March 12, 2026
From flash to backlash
Forcing Flash Attention onto a TPU and Learning the Hard Way
Free TPU stunt meets 'AI-wrote-this' drama and the harsh truth: this stuff is hard
TLDR: A dev tried to port a fast attention trick to free Google TPUs with JAX and discovered it’s far tougher than it looks. Comments exploded over suspected AI-written prose, a reminder that vendor tuning is hard, and nitpicks about memory details—proof that the compiler, not vibes, wins here.
An engineer tried to jam a faster “flash attention” trick (a way for AI models to focus efficiently) onto Google’s free TPU in Colab using JAX, expecting a quick port. Instead, they hit the wall: JAX compiles your Python into a frozen plan and won’t let you update arrays in place, turning simple “write here” loops into careful “pass state in, pass state out” dances. The kicker? The plain baseline already flies because the compiler fuses it all, so beating it by hand is no cakewalk.
This is where the comments lit up. Veteran gdiamos delivered the reality check: the boring grind—indexing, partitioning, scheduling, benchmarking—is brutal, and big vendors like Google have already done tons of it. Meanwhile, the vibe check went wild: refulgentis confessed a gut “I’m being slop’d” reaction to the writing, and FL33TW00D accused “Claude” fingerprints—em dashes and all—turning a tech post into an authenticity trial. Pedants grabbed popcorn too: ColonelPhantom nitpicked that the TPU’s on‑chip memory was compared to the wrong thing (“more like an L2 cache” than shared memory). The mood: half cheers for a gutsy learning write‑up, half side‑eye about AI‑assisted prose and tricky hardware analogies. Meme of the day: from Flash to backlash, while the compiler quietly does the real work
Key Points
- •The post ports a Flash Attention-style kernel from Triton on GPU to a TPU using JAX.
- •JAX traces Python functions into HLO, and XLA fuses and compiles them to device code (PTX for GPU, VLIW for TPU).
- •JAX’s immutable array model requires using lax.fori_loop and dynamic_update_slice instead of in-place updates.
- •fori_loop enables actual hardware loops, while the loop body must be a pure function carrying state in/out.
- •A standard causal attention implementation in JAX, jitted with XLA fusion, serves as the performance baseline on TPU.