Good point, Tao! Is this env enabled in all MKL-DNN CI?
> -----Original Message----- > From: Lv, Tao A [mailto:tao.a...@intel.com] > Sent: Friday, November 23, 2018 9:53 AM > To: dev@mxnet.incubator.apache.org > Subject: RE: MKLDNN performance in CI > > Thanks for bringing this up, Marco. It's really weird since most of those > tests > listed in "worth noting" are not related to mkldnn backend. > > I can understand that some tests for mkldnn operator may be slower > because MXNET_MKLDNN_DEBUG is enabled in the CI: > https://github.com/apache/incubator- > mxnet/blob/master/ci/docker/runtime_functions.sh#L713 > > -----Original Message----- > From: Marco de Abreu [mailto:marco.g.ab...@googlemail.com.INVALID] > Sent: Friday, November 23, 2018 9:22 AM > To: dev@mxnet.incubator.apache.org > Subject: MKLDNN performance in CI > > Hello, > > I have noticed that our Python tests have been increasing in duration > recently. > In order to analyse this further, I created the PR [1] which allows to record > test durations. Please note that I did not dive deep on these numbers and > that they have to be taken with a grain of salt since slaves have varying > resource utilizations. > > Please have a look at the two following logs: > Python3 CPU MKLDNN: > http://jenkins.mxnet-ci.amazon- > ml.com/blue/rest/organizations/jenkins/pipelines/mxnet- > validation/pipelines/unix-cpu/branches/PR- > 13377/runs/2/nodes/155/steps/409/log/?start=0 > Python3 CPU Openblas: > http://jenkins.mxnet-ci.amazon- > ml.com/blue/rest/organizations/jenkins/pipelines/mxnet- > validation/pipelines/unix-cpu/branches/PR- > 13377/runs/2/nodes/152/steps/398/log/?start=0 > > If you scroll to the end (note that there are multiple test stages and > summaries being printed in these logs), you will find the following > statements: > > Python3 CPU MKLDNN: "Ran 702 tests in 3042.102s" > Python3 CPU Openblas: "Ran 702 tests in 2158.458s" > > This shows that the MKLDNN is generally being about 40% slower than the > Openblas backend. If we go into the details, we can see that some tests are > significantly slower: > > Python3 CPU MKLDNN: > > >[success] 20.78% test_random.test_shuffle: 630.7165s [success] 17.79% > >test_sparse_operator.test_elemwise_binary_ops: 540.0487s [success] > >10.91% test_gluon_model_zoo.test_models: 331.1503s [success] 2.62% > >test_operator.test_broadcast_binary_op: 79.4556s [success] 2.45% > >test_operator.test_pick: 74.4041s [success] 2.39% > >test_metric_perf.test_metric_performance: 72.5445s [success] 2.38% > >test_random.test_negative_binomial_generator: 72.1751s [success] 1.84% > >test_operator.test_psroipooling: 55.9432s [success] 1.78% > >test_random.test_poisson_generator: 54.0104s [success] 1.72% > >test_gluon.test_slice_pooling2d_slice_pooling2d: 52.3447s [success] > >1.60% test_contrib_control_flow.test_cond: 48.6977s [success] 1.41% > >test_random.test_random: 42.8712s [success] 1.03% > >test_operator.test_layer_norm: 31.1242s > > > Python3 CPU Openblas: > > [success] 26.20% test_gluon_model_zoo.test_models: 563.3366s [success] > > 4.34% test_random.test_shuffle: 93.3157s [success] 4.31% > > test_random.test_negative_binomial_generator: 92.6899s [success] 3.78% > > test_sparse_operator.test_elemwise_binary_ops: 81.2048s [success] > > 3.30% test_operator.test_psroipooling: 70.9090s [success] 3.20% > > test_random.test_poisson_generator: 68.7500s [success] 3.10% > > test_metric_perf.test_metric_performance: 66.6085s [success] 2.79% > > test_operator.test_layer_norm: 59.9566s [success] 2.66% > > test_gluon.test_slice_pooling2d_slice_pooling2d: 57.1887s [success] > > 2.62% test_operator.test_pick: 56.2312s [success] 2.60% > > test_random.test_random: 55.8920s [success] 2.19% > > test_operator.test_broadcast_binary_op: 47.1879s [success] 0.96% > > test_contrib_control_flow.test_cond: 20.6908s > > Tests worth noting: > - test_random.test_shuffle: 700% increase - but I don't know how this may > be related to MKLDNN. Are we doing random number generation in either of > those backends? > - test_sparse_operator.test_elemwise_binary_ops: 700% increase > - test_gluon_model_zoo.test_models: 40% decrease - that's awesome and to > be expect :) > - test_operator.test_broadcast_binary_op: 80% increase > - test_contrib_control_flow.test_cond: 250% increase > - test_operator.test_layer_norm: 50% decrease - nice! > > As I have stated previously, these numbers might not mean anything since > the CI is not a benchmarking environment (sorry if these are false negatives), > but I thought it might be worth mentioning so Intel could follow up and dive > deeper. > > Does anybody here create 1:1 operator comparisons (e.g. running > layer_norm in the different backends to compare the performance) who > could provide us with those numbers? > > Best regards, > Marco > > [1]: https://github.com/apache/incubator-mxnet/pull/13377
