Add support for qdense_batchnorm in QKeras

[julesmuhizi]

Add support for qdense_batchnorm by folding qdense kernel with batchnorm parameters, then computing qdense_batchnorm output using the qdense inputs and folded kernel

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[zhuangh]

@julesmuhizi thank you so much for your PR, could you sign CLA first?

[julesmuhizi]

@zhuangh I'm waiting on employer authorization for the CLA. Will do as soon as I get authorized.

[julesmuhizi]

@googlebot I signed it!

[google-cla]

CLAs look good, thanks!

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[zhuangh]

@julesmuhizi thanks! could you also add a test for your code change.

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[nghielme]

@zhuangh here a test --> https://gist.github.com/nicologhielmetti/84df61987476b031eb8fc6103f7e2915
@julesmuhizi and I compared the performance with a QDense + Batchnorm layer to see how much is the gap between the two. It actually resulted small, probably due to slightly difference with the quantization operations between the two versions.

[vloncar]

@julesmuhizi Don't you also need to add the new layer to bn_folding_utils.py?

@zhuangh Related to folding of dense+bn, convert_to_folded_model will not include Dense layers. a bug?

[zhuangh]

thanks @vloncar

@lishanok has been reviewing this PR. we are thinking whether to add a follow-up code change for that or do it in this PR.

[lishanok]

@julesmuhizi Thank you for the commit. I reviewed it and it looked good. Not sure why your test generates different output values between folded and non-folded models. Can you write a test similar to bn_folding_test.py/test_same_training_and_prediction() where weights are set with values that quantization won't result in a loss of precision and make sure the two versions result in the same results?

@vloncar There are quite a number of utility function to modify in order to support a new folded layer type. For example, convert_folded_model_to_normal, qtools, print_qmodel_summary, bn_folding_test, model_quantize, convert_folded_model_to_normal, etc. Regarding tests, I would suggest to write tests similar to qpooling_test.py (tests for regular new layers) and bn_folding_test.py (tests specific to bn folding type of layers) to check if all the utility functions are updated to support the new layer.

[zhuangh]

courtesy ping @julesmuhizi

In case you miss, there is a suggestion regarding test case from @lishanok

thanks

[julesmuhizi]

Hi, I have been occupied with another project but will review and begin addressing the issues in the comment above. Thanks for the ping @zhuangh

[boubasse]

Hi,
I would like to know if this thread is still active. I'm interested in having the QDenseBatchNorm :)
Thank you for your work around QKeras, the use is really straightforward.

[zhuangh]

Hi @boubasse thanks for the reminder. @lishanok could you take a look?

[julesmuhizi]

Hi, This thread is active and the layer is implemented on a separate fork/branch that’s not been merged yet as I don’t know how to format the unit tests. https://github.com/julesmuhizi/qkeras/blob/qdense_batchnorm/qkeras/qdense_batchnorm.py

On Fri, May 13, 2022 at 11:58 AM Hao Zhuang ***@***.***> wrote: Hi @boubasse <https://github.com/boubasse> thanks for the reminder. @lishanok <https://github.com/lishanok> could you take a look? — Reply to this email directly, view it on GitHub <#74 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ANDBM3O4NNYXUCFLP3YY6KLVJZ33PANCNFSM43VS2B6Q> . You are receiving this because you were assigned.Message ID: ***@***.***>

-- Jules Muhizi (he/him/his) Harvard College | Class of 2022 Electrical Engineering Secondary in Romance Languages and Literature (Spanish)

[jmduarte]

@lishanok @zhuangh Sorry for the delay. We've added the requested test.

An (unrelated) autoqkeras test was also failing (presumably also on master) due to the same legacy optimizer issue that was fixed in 5b1fe84. So we adopted a similar solution to that for the Adam optimizer. Let us know if you want us to split that into a separate PR.

We think this is ready to be merged and a follow-up PR should handle updating the utility functions to support a new folded layer type (convert_folded_model_to_normal, qtools, print_qmodel_summary, model_quantize, convert_folded_model_to_normal, etc.) and additional tests (similar to qpooling_test.py and bn_folding_test.py).

[zhuangh]

Thanks, Javier

Hi Shan and Daniele, could you take a look? @lishanok and @danielemoro

[jmduarte]

Hi all, any chance you could take a look? Thanks!

@zhuangh @lishanok @danielemoro

[lishanok]

Sorry about the delay. We will take a look as soon as possible. -Shan

…

On Thu, Feb 16, 2023 at 11:09 AM Javier Duarte ***@***.***> wrote: Hi all, any chance you could take a look? Thanks! @zhuangh <https://github.com/zhuangh> @lishanok <https://github.com/lishanok> @danielemoro <https://github.com/danielemoro> — Reply to this email directly, view it on GitHub <#74 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ARFXNMM2T4PAEEHIBOOGSULWXZ3PTANCNFSM43VS2B6Q> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[jmduarte]

Hi, @zhuangh @lishanok @danielemoro any chance you could take a look? Thank you!

[jmduarte]

Hi @zhuangh @lishanok @danielemoro are you able to merge this?

[jmduarte]

hi @lishanok can you take a look at this? thanks!

[lishanok]

Since some time has passed and our main branch has gone through some changes, could you rebase your PR with master branch and see if it passes all tests? Thanks!

[jmduarte]

@lishanok thanks for looking! CI tests pass after rebase.

[AdrianAlan]

Hi. My team used this feature, and it worked well for us. We'd love to see it merged. Are there any blockers?