Fairseq
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.
Features:
- multi-GPU training on one machine or across multiple machines (data and model parallel)
- fast generation on both CPU and GPU with multiple search algorithms implemented:
- beam search
- Diverse Beam Search (Vijayakumar et al., 2016)
- sampling (unconstrained, top-k and top-p/nucleus)
- lexically constrained decoding (Post & Vilar, 2018)
- gradient accumulation enables training with large mini-batches even on a single GPU
- mixed precision training (trains faster with less GPU memory on NVIDIA tensor cores)
- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers
- flexible configuration based on Hydra allowing a combination of code, command-line and file based configuration
- full parameter and optimizer state sharding
- offloading parameters to CPU
We also provide pre-trained models for translation and language modeling
with a convenient torch.hub
interface:
en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')
en2de.translate('Hello world', beam=5)
# 'Hallo Welt'
See the PyTorch Hub tutorials for translation
and RoBERTa for more examples.
Requirements and Installation
- PyTorch version >= 1.5.0
- Python version >= 3.6
- For training new models, you'll also need an NVIDIA GPU and NCCL
- To install fairseq and develop locally:
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./
# on MacOS:
# CFLAGS="-stdlib=libc++" pip install --editable ./
# to install the latest stable release (0.10.x)
# pip install fairseq
- For faster training install NVIDIA's apex library:
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
--global-option="--deprecated_fused_adam" --global-option="--xentropy" \
--global-option="--fast_multihead_attn" ./
- For large datasets install PyArrow:
pip install pyarrow
- If you use Docker make sure to increase the shared memory size either with
--ipc=host
or--shm-size
as command line options tonvidia-docker run
.