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Megascale XLA

MegascaleXLA is a compiler + runtime system that powers large-scale TPU training. It implements collective communication primitives that allow multiple TPU slices to communicate, which allows running training jobs that span beyond the limits of a single ICI domain.

The debugging guide discusses how to identify and diagnose sources of performance issues such as slowness, hangs or errors in a multi-slice job driven by Megascale.

Terminology

• Slice

  • A slice is a collection of chips all located inside the same TPU Pod connected by high-speed inter chip interconnects (ICI). Slices are described in terms of chips or TensorCores, depending on the TPU version.
  • Multislice is a group of slices, extending TPU connectivity beyond the inter-chip interconnect (ICI) connections and leveraging the data-center network (DCN) for transmitting data beyond a slice. Data within each slice is still transmitted by ICI. Using this hybrid connectivity, Multislice enables parallelism across slices and lets you use a greater number of TPU cores for a single job than what a single slice can accommodate.
  • TPUs can be used to run a job either on a single slice or multiple slices.

• RapidEye

  • RapidEye is a system that aims to provide global ML debugging infrastructure to quickly identify and root cause issues caused by bad hardware or software bugs. It monitors MegaScale jobs to automatically detect, analyze, and classify hang events. The process involves collecting data from all job workers, coordinating responses when a hang occurs, and generating a summary digest file for each event.
  • RapidEye is enabled by default for all multislice workloads. The diagnosis can be found under the Pathways resource manager or MXLA coordinator (slice0 task0 for multi-controller JAX workload). RapidEye is also used for auto-removing bad TPUs and NICs based on fleetwide rapideye data.

• Megascale Collective

  • XLA collectives are supported via the Megascale XLA (MXLA) primitives which are not directly usable by the end user. At the time of writing this the MXLA primitives include collectives include AllGather, AllReduce, AllToAll, ReduceScatter and OneToOne. The reduction operations that are currently supported include summation, max/min and product.

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