I think the convention is that random generators in most modern languages are always seeded, and always deterministic. If a user seed isn't supplied, implementations generally provide their own seed, which they attempt to make unique. Often they generate a seed that takes into account the current time. This is at least the case for many mainstream languages.
Java implementation: https://docs.oracle.com/javase/8/docs/api/java/util/Random.html Remarks: "If two instances of Random are created with the same seed, and the same sequence of method calls is made for each, they will generate and return identical sequences of numbers." C#: https://msdn.microsoft.com/en-us/library/ctssatww(v=vs.110).aspx Remarks: "Providing an identical seed value to different Random objects causes each instance to produce identical sequences of random numbers. This is often done when testing apps that rely on random number generators." On Tue, Jan 9, 2018 at 4:27 PM, Chris Olivier <[email protected]> wrote: > wait wait — i don’t think that random number generators should return > deterministic lists of numbers. i’m asking if something says it’s supposed > to. i know they tend to, but my understanding is that they tend to because > of the challenge of generating true random numbers from hardware. IMHO the > ideal random number generator would not return a determinaiticnset if > numbers regardless of seed. > > On Tue, Jan 9, 2018 at 3:43 AM Pedro Larroy <[email protected]> > wrote: > > > For enabling parallel deterministic testing we can set an environment > > variable and set the same seed on different devices for those cases > > where we want it, leaving the default as it is. I think this would be > > an easy solution that wouldn't change any behaviour in training on > > multi-gpu. > > > > On Tue, Jan 9, 2018 at 10:48 AM, kellen sunderland > > <[email protected]> wrote: > > > Thanks Asmus, yes this is also the approach I would be in favour of. I > > > think we should optionally allow the user to specify if they want > > > deterministic behaviour independent of the GPU they run on. If MXNet > is > > > going to support more arbitrary linear algabra operations I could see a > > lot > > > of use cases for this. For example I want deterministic noise fed > into a > > > deep-RL simulation so that I can compare a few different algorithms > > without > > > variance, and do it in parallel on my machine (that happens to have two > > > GPUs). > > > > > > On Tue, Jan 9, 2018 at 10:36 AM, Asmus Hetzel > > <[email protected]> > > > wrote: > > > > > >> The issue is tricky. Number generators should return deterministic > sets > > >> of numbers as Chris said, but that usually only applies to > > non-distributed > > >> systems. And to some extend, we have already a distributed system as > > soon > > >> as one cpu and one gpu is involved. > > >> For the usual setup like distributed training, using different seeds > on > > >> different devices is a must. You distribute a process that involves > > random > > >> number generation and that means that you absolutely have to ensure > that > > >> the sequences on the devices do not correlate. So this behaviour is > > >> intended and correct. We also can not guarantee that random number > > >> generation is deterministic when running on CPU vs. running on GPU. > > >> So what we are dealing here is generating repeatable results, when the > > >> application/code section is running on a single GPU out of a bigger > set > > of > > >> available GPUs, but we do not have control on which one. The crucial > > line > > >> in mxnet is this one (resource.cc): > > >> > > >> const uint32_t seed = ctx.dev_id + i * kMaxNumGPUs + global_seed * > > >> kRandMagic; > > >> Here I think it would make sense to add a switch that optionally makes > > >> this setting independent of ctx.dev_id. But we would have to document > > >> really well that this is solely meant for specific types of > > debugging/unit > > >> testing. > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> Am Montag, 8. Januar 2018, 19:30:02 MEZ hat Chris Olivier < > > >> [email protected]> Folgendes geschrieben: > > >> > > >> Is it explicitly defined somewhere that random number generators > should > > >> always return a deterministic set of numbers given the same seed, or > is > > >> that just a side-effect of some hardware not having a better way to > > >> generate random numbers so they use a user-defined seed to kick off > the > > >> randomization starting point? > > >> > > >> On Mon, Jan 8, 2018 at 9:27 AM, kellen sunderland < > > >> [email protected]> wrote: > > >> > > >> > Hello MXNet devs, > > >> > > > >> > I wanted to see what people thought about the follow section of > code, > > >> which > > >> > I think has some subtle pros/cons: > > >> > https://github.com/apache/incubator-mxnet/blob/ > > >> > d2a856a3a2abb4e72edc301b8b821f0b75f30722/src/resource.cc#L188 > > >> > > > >> > Tobi (tdomhan) from sockeye pointed it out to me after he spent some > > time > > >> > debugging non-determinism in his model training. > > >> > > > >> > This functionality is well documented here: > > >> > https://mxnet.incubator.apache.org/api/python/ndarray. > > >> > html#mxnet.random.seed > > >> > but I don't think the current api meets all use cases due to this > > >> section: > > >> > > > >> > "Random number generators in MXNet are device specific. Therefore, > > random > > >> > numbers generated from two devices can be different even if they are > > >> seeded > > >> > using the same seed." > > >> > > > >> > I'm guessing this is a feature that makes distributed training > easier > > in > > >> > MXNet, you wouldn't want to train the same model on each GPU. > However > > >> the > > >> > downside of this is that if you run unit tests on a multi-gpu > system, > > or > > >> in > > >> > a training environment where you don't have control over which GPU > you > > >> use, > > >> > you can't count on deterministic behaviour which you can assert > > results > > >> > against. I have a feeling there are non-unit test use cases where > > you'd > > >> > also want deterministic behaviour independent of which gpu you > happen > > to > > >> > have your code scheduled to run on. > > >> > > > >> > How do others feel about this? Would it make sense to have some > > optional > > >> > args in the seed call to have the seed-per-device functionality > turned > > >> off? > > >> > > > >> > -Kellen > > >> > > > >> > > >> > > >
