TransformerBase \
--output_dir ende_ctranslate2
3. Translate tokenized inputs, for example with the Python API:
>>> import ctranslate2
>>> translator = ctranslate2.Translator("ende_ctranslate2/")
>>> translator.translate_batch([["▁H", "ello", "▁world", "!"]])
The ctranslate2
Python package will get you started in converting and executing models:
pip install ctranslate2
The package published on PyPI only supports CPU execution at the moment. Consider using a Docker image for GPU support (see below).
Requirements:
- OS: Linux
- pip version: >= 19.0
The opennmt/ctranslate2
repository contains images for multiple Linux distributions, with or without GPU support:
docker pull opennmt/ctranslate2:latest-ubuntu18-gpu
The images include:
- a translation client to directly translate files (only in Ubuntu images)
- Python 3 packages (with GPU support)
libctranslate2.so
library development files
See Building.
The core CTranslate2 implementation is framework agnostic. The framework specific logic is moved to a conversion step that serializes trained models into a simple binary format.
The following frameworks and models are currently supported:
OpenNMT-tf | OpenNMT-py | |
---|---|---|
Transformer (Vaswani et al. 2017) | ✓ | ✓ |
+ relative position representations (Shaw et al. 2018) | ✓ | ✓ |
If you are using a model that is not listed above, consider opening an issue to discuss future integration.
Conversion scripts are parts of the Python package and should be run in the same environment as the selected training framework:
ct2-opennmt-py-converter
ct2-opennmt-tf-converter
The converter Python API can also be used to convert Transformer models with any number of layers, hidden dimensions, and attention heads.
Models can also be converted directly from the supported training frameworks. See their documentation:
The converters support model quantization which is a way to reduce the model size and accelerate its execution. The --quantization
option accepts the following values:
int8
int16
However, some execution settings are not (yet) optimized for all quantization types. The following table documents the actual types used during the computation:
Model type | GPU | CPU (with MKL) |
---|---|---|
int16 | float | int16 |
int8 | int8 | int8 |
Quantization can also be configured later when starting a translation instance. See the compute_type
argument on translation clients.
Notes:
- only GEMM-based layers and embeddings are currently quantized
Each converter should populate a model specification with trained weights coming from an existing model. The model specification declares the variable names and layout expected by the CTranslate2 core engine.
See the existing converters implementation which could be used as a template.
The examples use the English-German model converted in the Quickstart. This model requires a SentencePiece tokenization.
echo "▁H ello ▁world !" | nvidia-docker run -i --rm -v $PWD:/data \
opennmt/ctranslate2:latest-ubuntu18-gpu --model /data/ende_ctranslate2 --device cuda
See docker run --rm opennmt/ctranslate2:latest-ubuntu18-gpu --help
for additional options.
>>> import ctranslate2
>>> translator = ctranslate2.Translator("ende_ctranslate2/")
>>> translator.translate_batch([["▁H", "ello", "▁world", "!"]])
See the Python reference for more advanced usages.
#include <iostream>
#include <ctranslate2/translator.h>
int main() {
ctranslate2::Translator translator("ende_ctranslate2/");
ctranslate2::TranslationResult result = translator.translate({"▁H", "ello", "▁world", "!"});
for (const auto& token : result.output())
std::cout << token << ' ';
std::cout << std::endl;
return 0;
}
See the Translator class for more advanced usages, and the TranslatorPool class for running translations in parallel.
CTranslate2 uses the following external libraries for acceleration:
- CPU requires:
- Intel MKL (>=2019.5)
- GPU requires:
CTranslate2 supports compiling for CPU only, GPU only, or both.
The Docker images are self contained and build the code from the active directory. The build
command should be run from the project root directory, e.g.:
docker build -t opennmt/ctranslate2:latest-ubuntu18 -f docker/Dockerfile.ubuntu .
See the docker/
directory for available images.
Intel MKL is the minimum requirement for building CTranslate2. The instructions below assume an Ubuntu system.
Note: This minimal installation only enables CPU execution. For GPU support, see how the GPU Dockerfile is defined.
Use the following instructions to install Intel MKL:
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
sudo apt-get update
sudo apt-get install intel-mkl-64bit-2020.0-088
Go to https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo for more details.
Under the project root, run the following commands:
mkdir build && cd build
cmake ..
make -j4
The cli/translate
binary will be generated. You can try it with the model converted in the Quickstart section:
echo "▁H ello ▁world !" | ./cli/translate --model ende_ctranslate2/
The result ▁Hallo ▁Welt !
should be displayed.
To enable the tests, you should configure the project with cmake -DWITH_TESTS=ON
. The binary tests/ctranslate2_test
runs all tests using Google Test. It expects the path to the test data as argument:
./tests/ctranslate2_test ../tests/data
We compare CTranslate2 with OpenNMT-py and OpenNMT-tf on their pretrained English-German Transformer models (available on the website). For this benchmark, CTranslate2 models are using the weights of the OpenNMT-py model.
Model size | |
---|---|
CTranslate2 (int8) | 110MB |
CTranslate2 (int16) | 197MB |
OpenNMT-tf | 367MB |
CTranslate2 (float) | 374MB |
OpenNMT-py | 542MB |
CTranslate2 models are generally lighter and can go as low as 100MB when quantized to int8. This also results in a fast loading time and noticeable lower memory usage during runtime.
We translate the test set newstest2014 and report:
- the number of target tokens generated per second (higher is better)
- the BLEU score of the detokenized output (higher is better)
Unless otherwise noted, translations are running beam search with a size of 4 and a maximum batch size of 32.
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings.
Configuration:
- GPU: NVIDIA Tesla V100
- CUDA: 10.0
- Driver: 410.48
Tokens/s | BLEU | |
---|---|---|
CTranslate2 1.2.1 | 3917.32 | 26.70 |
CTranslate2 1.2.1 (int8) | 2519.24 | 26.80 |
OpenNMT-tf 1.25.0 | 1338.26 | 26.90 |
OpenNMT-py 0.9.2 | 980.44 | 26.69 |
Configuration:
- CPU: Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz (with AVX2)
- Number of threads: 4 "intra threads", 1 "inter threads" (what's the difference?)
Tokens/s | BLEU | |
---|---|---|
CTranslate2 1.5.1 (int8 + vmap) | 647.64 | 26.59 |
CTranslate2 1.5.1 (int16 + vmap) | 534.63 | 26.63 |
CTranslate2 1.5.1 (int8) | 508.29 | 26.84 |
CTranslate2 1.5.1 (int16) | 411.26 | 26.68 |
CTranslate2 1.5.1 (float) | 399.72 | 26.69 |
OpenNMT-py 0.9.2 | 241.92 | 26.69 |
OpenNMT-tf 1.25.0 | 119.34 | 26.90 |
- Both CTranslate2 and OpenNMT-py drop finished translations from the batch which is especially benefitial on CPU.
- On GPU, int8 quantization is generally slower as the runtime overhead of int8<->float conversions is presently too high compared to the actual computation.
- On CPU, performance gains of quantized runs can be greater depending on settings such as the number of threads, batch size, beam size, etc.
- In addition to possible performance gains, quantization results in a much lower memory usage and can also act as a regularizer (hence the higher BLEU score in some cases).
We don't have numbers comparing memory usage yet. However, past experiments showed that CTranslate2 usually requires up to 2x less memory than OpenNMT-py.
- How does it relate to the original CTranslate project?
- What is the state of this project?
- Why and when should I use this implementation instead of PyTorch or TensorFlow?
- What hardware is supported?
- What are the known limitations?
- What are the future plans?
- What is the difference between
intra_threads
andinter_threads
? - Do you provide a translation server?
- How do I generate a vocabulary mapping file?
How does it relate to the original CTranslate project?
The original CTranslate project shares a similar goal which is to provide a custom execution engine for OpenNMT models that is lightweight and fast. However, it has some limitations that were hard to overcome:
- a strong dependency on LuaTorch and OpenNMT-lua, which are now both deprecated in favor of other toolkits
- a direct reliance on Eigen, which introduces heavy templating and a limited GPU support
CTranslate2 addresses these issues in several ways:
- the core implementation is framework agnostic, moving the framework specific logic to a model conversion step
- the internal operators follow the ONNX specifications as much as possible for better future-proofing
- the call to external libraries (Intel MKL, cuBLAS, etc.) occurs as late as possible in the execution to not rely on a library specific logic
The code has been generously tested in production settings so people can rely on it in their application. The project versioning follows Semantic Versioning 2.0.0. The following APIs are covered by backward compatibility guarantees:
- Converted models
- Python converters options
- Python symbols:
ctranslate2.Translator
ctranslate2.converters.OpenNMTPyConverter
ctranslate2.converters.OpenNMTTFConverter
- C++ symbols:
ctranslate2::models::Model
ctranslate2::TranslationOptions
ctranslate2::TranslationResult
ctranslate2::Translator
ctranslate2::TranslatorPool
- C++ translation client options
Other APIs are expected to evolve to increase efficiency, genericity, and model support.
Here are some scenarios where this project could be used:
- You want to accelarate standard translation models for production usage, especially on CPUs.
- You need to embed translation models in an existing C++ application.
- Your application requires custom threading and memory usage control.
- You want to reduce the model size on disk and/or memory.
However, you should probably not use this project when:
- You want to train custom architectures not covered by this project.
- You see no value in the key features listed at the top of this document.
The supported hardware mostly depends on the external libraries used for acceleration.
CPU
Intel MKL officially supports Intel CPUs only (see Key Specifications).
When running CTranslate2 on other CPU vendors, we expect the code to run but at lower speed. Optimized execution on AMD and ARM is a future work (contributions are welcome!).
GPU
CTranslate2 currently requires a NVIDIA GPU with a compute capability greater or equal to 3.0 (Kepler). The driver requirements depend on the CUDA version, see the CUDA Compatibility guide for more information.
The current approach only exports the weights from existing models and redefines the computation graph via the code. This implies a strong assumption of the graph architecture executed by the original framework.
We are actively looking to ease this assumption by supporting ONNX as model parts.
There are many ways to make this project better and faster. See the open issues for an overview of current and planned features. Here are some things we would like to get to:
- Better support of INT8 quantization, for example by quantizing more layers
- Support of running ONNX graphs
- Optimizations for non-Intel CPUs
- Support GPU execution with the Python packages published on PyPI
intra_threads
is the number of threads that is used per translation: increase this value to decrease the latency.inter_threads
is the maximum number of translations executed in parallel: increase this value to increase the throughput (this will also increase the memory usage as some internal buffers are duplicated for thread safety).
The total number of computing threads launched by the process is summarized by this formula:
num_threads = inter_threads * intra_threads
Note that these options are only defined for CPU translation and are forced to 1 when executing on GPU.
The OpenNMT-py REST server is able to serve CTranslate2 models. See the code integration to learn more.
See here.