Neutron3529 opened a new issue #18506:
URL: https://github.com/apache/incubator-mxnet/issues/18506


   ## 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.
   
   A PR(PR#18423) is submitted to overcome such misbehave.
   In addition, this PR also fix `SDMLLoss` which uses `KLDivLoss`'s result.
   
   ### Error Message
   (no error message)
   
   ## To Reproduce
   >>> o=mx.nd.array([[1,2,3]])
   >>> l=mx.nd.array([1])
   >>> lo=l.one_hot(o.shape[1])
   >>> mx.gluon.loss.SoftmaxCrossEntropyLoss()(o,l)#correct answer.
   
   [1.4076059]
   <NDArray 1 @cpu(0)>
   >>> mx.gluon.loss.KLDivLoss(from_logits=False)(o,lo)#not equals to the 
previous result.
   
   [0.46920195]
   <NDArray 1 @cpu(0)>
   >>> mx.gluon.loss.KLDivLoss(from_logits=False)(o,lo)*o.shape[1]#this is what 
the document say.
   
   [1.4076059]
   <NDArray 1 @cpu(0)>
   
   
   ## Environment
   
   We recommend using our script for collecting the diagnositc information. Run 
the following command and paste the outputs below:
   ```
   curl --retry 10 -s 
https://raw.githubusercontent.com/dmlc/gluon-nlp/master/tools/diagnose.py | 
python
   
   ----------Python Info----------
   Version      : 3.8.3
   Compiler     : GCC 10.1.0
   Build        : ('default', 'May 17 2020 18:15:42')
   Arch         : ('64bit', 'ELF')
   ------------Pip Info-----------
   Version      : 20.0.2
   Directory    : /usr/lib/python3.8/site-packages/pip
   ----------MXNet Info-----------
   Version      : 1.6.0
   Directory    : /home/neutron/.local/lib/python3.8/site-packages/mxnet
   Num GPUs     : 1
   Commit Hash   : 6eec9da55c5096079355d1f1a5fa58dcf35d6752
   ----------System Info----------
   Platform     : Linux-5.4.43-1-MANJARO-x86_64-with-glibc2.2.5
   system       : Linux
   node         : Neutron
   release      : 5.4.43-1-MANJARO
   version      : #1 SMP PREEMPT Wed May 27 20:25:12 UTC 2020
   ----------Hardware Info----------
   machine      : x86_64
   processor    : 
   架构:                           x86_64
   CPU 运行模式:                   32-bit, 64-bit
   字节序:                         Little Endian
   Address sizes:                   39 bits physical, 48 bits virtual
   CPU:                             12
   在线 CPU 列表:                  0-11
   每个核的线程数:                 2
   每个座的核数:                   6
   座:                             1
   NUMA 节点:                      1
   厂商 ID:                        GenuineIntel
   CPU 系列:                       6
   型号:                           158
   型号名称:                       Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz
   步进:                           10
   CPU MHz:                        2803.323
   CPU 最大 MHz:                   4100.0000
   CPU 最小 MHz:                   800.0000
   BogoMIPS:                       4401.32
   虚拟化:                         VT-x
   L1d 缓存:                       192 KiB
   L1i 缓存:                       192 KiB
   L2 缓存:                        1.5 MiB
   L3 缓存:                        9 MiB
   NUMA 节点0 CPU:                 0-11
   Vulnerability Itlb multihit:     KVM: Mitigation: Split huge pages
   Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional 
cache flushes, SMT vulnerable
   Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT 
vulnerable
   Vulnerability Meltdown:          Mitigation; PTI
   Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass 
disabled via prctl and seccomp
   Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and 
__user pointer sanitization
   Vulnerability Spectre v2:        Mitigation; Full generic retpoline, IBPB 
conditional, IBRS_FW, STIBP conditional, RSB filling
   Vulnerability Tsx async abort:   Not affected
   标记:                           fpu vme de pse tsc msr pae mce cx8 apic sep 
mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm p
                                    be syscall nx pdpe1gb rdtscp lm 
constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc 
cpuid aper
                                    fmperf pni pclmulqdq dtes64 monitor ds_cpl 
vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe 
                                    popcnt tsc_deadline_timer aes xsave avx 
f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd
                                     ibrs ibpb stibp tpr_shadow vnmi 
flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms 
invpcid mp
                                    x rdseed adx smap clflushopt intel_pt 
xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_w
                                    indow hwp_epp md_clear flush_l1d
   ----------Network Test----------
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0006 
sec, LOAD: 1.1995 sec.
   Timing for GluonNLP GitHub: https://github.com/dmlc/gluon-nlp, DNS: 0.0003 
sec, LOAD: 2.3723 sec.
   Timing for GluonNLP: http://gluon-nlp.mxnet.io, DNS: 0.4260 sec, LOAD: 
1.6315 sec.
   Timing for D2L: http://d2l.ai, DNS: 1.2683 sec, LOAD: 0.6324 sec.
   Timing for D2L (zh-cn): http://zh.d2l.ai, DNS: 0.2637 sec, LOAD: 0.6168 sec.
   Timing for FashionMNIST: 
https://repo.mxnet.io/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, 
DNS: 0.4871 sec, LOAD: 1.0879 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0196 sec, LOAD: 
1.7082 sec.
   Error open Conda: https://repo.continuum.io/pkgs/free/, HTTP Error 403: 
Forbidden, DNS finished in 0.31757044792175293 sec.
   
   ```
   


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