[EZ] Pass seed to data sampler.

[EugenHotaj]

We currently hardcode zero. I assume this is not intentional.

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❌ 1 New Failure, 2 Cancelled Jobs

As of commit 3708bf9 with merge base 779569e ():

NEW FAILURE - The following job has failed:

• GPU tests / gpu_test (3.10, stable) (gh)
  tests/recipes/test_full_finetune_distributed.py::TestFullFinetuneDistributedRecipe::test_training_state_on_resume_from_distributed_checkpoint_multi_rank[llama3/8B_full-llama3-tune-1-4-False]

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• GPU tests / gpu_test (3.11, stable) (gh)
• GPU tests / gpu_test (3.9, stable) (gh)
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[RdoubleA]

Getting the seed from self.seed is risky, since we are using self.seed = training.set_seed() in our recipes, which will actually return a different seed on every rank. I am not sure if the sampler should have the same seed across ranks, but I imagine to ensure all ranks are not shuffling the data differently we would want the same seed. It would be better to set it from cfg.seed directly to keep the same data seed across all ranks.

Tagging @andrewkho for thoughts on setting the seed in DistributedSampler.

[EugenHotaj]

@RdoubleA I might be misunderstanding the code but I thought set_seed returned the seed you passed it in from the config (or if you don't pass anything, a global seed). I guess my intention here is to just pass in the seed from the config. Lmk if there is a better way to do it (maybe pass in cfg.seed directly?)

[RdoubleA]

Ah my bad, you're right. It's returning the global seed not the local seed. In that case this should be ok

[RdoubleA]

@EugenHotaj actually we might need to do something like this to get the seed directly from the config and maintaining BC with existing configs:
seed=cfg.get("seed", None) or 0

[EugenHotaj]

@RdoubleA done

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Codecov Report

Attention: Patch coverage is 0% with 2 lines in your changes missing coverage. Please review.

Project coverage is 23.97%. Comparing base (779569e) to head (3708bf9).

Files with missing lines	Patch %	Lines
tests/recipes/test_lora_finetune_single_device.py	0.00%	2 Missing ⚠️

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- Coverage   65.34%   23.97%   -41.37%     
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  Lines       21207    21207               
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- Misses       7350    16123     +8773     

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[EugenHotaj]

@RdoubleA I'm not sure what's happening with the TestFullFinetuneDistributedRecipe::test_training_state_on_resume_from_distributed_checkpoint_multi_rank test that keeps failing. It keeps deadlocking for some reason:

3|5|Loss: 11.97959041595459:  50%|█████     | 1/2 [00:00<00:00,  1.63it/s][rank0]:[E120 05:15:13.042532067 ProcessGroupNCCL.cpp:616] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=420, OpType=_REDUCE_SCATTER_BASE, NumelIn=8208384, NumelOut=4104192, Timeout(ms)=1800000) ran for 1800059 milliseconds before timing out.
[rank0]:[E120 05:15:13.042708659 ProcessGroupNCCL.cpp:1785] [PG ID 0 PG GUID 0(default_pg) Rank 0] Exception (either an error or timeout) detected by watchdog at work: 420, last enqueued NCCL work: 426, last completed NCCL work: 419.
[rank1]:[E120 05:15:13.091641415 ProcessGroupNCCL.cpp:616] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=423, OpType=_REDUCE_SCATTER_BASE, NumelIn=64, NumelOut=32, Timeout(ms)=1800000) ran for 1800096 milliseconds before timing out.
[rank1]:[E120 05:15:13.091715615 ProcessGroupNCCL.cpp:1785] [PG ID 0 PG GUID 0(default_pg) Rank 1] Exception (either an error or timeout) detected by watchdog at work: 423, last enqueued NCCL work: 424, last completed NCCL work: 422.
[rank1]:[E120 05:15:14.970203483 ProcessGroupNCCL.cpp:1834] [PG ID 0 PG GUID 0(default_pg) Rank 1] Timeout at NCCL work: 423, last enqueued NCCL work: 424, last completed NCCL work: 422.
[rank1]:[E120 05:15:14.970217275 ProcessGroupNCCL.cpp:630] [Rank 1] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[rank1]:[E120 05:15:14.970222520 ProcessGroupNCCL.cpp:636] [Rank 1] To avoid data inconsistency, we are taking the entire process down.
[rank1]:[E120 05:15:14.971615376 ProcessGroupNCCL.cpp:1595] [PG ID 0 PG GUID 0(default_pg) Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=423, OpType=_REDUCE_SCATTER_BASE, NumelIn=64, NumelOut=32, Timeout(ms)=1800000) ran for 1800096 milliseconds before timing out.
Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:618 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7f6f92b6c446 in /home/ec2-user/actions-runner/_work/torchtune/torchtune/3/envs/test/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x282 (0x7f6f485cc772 in /home/ec2-user/actions-runner/_work/torchtune/torchtune/3/envs/test/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x233 (0x7f6f485d3bb3 in /home/ec2-user/actions-runner/_work/torchtune/torchtune/3/envs/test/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x7f6f485d561d in /home/ec2-user/actions-runner/_work/torchtune/torchtune/3/envs/test/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #4: <unknown function> + 0x145c0 (0x7f6f9301d5c0 in /home/ec2-user/actions-runner/_work/torchtune/torchtune/3/envs/test/lib/python3.10/site-packages/torch/lib/libtorch.so)
frame #5: <unknown function> + 0x9f832 (0x7f6fa0c9f832 in /lib64/libc.so.6)
frame #6: <unknown function> + 0x3f480 (0x7f6fa0c3f480 in /lib64/libc.so.6)

Doesn't seem like it would be related to these changes (or maybe surfacing some latent bug), but any idea what's going on?

[EugenHotaj]

^ @RdoubleA any thoughts on the failure I mentioned above?

[RdoubleA]

@EugenHotaj Sorry I've been meaning to investigate this more. I do think it's related to changing the seed though, the failing test is using DCP's resume from checkpoint. DCP was only recently added, so my guess is it's not setting the seed correctly when resuming training.

Will update here once I debug more.