GitLab

* [syncBN]
  added syncBN in native pure python apex
  added fused cuda kernels used for sync BN. Using welford for mean/var
    optional installation using 'python setup.py install --cuda_ext'
  added unit test with side to side comparison between apex sync BN with
    PyTorch BN. Notice that for pytorch BN implementation, because of
    numerical issue for mean/var, the output will be slightly off.

* [syncBN PR]
  added fp16 support
  addressing review comments on:
    1. updating last pow 2
    2. look for import error when importing syncBN kernel

* [syncBN PR]
  added convert function to insert SyncBatchNorm
  refactored some kernel code

* fixing type issue (fp16/fp32/fp64)
added Kahan summation
editing unit test to use pytorch primitive ops with double, passing reasonable tests now

* updating tensor creation calls

* fixing the all_reduce contiguous tensor

* transposed all reduce results

* [syncBN]
support fp16 input & fp32 layer for apex fp16
partially fixing launch configs
enabling imagenet example to run with --sync_bn

* [syncBN PR]
Documentation added

* adjusting README

* adjusting again

* added some doc to imagenet example

* [syncBN]
  warp-level reduction
  bug fix: warp reduction logic updated. check for dummy element to avoid nan.
  improved launch config for better reduction kernels. Further improvements
would be to increase grid size.

* [syncBN]
  fixing undefined behavior in __shfl_down_sync from divergent threads in warp
reduction.
  changing at::native::empty to at::empty (upstream comments)

Fixes to validation in imagenet example scripts. Precision and loss reporting modified to be consistent with train.