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PyTorch on XLA Devices

PyTorch runs on XLA devices, like TPUs, with the torch_xla package. This document describes how to run your models on these devices.

Creating an XLA Tensor

PyTorch/XLA adds a new xla device type to PyTorch. This device type works just like other PyTorch device types. For example, here's how to create and print an XLA tensor:

import torch
import torch_xla
import torch_xla.core.xla_model as xm

t = torch.randn(2, 2, device=xm.xla_device())
print(t.device)
print(t)

This code should look familiar. PyTorch/XLA uses the same interface as regular PyTorch with a few additions. Importing torch_xla initializes PyTorch/XLA, and xm.xla_device() returns the current XLA device. This may be a CPU or TPU depending on your environment.

XLA Tensors are PyTorch Tensors

PyTorch operations can be performed on XLA tensors just like CPU or CUDA tensors.

For example, XLA tensors can be added together:

t0 = torch.randn(2, 2, device=xm.xla_device())
t1 = torch.randn(2, 2, device=xm.xla_device())
print(t0 + t1)

Or matrix multiplied:

print(t0.mm(t1))

Or used with neural network modules:

l_in = torch.randn(10, device=xm.xla_device())
linear = torch.nn.Linear(10, 20).to(xm.xla_device())
l_out = linear(l_in)
print(l_out)

Like other device types, XLA tensors only work with other XLA tensors on the same device. So code like

l_in = torch.randn(10, device=xm.xla_device())
linear = torch.nn.Linear(10, 20)
l_out = linear(l_in)
print(l_out)
# Input tensor is not an XLA tensor: torch.FloatTensor

will throw an error since the torch.nn.Linear module is on the CPU.

Running Models on XLA Devices

Building a new PyTorch network or converting an existing one to run on XLA devices requires only a few lines of XLA-specific code. The following snippets highlight these lines when running on a single device and multiple devices with XLA multi-processing.

Running on a Single XLA Device

The following snippet shows a network training on a single XLA device:

import torch_xla.core.xla_model as xm

device = xm.xla_device()
model = MNIST().train().to(device)
loss_fn = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

for data, target in train_loader:
  optimizer.zero_grad()
  data = data.to(device)
  target = target.to(device)
  output = model(data)
  loss = loss_fn(output, target)
  loss.backward()

  optimizer.step()
  xm.mark_step()

This snippet highlights how easy it is to switch your model to run on XLA. The model definition, dataloader, optimizer and training loop can work on any device. The only XLA-specific code is a couple lines that acquire the XLA device and mark the step. Calling xm.mark_step() at the end of each training iteration causes XLA to execute its current graph and update the model's parameters. See XLA Tensor Deep Dive for more on how XLA creates graphs and runs operations.

Running on Multiple XLA Devices with Multi-processing

PyTorch/XLA makes it easy to accelerate training by running on multiple XLA devices. The following snippet shows how:

import torch_xla
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as pl

def _mp_fn(index):
  device = xm.xla_device()
  mp_device_loader = pl.MpDeviceLoader(train_loader, device)

  model = MNIST().train().to(device)
  loss_fn = nn.NLLLoss()
  optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

  for data, target in mp_device_loader:
    optimizer.zero_grad()
    output = model(data)
    loss = loss_fn(output, target)
    loss.backward()
    xm.optimizer_step(optimizer)

if __name__ == '__main__':
  torch_xla.launch(_mp_fn, args=())

There are three differences between this multi-device snippet and the previous single device snippet. Let's go over then one by one.

  • torch_xla.launch()
    • Creates the processes that each run an XLA device.
    • This function is a wrapper of multithreading spawn to allow user run the script with torchrun command line also. Each process will only be able to access the device assigned to the current process. For example on a TPU v4-8, there will be 4 processes being spawn up and each process will own a TPU device.
    • Note that if you print the xm.xla_device() on each process you will see xla:0 on all devices. This is because each process can only see one device. This does not mean multi-process is not functioning. The only execution is with PJRT runtime on TPU v2 and TPU v3 since there will be #devices/2 processes and each process will have 2 threads(check this doc for more details).
  • MpDeviceLoader
    • Loads the training data onto each device.
    • MpDeviceLoader can wrap on a torch dataloader. It can preload the data to the device and overlap the dataloading with device execution to improve the performance.
    • MpDeviceLoader also call xm.mark_step for you every batches_per_execution(default to 1) batch being yield.
  • xm.optimizer_step(optimizer)
    • Consolidates the gradients between devices and issues the XLA device step computation.
    • It is pretty much a all_reduce_gradients + optimizer.step() + mark_step and returns the loss being reduced.

The model definition, optimizer definition and training loop remain the same.

NOTE: It is important to note that, when using multi-processing, the user can start retrieving and accessing XLA devices only from within the target function of torch_xla.launch() (or any function which has torch_xla.launch() as parent in the call stack).

See the full multiprocessing example for more on training a network on multiple XLA devices with multi-processing.

Running on TPU Pods

Multi-host setup for different accelerators can be very different. This doc will talk about the device independent bits of multi-host training and will use the TPU + PJRT runtime(currently available on 1.13 and 2.x releases) as an example.

Before you being, please take a look at our user guide at here which will explain some Google Cloud basis like how to use gcloud command and how to setup your project. You can also check here for all Cloud TPU Howto. This doc will focus on the PyTorch/XLA perspective of the Setup.

Let's assume you have the above mnist example from above section in a train_mnist_xla.py. If it is a single host multi device training, you would ssh to the TPUVM and run command like

PJRT_DEVICE=TPU python3 train_mnist_xla.py

Now in order to run the same models on a TPU v4-16 (which has 2 host, each with 4 TPU devices), you will need to

  • Make sure each host can access the training script and training data. This is usually done by using the gcloud scp command or gcloud ssh command to copy the training scripts to all hosts.
  • Run the same training command on all hosts at the same time.
gcloud alpha compute tpus tpu-vm ssh $USER-pjrt --zone=$ZONE --project=$PROJECT --worker=all --command="PJRT_DEVICE=TPU python3 train_mnist_xla.py"

Above gcloud ssh command will ssh to all hosts in TPUVM Pod and run the same command at the same time..

NOTE: You need to run run above gcloud command outside of the TPUVM vm.

The model code and training script is the same for the multi-process training and the multi-host training. PyTorch/XLA and the underlying infrastructure will make sure each device is aware of the global topology and each device's local and global ordinal. Cross-device communication will happen across all devices instead of local devices.

For more details regarding PJRT runtime and how to run it on pod, please refer to this doc. For more information about PyTorch/XLA and TPU pod and a complete guide to run a resnet50 with fakedata on TPU pod, please refer to this guide.

XLA Tensor Deep Dive

Using XLA tensors and devices requires changing only a few lines of code. But even though XLA tensors act a lot like CPU and CUDA tensors, their internals are different. This section describes what makes XLA tensors unique.

XLA Tensors are Lazy

CPU and CUDA tensors launch operations immediately or eagerly. XLA tensors, on the other hand, are lazy. They record operations in a graph until the results are needed. Deferring execution like this lets XLA optimize it. A graph of multiple separate operations might be fused into a single optimized operation, for example.

Lazy execution is generally invisible to the caller. PyTorch/XLA automatically constructs the graphs, sends them to XLA devices, and synchronizes when copying data between an XLA device and the CPU. Inserting a barrier when taking an optimizer step explicitly synchronizes the CPU and the XLA device. For more information about our lazy tensor design, you can read this paper.

Memory Layout

The internal data representation of XLA tensors is opaque to the user. They do not expose their storage and they always appear to be contiguous, unlike CPU and CUDA tensors. This allows XLA to adjust a tensor's memory layout for better performance.

Moving XLA Tensors to and from the CPU

XLA tensors can be moved from the CPU to an XLA device and from an XLA device to the CPU. If a view is moved then the data its viewing is also copied to the other device and the view relationship is not preserved. Put another way, once data is copied to another device it has no relationship with its previous device or any tensors on it. Again, depending on how your code operates, appreciating and accommodating this transition can be important.

Saving and Loading XLA Tensors

XLA tensors should be moved to the CPU before saving, as in the following snippet:

import torch
import torch_xla
import torch_xla.core.xla_model as xm

device = xm.xla_device()

t0 = torch.randn(2, 2, device=device)
t1 = torch.randn(2, 2, device=device)

tensors = (t0.cpu(), t1.cpu())

torch.save(tensors, 'tensors.pt')

tensors = torch.load('tensors.pt')

t0 = tensors[0].to(device)
t1 = tensors[1].to(device)

This lets you put the loaded tensors on any available device, not just the one on which they were initialized.

Per the above note on moving XLA tensors to the CPU, care must be taken when working with views. Instead of saving views it is recommended that you recreate them after the tensors have been loaded and moved to their destination device(s).

A utility API is provided to save data by taking care of previously moving it to CPU:

import torch
import torch_xla
import torch_xla.core.xla_model as xm

xm.save(model.state_dict(), path)

In case of multiple devices, the above API will only save the data for the master device ordinal (0).

In case where memory is limited compared to the size of the model parameters, an API is provided that reduces the memory footprint on the host:

import torch_xla.utils.serialization as xser

xser.save(model.state_dict(), path)

This API streams XLA tensors to CPU one at a time, reducing the amount of host memory used, but it requires a matching load API to restore:

import torch_xla.utils.serialization as xser

state_dict = xser.load(path)
model.load_state_dict(state_dict)

Directly saving XLA tensors is possible but not recommended. XLA tensors are always loaded back to the device they were saved from, and if that device is unavailable the load will fail. PyTorch/XLA, like all of PyTorch, is under active development and this behavior may change in the future.

Compilation Caching

The XLA compiler converts the traced HLO into an executable which runs on the devices. Compilation can be time consuming, and in cases where the HLO doesn't change across executions, the compilation result can be persisted to disk for reuse, significantly reducing development iteration time.

Note that if the HLO changes between executions, a recompilation will still occur.

This is currently an experimental opt-in API, which must be activated before any computations are executed. Initialization is done through the initialize_cache API:

import torch_xla.runtime as xr
xr.initialize_cache('YOUR_CACHE_PATH', readonly=False)

This will initialize a persistent compilation cache at the specified path. The readonly parameter can be used to control whether the worker will be able to write to the cache, which can be useful when a shared cache mount is used for an SPMD workload.

Further Reading

Additional documentation is available at the PyTorch/XLA repo. More examples of running networks on TPUs are available here.