Universal checkpoint for zero stage 3

[xylian86]

This PR enables the universal checkpoint for zero stage 3.

Notes:

• The current implementation supports Data parallelism.
• Development is ongoing for universal checkpoint Stage 3 with tensor-slicing model parallelism.
• Pipeline parallelism is not supported by ZeRO Stage 3, and hence is not included in this universal checkpoint implementation.

In this PR:

• I've updated deepspeed/checkpoint/ds_to_universal.py to support converting Zero checkpoints into Universal checkpoints.
• I've updated deepspeed/runtime/zero/stage3.py to enable loading Universal checkpoints using the Stage 3 optimizer.

[xylian86]

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[tohtana]

We have a test for universal checkpointing. It currently supports DP scaling only but it would be good to test ZeRO3 feature using this test. You can just add "3" to the test argument.

[tohtana]

Thank you for introducing this interesting approach.
We have a similar sorting in _merge_zero_shards but are using a different approach but it is not good to have two different sorting implementations for the same purpose. Can you replace this one with natural_keys?

[xylian86]

Thanks for the suggestions!
For this natural_keys function, I actually reuse it from zero_to_fp32.py.

You’re right; it’s not ideal to have two different implementations for the same function. How about I replace the one in _merge_zero_shards with this natural_keys?

[tohtana]

This sounds good, thank you

[xylian86]

Convergence curve for ZeRO 3 using the current implementation

[Lomax314]

Hello, thank you for your work!
I've encountered some issues during my practice and would appreciate your help in analyzing them.
I am using Zero Stage=3 (Only DeepSpeed)for training and use model.save_checkpoint to save the optimizer state(optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr)). The conversion of the original optimizer state to the universal_checkpoint format and loading the uni format part did not report any errors. However, the training loss did not continue from where it left off but started to gradually decrease from 9, which is a very high loss value. I am puzzled by this and would appreciate your assistance in analyzing it.

Additionally, I used model.state_dict() and uni_model.state_dict() to get the model state before and after the conversion and compared them using compare_state_dicts. The result triggered an assertion, as torch.equal(s0.to('cpu'), s1.to('cpu')) evaluated to False.

[ArtificialZeng]

This PR enables the universal checkpoint for zero stage 3.

Notes:

• The current implementation supports Data parallelism.
• Development is ongoing for universal checkpoint Stage 3 with tensor-slicing model parallelism.
• Pipeline parallelism is not supported by ZeRO Stage 3, and hence is not included in this universal checkpoint implementation.

In this PR:

• I've updated deepspeed/checkpoint/ds_to_universal.py to support converting Zero checkpoints into Universal checkpoints.
• I've updated deepspeed/runtime/zero/stage3.py to enable loading Universal checkpoints using the Stage 3 optimizer.

how to load

[xylian86]

@ArtificialZeng We have released examples in the Megatron-DeepSpeed repository. You can find them at:
https://github.com/microsoft/Megatron-DeepSpeed/tree/main/examples_deepspeed/universal_checkpointing.
Please let us know if you encounter any issues.