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19a6487ad4
`state_dict` is a very common variable name people use to represent a local state_dict and `load_state_dict` conflicts with DCP's `load_state_dict`. This PR changes `state_dict` to `get_state_dict`. `get_state_dict` is more close to what is this API does -- users use the API to get the current state_dict for saving or for loading (passed to DCP for loading in-place).. This PR also changes `load_state_dict` to `set_state_dict`. `set_state_dict` is less ideal compared to `get_state_dict` but is symetric. We can still change the API name before it goes to beta. This PR also simplies the API signatures. `model_only` is removed and `optim_only` only exists for `get_state_dict`. Differential Revision: [D50213931](https://our.internmc.facebook.com/intern/diff/D50213931/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/111120 Approved by: https://github.com/wz337 ghstack dependencies: #111106, #111107, #111275, #111109, #111110
83 lines
2.6 KiB
ReStructuredText
83 lines
2.6 KiB
ReStructuredText
.. role:: hidden
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:class: hidden-section
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Distributed Checkpoint - torch.distributed.checkpoint
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=====================================================
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Distributed Checkpoint (DCP) support loading and saving models from multiple ranks in parallel.
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It handles load-time resharding which enables saving in one cluster topology and loading into another.
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DCP is different than `torch.save` and `torch.load` in a few significant ways:
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* It produces multiple files per checkpoint, with at least one per rank.
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* It operates in place, meaning that the model should allocate its data first and DCP uses that storage instead.
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The entrypoints to load and save a checkpoint are the following:
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.. automodule:: torch.distributed.checkpoint
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.. currentmodule:: torch.distributed.checkpoint
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.. autofunction:: load_state_dict
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.. autofunction:: save_state_dict
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This `example <https://github.com/pytorch/pytorch/blob/main/torch/distributed/checkpoint/examples/fsdp_checkpoint_example.py>`_ shows how to use Pytorch Distributed Checkpoint to save a FSDP model.
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The following types define the IO interface used during checkpoint:
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.. autoclass:: torch.distributed.checkpoint.StorageReader
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:members:
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.. autoclass:: torch.distributed.checkpoint.StorageWriter
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:members:
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The following types define the planner interface used during checkpoint:
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.. autoclass:: torch.distributed.checkpoint.LoadPlanner
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:members:
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.. autoclass:: torch.distributed.checkpoint.LoadPlan
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:members:
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.. autoclass:: torch.distributed.checkpoint.ReadItem
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:members:
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.. autoclass:: torch.distributed.checkpoint.SavePlanner
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:members:
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.. autoclass:: torch.distributed.checkpoint.SavePlan
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:members:
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.. autoclass:: torch.distributed.checkpoint.WriteItem
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:members:
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We provide a filesystem based storage layer:
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.. autoclass:: torch.distributed.checkpoint.FileSystemReader
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:members:
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.. autoclass:: torch.distributed.checkpoint.FileSystemWriter
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:members:
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We provide default implementations of `LoadPlanner` and `SavePlanner` that
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can handle all of torch.distributed constructs such as FSDP, DDP, ShardedTensor and DistributedTensor.
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.. autoclass:: torch.distributed.checkpoint.DefaultSavePlanner
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:members:
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.. autoclass:: torch.distributed.checkpoint.DefaultLoadPlanner
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:members:
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We provide a set of APIs to help users do get and set state_dict easily. This is
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an experimental feature and is subject to change.
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.. autofunction:: torch.distributed.checkpoint.state_dict.get_state_dict
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.. autofunction:: torch.distributed.checkpoint.state_dict.set_state_dict
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.. autoclass:: torch.distributed.checkpoint.state_dict.StateDictOptions
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:members:
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