* Changes for bfloat16 Zero2

* ZeRO stage3 optimizations, with some bug fixes

optimizations for stage3:
- prefetching improvements
- batching allgather calls to amortize fixed overhead and improve
  bandwidth utilization
- batching reduce_scatter calls to amortize fixed overhead and
  improve bandwidth utilization
- using *_base variants of allgather and reduce scatter to reduce memory
  allocations and data movement
- more fine grained synchronization for communication that allows
  blocking on less work
- precomputation of fetching code - using a fetch queue rather than
  deciding what to (pre)fetch at each iteration
- limiting queued coalesced communication ops to reduce memory pressure
  on pytorch cuda caching allocator (not elegant solution)

optimizations for stage3-offload:
- made some host-device tensor copies async to improve performance

bug fixes and qol improvements:
- fix init context method when parent modules modify child weights
- speed up model initialization by moving model to GPU before weight
  initialization
- fixed unit test imports so that unit tests can be run from any
  directory
- change performance logging to include memory consumption
- add logging w/ model size when done partitioning model

new features
- bfloat16 support for ZeRO 3

* fix import in ut

* ran yapf

* improvements to cache flush warn log

* backwards compatibility with older versions of pytorch

* handle edge case where reduced tensor smaller than world size

* moved event synchronization to allgather handle wait() call

* removed unnecessary barrier call

* formatting fix after resolving merge conflict

* skip nvme prefetch when trace not complete

* opportunistically avoid memory allocation in allgather coalesced where possible

* fix indentation after merge

* fixes to account for parameter offload

* accounting for torch.cuda.memory_stats not being available

* moved partition_all_params to optimizer step

* allgathering on params before item gets called

* fix param status checks

needed after moving partition_all_parameters call to optimizer step

* fix grad accumulation with optimizer offload

* grad norm computation fix for optimizer offload

* change post divide in reduce-scatter to pre divide

* fix gradient race condition w/ optimizer offload

* improve inf/nan gradient tracking

* don't prefetch when not in training mode

* format fix after merging

* fix prefetching issue when using NVME offload

* improved defragmentation for fp16 parameters

* relative imports for bf16 tests

* changes for bwd compatibility with pytorch 1.2

* remove buffered_reduce_fallback

* removed unused parameter offset bookkeeping

* fixed tracking for multiple param groups

* unbroke bfloat16 config after merge conflict

* using base allgather params when only 1 param

* cleanup/fixes for fp16 partition defragmentation

* switch to CRLF

* convert to same new-line style as master

* align new line with master

* Fix merge issues

* switch to CRLF

* fix to LF line endings

* minor merge fixes

* remove extra bfloat16_enabled definition

* asserting params inflight for AllGatherHandle

* remove get_cuda_mem_allocated_str

* Format fixes

* fix bfloat16 zero stage check (broken after merge commit)

* +self.communication_data_type, -self.allreduce_always_fp32; delete dead code

* Add self.reduce_scatter

* Format fix

* Fix merge issues

* iterate over params_to_fetch rather than make another iterator

* add some TODOs

* remove unnecessary division by micro_step_id

* rename config keys "bfloat16" -> "bf16"

* rename stage3_gather_fp16_weights_on_model_save -> stage3_gather_16bit_weights_on_model_save

* add unit test to check backwards compatibility for gather_16bit_weights

* added test to confirm bf16 key bwd compatibility

* Format fixes
Co-authored-by: NRana Ali Amjad <raamjad@amazon.com>
Co-authored-by: NJustin Chiu <justchiu@amazon.com>
Co-authored-by: NOlatunji Ruwase <olruwase@microsoft.com>
Co-authored-by: NJeff Rasley <jerasley@microsoft.com>