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a fast and user-friendly runtime for transformer inference (Bert, Albert, GPT2, Decoders, etc) on CPU and GPU.

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TurboTransformers: a fast and user-friendly runtime for transformer inference on CPU and GPU


Make transformers serving fast by adding a turbo to your inference engine!

The WeChat AI open-sourced TurboTransformers with the following characteristics.

  1. Supporting both Transformers Encoder and Decoder.
  2. Supporting real-time variable length inputs. No time-consuming offline tuning is required. You can change the batch size and the sequence length of the request in real-time.
  3. Excellent CPU / GPU performance. Backend is implemented with hand-crafted OpenMP and CUDA code and involved with some innovative tricks.
  4. Perfect Usability. Supports python and C++ APIs. It can be used as a plugin for PyTorch. The end-to-end acceleration is obtained by adding a few lines of python code.
  5. Smart Batching. Minimize zero-padding overhead for a batch of requests of different lengths.
  6. Memory Efficiency. A new model-aware allocator ensures a small memory footprint during the variable-length request serving.

TurboTransformers has been applied to multiple online BERT service scenarios in Tencent. For example, It brings 1.88x acceleration to the WeChat FAQ service, 2.11x acceleration to the public cloud sentiment analysis service, and 13.6x acceleration to the QQ recommendation system. Moreover, it has already been applied to build services such as Chitchating, Searching, and Recommendation.

The following table is a comparison of TurboTransformers and related work.

| Related Works | Performance | Need Preprocess | Variable Length | Usage | |------------------|---|---|---|---| | pytorch JIT (CPU) | Fast | Yes | No | Hard | | TensorRT (GPU) | Fast | Yes | No | Hard | | tf-Faster Transformers (GPU) | Fast | Yes | No | Hard | | ONNX-runtime (CPU/GPU) | Fast/Fast | No | Yes | Medium | | tensorflow-1.x (CPU/GPU) | Slow/Medium | Yes | No | Easy | | pytorch (CPU/GPU) | Medium/Medium | No | Yes | Easy | | turbo-transformers (CPU/GPU) | Fastest/Fastest | No | Yes | Easy |

Supported Models

We currenly support the following transformer models.

Boost BERT Inference in 2 Lines of Python Code

import torch
import transformers
import turbo_transformers

if name == "main": turbo_transformers.set_num_threads(4) torch.set_num_threads(4) model_id = "bert-base-uncased" model = transformers.BertModel.from_pretrained(model_id) model.eval() cfg = model.config

input_ids = torch.tensor(
    ([12166, 10699, 16752, 4454], [5342, 16471, 817, 16022]),
position_ids = torch.tensor(([1, 0, 0, 0], [1, 1, 1, 0]), dtype=torch.long)
segment_ids = torch.tensor(([1, 1, 1, 0], [1, 0, 0, 0]), dtype=torch.long)
torch_res = model(
    input_ids, position_ids=position_ids, token_type_ids=segment_ids
)  # sequence_output, pooled_output, (hidden_states), (attentions)
torch_seqence_output = torch_res[0][:, 0, :]
tt_model = turbo_transformers.BertModel.from_torch(model)
res = tt_model(
    input_ids, position_ids=position_ids,
    token_type_ids=segment_ids)  # pooled_output, sequence_output
tt_seqence_output = res[0]


Note that the building scripts only applie to specific OS and software (Pytorch, OpenNMT, transformers, etc.) versions. Please adjust them according to your needs.


git clone --recursive
  1. build docker images and containers on your machine. ``` sh tools/

    optional: If you want to compare the performance of onnxrt-mkldnn during benchmark, you need to set BUILD_TYPE=dev to compile onnxruntime into the docker image, as follows

    env BUILDTYPE=dev sh tools/ docker run -it --rm --name=turbort -v $PWD:/workspace yourimage_name /bin/bash ```

  2. Install turbo in docker

Method 1: I want to unitest ``` cd /workspace sh tools/ $PWD -DWITHGPU=OFF

you can switch between Openblas and MKL by modifying this line in CMakeList.txt

set(BLAS_PROVIDER "mkl" CACHE STRING "Set the blas provider library, in [openblas, mkl, blis]")

Method 2: I do not want to unitest

cd /workspace mkdir -p build && cd build cmake .. -DWITHGPU=OFF make -j 4 pip install

find . -name *whl
3. Run benchmark (optional) in docker, compare with pytorch, torch-JIT, onnxruntime
cd benchmark bash run

4. Install conda packages in docker (optional)
sh tool/

The conda package will be in /workspace/dist/*.tar.bz2

When using turbotransformers in other environments outside this container: conda install yourroot_path/dist/*.tar.bz2

*We also prepared a docker image containing CPU version of TurboTransformers, as well as other related works, i.e. onnxrt v1.2.0 and pytorch-jit on dockerhub*

docker pull thufeifeibear/turbotransformerscpu:latest ```


git clone --recursive
  1. build docker images and containers on your machine. ```

    You can modify the environment variables in the script to specify the cuda version and operating system version

    sh tools/ $PWD nvidia-docker run --gpus all --net=host --rm -it -v $PWD:/workspace -v /etc/passwd:/etc/passwd --name=yourcontainername REPOSITORY:TAG

    for example: nvidia-docker run --gpus all --net=host --rm -it -v $PWD:/workspace -v /etc/passwd:/etc/passwd --name=turbogpuenv thufeifeibear:0.1.1-cuda9.0-ubuntu16.04-gpu-dev

  2. Install pip package in docker and unitest test

    cd /workspace
    sh tools/ $PWD -DWITH_GPU=ON
  3. Run benchmark (optional) in docker container, compare with pytorch

    cd benchmark
    We also prepared a docker image containing GPU version of TurboTransformers.
    docker pull thufeifeibear/turbo_transformers_gpu:latest


TurboTransformers provides C++ / python API interfaces. We hope to do our best to adapt to a variety of online environments to reduce the difficulty of development for users.

Pretrained Model Loading

The first step in using turbo is to load a pre-trained model. We provide a way to load PyTorch and TensorFlow pre-trained models in huggingface/transformers. The specific conversion method is to use the corresponding script in ./tools to convert the pre-trained model into an npz format file, and turbo uses the C ++ or python interface to load the npz format model. In particular, we consider that most of the pre-trained models are in PyTorch format and used with python. We provide a shortcut for calling directly in python for the PyTorch saved model.



python APIs

Refer to examples of supported models in ./example/python. TurboNLP/Translate-Demo shows a demo of applying TurboTransformer in Translatetion Task. Since the user of BERT acceleration always requires a customized post-processing process for the task, we provide an example of how to write a sequence classification application.

C++ APIs

Refer to ./example/cpp for an example. Our example provides the GPU and two CPU multi-thread calling methods. One is to do one BERT inference using multiple threads; the other is to do multiple BERT inference, each of which using one thread. Users can link turbo-transformers to your code through add_subdirectory.

Smart Batching (Minimize Zero-Padding Overhead in Batching)

Usually, feeding a batch of requests of different lengths into a bert model for inference, zero-padding is required to make all the requests have the same length. For example, serving requests list of lengths (100, 10, 50), you need a preprocessing stage to pad them as lengths (100, 100, 100). In this way, 90% and 50% of the last two sequence computation are wasted. As indicated in Effective Transformer, it is not necessary to pad the input tensors. As an alternative, you have to pad the batch-gemm operations inside multi-headed attentions, which accounts for a small proportion of the entire BERT computation. Therefore most gemm operations are processed without zero-padding. Turbo provides a model as

including a smart batching technique. The example is presented in ./example/python/

How to contribute new models

How to know hotspots of your code?

How to add a new layer?


Currently (June 2020), In the near futuer, we will add support for low-precision models (CPU int8, GPU FP16). Looking forwards to your contribution!


BSD 3-Clause License

Known Issues

  1. The results of Turbo Transformers may be different from the results of PyTorch after 2 digits behind the decimal point. The diff mainly comes from Bert Output Layer. We use a approximate GELU algorithm, which may be different from PyTorch.
  2. Turbo and PyTorch share the same MKL. MKL of PyTorch 1.5.0 may slow in Turbo. Reasons needs to be determined. Download PyTorch version to 1.1.0 will improve Turbo's Performance.
  3. onnxruntime-cpu==1.4.0 and onnxruntime-gpu==1.3.0 can not work simultaneously.


  1. Janurary 2021 v0.6.0, TurboTransformers supports smart batching.
  2. July 2020 v0.4.0, TurboTransformers used onnxruntime as cpu backend, supports GPT2. Anded a Quantized BERT.
  3. July 2020 v0.3.1, TurboTransformers added support for ALbert, Roberta on CPU/GPU.
  4. June 2020 v0.3.0, TurboTransformers added support for Transformer Decoder on CPU/GPU.
  5. June 2020 v0.2.1, TurboTransformers added BLIS as a BLAS provider option. Better performance on AMD CPU.
  6. April 2020 v0.0.1, TurboTransformers released, and achieved state-of-the-art BERT inference speed on CPU/GPU.

Cite us

Cite this paper, if you use TurboTransformers in your research publication.

  title={TurboTransformers: An Efficient GPU Serving System For Transformer Models},
  author={Fang, Jiarui and Yu, Yang and Zhao, Chengduo and Zhou, Jie},
  journal={arXiv preprint arXiv:2010.05680},

The artifacts of the paper can be found at branch


Contact us

Although we recommand you post your problem with github issues, you can also join in our Turbo user group. 1. Scan this QR code and add our contactor as your WeChat friend. 2. QQ Group, Name: TurboTransformers, Number : 1109315167.

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