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:
To install PyTorch/XLA stable build in a new TPU VM:
pip install torch~=2.6.0 'torch_xla[tpu]~=2.6.0' \
-f https://storage.googleapis.com/libtpu-releases/index.html \
-f https://storage.googleapis.com/libtpu-wheels/index.html
# Optional: if you're using custom kernels, install pallas dependencies
pip install 'torch_xla[pallas]' \
-f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html \
-f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
To install PyTorch/XLA nightly build in a new TPU VM:
pip3 install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
pip install 'torch_xla[tpu] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0.dev-cp310-cp310-linux_x86_64.whl' -f https://storage.googleapis.com/libtpu-releases/index.html -f https://storage.googleapis.com/libtpu-wheels/index.html
Starting from Pytorch/XLA 2.6, we'll provide wheels and docker images built with two C++ ABI flavors: C++11 and pre-C++11. Pre-C++11 is the default to align with PyTorch upstream, but C++11 ABI wheels and docker images have better lazy tensor tracing performance.
To install C++11 ABI flavored 2.6 wheels (Python 3.10 example):
pip install torch==2.6.0+cpu.cxx11.abi \
https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0%2Bcxx11-cp310-cp310-manylinux_2_28_x86_64.whl \
'torch_xla[tpu]' \
-f https://storage.googleapis.com/libtpu-releases/index.html \
-f https://storage.googleapis.com/libtpu-wheels/index.html \
-f https://download.pytorch.org/whl/torch
The above command works for Python 3.10. We additionally have Python 3.9 and 3.11 wheels:
To access C++11 ABI flavored docker image:
us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.6.0_3.10_tpuvm_cxx11
If your model is tracing bound (e.g. you see that the host CPU is busy tracing the model while TPUs are idle), switching to the C++11 ABI wheels/docker images can improve performance. Mixtral 8x7B benchmarking results on v5p-256, global batch size 1024:
PyTorch/XLA now provides GPU support through a plugin package similar to libtpu:
pip install torch~=2.5.0 torch_xla~=2.5.0 https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla_cuda_plugin-2.5.0-py3-none-any.whl
The newest stable version where PyTorch/XLA:GPU wheel is available is torch_xla
2.5. We do not offer a PyTorch/XLA:GPU wheel in the PyTorch/XLA 2.6 release. We
understand this is important and plan to reinstate GPU support by the 2.7 release.
PyTorch/XLA remains an open-source project and we welcome contributions from the
community to help maintain and improve the project. To contribute, please start
with the contributors guide.
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
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)
+ # xla.launch automatically selects the correct world size
+ xla.launch(_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_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())
+ 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)
+ xla.launch(_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
The AI-Hypercomputer/tpu-recipes repo. contains examples for training and serving many LLM and diffusion models.
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 corresponding to your installed torch_xla, install the optional tpu dependencies after installing the main build with
pip install torch_xla[tpu] \
-f https://storage.googleapis.com/libtpu-wheels/index.html \
-f https://storage.googleapis.com/libtpu-releases/index.html
GPU and nightly builds are available in our public GCS bucket.
| Version | Cloud GPU VM Wheels |
|---|---|
| 2.5 (CUDA 12.1 + Python 3.9) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp39-cp39-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.1 + Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.1 + Python 3.11) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp311-cp311-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.4 + Python 3.9) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp39-cp39-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.4 + Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.4 + Python 3.11) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp311-cp311-manylinux_2_28_x86_64.whl |
| nightly (Python 3.8) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0.dev-cp38-cp38-linux_x86_64.whl |
| nightly (Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0.dev-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-2.6.0.dev-cp38-cp38-linux_x86_64.whl |
pip3 install torch==2.6.0.dev20240925+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
pip3 install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly%2B20240925-cp310-cp310-linux_x86_64.whl
The torch wheel version 2.6.0.dev20240925+cpu can be found at https://download.pytorch.org/whl/nightly/torch/.
You can also add yyyymmdd after torch_xla-2.6.0.dev to get the nightly wheel of a specified date. Here is an example:
pip3 install torch==2.5.0.dev20240820+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
pip3 install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.5.0.dev20240820-cp310-cp310-linux_x86_64.whl
The torch wheel version 2.6.0.dev20240925+cpu can be found at https://download.pytorch.org/whl/nightly/torch/.
By default, torch is built with pre-C++11 version of ABI (see https://github.com/pytorch/pytorch/issues/51039).
torch_xla follows that and ships pre-C++11 builds by default. However, the lazy
tensor tracing performance can be improved by building the code with C++11 ABI.
As a result, we provide C++11 ABI builds for interested users to try, especially
if you find your model performance bottlenecked in Python lazy tensor tracing.
You can add .cxx11 after yyyymmdd to get the C++11 ABI variant of a
specific nightly wheel. Here is an example to install nightly builds from
10/28/2024:
pip3 install torch==2.6.0.dev20241028+cpu.cxx11.abi --index-url https://download.pytorch.org/whl/nightly
pip3 install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0.dev20241028.cxx11-cp310-cp310-linux_x86_64.whl
As of 12/11/2024, the torch_xla C++11 ABI wheel is named differently and can be installed as follows:
pip3 install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0.dev20241211+cxx11-cp310-cp310-linux_x86_64.whl
The torch wheel version 2.6.0.dev20241028+cpu.cxx11.abi can be found at https://download.pytorch.org/whl/nightly/torch/.
| Version | Cloud TPU VMs Wheel |
|---|---|
| 2.5 (Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.4 (Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.4.0-cp310-cp310-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 |
| 2.2 (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 |
| 2.1 (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 |
| Version | GPU Wheel |
|---|---|
| 2.5 (CUDA 12.1 + Python 3.9) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp39-cp39-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.1 + Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.1 + Python 3.11) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp311-cp311-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.4 + Python 3.9) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp39-cp39-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.4 + Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.5 (CUDA 12.4 + Python 3.11) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp311-cp311-manylinux_2_28_x86_64.whl |
| 2.4 (CUDA 12.1 + Python 3.9) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.4.0-cp39-cp39-manylinux_2_28_x86_64.whl |
| 2.4 (CUDA 12.1 + Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.4.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.4 (CUDA 12.1 + Python 3.11) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.4.0-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 |
| 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 + 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 |
| 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 |
| Version | Cloud TPU VMs Docker |
|---|---|
| 2.6 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.6.0_3.10_tpuvm |
| 2.5 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.5.0_3.10_tpuvm |
| 2.4 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.4.0_3.10_tpuvm |
| 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 |
| nightly python | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm |
| nightly python (C++11 ABI) | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_cxx11 |
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 12.4 Docker |
|---|---|
| 2.5 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.5.0_3.10_cuda_12.4 |
| 2.4 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.4.0_3.10_cuda_12.4 |
| Version | GPU CUDA 12.1 Docker |
|---|---|
| 2.5 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.5.0_3.10_cuda_12.1 |
| 2.4 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.4.0_3.10_cuda_12.1 |
| 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 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.0_3.8_cuda_11.8 |
To run on compute instances with GPUs.
If PyTorch/XLA isn't performing as expected, see the troubleshooting guide, which has suggestions for debugging and optimizing your network(s).
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!
See the contribution guide.
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.
You can find additional useful reading materials in
FAQs
XLA bridge for PyTorch
We found that torch-xla demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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