fix: Allow for multi node training for accelerated moe #129
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@kmehant i understand the fix, but can you update the description for record keeping purposes |
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@fabianlim |
fabianlim
approved these changes
Feb 24, 2025
| @@ -65,7 +66,7 @@ def augmentation( | |||
| rank, world_size = 0, 1 | |||
| if torch.distributed.is_initialized(): | |||
| world_size = torch.distributed.get_world_size() | |||
| rank = torch.distributed.get_rank() | |||
| rank = int(os.environ["LOCAL_RANK"]) | |||
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can we make it consistent and follow the new style.
Suggested change
| rank = int(os.environ["LOCAL_RANK"]) | |
| # we do not need to use the fallback as this is wrapped in an `is_initialized` block | |
| rank = torch.distributed.get_node_local_rank() |
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@fabianlim Have included this suggestion thanks.
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Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com> Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com> Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>
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@fabianlim requesting your merge. |
fabianlim
approved these changes
Feb 27, 2025
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Current implementation uses global rank of the process to prepare the device index which would not work in a multi node setting. Therefore, we would need to use local rank since devices are not continuously indexed across the nodes.