CTranslate2

CTranslate2 is a C++ and Python library for efficient inference with Transformer models.

The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU.

The following model types are currently supported:

• Encoder-decoder models: Transformer base/big, M2M-100, NLLB, BART, mBART, Pegasus, T5, Whisper
• Decoder-only models: GPT-2, GPT-J, GPT-NeoX, OPT, BLOOM, MPT, Llama, Mistral, Gemma, CodeGen, GPTBigCode, Falcon
• Encoder-only models: BERT, DistilBERT, XLM-RoBERTa

Compatible models should be first converted into an optimized model format. The library includes converters for multiple frameworks:

• OpenNMT-py
• OpenNMT-tf
• Fairseq
• Marian
• OPUS-MT
• Transformers

The project is production-oriented and comes with backward compatibility guarantees, but it also includes experimental features related to model compression and inference acceleration.

Key features

• Fast and efficient execution on CPU and GPU
  The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many advanced optimizations: layer fusion, padding removal, batch reordering, in-place operations, caching mechanism, etc.
• Quantization and reduced precision
  The model serialization and computation support weights with reduced precision: 16-bit floating points (FP16), 16-bit brain floating points (BF16), 16-bit integers (INT16), 8-bit integers (INT8) and AWQ quantization (INT4).
• Multiple CPU architectures support
  The project supports x86-64 and AArch64/ARM64 processors and integrates multiple backends that are optimized for these platforms: Intel MKL, oneDNN, OpenBLAS, Ruy, and Apple Accelerate.
• Automatic CPU detection and code dispatch
  One binary can include multiple backends (e.g. Intel MKL and oneDNN) and instruction set architectures (e.g. AVX, AVX2) that are automatically selected at runtime based on the CPU information.
• Parallel and asynchronous execution
  Multiple batches can be processed in parallel and asynchronously using multiple GPUs or CPU cores.
• 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.
• Lightweight on disk
  Quantization can make the models 4 times smaller on disk with minimal accuracy loss.
• Simple integration
  The project has few dependencies and exposes simple APIs in Python and C++ to cover most integration needs.
• Configurable and interactive decoding
  Advanced decoding features allow autocompleting a partial sequence and returning alternatives at a specific location in the sequence.
• Support tensor parallelism for distributed inference
  Very large model can be split into multiple GPUs. Following this documentation to set up the required environment.

Some of these features are difficult to achieve with standard deep learning frameworks and are the motivation for this project.

Installation and usage

CTranslate2 can be installed with pip:

pip install ctranslate2

The Python module is used to convert models and can translate or generate text with few lines of code:

translator = ctranslate2.Translator(translation_model_path)
translator.translate_batch(tokens)

generator = ctranslate2.Generator(generation_model_path)
generator.generate_batch(start_tokens)

See the documentation for more information and examples.

Benchmarks

We translate the En->De test set newstest2014 with multiple models:

• OpenNMT-tf WMT14: a base Transformer trained with OpenNMT-tf on the WMT14 dataset (4.5M lines)
• OpenNMT-py WMT14: a base Transformer trained with OpenNMT-py on the WMT14 dataset (4.5M lines)
• OPUS-MT: a base Transformer trained with Marian on all OPUS data available on 2020-02-26 (81.9M lines)

The benchmark reports the number of target tokens generated per second (higher is better). The results are aggregated over multiple runs. See the benchmark scripts for more details and reproduce these numbers.

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.

CPU

Executed with 4 threads on a c5.2xlarge Amazon EC2 instance equipped with an Intel(R) Xeon(R) Platinum 8275CL CPU.

GPU

Executed with CUDA 11 on a g5.xlarge Amazon EC2 instance equipped with a NVIDIA A10G GPU (driver version: 510.47.03).

Additional resources

• Documentation
• Forum
• Gitter