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Update numpy and pytorch seeding for dataloader and multiple processes per machine. #299
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…s per machine. Summary: *Current state:* Currently we set different seeds per-nodes, but the same seed among all training processes on a node. However, each of our Dataloader process seeds are all different each epoch, but non-deterministic. *Proposed State:* Different random number seeds for each dist_rank, and different, deterministic, for each Dataloader process seeds per epoch. https://fb.quip.com/hVIcAahpVLo2 *Effects:* Fixes randomization for a few losses, hooks, and trunks. Fixes randomization when using the Fork multiprocessing option for transformations. Fixes collapse of seeds to 0 when config seed set to 0. There are 3 changes summarized as: 1. Use dist_rank instead of node_id for seeding training processes. 2. Create worker_init_fn to manually set numpy seed for dataloader workers. 3. Add knowledge of training process seed to sampler. Differential Revision: D27784137 fbshipit-source-id: 023c1ded41a4ed7c0b2caaedca5b5d1999d1ce42
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…s per machine. (#299) Summary: Pull Request resolved: #299 *Current state:* Currently we set different seeds per-nodes, but the same seed among all training processes on a node. However, each of our Dataloader process seeds are all different each epoch, but non-deterministic. *Proposed State:* Different random number seeds for each dist_rank, and different, deterministic, for each Dataloader process seeds per epoch. https://fb.quip.com/hVIcAahpVLo2 *Effects:* Fixes randomization for a few losses, hooks, and trunks. Fixes randomization when using the Fork multiprocessing option for transformations. Fixes collapse of seeds to 0 when config seed set to 0. There are 3 changes summarized as: 1. Use dist_rank instead of node_id for seeding training processes. 2. Create worker_init_fn to manually set numpy seed for dataloader workers. 3. Add knowledge of training process seed to sampler. Reviewed By: prigoyal, QuentinDuval Differential Revision: D27784137 fbshipit-source-id: 55008e1a10d88637f7b8ff44c45fd4658706a465
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Summary:
Current state:
Currently we set different seeds per-nodes, but the same seed among all training processes on a node. However, each of our Dataloader process seeds are all different each epoch, but non-deterministic.
Proposed State:
Different random number seeds for each dist_rank, and different, deterministic, for each Dataloader process seeds per epoch.
https://fb.quip.com/hVIcAahpVLo2
Effects:
Fixes randomization for a few losses, hooks, and trunks. Fixes randomization when using the Fork multiprocessing option for transformations. Fixes collapse of seeds to 0 when config seed set to 0.
There are 3 changes summarized as:
Differential Revision: D27784137