PyTorch/XLA

Current CI status:

PyTorch/XLA is a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs. You can try it right now, for free, on a single Cloud TPU VM with Kaggle!

Take a look at one of our Kaggle notebooks to get started:

• Stable Diffusion with PyTorch/XLA 2.0
• Distributed PyTorch/XLA Basics

Getting Started

PyTorch/XLA is now on PyPI!

To install PyTorch/XLA a new TPU VM:

pip install torch~=2.3.0 torch_xla[tpu]~=2.3.0 -f https://storage.googleapis.com/libtpu-releases/index.html

To update your existing training loop, make the following changes:

-import torch.multiprocessing as mp
+import torch_xla as xla
+import torch_xla.core.xla_model as xm
+import torch_xla.distributed.xla_multiprocessing as xmp

 def _mp_fn(index):
   ...

+  # Move the model paramters to your XLA device
+  model.to(xla.device())

   for inputs, labels in train_loader:
+    with xla.step():
+      # Transfer data to the XLA device. This happens asynchronously.
+      inputs, labels = inputs.to(xla.device()), labels.to(xla.device())
       optimizer.zero_grad()
       outputs = model(inputs)
       loss = loss_fn(outputs, labels)
       loss.backward()
-      optimizer.step()
+      # `xm.optimizer_step` combines gradients across replicas
+      xm.optimizer_step(optimizer)

 if __name__ == '__main__':
-  mp.spawn(_mp_fn, args=(), nprocs=world_size)
+  # xmp.spawn automatically selects the correct world size
+  xmp.spawn(_mp_fn, args=())

If you're using DistributedDataParallel, make the following changes:

 import torch.distributed as dist
-import torch.multiprocessing as mp
+import torch_xla as xla
+import torch_xla.distributed.xla_multiprocessing as xmp
+import torch_xla.distributed.xla_backend

 def _mp_fn(rank):
   ...

-  os.environ['MASTER_ADDR'] = 'localhost'
-  os.environ['MASTER_PORT'] = '12355'
-  dist.init_process_group("gloo", rank=rank, world_size=world_size)
+  # Rank and world size are inferred from the XLA device runtime
+  dist.init_process_group("xla", init_method='xla://')
+
+  model.to(xm.xla_device())
+  # `gradient_as_bucket_view=True` required for XLA
+  ddp_model = DDP(model, gradient_as_bucket_view=True)

-  model = model.to(rank)
-  ddp_model = DDP(model, device_ids=[rank])

   for inputs, labels in train_loader:
+    with xla.step():
+      inputs, labels = inputs.to(xla.device()), labels.to(xla.device())
       optimizer.zero_grad()
       outputs = ddp_model(inputs)
       loss = loss_fn(outputs, labels)
       loss.backward()
       optimizer.step()

 if __name__ == '__main__':
-  mp.spawn(_mp_fn, args=(), nprocs=world_size)
+  xmp.spawn(_mp_fn, args=())

Additional information on PyTorch/XLA, including a description of its semantics and functions, is available at PyTorch.org. See the API Guide for best practices when writing networks that run on XLA devices (TPU, CUDA, CPU and...).

Our comprehensive user guides are available at:

Documentation for the latest release

Documentation for master branch

PyTorch/XLA tutorials

• Cloud TPU VM quickstart
• Cloud TPU Pod slice quickstart
• Profiling on TPU VM
• GPU guide

Available docker images and wheels

Python packages

PyTorch/XLA releases starting with version r2.1 will be available on PyPI. You can now install the main build with pip install torch_xla. To also install the Cloud TPU plugin, install the optional tpu dependencies after installing the main build with

pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html

GPU and nightly builds are available in our public GCS bucket.

Version	Cloud TPU/GPU VMs Wheel
2.3 (Python 3.8)	https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.3.0-cp38-cp38-manylinux_2_28_x86_64.whl
2.3 (Python 3.10)	https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.3.0-cp310-cp310-manylinux_2_28_x86_64.whl
2.3 (CUDA 12.1 + Python 3.8)	https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.3.0-cp38-cp38-manylinux_2_28_x86_64.whl
2.3 (CUDA 12.1 + Python 3.10)	https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.3.0-cp310-cp310-manylinux_2_28_x86_64.whl
nightly (Python 3.8)	https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly-cp38-cp38-linux_x86_64.whl
nightly (Python 3.10)	https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly-cp310-cp310-linux_x86_64.whl
nightly (CUDA 12.1 + Python 3.8)	https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-nightly-cp38-cp38-linux_x86_64.whl

You can also add +yyyymmdd after torch_xla-nightly to get the nightly wheel of a specified date. To get the companion pytorch nightly wheel, replace the torch_xla with torch on above wheel links.

older versions

Version	Cloud TPU VMs Wheel
2.2 (Python 3.8)	https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.2.0-cp38-cp38-manylinux_2_28_x86_64.whl
2.2 (Python 3.10)	https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.2.0-cp310-cp310-manylinux_2_28_x86_64.whl
2.2 (CUDA 12.1 + Python 3.8)	https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.2.0-cp38-cp38-manylinux_2_28_x86_64.whl
2.2 (CUDA 12.1 + Python 3.10)	https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.2.0-cp310-cp310-manylinux_2_28_x86_64.whl
2.1 (XRT + Python 3.10)	https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch_xla-2.1.0%2Bxrt-cp310-cp310-manylinux_2_28_x86_64.whl
2.1 (Python 3.8)	https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.1.0-cp38-cp38-linux_x86_64.whl
2.0 (Python 3.8)	https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-2.0-cp38-cp38-linux_x86_64.whl
1.13	https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.13-cp38-cp38-linux_x86_64.whl
1.12	https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.12-cp38-cp38-linux_x86_64.whl
1.11	https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.11-cp38-cp38-linux_x86_64.whl
1.10	https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.10-cp38-cp38-linux_x86_64.whl

Note: For TPU Pod customers using XRT (our legacy runtime), we have custom wheels for torch and torch_xla at https://storage.googleapis.com/tpu-pytorch/wheels/xrt.

Package	Cloud TPU VMs Wheel (XRT on Pod, Legacy Only)
torch_xla	https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch_xla-2.1.0%2Bxrt-cp310-cp310-manylinux_2_28_x86_64.whl
torch	https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch-2.1.0%2Bxrt-cp310-cp310-linux_x86_64.whl

Version	GPU Wheel + Python 3.8
2.1+ CUDA 11.8	https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/11.8/torch_xla-2.1.0-cp38-cp38-manylinux_2_28_x86_64.whl
2.0 + CUDA 11.8	https://storage.googleapis.com/tpu-pytorch/wheels/cuda/118/torch_xla-2.0-cp38-cp38-linux_x86_64.whl
2.0 + CUDA 11.7	https://storage.googleapis.com/tpu-pytorch/wheels/cuda/117/torch_xla-2.0-cp38-cp38-linux_x86_64.whl
1.13	https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.13-cp38-cp38-linux_x86_64.whl
nightly + CUDA 12.0 >= 2023/06/27	https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.0/torch_xla-nightly-cp38-cp38-linux_x86_64.whl
nightly + CUDA 11.8 <= 2023/04/25	https://storage.googleapis.com/tpu-pytorch/wheels/cuda/118/torch_xla-nightly-cp38-cp38-linux_x86_64.whl
nightly + CUDA 11.8 >= 2023/04/25	https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/11.8/torch_xla-nightly-cp38-cp38-linux_x86_64.whl

Version	GPU Wheel + Python 3.7
1.13	https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.13-cp37-cp37m-linux_x86_64.whl
1.12	https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.12-cp37-cp37m-linux_x86_64.whl
1.11	https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.11-cp37-cp37m-linux_x86_64.whl

Version	Colab TPU Wheel
2.0	https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-2.0-cp310-cp310-linux_x86_64.whl

Docker

Version	Cloud TPU VMs Docker
2.3	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.3.0_3.10_tpuvm
2.2	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.2.0_3.10_tpuvm
2.1	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_tpuvm
2.0	gcr.io/tpu-pytorch/xla:r2.0_3.8_tpuvm
1.13	gcr.io/tpu-pytorch/xla:r1.13_3.8_tpuvm
nightly python	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm

To use the above dockers, please pass --privileged --net host --shm-size=16G along. Here is an example:

docker run --privileged --net host --shm-size=16G -it us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm /bin/bash

Version	GPU CUDA 12.1 Docker
2.3	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.3.0_3.10_cuda_12.1
2.2	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.2.0_3.10_cuda_12.1
2.1	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_cuda_12.1
nightly	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1
nightly at date	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1_YYYYMMDD

Version	GPU CUDA 11.8 + Docker
2.1	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_cuda_11.8
2.0	gcr.io/tpu-pytorch/xla:r2.0_3.8_cuda_11.8
nightly	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_11.8
nightly at date	us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_11.8_YYYYMMDD

older versions

Version	GPU CUDA 11.7 + Docker
2.0	gcr.io/tpu-pytorch/xla:r2.0_3.8_cuda_11.7

Version	GPU CUDA 11.2 + Docker
1.13	gcr.io/tpu-pytorch/xla:r1.13_3.8_cuda_11.2

Version	GPU CUDA 11.2 + Docker
1.13	gcr.io/tpu-pytorch/xla:r1.13_3.7_cuda_11.2
1.12	gcr.io/tpu-pytorch/xla:r1.12_3.7_cuda_11.2

To run on compute instances with GPUs.

Troubleshooting

If PyTorch/XLA isn't performing as expected, see the troubleshooting guide, which has suggestions for debugging and optimizing your network(s).

Providing Feedback

The PyTorch/XLA team is always happy to hear from users and OSS contributors! The best way to reach out is by filing an issue on this Github. Questions, bug reports, feature requests, build issues, etc. are all welcome!

Contributing

See the contribution guide.

Disclaimer

This repository is jointly operated and maintained by Google, Meta and a number of individual contributors listed in the CONTRIBUTORS file. For questions directed at Meta, please send an email to opensource@fb.com. For questions directed at Google, please send an email to pytorch-xla@googlegroups.com. For all other questions, please open up an issue in this repository here.

Additional Reads

You can find additional useful reading materials in

• Performance debugging on Cloud TPU VM
• Lazy tensor intro
• Scaling deep learning workloads with PyTorch / XLA and Cloud TPU VM
• Scaling PyTorch models on Cloud TPUs with FSDP

Related Projects

• OpenXLA
• HuggingFace
• JetStream