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https://github.com/opennmt/ctranslate2

Fast inference engine for Transformer models
https://github.com/opennmt/ctranslate2

avx avx2 cpp cuda deep-learning deep-neural-networks gemm inference intrinsics machine-translation mkl neon neural-machine-translation onednn openmp opennmt parallel-computing quantization thrust transformer-models

Last synced: 5 days ago
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Fast inference engine for Transformer models

Host: GitHub
URL: https://github.com/opennmt/ctranslate2
Owner: OpenNMT
License: mit
Created: 2019-09-23T08:10:42.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2024-11-05T08:50:15.000Z (about 2 months ago)
Last Synced: 2024-11-06T09:12:39.288Z (about 2 months ago)
Topics: avx, avx2, cpp, cuda, deep-learning, deep-neural-networks, gemm, inference, intrinsics, machine-translation, mkl, neon, neural-machine-translation, onednn, openmp, opennmt, parallel-computing, quantization, thrust, transformer-models
Language: C++
Homepage: https://opennmt.net/CTranslate2
Size: 14 MB
Stars: 3,380
Watchers: 59
Forks: 300
Open Issues: 189
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

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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](#benchmarks) 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, Qwen2

* 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](https://opennmt.net/CTranslate2/guides/opennmt_py.html)

* [OpenNMT-tf](https://opennmt.net/CTranslate2/guides/opennmt_tf.html)

* [Fairseq](https://opennmt.net/CTranslate2/guides/fairseq.html)

* [Marian](https://opennmt.net/CTranslate2/guides/marian.html)

* [OPUS-MT](https://opennmt.net/CTranslate2/guides/opus_mt.html)

* [Transformers](https://opennmt.net/CTranslate2/guides/transformers.html)

The project is production-oriented and comes with [backward compatibility guarantees](https://opennmt.net/CTranslate2/versioning.html), 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](#benchmarks) 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](https://opennmt.net/CTranslate2/quantization.html): 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](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onemkl.html), [oneDNN](https://github.com/oneapi-src/oneDNN), [OpenBLAS](https://www.openblas.net/), [Ruy](https://github.com/google/ruy), and [Apple Accelerate](https://developer.apple.com/documentation/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](https://opennmt.net/CTranslate2/python/overview.html) and C++ to cover most integration needs.

* **Configurable and interactive decoding**
[Advanced decoding features](https://opennmt.net/CTranslate2/decoding.html) 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](docs/parallel.md#model-and-tensor-parallelism) 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:

```bash

pip install ctranslate2

```

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

```python

translator = ctranslate2.Translator(translation_model_path)

translator.translate_batch(tokens)

generator = ctranslate2.Generator(generation_model_path)

generator.generate_batch(start_tokens)

```

See the [documentation](https://opennmt.net/CTranslate2) for more information and examples.

## Benchmarks

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

* [OpenNMT-tf WMT14](https://opennmt.net/Models-tf/#translation): a base Transformer trained with OpenNMT-tf on the WMT14 dataset (4.5M lines)

* [OpenNMT-py WMT14](https://opennmt.net/Models-py/#translation): a base Transformer trained with OpenNMT-py on the WMT14 dataset (4.5M lines)

* [OPUS-MT](https://github.com/Helsinki-NLP/OPUS-MT-train/tree/master/models/en-de#opus-2020-02-26zip): 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](tools/benchmark) 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*](https://aws.amazon.com/ec2/instance-types/c5/) 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*](https://aws.amazon.com/ec2/instance-types/g5/) Amazon EC2 instance equipped with a NVIDIA A10G GPU (driver version: 510.47.03).

## Additional resources

* [Documentation](https://opennmt.net/CTranslate2)

* [Forum](https://forum.opennmt.net)

* [Gitter](https://gitter.im/OpenNMT/CTranslate2)