Use streaming transfers #101
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Added in a new streaming_save/load implementations that don't use an extra layer of zipfile serialization Co-authored-by: Tristan Rice <[email protected]> Co-authored-by: Krishn Parasar <[email protected]>
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… methods (#146555) 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: meta-pytorch/torchft#101 meta-pytorch/torchft#54 Test Plan: pytest test/distributed/test_serialization.py Pull Request resolved: #146555 Approved by: https://github.com/fegin, https://github.com/mikaylagawarecki Co-authored-by: Krishn Parasar <[email protected]>
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… methods (#146555) 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: meta-pytorch/torchft#101 meta-pytorch/torchft#54 Test Plan: pytest test/distributed/test_serialization.py Pull Request resolved: #146555 Approved by: https://github.com/fegin, https://github.com/mikaylagawarecki Co-authored-by: Krishn Parasar <[email protected]>
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Added in a new streaming_save/load implementations that don't use an extra layer of zipfile serialization
This is a rework of #54 that supports DTensor
Test plan: