pytorch

51f687fd - Add overlap with DDP to ZeRO (two approaches) (#62157)

Add overlap with DDP to ZeRO (two approaches) (#62157) Summary: **Overview:** This adds two approaches to overlapping `DistributedDataParallel.backward()` with `ZeroRedundancyOptimizer.step()` by providing two hook constructors: `hook_with_zero_step()` and `hook_with_zero_step_interleaved()`. The former waits for all backward computation to finish before starting optimizer computation, while the latter launches a partial optimizer computation using the contents of a gradient bucket once that bucket's all-reduce completes. The two approaches each suffer from their own weaknesses, and which one to use depends on the specific hardware configuration. Both approaches can share changes to `ZeroRedundancyOptimizer`. A user should pass `overlap_with_ddp=True` to `ZeroRedundancyOptimizer`, construct a DDP communication hook using either `hook_with_zero_step()` or `hook_with_zero_step_interleaved()`, and register that communication hook. `ZeroRedundancyOptimizer.step()` should still be called in the training loop, though the optimizer computation and communication will be offloaded to originate from the communication hook. Currently, the first two iterations are vacuous, meaning they do not result in parameter updates and the inputs are ignored. This is required to finalize the DDP bucket strategy and to then initialize the `ZeroRedundancyOptimizer`'s local optimizer based on that bucketing. Pull Request resolved: https://github.com/pytorch/pytorch/pull/62157 Test Plan: The existing `ZeroRedundancyOptimizer` tests pass, and new unit tests for both hooks pass: - ~~`test_ddp_with_zero_step_parity_cpu`~~ (removed for now due to flakiness in CI -- under investigation, could possibly be similar Gloo issue as with `hook_with_zero_step_interleaved()`) - `test_ddp_with_zero_step_parity_gpu` - `test_ddp_with_zero_step_interleaved_parity_gpu` These were tested on the AI AWS cluster. An analogous `test_ddp_with_zero_step_interleaved_parity_cpu` is missing due to existing bugs with Gloo. See https://github.com/pytorch/pytorch/pull/62302. Both approaches have been verified using an internal accuracy benchmark. Reviewed By: mrshenli Differential Revision: D29971046 Pulled By: andwgu fbshipit-source-id: a7234c23c7ea253f144a698fd7e3c0fe039de5e8