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Fast inference engine for Transformer models

Project description

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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, 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:

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), and 8-bit integers (INT8).
  • 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.

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

Tokens per second Max. memory BLEU
OpenNMT-tf WMT14 model
OpenNMT-tf 2.31.0 (with TensorFlow 2.11.0) 209.2 2653MB 26.93
OpenNMT-py WMT14 model
OpenNMT-py 3.0.4 (with PyTorch 1.13.1) 275.8 2012MB 26.77
- int8 323.3 1359MB 26.72
CTranslate2 3.6.0 658.8 849MB 26.77
- int16 733.0 672MB 26.82
- int8 860.2 529MB 26.78
- int8 + vmap 1126.2 598MB 26.64
OPUS-MT model
Transformers 4.26.1 (with PyTorch 1.13.1) 147.3 2332MB 27.90
Marian 1.11.0 344.5 7605MB 27.93
- int16 330.2 5901MB 27.65
- int8 355.8 4763MB 27.27
CTranslate2 3.6.0 525.0 721MB 27.92
- int16 596.1 660MB 27.53
- int8 696.1 516MB 27.65

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

GPU

Tokens per second Max. GPU memory Max. CPU memory BLEU
OpenNMT-tf WMT14 model
OpenNMT-tf 2.31.0 (with TensorFlow 2.11.0) 1483.5 3031MB 3122MB 26.94
OpenNMT-py WMT14 model
OpenNMT-py 3.0.4 (with PyTorch 1.13.1) 1795.2 2973MB 3099MB 26.77
FasterTransformer 5.3 6979.0 2402MB 1131MB 26.77
- float16 8592.5 1360MB 1135MB 26.80
CTranslate2 3.6.0 6634.7 1261MB 953MB 26.77
- int8 8567.2 1005MB 807MB 26.85
- float16 10990.7 941MB 807MB 26.77
- int8 + float16 8725.4 813MB 800MB 26.83
OPUS-MT model
Transformers 4.26.1 (with PyTorch 1.13.1) 1022.9 4097MB 2109MB 27.90
Marian 1.11.0 3241.0 3381MB 2156MB 27.92
- float16 3962.4 3239MB 1976MB 27.94
CTranslate2 3.6.0 5876.4 1197MB 754MB 27.92
- int8 7521.9 1005MB 792MB 27.79
- float16 9296.7 909MB 814MB 27.90
- int8 + float16 8362.7 813MB 766MB 27.90

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

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