Using JAX in multi-host and multi-process environments¶

Introduction¶

This guide explains how to use JAX in environments such as Cloud TPU pods where accelerators are spread across multiple CPU hosts or JAX processes. We’ll refer to these as “multi-process” environments.

This guide specifically focuses on how to use collective communication operations (e.g. jax.lax.psum()) in multi-process settings, although other communication methods may be useful too depending on your use case (e.g. RPC, mpi4jax). If you’re not already familiar with JAX’s collective operations, we recommend starting with the Parallel Evaluation in JAX notebook. An important requirement of multi-process environments in JAX is direct communication links between accelerators, e.g. the high-speed interconnects for Cloud TPUs or NCCL for GPUs. These links are what allow collective operations to run across multiple processes’ worth of accelerators.

Multi-process programming model¶

Key concepts:

• You must run at least one JAX process per host.

• Each process has a distinct set of local devices it can address. The global devices are the set of all devices across all processes.

• Use standard JAX parallelism APIs like pmap() and xmap(). Each process “sees” local input and output to parallelized functions, but communication inside the computations is global.

• Make sure all processes run the same parallel computations in the same order.

Launching JAX processes¶

Unlike other distributed systems where a single controller node manages many worker nodes, JAX uses a “multi-controller” programming model where each JAX Python process runs independently, sometimes referred to as a Single Program, Multiple Data (SPMD) model. Generally, the same JAX Python program is run in each process, with only slight differences between each process’s execution (e.g. different processes will load different input data). Furthermore, you must manually run your JAX program on each host! JAX doesn’t automatically start multiple processes from a single program invocation.

(This is why this guide isn’t offered as a notebook – we don’t currently have a good way to manage multiple Python processes from a single notebook.)

Local vs. global devices¶

Before we get to running multi-process computations from your program, it’s important to understand the distinction between local and global devices.

A process’s local devices are those that it can directly address and launch computations on. For example, in a Cloud TPU pod, each host can only launch computations on the 8 TPU cores attached directly to that host (see the Cloud TPU System Architecture documentation for more details). You can see a process’s local devices via jax.local_devices().

The global devices are the devices across all processes. A computation can span devices across processes and perform collective operations via the direct communication links between devices, as long as each process launches the computation on its local devices. You can see all available global devices via jax.devices(). A process’s local devices are always a subset of the global devices.

Running multi-process computations¶

So how do you actually run a computation involving cross-process communication? Use the same parallel evaluation APIs that you would in a single process!

For example, pmap() can be used to run a parallel computation across multiple processes. (If you’re not already familiar with how to use pmap() to run across multiple devices within a single process, check out the Parallel Evaluation in JAX notebook.) Each process should call the same pmapped function and pass in arguments to be mapped across its local devices (i.e., the pmapped axis size is equal to the number of local devices). Similarly, the function will return outputs sharded across local devices only. Inside the function, however, collective communication operations are run across all global devices, across all processes. Conceptually, this can be thought of as running a pmap over a single array sharded across hosts, where each host “sees” only its local shard of the input and output.

Here’s an example of multi-process pmap in action:

# The following is run in parallel on each host in a Cloud TPU v3-32 pod slice
>>> import jax
>>> jax.device_count()  # total number of TPU cores in pod slice
32
>>> jax.local_device_count()  # number of TPU cores attached to this host
8
# The psum is performed over all mapped devices across the pod slice
>>> xs = jax.numpy.ones(jax.local_device_count())
>>> jax.pmap(lambda x: jax.lax.psum(x, 'i'), axis_name='i')(xs)
ShardedDeviceArray([32., 32., 32., 32., 32., 32., 32., 32.], dtype=float32)


xmap() works similarly when using a physical hardware mesh (see the xmap tutorial if you’re not familiar with the single-process version). Like pmap(), the inputs and outputs are local and any parallel communication inside the xmapped function is global. The mesh is also global.

TODO: xmap example

It’s very important that all processes run the same cross-process computations in the same order. Running the same JAX Python program in each process is usually sufficient. Some common pitfalls to look out for that may cause differently-ordered computations despite running the same program:

• Processes passing differently-shaped inputs to the same parallel function can cause hangs or incorrect return values. Differently-shaped inputs are safe so long as they result in identically-shaped per-device data shards across processes; e.g. passing in different leading batch sizes in order to run on different numbers of local devices per process is ok, but having each process pad its batch to a different max example length is not.

• “Last batch” issues where a parallel function is called in a (training) loop, and one or more processes exit the loop earlier than the rest. This will cause the rest to hang waiting for the already-finished processes to start the computation.