[MXNET-891] Support tuple of scales in upsample operator

[vandanavk]

Description

This PR adds the option to specify different scales for different dimensions.
Ref: #12602

Fixes #12851

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
• Changes are complete (i.e. I finished coding on this PR)
• All changes have test coverage:
• Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
• Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
• Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
• Code is well-documented:
• For user-facing API changes, API doc string has been updated.
• For new C++ functions in header files, their functionalities and arguments are documented.
• For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
• Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
• To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

• Change scale to tuple
• Add custom nearest neighbor implementation
• Allow same scales for bilinear upsampling
• Add ONNX import/export for Upsampling operator

Comments

• Fix for bilinear upsampling documentation and test are at Fix documentation for bilinear upsampling and add unit test #14035

[vandanavk]

@mxnet-label-bot add [pr-work-in-progress, ONNX, Operator]

[vandanavk]

@mxnet-label-bot update [pr-awaiting-review, ONNX, Operator]

[ChaiBapchya]

Thanks for the contribution! Looks like quite a few CI jobs have failed - have a look.

[ChaiBapchya]

how about using index_t as datatype for all of them
Since for large operator support it was found to be useful -#13418

[vandanavk]

Sure, I'll make this change

[ankkhedia]

@Roshrini Ping for review!

[vandanavk]

@anirudh2290 @samskalicky @apeforest for review

[samskalicky]

LGTM

[anirudhacharya]

can this nested for loop be vectorized?

[vandanavk]

@mxnet-label-bot update [ONNX, Operator, pr-work-in-progress]

[pinaraws]

@vandanavk Thanks for your contribution! Could you please rebase and resolve conflicts?

[piyushghai]

@vandanavk Gentle ping...

[Roshrini]

@vandanavk Can you resolve conflicts and look into build failures?

[huangzhiyuan]

@vandanavk, thanks for your contribution, and I'm interested in this feature. But I have a question about it, have you tested the effect of this fix on the performance of the original int scale in upsample operator? I have a simple test script, and the result are below:

sym = mx.symbol.UpSampling(data, scale=2, sample_type='nearest')

OP	ms	ms/call	calls
Upsampling (origin)	99.873	0.9511714285714287	105
Upsampling (with this fix)	736.018	7.009695238095238	105

could you help check the performance drop after you rebase and resolve conflicts?

[pengzhao-intel]

Significant performance difference from the new implementation on the CPU.
@vandanavk could you help to fix it? Feel free to let me know if anything I can help :)

[vandanavk]

@huangzhiyuan thanks for the script and performance numbers. I haven't had a chance to look into the performance yet. Will work on it soon.

[karan6181]

@vandanavk Did you get a chance to work on it ? Thanks!

[AnaRhisT94]

How can I use the UpSampling code here?
Just copying and pasting that code into _op_transalations.py doesn’t work. it spits the error the 'UpSampling' isn't found. It’s probably because there’s a ‘get_inputs’ function which I don’t know where is located. and also it uses ‘onnx.’ i guess I should import onnx in the same file?

[ThomasDelteil]

Any updates on that? This would be quite appreciated: https://stackoverflow.com/questions/56246010/the-upsampling-operation-is-not-supported-when-convert-mxnet-model-to-onnx-model

[vandanavk]

@benhe2011 is working on optimizing the code based on @huangzhiyuan's comments.

[vandanavk]

Closing in favor of #15811.