[Discussion] 1.6.0 Roadmap

[szha]

Let's start a discussion here about the roadmap towards 1.6.0. We are looking for:

New features that are useful to your research and development.
Improvements and patches to existing features.
If you have any item that you'd like to propose to have in the roadmap, please do:

Create (or locate existing) issue/pull request for the item, note the issue/pull request number.
Comment in this issue: 1) the above issue number, 2) one sentence of what the item is about and why it's useful to you.
Indicate whether you'd be willing to help out on the item.
Share the ETA if you're driving the item and have an guesstimate on when it will be done.

Feel free to include items that weren't included in past roadmap discussions that you still wish to include in this release.
cc @apache/mxnet-committers

[mxnet-label-bot]

Hey, this is the MXNet Label Bot.
Thank you for submitting the issue! I will try and suggest some labels so that the appropriate MXNet community members can help resolve it.
Here are my recommended labels: Feature

[sandeep-krishnamurthy]

1. Can we work towards INT-64 support (Large Tensor) as DEFAULT. This would make it easy for Large Tensor use cases such as DGL, recommendation system models to use MXNet easily. Today, they need to compile from source. (CC @apeforest @access2rohit)

[anirudhacharya]

1. Can we work towards INT-64 support (Large Tensor) as DEFAULT. This would make it easy for Large Tensor use cases such as DGL, recommendation system models to use MXNet easily. Today, they need to compile from source. (CC @apeforest @access2rohit)

There are a few regressions that have been introduced since the large tensor support was added. For example, there are a few hierarchical attention networks which were training fine until 1.3.1 but since the addition of large tensor support, results in NaNs in weight vectors and gradient calculations. We probably should ensure all regressions are fixed with regard to this feature.

Related issues and PRs that might be relevant( the above issue is in addition to what is discussed in the below links) -

• [MXNET-623] Fixing an integer overflow bug in large NDArray #11742 (comment)
• performance degradation from 1.3.1 to 1.4.0 #14496
• add a compiler flag to use int64 as tensor size #14570

[pengzhao-intel]

@sandeep-krishnamurthy Agree INT64 enhancements.
We plan to upgrade MKLDNN to 1.0 in r1.6 so that MKLDNN backend can work for INT64 index.

[pengzhao-intel]

CPU related proposal for 1.6 (keep updating in the following several days).

1. WIP, MKL-DNN upgrade to 1.0

• the proposal will be sent out soon in dev@ @TaoLv
  https://github.com/apache/incubator-mxnet/projects/16

2. DONE, MKL-DNN Subgraph Fusion as default @ZhennanQin [MKLDNN] Enable subgraph backend mkldnn by default. #15518

3. DONE, Quantization API improvement @xinyu-intel [MKLDNN]Enhance Quantization APIs and Tutorial #15448

4. WIP, RNN (vRNN/LSTM/GRU) with MKL-DNN acceleration @ciyongch @zixuanweeei

5. Enable new models, like NCF, @xinyu-intel

[wkcn]

Agree INT64 enhancements, too.
For the regression problem, a good solution is to choose data type by the data size in the performance bottleneck.

The following features are useful for research and development:

1. Need register_backward_hook() function in mxnet Need register_backward_hook() function in mxnet #15411
   backward_hook is useful to watch gradient for debug, and modify gradient like normalizing it.

[Neutron3529]

I think make (pre-trained) NN editable directly may helpful.
For example, I want to train a NN, whose input is (data,aux_data) and its output is 'predict'.
with the progression of training, I may found that aux_data is useless and may introduce additional bias in both training and testing.
Fortunately, since L2-regularization will make all the params related to aux_data near 0, I could directly set all the params related to aux_data to 0.
But, the net with new parameters are slower than a new net with the pre-trained params.
I think it is useful to enable direct editing for NNs.
I think it will be more fancy:

batch_size=1
data=mx.sym.var('data',shape=(batch_size,1))
aux_data=mx.sym.var('aux_data',shape=(batch_size,1))
combine=mx.sym.Concat(data,aux,dim=-1)
out=mx.sym.FullyConnected(combine,num_hidden=3).softmax()
net=mx.mod.Module(symbol=out,data_names=('data','aux_data'))
#(train and found that aux is useless)
net.sym#`.sym` returns the names of symbols used in the net
net.pop('aux_data')#will remove aux from net, and further, the net does not need `Concat` function anymore, and the params for `out` reduce 3 since `aux` is removed.

Since it is very inconvenient to change the params in ParameterDict(I only knows that use mx.init.Constant may help, but that is too inconvenient for me.), add a pop method to the net may help.

---

What's more, I think MXNet needs a relocatable .dll/.so file
libmxnet.dll is too large and takes too much time to load in my Windows 10

I asked how to decrease the dll size (since there is too much archs in the single .dll file and what I want is just -arch=sm_61 for my GTX 1060)

The reply is to use nvprune, I tried it and it gives me an error:

nvprune fatal   : Input file 'libmxnet.dll' not relocatable

Make the libmxnet.dll/libmxnet.so relocatable will provides the possibility to further decrease the size of the dll file. And may further more decrease the import time.
It seems the symbol are contained in libmxnet.lib, but I can not merge these symbols into libmxnet.dll. If someone find a way to redistribute a libmxnet.dll with symbols, we may take less time import mxnet in python

[sandeep-krishnamurthy]

It would be nice to add support for default checkpointing in Estimator API. For reference, TF estimator APIs does provide default checkpointing of models trained with Estimator API, which is very useful for users.

Also, can we plan to graduate Estimator API in MXNet 1.6? This is super useful API for MXNet users.

@roywei - Any comments?

[apeforest]

Thanks to @ChaiBapchya we now have performance comparison data between int32 and int64: https://docs.google.com/spreadsheets/d/1GpdNquQb71Is5B-li99JDuiLeEZd-eSjHIIowzGrwxc/edit#gid=843443107

[ptrendx]

We have multiple improvements to BERT inference and training speed that we would like to be part of 1.6 release:

• Softmax optimizations (Softmax optimization for GPU #15545 )
• Pointwise fusion for GPU (Pointwise fusion for GPU #15167 )
• Eliminate common expressions (Eliminate common expressions #15657 )
• Bias speed improvements (FullyConnected Bias performance improvement on GPU #16039 )
• Aggregated AdamW optimizer (Aggregated adamw update #16398)
• Aggregated zeroing of the gradients (Aggregated zero grad #16446)
• Aggregated sum of squares operator (also used in LARS, Add fast implementation of LARS #16122)
• Embedding gradient optimization (Embedding gradient performance optimization on GPU #16355)
• Faster multihead attention operator (Add MXNet Ops for fast multihead attention #16408)

[roywei]

Moving fixing nightly failure from 1.5.1 scope to 1.6.0 as they are failing on master branch not 1.5.x branch.
#15613 (comment)

nightly test failure need to be fixed:
#15374

http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/NightlyTestsForBinaries/detail/master/395/pipeline/

[szha]

@reminisce @haojin2 given that numpy operators would be a major topic in 1.6 release, it would be great if you could add what you intend to include in this release.