resolve conflict in data parallel model
* master: (201 commits)
  Add cost inference to ConvGradient and WeightedSum operators (pytorch#10744)
  Move collapse dims into a single place (pytorch#11272)
  Fix some more warnings (pytorch#11257)
  Fix the batchnorm onnx exporting when affine=False
  Improve error message to include return types too (pytorch#11245)
  Check doxygen output in travis (pytorch#11124)
  Accept more numpy scalars as doubles (pytorch#9659)
  Fixed log message (pytorch#10874)
  Fix to distribution.__repr__ with lazy attributes (pytorch#11263)
  Add import export step to end to end tests
  Add complex hooks for out of tree complex implementation. (pytorch#11216)
  Unify opt flag for cmake codegen (pytorch#11227)
  nomnigraph - fix memory error in NN subgraph matchOp (pytorch#11127)
  Port PackedSequences functions to C++ (pytorch#11224)
  Treat numerical differences as warnings instead of errors when tracing (pytorch#11246)
  add a Float16UniformFill (pytorch#11123)
  Implement torch.tensordot (pytorch#10025)
  keep net type info when generating model complete net (pytorch#11032)
  Get rid of some uses of type() (pytorch#11215)
  Reorganize methods in Type, add CPUTypeDefault/CUDATypeDefault (pytorch#11205)
  ...

Summary:
Pull Request resolved: pytorch#11123

this adds an operator that fills a tensor with a uniform(min, max)
the implementation is to use the fp32 generator and convert to fp16

if performance becomes an issue we could resort to intrinsics

Reviewed By: jspark1105, chocjy

Differential Revision: D9598142

fbshipit-source-id: 5aeab99acf7c3596fa6c33611d9d2c484f7c1145