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CTranslate

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Lightweight C++ translator for OpenNMT Torch models (deprecated)

// repository documentation

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**This project is considered obsolete as the Torch framework is no longer maintained. For compatibility with OpenNMT-tf or OpenNMT-py, please check out [CTranslate2](https://github.com/OpenNMT/CTranslate2).**

[![Build Status](https://api.travis-ci.org/OpenNMT/CTranslate.svg?branch=master)](https://travis-ci.org/OpenNMT/CTranslate)

# CTranslate

CTranslate is a C++ implementation of OpenNMT's `translate.lua` script with no LuaTorch dependencies. It facilitates the use of OpenNMT models in existing products and on various platforms using [Eigen](http://eigen.tuxfamily.org) as a backend.

CTranslate provides optimized CPU translation and optionally offloads matrix multiplication on a CUDA-compatible device using [cuBLAS](http://docs.nvidia.com/cuda/cublas/). It only supports OpenNMT models released with the [`release_model.lua`](https://github.com/OpenNMT/OpenNMT/tree/master/tools#release-model) script.

## Dependencies

* [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) >= 3.3
* [Boost](http://www.boost.org/) (`log`, when `-DWITH_BOOST_LOG=ON`; `program_options`, when `-DLIB_ONLY=OFF`)

### Optional

* [CUDA](https://developer.nvidia.com/cuda-toolkit) for matrix multiplication offloading on a GPU
* [Intel® MKL](https://software.intel.com/en-us/intel-mkl) for an alternative BLAS backend

## Compiling

*CMake and a compiler that supports the C++11 standard are required to compile the project.*

```
git submodule update --init
mkdir build
cd build
cmake ..
make
```

It will produce the dynamic library `libonmt.so` (or `.dylib` on Mac OS, `.dll` on Windows) and the translation client `cli/translate`.

CTranslate also bundles OpenNMT's [Tokenizer](https://github.com/OpenNMT/Tokenizer) which provides the tokenization tools `lib/tokenizer/cli/tokenize` and `lib/tokenizer/cli/detokenize`.

### Options

* To give hints about Eigen location, use the `-DEIGEN3_ROOT=<path to Eigen library>` option.
* To compile only the library, use the `-DLIB_ONLY=ON` flag.
* To disable [OpenMP](http://www.openmp.org), use the `-DWITH_OPENMP=OFF` flag.
* To enable optimization through quantization in matrix multiplications, use the `-DWITH_QLINEAR=AVX2|SSE` flag (`OFF` by default) and set the appropriate extended instructions set via `-DCMAKE_CXX_FLAGS`:
  * `-DWITH_QLINEAR=AVX2` requires at least `-mavx2`
  * `-DWITH_QLINEAR=SSE` requires at least `-mssse3`

### Performance tips

* Use extended instructions sets:
  * if you are not cross-compiling, add `-DCMAKE_CXX_FLAGS="-march=native"` to the `cmake` command above to optimize for speed;
  * otherwise, select a recent [SIMD extensions](https://gcc.gnu.org/onlinedocs/gcc-5.5.0/gcc/x86-Options.html#x86-Options) to improve performance while meeting portability requirements.
* Consider installing [Intel® MKL](https://software.intel.com/en-us/intel-mkl) when you are targetting Intel®-powered platforms. If found, the project will automatically link against it.
* Consider using quantization options as described above.
* When using `cli/translate`, consider fine-tuning the level of parallelism:
  * the `--parallel` option enables concurrent translation of `--batch_size` sentences
  * the `--threads` option enables each translation to use multiple threads
  * *Bottom-line:* if you want optimal throughput for a collection of sentences, increase `--parallel` and set `--threads` to 1; if you want minimal latency for a single batch, set `--parallel` to 1, and increase `--threads`.

## Using

### Clients

See `--help` on the clients to discover available options and usage. They have the same interface as their Lua counterpart.

### Library

This project is also a convenient way to load OpenNMT models and translate texts in existing software.

Here is a very simple example:

```cpp
#include <iostream>

#include <onmt/onmt.h>

int main()
{
  // Create a new Translator object.
  auto translator = onmt::TranslatorFactory::build("enfr_model_release.t7");

// Translate a tokenized sentence.
  std::cout << translator->translate("Hello world !") << std::endl;

return 0;
}

```

For a more advanced usage, see:

* `include/onmt/TranslatorFactory.h` to instantiate a new translator
* `include/onmt/ITranslator.h` (the `Translator` interface) to translate sequences or batch of sequences
* `include/onmt/TranslationResult.h` to retrieve results and attention vectors
* `include/onmt/Threads.h` to programmatically control the number of threads to use

Also see the headers available in the [Tokenizer](https://github.com/OpenNMT/Tokenizer) that are accessible when linking against CTranslate.

## Supported features

CTranslate focuses on supporting model configurations that are likely to be used in production settings. It covers models trained with the default options, plus some variants:

* additional input or output word features
* `brnn` encoder (with `sum` or `concat` merge policy)
* `dot` attention
* residual connections
* no input feeding

Additionally, CTranslate misses some advanced features of `translate.lua`:

* gold data score
* hypotheses filtering
* beam search normalization

CTranslate

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