Just to summarize base on the concerns Marco raised and discussed abvove: - AMD CPU (it should work with MKLDNN: https://cwiki.apache.org/confluence/display/MXNET/MXNet+with+Intel+MKL-DNN+-+Performance+Benchmarking ) - ARM CPU (we don't have it today w/o MKLDNN either) - Windows (Windows support is there regardless of MKLDNN or not) - GPU and MKLDNN enabled (already supported) - Fully reproducible results (medical and financial sector requested that and we have some flags for cuda) (The nondeterminism exists even today w/o MKLDNN. We should address it regardless of MLKDNN)
Marco, please let us know if your concerns are properly addressed? Given that MKLDNN gives significant performance speed up in CPU, I am inclined to make it default in pip build. Best, Lin On Tue, Nov 19, 2019 at 8:08 AM Chris Olivier <cjolivie...@gmail.com> wrote: > Thanks, Patric. I was just trying to point out that there was currently no > guarantee of deterministic results without MKL, so there’s not necessarily > an expectation of determinism with MKL (ie requirement isn’t relaxed). > > On Mon, Nov 18, 2019 at 9:38 PM Zhao, Patric <patric.z...@intel.com> > wrote: > > > It may be a concern but little noise can't affect the final results if > the > > algorithm is stable in numerical. > > The MKLDNN backend with mxnet-mkl has been used for 2 years and we didn't > > see the coverage issue caused by multiple threading. > > In other words, GPU programming mode works well on training where the > > non-deterministic also exists from multiple threads. > > > > Parts of training accuracy was pasted in the first PR when MKLDNN is > > integrated. > > > https://github.com/apache/incubator-mxnet/pull/8302#issuecomment-359674818 > > > > In conclusion, it may happen with very little probability. I believe we > > can get a solution in case it happens someday. > > > > Thanks, > > > > --Patric > > > > > > > -----Original Message----- > > > From: Chris Olivier <cjolivie...@gmail.com> > > > Sent: Tuesday, November 19, 2019 11:51 AM > > > To: dev@mxnet.incubator.apache.org > > > Cc: Tao Lv <mutou...@gmail.com> > > > Subject: Re: Proposal to make MKLDNN as default CPU backend > > > > > > (for non mkl dropout, for instance) > > > > > > On Mon, Nov 18, 2019 at 7:50 PM Chris Olivier <cjolivie...@gmail.com> > > > 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 > > > > <alfredo.lu...@airbnb.com.invalid> 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 (mutou...@gmail.com) > > > 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 > > > >> <marco.g.ab...@gmail.com> > > > >> 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 <sska...@amazon.com.invalid> 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 < > > > >> alfredo.lu...@airbnb.com > > > >> > .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 ( > > > >> > > > sska...@amazon.com.invalid) 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 > > > >> > > > <alfredo.lu...@airbnb.com.INVALID> 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 ( > > > >> > > >> sska...@amazon.com.invalid) 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 < > > > >> > marco.g.ab...@gmail.com> > > > >> > > >> 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 <patric.z...@intel.com> 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/bfeae6ee46374112eb4dff1470c26295 > > > >> 9101e4bffb19930926963535@%3Cdev.mxnet.apache.org%3E > > > >> > > >>>> [3] https://github.com/apache/incubator-mxnet/pull/15518 > > > >> > > >>>> [4] > > > >> > > >>>> > > > >> > > >> > > > >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > > https://lists.apache.org/thread.html/f46ab920f18795496eafe713e6e9e561 > > > >> c684e06189085cec17b401dc@%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 > > > >> > > > > > > >
