I did trained some research models using the existing PyTorch/XLA on TPUs, and it was a mess of undocumented behavior and bugs (silently hanging after 8 hours of training!).
If anyone is trying to use PyTorch on TPU before TorchTPU is released, you can check out the training pipeline that I ended up building to support my research: https://github.com/aklein4/easy-torch-tpu
These parts here somehow trigger me:
- Enter TorchTPU. As an engineering team, our mandate was to build a stack that leads with usability, portability, and excellent performance.
- Engineering the TorchTPU Stack: The Technical Reality
- Eager First: Flexibility Without Compromise
- The breakthrough, however, is our fused eager mode.
- The Road Ahead: 2026 and Beyond
I have mixed feelings about this. On one hand, we all seem to be using the same tools and converging to the same style. On the other hand, if we all use the same models with the same system prompts, we might lose a lot of creativity and diversity in online content.
If you don't, I envy you.
shit, maybe China will start selling Huawei Ascend chips internationally.
From the text of the blog post: "Portability doesn't eliminate hardware realities, so TorchTPU facilitates a tiered workflow: establish correct execution first, then use our upcoming deep-dive guidelines to identify and refactor suboptimal architectures, or to inject custom kernels, for optimal hardware utilization."
