Neutron3529 opened a new pull request #18423:
URL: https://github.com/apache/incubator-mxnet/pull/18423


   ## Description ##
   In the current version of KLDivLoss, the return value is not the same value 
calculated by SoftmaxCrossEntropyLoss, which is not documented. It may due to 
the incorrect settings which using mean rather than sum dealing with the return 
value.
   I provide a fix of this setting, which will keep the return value of 
`KLDivLoss` and `SoftmaxCrossEntropyLoss` almost the same when 
`from_logits=False` and `sparse_label=False` are set to these functions 
seperately.
   Now, the behave of KLDivLoss is exactly the same to what the document say.
   
   to reproduce the misbehave in the current master branch:
   ```
   import mxnet as mx
   a=mx.nd.array([[-1,1],[1,-1]])
   b=mx.nd.array([1,0]).one_hot(2)
   TrueLoss=mx.gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=False)
   FalseLoss=mx.gluon.loss.KLDivLoss(from_logits=False)
   c=TrueLoss(a,b)
   d=FalseLoss(a,b)*a.shape[-1]
   assert((c-d).abs().sum()==0 and a.shape[-1]==2)
   ```
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [x] Changes are complete (i.e. I finished coding on this PR)
   - [x] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [x] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
   - [x] To the best of my knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [x] Now, the behave of KLDivLoss is exactly the same to what the document 
say.
   
   ## Comments ##
   - If this change is a backward incompatible change, why must this change be 
made.
   - Interesting edge cases to note here
   


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