Distributed training with XLA

I have noticed that XLA can now compile MirroredStrategy, so we can use jit compiler on multi gpu and get a better performance. But I’d like to know how xla optimize distributed training. Or XLA just optimize the process running independently on each GPU, and doesn’t optimize any communication or synchronization.

@wang_dongan Welcome to Tensorflow Forum!

XLA (Accelerated Linear Algebra) is a domain-specific compiler for machine learning workloads that can optimize the execution of operations on various accelerators, including GPUs. XLA is primarily used with TensorFlow to optimize the execution of computational graphs, and it can be used in the context of distributed training as well. However, XLA itself is focused on optimizing computation and doesn’t directly handle communication or synchronization in distributed training.

When it comes to distributed training with TensorFlow and XLA, there are two key components to consider:

  1. Computation Optimization: XLA optimizes the computation that happens on each device (e.g., each GPU). It does this by fusing and scheduling operations in a way that can improve execution speed. XLA performs various optimization techniques like operator fusion, kernel specialization, and layout optimization to make better use of the underlying hardware. These optimizations can lead to improved performance for individual GPUs.
  2. Distributed Training: The orchestration of distributed training (e.g., synchronous data-parallel training using MirroredStrategy in TensorFlow) typically involves communication and synchronization between devices (GPUs or TPUs). XLA does not directly manage the communication or synchronization aspects of distributed training. These aspects are usually handled by the distributed training strategy (e.g., MirroredStrategy) and the communication library used (e.g., TensorFlow’s distributed runtime or Horovod). These libraries ensure that gradients are communicated and aggregated across devices, and synchronization points are maintained for training stability.

So, while XLA can optimize the computation happening on individual devices and make better use of GPU resources, it doesn’t directly optimize the communication or synchronization in a distributed training setup. The optimization of communication and synchronization in distributed training is typically handled by the higher-level frameworks and libraries used in conjunction with XLA, like TensorFlow’s distribution strategy and distributed runtime.

XLA’s primary role is to optimize the computation on each GPU, while communication and synchronization for distributed training are responsibilities of the distributed training strategy and libraries used in the TensorFlow ecosystem.

Let us know if this helps!