It's probably due to the fact that it's a cloud service. You have no guarantee that your next request will go to the same machine. So even with an identical seed, and temp 0 you might get different hardware and hence different accuracy/noise in the floating point operations.
replyHow can there be noise in floating point operations? I could buy like completion order for parallized batches i.e. adding a+b+c is different from a+c+b etc.
replyIEEE-754 doesn't mandate exact results for functions like exp(x). It mandates things like "within 2 ULP of the true answer." Hardware vendors are free to implement these functions in any way that meets the error tolerance.
replyBatching order, as you mentioned, matters a lot, and for any heavily optimized kernels it will change from one machine to the next. You also have the choice of backend numerical library from, e.g., different OS versions. There are floating-point bugs from time to time, especially in GPUs. Many operations (like transcendentals) are usually given a couple bits of wiggle room in the result. Another program executing could have changed the floating-point rounding mode on one device. More aggressive ML optimizers might automatically apply various forms of reduced precision to the requested high-level operation. If you have enough optimizations enabled, you might non-deterministically get compiled instructions like fmadd so that any one build of your library is deterministic (excluding other ideas mentioned above) but different machines with different builds (because of a staged rollout, different architectures, engineering mistakes, etc) can have different outputs. And so on.
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replyWhile the IEEE 754 standard ensures that individual basic operations are deterministic and strictly bounded, it does not guarantee that an entire program will yield bit-identical results on all CPUs.
replyCPUs and their execution environments introduce subtle hardware variations, architecture choices, and compiler optimizations that break bit-level consistency.
(same for GPU/TPU, ...)
Parent is correct - the math is very deterministic if you can guarantee it’s running repeatedly on the same machine and you’re not processing “random” requests in parallel. The compiler is irrelevant because once the code is generated it’s not getting recompiled and thus isn’t a source of non determinism (and generally if you don’t touch the math the compiler will consistently emit the same underlying machine code).
replyThis sub-thread was about cloud environments, where different requests may be served by different hardware. And it's in fact very likely that there will be a mix of different hardware from different vendors, in any particular LLM cloud for now.
replyIt is, after all, a fundamentally voltage-based process, and the logical “no-man’s land” is chosen to limit the likelihood of a weak component producing faulty logic, but it’s impractical to run through the set of all possible starting states and to verify that after an unbounded number of clock steps the machine reaches a predictable end state on all of the devices being manufactured.
replyStable seeding is not enough. A lot of modern, fast compute kernels are nondeterministic. Floating point multiplication/addition is not strictly associative and e.g. reductions can combine results from different threads in different orders (e.g. through atomic ops). You can write kernels to be deterministic, but it is generally less efficient.
replyThey are only non-deterministic when you’re doing batching and a kernel ends up running across a “random” set of token streams. If you’re only processing one user’s request, they’re very much deterministic.
replythat's incorrect in the presence of batching. it's tough work making it truly deterministic:
replyIt's not that hard. What is hard is making it truly deterministic and retain high throughput.
replyPRNG is deterministic.
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