reland of https://github.com/pytorch/pytorch/pull/116559, which was reverted by internal.
The underlying reason for the revert is that the torch.dynamo.disable can't be used by the
pytorch codebase, as it's conflicting with some torch.deploy together, although the later one
only run some inference, but it somehow take that weird dependency on fsdp..
We have seen this issue with our functional collectives that we can't
use any dynamo components otherwise torch.deploy would complain..
verified internally that after removing torch.dynamo.disable the test
passed again
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117020
Approved by: https://github.com/awgu
Context: Existing FSDPExtension have some bug in the case when the
unflatten tensor involves some compute/communications in cuda stream,
the current logic of FSDPExtension unflatten tensor happens in the
unshard stream, which makes runtime lost sync with the compute stream,
and if there're some dependencies between the compute stream and the
unflatten tensor logic, currently it would lose sync point, which could
possibly lead to NaN.
This PR make the FSDPExtension to record the compute stream and let
DTensorExtension to directly use the compute stream for unflatten_tensor.
In long term we might want to directly make the FSDP runtime logic to only
make the unshard happen in unshard stream, and use unshard views to
happen in the compute stream. We currently fix this in the Extension
directly as this is the simplest thing to do without affecting FSDP
runtime logic
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116559
Approved by: https://github.com/awgu, https://github.com/fduwjj, https://github.com/yifuwang
ghstack dependencies: #116426
Summary:
Rename _device_mesh.py to device_mesh.py, update all callsites, add documentation.
We created stubs for public class and methods in torch.distributed.device_mesh so that torch.distributed.device_mesh can be imported with or without distributed is available().
Original diff reverted: D51629761
Original PR reverted: https://github.com/pytorch/pytorch/pull/115099
Prior to landing, CI signals are all passed. Shipit added the "ci/trunk" label to the PR and DID NOT wait for it and went ahead committing. More context can be found in the reverted PR above.
Test Plan: CI.
Differential Revision: D51861018
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115193
Approved by: https://github.com/fegin
Summary:
Rename _device_mesh.py to device_mesh.py, update all callsites, adds documentation.
Original diff reverted: D51629761
Original PR reverted: https://github.com/pytorch/pytorch/pull/114991
It was failing because failing a public module binding tests in MacOS, and this is due to the change in import order for torch/distributed/fsdp/_common_utils.py. Since this original import would still work, we remove the changes in this file.
Test Plan: CI.
Differential Revision: D51825114
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115099
Approved by: https://github.com/wanchaol, https://github.com/fegin
Fixes: #113193
`pydocstyle <all_files_in_issue> --count`
- Before: 345
- After: 130
For deprecated methods, I have added a `noqa` to ignore them. I was not able to find the file `torch/distributed/tensor/parallel/multihead_attention_tp.py`, so I've ignored it for this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113241
Approved by: https://github.com/kit1980
This PR adds a all_gather_dtensor() method to fsdp/_fsdp_extensions.py and the actual implementation in tensor/parallel/fsdp.py. This enables FSDP to load 2D DTensor state_dict into model when calling `model.load_state_dict()`.
cc. @fegin
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110925
Approved by: https://github.com/fegin
ghstack dependencies: #110831, #110846
Replacing https://github.com/pytorch/pytorch/pull/109553 as it gets reverted.
This PR enables training with new 2D flow and adds associated test. In addition, this PR moves the tensor/parallel/_data_parallel_utils.py that are fsdp specific back to tensor/parallel/fsdp.py to avoid circular dependency for ddp.py and test/distributed/tensor/parallel/test_ddp_2d_parallel.py.
state_dict related changes would be in later PRs.
cc. @fegin, @fduwjj, @wanchaol, @awgu
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110034
Approved by: https://github.com/fduwjj
This PR get rids of the dim_groups attribute from DeviceMesh, the main
motivation behind this is that we should let c10d store the process
groups during its creation instead of DeviceMesh, DeviceMesh should just
handle ranks correctly.
This could enable DTensor becomes picklable! (torch.save/load could be
possible), which I will give it a try in the next PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103105
Approved by: https://github.com/XilunWu, https://github.com/fduwjj
