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Summary: This is intended for use with torchft when we need to do a streaming state dict transfer. This is strictly superior to the prior streaming method in torchft as this supports all tensor subclasses such as DTensor. This supports 100% of the inputs to torch.save/load but is not wire compatible nor intended to have any backwards compatibility. Security wise this fully supports weights_only and defaults to True. It does use pickle for some metadata but uses weights_only for the metadata. Adapted from: https://github.com/pytorch/torchft/pull/101 https://github.com/pytorch/torchft/pull/54 Test Plan: pytest test/distributed/test_serialization.py Pull Request resolved: https://github.com/pytorch/pytorch/pull/146555 Approved by: https://github.com/fegin, https://github.com/mikaylagawarecki Co-authored-by: Krishn Parasar <76171905+Krishn1412@users.noreply.github.com> |
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| .. | ||
| _composable | ||
| _shard | ||
| _sharded_tensor | ||
| _sharding_spec | ||
| _symmetric_memory | ||
| _tensor | ||
| _tools | ||
| algorithms | ||
| autograd | ||
| benchmarks | ||
| checkpoint | ||
| elastic | ||
| examples | ||
| fsdp | ||
| launcher | ||
| nn | ||
| optim | ||
| pipelining | ||
| rpc | ||
| tensor | ||
| __init__.py | ||
| _checkpointable.py | ||
| _composable_state.py | ||
| _functional_collectives.py | ||
| _functional_collectives_impl.py | ||
| _serialization.py | ||
| _state_dict_utils.py | ||
| argparse_util.py | ||
| c10d_logger.py | ||
| collective_utils.py | ||
| constants.py | ||
| CONTRIBUTING.md | ||
| device_mesh.py | ||
| distributed_c10d.py | ||
| launch.py | ||
| logging_handlers.py | ||
| remote_device.py | ||
| rendezvous.py | ||
| run.py | ||
| utils.py | ||