This function is needed by all ReadPlanner subclasses that are trying to implement support for a custom distributed tensor.
Better expose it than have users reimplement this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97570
Approved by: https://github.com/wz337
Summary:
The current `optim_state_dict()` does not require users to call `optim.state_dict()` first while `optim_state_dict_to_load()` requires users to call `optim.load_state_dict()`. This PR make both APIs provide the option for users not having to call the extra API.
This PR also changes the arguments order of `optim_state_dict_to_load` which is a breaking change. So we should do this asap before the API is adopted in production cases.
Test Plan: CI
Differential Revision: D43925068
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96534
Approved by: https://github.com/rohan-varma
This is the last PR for integrating 2D into core distributed.
This PR does the following:
1. Add optimizer.py: this adds ability to load a state_dict in conjunction with FSDP sharded optimzer state.
2. Update default_planner.py to support 2D checkpoint.
3. Add test_fsdp_optim_state.py as a unit test for No. 1.
4. Fix bug in torch/testing/_internal/distributed/checkpoint_utils.py
5. Rename the filename for the APIs that should be private. Will organize and cleanup further in following PRs. #90328
Docstring and integration test will be added in the following PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90212
Approved by: https://github.com/wanchaol
