ParaGen

ParaGen is a PyTorch deep learning framework for parallel sequence generation. Apart from sequence generation, ParaGen also enhances various NLP tasks, including sequence-level classification, extraction and generation.

Requirements and Installation

• Install third-party dependent package:

  # Ubuntu
  apt-get install libopenmpi-dev libssl-dev openssh-server
  # CentOS
  yum install openmpi openssl openssh-server
  # Conda
  conda install -c conda-forge mpi4py

• To install ParaGen from source:

  cd ParaGen
  pip install -e .

• For distributed training on multiple GPUs, run ParaGen with torch.distributed

  python -m torch.distributed.launch --nproc_per_node {GPU_NUM} paragen/entries/run.py --configs {config_file}

  You can also use horovod for distributed training. Install horovod with

  # require CMake to install horovod. (https://cmake.org/install/)
  HOROVOD_WITH_PYTORCH=1 HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=${NCCL_ROOT_DIR} pip install horovod

  Then run ParaGen with horovod:

  horovodrun -np {GPU_NUM} -H localhost:{GPU_NUM} paragen-run --config {config_file}

• Install lightseq to faster train:

  pip install lightseq

Getting Started

Before using ParaGen, it would be helpful to overview how ParaGen works.

ParaGen is designed as a task-oriented framework, where task is regarded as the core of all the codes. A specific task selects all the components for support itself, such as model architectures, training strategies, dataset, and data processing. Any component within ParaGen can be customized, while the existing modules and methods are used as a plug-in library.

As tasks are considered as the core of ParaGen, it works with various modes, such as train, evaluate, preprocess and serve. Tasks act differently under different modes, by reorganizing the components without code modification.

Please refer to examples for detailed instructions.

ParaGen Usage and Contribution

We welcome any experimental algorithms on ParaGen.

• Install ParaGen;
• Create your own paragen-plugin libraries under third_party;
• Experiment your own algorithms;
• Write a reproducible shell script;
• Create a merge request and assign reviewers to any of us.