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* 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: Rana Ali Amjad <raamjad@amazon.com> Co-authored-by: Justin Chiu <justchiu@amazon.com> Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com> Co-authored-by: Jeff Rasley <jerasley@microsoft.com>