Hi Anirudh, The LSTM performance bug is fixed by MKL-DNN and PR in here (https://github.com/apache/incubator-mxnet/pull/13417).
I am still working on MKL-DNN team to get a patch release for MXNet 1.4 in 1 or 2 days. Will update the status soon. Thanks everyone. --Patric > -----Original Message----- > From: Anirudh Subramanian [mailto:anirudh2...@gmail.com] > Sent: Tuesday, November 27, 2018 6:16 AM > To: dev@mxnet.incubator.apache.org > Subject: Re: Include MKLDNN into default mxnet pip package > > Hi Tao, > > I agree with Steffen that we can start with a stable release for MKLDNN for > 1.4.0. For your suggestion on using 0.17, can you provide info on what > versioning mechanism MKLDNN uses. Once a MKLDNN release is out and > there are some regressions found like the LSTM regression, would it be > possible to do a patch release for it or maintain a release branch for it ? > > Anirudh > > On Sun, Nov 25, 2018 at 5:03 PM Lv, Tao A <tao.a...@intel.com> wrote: > > > Hi Steffen, > > > > I think all the commits on MKL-DNN master branch are well tested for > > MKL-DNN development team. If we really want to have a release commit > > in the coming 1.4 mxnet release, my suggestion is 0.17 MKL-DNN release. > > > > Thank you, > > Tao > > > > Sent from my iPhone > > > > > On Nov 26, 2018, at 8:09 AM, Steffen Rochel > > > <steffenroc...@gmail.com> > > wrote: > > > > > > +1 to make MKL-DNN default. > > > I'm tracking https://github.com/apache/incubator-mxnet/issues/13369 > > > as open issue to be addressed for 1.4.0 I do agree that we should > > > move to a model to include released > > dependencies > > > instead of just taking bleeding edge snapshots. > > > However, speed of development is important as well. > > > As a compromise for 1.4.0 release with MKL-DNN: can the MKL-DNN > > development > > > team provide us with a well tested tag/commit id to include in 1.4.0 > > > release? > > > Steffen > > > > > >> On Wed, Nov 21, 2018 at 11:42 PM Lv, Tao A <tao.a...@intel.com> > wrote: > > >> > > >> Thanks for the information, Kellen and Naveen. > > >> > > >> Better than onnx-tensorrt, MKL-DNN has already provided versioning > > >> and release tags. My concern is that as MKL-DNN is still under > > >> intensive development, if it has a new feature or bug fix on its > > >> master branch, > > do we > > >> really want to wait for next release to get it supported in MXNet? > > >> > > >> Take the LSTM regression as an example, probably MKL-DNN will give > > >> a fix or improvement on its master branch soon, do we need to wait > > >> for 0.18 release to get it fixed for mxnet user? AFAIK, tensorflow > > >> is also using normal commit id, not release, as the dependency for MKL- > DNN. > > >> > > >> Regarding the LSTM regression, we are using internal JIRA tickets > > >> rather than github issues to track the defects of MKL-DNN. But I > > >> agree with > > you, > > >> we need update the progress of it in Alex's issue. > > >> > > >> Thanks, > > >> -tao > > >> > > >> -----Original Message----- > > >> From: kellen sunderland [mailto:kellen.sunderl...@gmail.com] > > >> Sent: Thursday, November 22, 2018 10:55 AM > > >> To: dev@mxnet.incubator.apache.org > > >> Subject: Re: Include MKLDNN into default mxnet pip package > > >> > > >> Agree with your point about other repos also not being based on > > versioning > > >> Tao. I would point out that I've given some that I've worked with > > similar > > >> feedback: https://github.com/onnx/onnx-tensorrt/issues/68 > > >> > > >>> On Wed, Nov 21, 2018 at 6:48 PM Naveen Swamy > <mnnav...@gmail.com> > > wrote: > > >>> > > >>> Tao, > > >>> > > >>> You are right there are many submodules in 3rd party. We have to > > >>> start somewhere and I believe this one is a good candidate to start > with. > > >>> This is not to cater to release of MXNet or to tie them with the > > >>> releases of the submodules but instead to pick only stable > > >>> releases and not to pick up bleeding edge commits from the tip of > > >>> the master, this gives us confidence in the submodule that MXNet > > >>> users are depending on that especially if we make MKLDNN the default. > > >>> > > >>> Good to know it is known already as a regression.Alex has created > > >>> this issue https://github.com/apache/incubator-mxnet/issues/13369, > > >>> please add details and link the corresponding issue in MKLDNN(I > > >>> couldn't > > find). > > >>> > > >>> -Naveen > > >>> > > >>>> On Wed, Nov 21, 2018 at 6:04 PM Lv, Tao A <tao.a...@intel.com> > wrote: > > >>>> > > >>>> Here are my answers for the questions from Kellen and Naveen > > >>>> about MKL-DNN. It doesn't mean that I'm supportive for making > > >>>> MKL-DNN default here. > > >>>> > > >>>> @Kellen, > > >>>> > > >>>> FYI, here is a list for those platforms which are officially > > >>>> supported by MKL-DNN. > > >>>> https://github.com/intel/mkl-dnn#system-requirements > > >>>> > > >>>> Most of computation intensive kernels in MKL-DNN are JITed. So > > >>>> they are supposed to generate code according to the platform > > >>>> during runtime. For non-JIT code in MKL-DNN, same as other code > > >>>> in MXNet, it will generate instructions according to the > > >>>> options/flags of compiler. We can set -DARCH_OPT_FLAGS when > build > > >>>> MKL-DNN to avoid optimization for compiling machine. That's > > >>>> exactly what we are doing > > >> for MKL-DNN build in MXNet. > > >>> Even > > >>>> without MKL-DNN, I noticed there were issues about illegal > > >>>> instructions > > >>> of > > >>>> MXNet when users import the pip package on a lower end machine > > >>>> which probably only supports SSE. > > >>>> > > >>>> @Naveen, > > >>>> > > >>>> The LSTM issue has already been identified as a regression from > > >>>> the > > >>> recent > > >>>> version of MKL-DNN. Hopefully it will be fixed soon with a new > > >>>> update of MKL-DNN. > > >>>> > > >>>> MXNet has many submodule dependencies under the 3rd party folder. > > >>>> Seems > > >>> we > > >>>> don't require release versions for most of these dependencies. > > >>>> The > > >>> release > > >>>> period of MKL-DNN and MXNet are not matched very well. I think it > > >>>> would > > >>> be > > >>>> a risk for MXNet release if it hardly depends on the release of a > > >>>> submodule, no need to say depends on the releases of all submodules. > > >>>> > > >>>> -tao > > >>>> > > >>>> -----Original Message----- > > >>>> From: Naveen Swamy [mailto:mnnav...@gmail.com] > > >>>> Sent: Thursday, November 22, 2018 9:08 AM > > >>>> To: dev@mxnet.incubator.apache.org > > >>>> Cc: d...@mxnet.apache.org > > >>>> Subject: Re: Include MKLDNN into default mxnet pip package > > >>>> > > >>>> Hi Alex, > > >>>> > > >>>> Thanks for promptly running the numbers on AMD and reporting here. > > >>>> > > >>>> Can you please update the AMD numbers here for posterity > > >>>> > > >>> > https://cwiki.apache.org/confluence/display/MXNET/MXNet+with+Intel > > >>> +MKL > > >>> -DNN+-+Performance+Benchmarking > > >>>> ? > > >>>> > > >>>> are there any outstanding issues when MKLDNN is enabled? from my > > >>>> offline conversation I am briefly aware performance issues with > > >>>> LSTM, is there an GitHub issue for it? > > >>>> > > >>>> MKLDNN is a submodule dependency, are we pulling the latest > > >>>> commit or releases ? If not we should move to releases before we > > >>>> make it a > > >>> default. > > >>>> Ideally we should use platform specific distributions (-dev > > >>>> packages) at least we should rely on well tested releases. > > >>>> > > >>>> > > >>>> Thanks, Naveen > > >>>> > > >>>> On Wed, Nov 21, 2018 at 4:55 PM Zai, Alexander > > >>> <alex...@amazon.com.invalid > > >>>>> > > >>>> wrote: > > >>>> > > >>>>> AMD benchmarks have been published. We are seeing a x15.8 > > >>>>> speedup with > > >>>>> Resnet50 (batch size 32) on AWS's new m5a.24xlarge machine. With > > >>>>> a smaller network (Mobilenet - batch size 32) the speedup is > > >>>>> more significant at x38.7. Let's have a vote to see if the PR to > > >>>>> have MKLDNN enabled by default > > >>>>> (https://github.com/apache/incubator-mxnet/pull/12591) can be > > >>>>> merged before 1.4.0 release. > > >>>>> > > >>>>> On 10/19/18, 9:17 AM, "Pedro Larroy" > > >>>>> <pedro.larroy.li...@gmail.com> > > >>>>> wrote: > > >>>>> > > >>>>> I did pip install mxnet-mkl==1.3.1b20181018 on an AMD Ryzen > > >>>>> 1950X and unit > > >>>>> tests are passing. > > >>>>> > > >>>>> Is this build using AVX512? in /proc/cpuinfo I see only "avx" > > >>> flag. > > >>>>> There's no "avx2" like on recent intel cpus. > > >>>>> > > >>>>> Pedro. > > >>>>> > > >>>>> On Fri, Oct 19, 2018 at 5:12 PM Hagay Lupesko > > >>>>> <lupe...@gmail.com> > > >>>>> wrote: > > >>>>> > > >>>>>> Awesome collaborative effort across many contributors and > > >>>> companies! > > >>>>>> > > >>>>>> The boost is impressive and for MXNet users to get this > > >>>>> boost "out of the > > >>>>>> box" is a great benefit and makes MXNet an even better choice. > > >>>>>> > > >>>>>> Alex - can you clarify whether there are any down sides with > > >>>>> regards to > > >>>>>> noon AVX-512 architectures, AMD CPUs, etc? Will it > > >>>>> gracefully fallback? > > >>>>>> > > >>>>>> Hagay > > >>>>>> > > >>>>>> > > >>>>>> On Fri, Oct 19, 2018, 15:46 Sergio Fernández > > >>>>> <wik...@apache.org> > > >>>>> wrote: > > >>>>>> > > >>>>>>> If there is no downside on platforms not supporting AVX512 > > >>>>> instructions, > > >>>>>>> then +1 > > >>>>>>> > > >>>>>>> > > >>>>>>> On Wed, Oct 17, 2018, 14:10 Alex Zai <aza...@gmail.com> > > >> wrote: > > >>>>>>> > > >>>>>>>> Hey all, > > >>>>>>>> We have been working hard these past few months to > > >>>>> integrate > > >>>> and > > >>>>>>> stabilize > > >>>>>>>> Intel’s MKLDNN deep learning CPU accelerator into Mxnet > > >>>>> and have made > > >>>>>>>> incredible progress. On CPUs with AVX512 instructions > > >>>>> (such as > > >>>>> c5.18x) > > >>>>>> we > > >>>>>>>> have seen performance increase up to 12x and on other > > >>>>> platforms (Macs, > > >>>>>>>> AVX2) we seen a speedup of 1.5+. Full list of benchmarks > > >>>>> can be found > > >>>>>>> here > > >>>>>>>> ( > > >>>>>>>> > > >>>>>>> > > >>>>>> > > >>>>> > > >>>> > > >>> > https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=9 > > >>> 5650 > > >>> 764 > > >>>>>>>> and https://github.com/apache/incubator-mxnet/pull/12591 > > >> ). > > >>>>>>>> > > >>>>>>>> Currently, using this accelerator requires the developer > > >>>>> to either pip > > >>>>>>>> install the mxnet-mkl version of mxnet or to build it > > >>>>> themselves from > > >>>>>>>> source. Given that we should try to provide the best > > >>>>> performance "out > > >>>>>> of > > >>>>>>>> the box” with mxnet we should include this in the > > >>>>> default > > >>>> build. > > >>>>> The > > >>>>>>> mkldnn > > >>>>>>>> library is included with in the pip package build so it > > >>>>> does > > >>>> not > > >>>>>> require > > >>>>>>> an > > >>>>>>>> external dependency. > > >>>>>>>> > > >>>>>>>> There were concerns that MKLDNN could cause regressions > > >>>>> on certain > > >>>>>>>> platforms (as it did with the tensorflow version a while > > >>>>> back); but we > > >>>>>>>> added a env flag (MXNET_MKLDNN_ENABLED) that allows > > >>>>> users to turn of > > >>>>>> this > > >>>>>>>> feature during runtime. Please bring up any other > > >>>>> concerns you may have > > >>>>>>> and > > >>>>>>>> your thoughts on including this accelerator in the > > >>>>> default > > >>>> build. > > >>>>>>>> > > >>>>>>>> Best, > > >>>>>>>> Alex > > >>>>>>>> > > >>>>>>> > > >>>>>> > > >>>>> > > >>>>> > > >>>>> > > >>>> > > >>> > > >> > >
