TorchTPU is a native PyTorch back end for Google TPUs that implements PyTorch ATen kernels and a compilation cache to run and optimize XLA computations on TPU devices. It supports distributed training (data and tensor parallelism), integrates with torch.compile() for graph-mode execution, and provides utilities for debugging and benchmarking. The project includes end-to-end examples for large models and common benchmarks (Llama, Qwen, ResNet, DLRM, minGPT, UNet), user-facing APIs, and interactive Marimo notebooks (static copies available in docs) that are intended to be run from a TPU VM (a v6e GCE instance is recommended).
Installation supports Python 3.11+ and a public pip release (torch_tpu==0.2.0) with the CPU torch index URL; building from source uses Bazel/Bazelisk with an explicit wheel build workflow (bazel build config=wheel_common //ci/wheel:torch_tpu_wheel) and defaults to optimized (-c opt) builds. The repo documents nuances for wheel builds (shared libxla_base.so handling), options to disable remote build execution (config=no_rbe), and VS Code/clangd setup for C++ development. Contributions are welcome under an Apache 2.0 license; docs, examples, and build scripts are included to help developers reproduce, extend, and deploy PyTorch models on TPUs.
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