[FSDP][optim_state_dict] Let optim_state_dict ignore the non-FSDP managed parameters that do not reside on the rank #94129
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…aged parameters that do not reside on the rank When FSDP is used with other parallelism (e.g., TorchRec), some parameters that are not managed by FSDP may not reside on all the ranks (TorchRec is model parallelism). When `use_orig_params=True` , FSDP will synchronize the FQNs among ranks. As a result, a rank may get the FQNs that the rank does not actually own. If the FQN belongs to a TorchRec managed parameter, FSDP has to ignore the parameter state. Otherwise FSDP does not know how to store the state. This PR add the logic to ignore the parameters that are not managed by FSDP and are not on the rank. Differential Revision: [D42982778](https://our.internmc.facebook.com/intern/diff/D42982778/) [ghstack-poisoned]
fegin
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/94129
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…on-FSDP managed parameters that do not reside on the rank" When FSDP is used with other parallelism (e.g., TorchRec), some parameters that are not managed by FSDP may not reside on all the ranks (TorchRec is model parallelism). When `use_orig_params=True` , FSDP will synchronize the FQNs among ranks. As a result, a rank may get the FQNs that the rank does not actually own. If the FQN belongs to a TorchRec managed parameter, FSDP has to ignore the parameter state. Otherwise FSDP does not know how to store the state. This PR add the logic to ignore the parameters that are not managed by FSDP and are not on the rank. Differential Revision: [D42982778](https://our.internmc.facebook.com/intern/diff/D42982778/) [ghstack-poisoned]
rohan-varma
approved these changes
Feb 6, 2023
| dist.all_reduce(sparse) | ||
| return self.dense(sparse) | ||
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| models = [FakeMPModel().cuda(), FakeMPModel().cuda()] |
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Do we need to test wrapping the entire FakeModel with FSDP and telling FSDP to ignore the sparse0 / sparse1? Wondering which approach most accurately simulates the FSDP / TRec integration case.
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afaik, this way to wrap the model, the root module not being a FSDP model but having model parallelism -- is more close to the real use case.
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| # Train one batch and see if optim_state_dict are the same. | ||
| batch = torch.rand(5, 8) | ||
| batch = torch.rand(5, 8).cuda() |
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nit: torch.rand(5, 8, device=cuda) creates the tensor directly on the device.
…on-FSDP managed parameters that do not reside on the rank" When FSDP is used with other parallelism (e.g., TorchRec), some parameters that are not managed by FSDP may not reside on all the ranks (TorchRec is model parallelism). When `use_orig_params=True` , FSDP will synchronize the FQNs among ranks. As a result, a rank may get the FQNs that the rank does not actually own. If the FQN belongs to a TorchRec managed parameter, FSDP has to ignore the parameter state. Otherwise FSDP does not know how to store the state. This PR add the logic to ignore the parameters that are not managed by FSDP and are not on the rank. Differential Revision: [D42982778](https://our.internmc.facebook.com/intern/diff/D42982778/) [ghstack-poisoned]
fegin
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Feb 6, 2023
…aged parameters that do not reside on the rank Pull Request resolved: #94129 When FSDP is used with other parallelism (e.g., TorchRec), some parameters that are not managed by FSDP may not reside on all the ranks (TorchRec is model parallelism). When `use_orig_params=True` , FSDP will synchronize the FQNs among ranks. As a result, a rank may get the FQNs that the rank does not actually own. If the FQN belongs to a TorchRec managed parameter, FSDP has to ignore the parameter state. Otherwise FSDP does not know how to store the state. This PR add the logic to ignore the parameters that are not managed by FSDP and are not on the rank. ghstack-source-id: 179458431 Differential Revision: [D42982778](https://our.internmc.facebook.com/intern/diff/D42982778/)
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@pytorchbot merge -f "The failing test is not related" |
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Stack from ghstack (oldest at bottom):
When FSDP is used with other parallelism (e.g., TorchRec), some parameters that are not managed by FSDP may not reside on all the ranks (TorchRec is model parallelism). When
use_orig_params=True, FSDP will synchronize the FQNs among ranks. As a result, a rank may get the FQNs that the rank does not actually own. If the FQN belongs to a TorchRec managed parameter, FSDP has to ignore the parameter state. Otherwise FSDP does not know how to store the state.This PR add the logic to ignore the parameters that are not managed by FSDP and are not on the rank.
Differential Revision: D42982778