(for non mkl dropout, for instance) On Mon, Nov 18, 2019 at 7:50 PM Chris Olivier <[email protected]> wrote:
> To address the deterministic item, I know for a fact that training will > not be deterministic in some cases where the “parallel random” class is > utilized in parallel threads, such as OMP, if the number of cores is > different, even with the same seed, because threads are seeded > independently and different number of threads will end up generating > different random number sequences. Dropout operator being an example. > > On Mon, Nov 18, 2019 at 6:39 PM Alfredo Luque > <[email protected]> wrote: > >> For AMD CPUs, you’d want to perform validation because now MKL-DNN would >> be >> enabled by default. Historically, other intel libraries (along with the >> ICC >> compiler) have had performance issues on AMD CPUs. It’s just worth double >> checking to make sure that’s not the case here. Perhaps some MKL-DNN >> authors can chime in though. It’s not sufficient to double check that an >> AVX2 package passes tests. >> >> Agreed in the case we’re not releasing ARM binaries. >> >> The reproducibility argument is around the results being numerically >> reproducible. That is, eg; if I train a model with some fixed set of data, >> some random seed, etc. and then run inference on it do I get the exact >> same >> floating point values for the weights and results? Does MxNet already >> offer >> this without MKL-DNN? >> >> On November 18, 2019 at 6:32:07 PM, Tao Lv ([email protected]) wrote: >> >> Regarding the cases listed by Marco: >> - AMD CPU >> From my architecture knowledge, what works on C4 instances (with AVX2 >> support) should also work well on m5a, right? I think mxnet-mkl and >> mxnet-cuxxmkl packages have been fully validated on AVX2 machines. >> Also, we didn't perform any validation on AMD CPU before, why we need do >> that for this time? >> >> - ARM CPU >> I don't know we're releasing any convenience binaries for ARM CPU. This >> proposal mainly targets those pypi packages. >> >> - Windows >> Already validated by CI. We're also releasing mxnet-mkl packages for Win. >> >> - GPU and MKLDNN enabled >> Already validated by CI and mxnet-cuxxmkl packages have been released for >> several versions. >> >> - Fully reproducible results (medical and financial sector requested that >> and we have some flags for cuda) >> Not sure I understand this case. We already have MKL-DNN backend for a >> while. Functionality and correctness of it have been verified by MXNet >> users. >> >> -tao >> >> On Tue, Nov 19, 2019 at 4:41 AM Marco de Abreu <[email protected]> >> wrote: >> >> > Sorry, my intent with the "non-standard" phrase was not about general >> MXNet >> > but rather from MKLDNNs point of view, considering that it's being >> > developed by Intel, I assumed that MKLDNN might consider non-intel >> > use-cases non standard. >> > >> > -Marco >> > >> > Skalicky, Sam <[email protected]> schrieb am Mo., 18. Nov. >> 2019, >> > 21:34: >> > >> > > Thanks Alfredo, if you can create a GitHub issue with notes/steps we >> can >> > > add this to the todo list for integrating with the MXNet CI to test on >> > m5a >> > > instances too. Then we can start tracking this on a regular basis. It >> > would >> > > be great to actually test on ARM instances now that AWS has A1 >> instances >> > > too…..ill add it to the wish list ;-D >> > > >> > > Sam >> > > >> > > > On Nov 18, 2019, at 12:32 PM, Alfredo Luque < >> [email protected] >> > .INVALID> >> > > wrote: >> > > > >> > > > Happy to run some benchmarks on an AWS m5a instance (Epyc) and first >> > > > generation AMD Threadripper Gen 1 if someone has something easy to >> run >> > > and >> > > > representative. >> > > > >> > > > On November 18, 2019 at 12:29:31 PM, Skalicky, Sam ( >> > > > [email protected]) wrote: >> > > > >> > > > Thanks a good idea Alfredo, are you able to help test on AMD CPUs? >> Or >> > is >> > > > there someone else in the mxnet dev@ community who can help? >> > > > >> > > > Sam >> > > > >> > > >> On Nov 18, 2019, at 12:27 PM, Alfredo Luque >> > > > <[email protected]> wrote: >> > > >> >> > > >> Verifying that there isn’t a slowdown on AMD CPUs (eg; Ryzen / >> Epyc) >> > > > would >> > > >> definitely make sense as a requirement. It seems odd to classify >> that >> > as >> > > > a >> > > >> “nonstandard” use case. >> > > >> >> > > >> On November 18, 2019 at 12:20:33 PM, Skalicky, Sam ( >> > > >> [email protected]) wrote: >> > > >> >> > > >> Thanks Patric & team for your work over the years to make MXNet >> fast >> > > with >> > > >> MKLDNN! >> > > >> >> > > >> I think it would be great to make MKLDNN enabled by default. We >> will >> > > need >> > > >> to continue producing variants without MKLDNN for those who don’t >> want >> > > it >> > > >> (Marco enumerated some use cases). How do you propose to identify >> the >> > > pip >> > > >> wheels with/without MKLDNN? Previously we had: mxnet-mkl and >> > > > mxnet-cu101mkl >> > > >> with MKLDNN. If the plain “mxnet” pip wheel now contains MKLDNN >> what >> > do >> > > > you >> > > >> propose we call the build without MKLDNN? mxnet-nomkl? >> > > >> >> > > >> Thanks! >> > > >> Sam >> > > >> >> > > >>> On Nov 18, 2019, at 11:08 AM, Marco de Abreu < >> > [email protected]> >> > > >> wrote: >> > > >>> >> > > >>> Hi Patric, >> > > >>> >> > > >>> First of all, thanks a lot to you and your team for all the effort >> on >> > > >> MXNet >> > > >>> and mkldnn! >> > > >>> >> > > >>> Generally I'm inclined towards your proposal, but I'm thinking >> about >> > > the >> > > >>> non-standard use cases: >> > > >>> - AMD CPU >> > > >>> - ARM CPU >> > > >>> - Windows >> > > >>> - GPU and MKLDNN enabled >> > > >>> - Fully reproducible results (medical and financial sector >> requested >> > > > that >> > > >>> and we have some flags for cuda) >> > > >>> >> > > >>> Is mkldnn fully compatible with these use cases? If not, what >> would >> > > >> happen? >> > > >>> If yes, do we have performance numbers? >> > > >>> >> > > >>> Best regards, >> > > >>> Marco >> > > >>> >> > > >>> Zhao, Patric <[email protected]> schrieb am Mo., 18. Nov. >> 2019, >> > > >> 14:00: >> > > >>> >> > > >>>> Hi MXNet community, >> > > >>>> >> > > >>>> From the first MKLDNN backend integrated in release 1.2, the >> > community >> > > >> is >> > > >>>> continuously improving the quality and performance of MKLDNN CPU >> > > >> backend. >> > > >>>> Nowadays, the MKLDNN backend is widely used for the inference, >> > > >> especially >> > > >>>> for INT8 inference, and we got lots of very positive feedbacks >> from >> > > >> MXNet >> > > >>>> users. >> > > >>>> >> > > >>>> Achieved milestones as below: >> > > >>>> >> > > >>>> - MKLDNN integrated into Apache MXNet from release 1.2, Feb, 2018 >> > [1] >> > > >>>> - MKLDNN backend as default CPU backend from source building, >> Jan, >> > > 2019 >> > > >> [2] >> > > >>>> - MKLDNN subgraph optimization as default for the inference, Jul, >> > 2019 >> > > >> [3] >> > > >>>> - MKLDNN major version upgrade in release 1.6, Oct, 2019 [4] >> > > >>>> >> > > >>>> To make more successful and technical leadership for Apache MXNet >> in >> > > > the >> > > >>>> industry, I propose to make MKLDNN as default CPU backend in all >> > > binary >> > > >>>> distribution from the next release. >> > > >>>> The new milestone includes: >> > > >>>> >> > > >>>> - Static link MKLDNN library in the binary avoiding the mismatch >> > > > version >> > > >>>> in the runtime [5] >> > > >>>> - Make nightly build with MKLDNN default from master pre 1.7 >> release >> > > >>>> - Binary distribution with MKLDNN default from 1.7 release. >> > > >>>> >> > > >>>> What will be changed: >> > > >>>> >> > > >>>> - mxnet and mxnet-cuXX binary will be built with MKLDNN=1 >> > > >>>> - mxnet-mkl and mxnet-cuXXmkl will be not changed in the minor >> > release >> > > >>>> (1.x) and plan to remove in next major release (2.0) >> > > >>>> >> > > >>>> Suggestions and comments are highly appreciated. >> > > >>>> >> > > >>>> Thanks, >> > > >>>> >> > > >>>> --Patric >> > > >>>> >> > > >>>> >> > > >>>> [1] https://github.com/apache/incubator-mxnet/pull/9677 >> > > >>>> [2] >> > > >>>> >> > > >> >> > > > >> > > >> > >> >> https://lists.apache.org/thread.html/bfeae6ee46374112eb4dff1470c262959101e4bffb19930926963535@%3Cdev.mxnet.apache.org%3E >> > > >>>> [3] https://github.com/apache/incubator-mxnet/pull/15518 >> > > >>>> [4] >> > > >>>> >> > > >> >> > > > >> > > >> > >> >> https://lists.apache.org/thread.html/f46ab920f18795496eafe713e6e9e561c684e06189085cec17b401dc@%3Cdev.mxnet.apache.org%3E >> > > >>>> [5] https://github.com/apache/incubator-mxnet/pull/16731 >> > > >>>> >> > > >> >> > > >> — >> > > >> Alfredo Luque >> > > >> Software Engineer >> > > >> Machine Learning Infrastructure >> > > >> Airbnb >> > > >> San Francisco, CA >> > > > >> > > > — >> > > > Alfredo Luque >> > > > Software Engineer >> > > > Machine Learning Infrastructure >> > > > Airbnb >> > > > San Francisco, CA >> > > >> > > >> > >> >> — >> Alfredo Luque >> Software Engineer >> Machine Learning Infrastructure >> Airbnb >> San Francisco, CA >> >
