  This paper presents the design, implementation, and evaluation of the PyTorch
distributed data parallel module. PyTorch is a widely-adopted scientific
computing package used in deep learning research and applications. Recent
advances in deep learning argue for the value of large datasets and large
models, which necessitates the ability to scale out model training to more
computational resources. Data parallelism has emerged as a popular solution for
distributed training thanks to its straightforward principle and broad
applicability. In general, the technique of distributed data parallelism
replicates the model on every computational resource to generate gradients
independently and then communicates those gradients at each iteration to keep
model replicas consistent. Despite the conceptual simplicity of the technique,
the subtle dependencies between computation and communication make it
non-trivial to optimize the distributed training efficiency. As of v1.5,
PyTorch natively provides several techniques to accelerate distributed data
parallel, including bucketing gradients, overlapping computation with
communication, and skipping gradient synchronization. Evaluations show that,
when configured appropriately, the PyTorch distributed data parallel module
attains near-linear scalability using 256 GPUs.
