pytorch

522041a0 - [FSDP] Add full optim state dict (#74215)

[FSDP] Add full optim state dict (#74215) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/74215 ### Overview of API This PR introduces full optimizer state dict checkpointing. - This allows users to save the optimizer state for a `torch.nn.Module` (not necessarily a `FullyShardedDataParallel` instance) that contains `FullyShardedDataParallel` instances and later load that optimizer state. - This supports loading to a module with a different world size, but the `FSDP` wrapping scheme must be the same. To **save** the optimizer state, run the following (on all ranks): ``` model: torch.nn.Module = ... optim = torch.optim.Adam(model.parameters(), ...) # Train for some steps... full_osd = FSDP.full_optim_state_dict(model, optim) # returns non-empty dict only on rank 0 if rank == 0: torch.save(full_osd, ...) ``` To **load** the optimizer state, run the following (on all ranks): ``` new_model: torch.nn.Module = ... # may use different world size full_osd = torch.load(...) sharded_osd = FSDP.shard_full_optim_state_dict(full_osd, new_model) optim = torch.optim.Adam(new_model.parameters(), ...) optim.load_state_dict(sharded_osd) ``` To support **multiple parameter groups**, we require using an additional argument `optim_input`, which is the first argument that the user passes into the optimizer constructor. ``` optim_input = ... optim = torch.optim.Adam(optim_input, ...) FSDP.full_optim_state_dict(model, optim, optim_input) # one more argument ... new_optim_input = ... new_optim = torch.optim.Adam(new_optim_input, ...) FSDP.shard_full_optim_state_dict(full_osd, new_model, new_optim_input) # one more argument ``` One caveat is that the user should be careful of generators, which are exhausted after their first use. The `optim_input` passed into the `FSDP` APIs should be refreshed version of the generator if using generators. ### Test Plan **`full_optim_state_dict()`** - [x] `full_optim_state_dict()` for a non-`FSDP` root model matches that of an equivalent local model, up to parameter IDs being rearranged, when optimizer input is `model.parameters()`. - [x] `full_optim_state_dict()` for a non-`FSDP` root model matches that of an equivalent local model, up to parameter IDs being rearranged, when optimizer input is multiple parameter groups (changing parameter order). **`shard_full_optim_state_dict()`** - [x] `shard_full_optim_state_dict()` for a non-`FSDP` root model matches the local `optim.state_dict()` of the same model with halved world size, when optimizer input is `model.parameters()`. - [x] `shard_full_optim_state_dict()` for a non-`FSDP` root model matches the local `optim.state_dict()` of the same model with halved world size, when optimizer input is multiple parameter groups (changing parameter order). - [x] `shard_full_optim_state_dict()` raises a `ValueError` when changing the `FSDP` wrapping scheme. On the AWS cluster, the TTS contribution for these tests is ~45 seconds. ### Developer Notes **Relaxing the Problem** For optimizer state checkpointing, we have relaxed the problem to **not support changing the `FSDP` wrapping scheme** between save and load time. It is unclear how to solve without this relaxation. This was the least restrictive way to relax the problem since it does not affect most expected use cases. Rather, the expected change between save and load time is the **world size**, which this implementation **does support**. Even with the relaxation, the `optim_input` argument is necessary to determine the `flat_param_id_to_param` mapping, which is important to know which parameter IDs in the flattened space correspond to `FlatParameter`s that hence need to be unflattened. **Differences with Local Equivalent** Suppose `full_osd = full_optim_state_dict()` and `local_osd = state_dict()` for a purely local equivalent. The difference between `full_osd` and `local_osd` is that the parameter IDs of unflattened parameters comprising a single flattened parameter are always consecutive in `full_osd`, while they may be non-consecutive in `local_osd`. Suppose in the following that each layer has 1 parameter `param`: ``` FSDP(model) layer1 FSDP(layer2) layer3 ``` `layer1.param` and `layer3.param` are flattened and attributed to `model`. `layer2.param` is flattened and attributed to itself. - In `local_osd`, the parameter IDs would be `0: layer1.param`, `1: layer2.param`, and `2: layer3.param`. - In `full_osd`, the parameter IDs would be `0: layer1.param`, `1: layer3.param`, and `2: layer2.param`. (Parameter IDs of unflattened parameters sharing a flattened parameter are consecutive.) The idea is that as long as `full_optim_state_dict()` and `shard_full_optim_state_dict()` are internally consistent, then there is no need to match the local equivalent (assuming no change in `FSDP` wrapping). ### Follow-Ups **API** - If needed, we can follow-up this PR by adding an argument `key_by_name: bool = False` to both methods that may be set to `True` to key parameters by `str` names instead of `int` parameter IDs. We still need to investigate if keying by name enables changing the `FSDP` wrapping scheme. **Refactoring** - In this optimizer state checkpointing, all optimizer state is saved to CPU on rank 0 (set as `OPTIM_TARGET_RANK`). We should unify and refactor these assumptions with model state checkpointing. **Testing** - The code path for unused parameters is not tested. The testing and any needed implementation fixes can be done in a follow-up. - The code path for non-tensor states (e.g. `Adam` `"step"` as `float` instead of as zero-dimension `FloatTensor`) is not tested. However, it is identical to that of zero-dimension tensor states, so I have some confidence. If needed, I can add tests for it in a follow-up. - Would I have to write my own optimizer? I do not want to introduce dependencies on third party libraries like Nvidia `apex`. - We may want to add end-to-end checkpointing tests that include both model state dict and optimizer state dict. Test Plan: Imported from OSS Reviewed By: zhaojuanmao Differential Revision: D35045121 Pulled By: awgu fbshipit-source-id: 33c650dc960acbd7613d4f444a852b9f76ca4a9b (cherry picked from commit 2bbc2e344296dc455cf686f3a9b097989504be81)