OneRaynyDay opened a new pull request #11833: [MXNET-688] Fix quantization divide by zero errors URL: https://github.com/apache/incubator-mxnet/pull/11833 ## Description ## The current quantization strategy for `calib_mode='entropy'` is to calculate the KL divergence for different thresholds and choose the best threshold. This assumes that the random variable is nonzero for all reals and is a continuous random variable. Because we are discretizing the distribution, we smooth the distribution over the range `[-threshold, threshold]`. What we are not considering is that the entire sampled distribution may be not in the range `[-threshold, threshold]` and thus we end up with all zeros in the sampled candidate `p` distribution inside of `_get_optimal_threshold`. I have added a check that the distribution(possibly unnormalized) is proper before attempting to smooth or else we'll run into a divide by 0 error. In most cases, activation functions and layers for classification type problems output numbers symmetric around 0. This is not the case for a regressor's last layer, and there are various other examples where the activation distribution is not around 0, and this was a major blockage for airbnb's adoption into mxnet's quantization capabilities. ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes) - [ ] Changes are complete (i.e. I finished coding on this PR) - [ ] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [ ] Code is well-documented: - For user-facing API changes, API doc string has been updated. - For new C++ functions in header files, their functionalities and arguments are documented. - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$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 ### - Added tests and extra code inside of quantization.py to set all kl divergence to infinity if the probability distribution formed is all zeros. Also there are some typos and one-off-errors in the original implementation that are now fixed.
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