Skip to content

pytorch/xla

Repository files navigation

PyTorch/XLA

Current CI status: GitHub Actions 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:

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

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 (Python 3.11) https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.3.0-cp311-cp311-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
2.3 (CUDA 12.1 + Python 3.11) https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.3.0-cp311-cp311-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

Related Projects