Add support for AC budget API

[tohskai]

Inspired by the blogpost:
https://pytorch.org/blog/activation-checkpointing-techniques/

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[wwwjn]

I read the blog and find the memory budget idea is cool. Have you try out the implementation on some model (eg, llama3) with torch.compile? I'm curious does it works end to end and if the performance are better

[tohskai]

I read the blog and find the memory budget idea is cool. Have you try out the implementation on some model (eg, llama3) with torch.compile? I'm curious does it works end to end and if the performance are better

I haven't done runs on llama3, but on our benchmarks on it showed significant improvements over regular SAC. This is why I wanted to upstream this :)

But our model is quite different, so it's totally reasonable to see less gains.

[wwwjn]

Thanks for sharing! We would love to see more verifications - eg, correctness and loss curves , and performance analysis on titan supported models (llama3, etc)

cc @soulitzer for reviewing

[tianyu-l]

I haven't done runs on llama3, but on our benchmarks on it showed significant improvements over regular SAC. This is why I wanted to upstream this :)

I agree with @wwwjn that

We would love to see more verifications - eg, correctness and loss curves , and performance analysis on titan supported models (llama3, etc)

Please refer to https://github.com/pytorch/torchtitan/blob/main/CONTRIBUTING.md#proof-of-value

[tohskai]

@wwwjn @soulitzer @tianyu-l

Should this support selection of activation_memory_budget_solver and activation_memory_budget_runtime_estimator? What about visualize_memory_budget_pareto? I found them to be useful, and the discoverability is low, but given that this is unstable api and potentially feature overload, I would prefer to hear your opinion.

https://github.com/pytorch/pytorch/blob/main/torch/_functorch/config.py#L147-L169

[tohskai]

I ran models/llama3/train_configs/llama3_8b.toml on 8xH100:

Would that suffice for Proof of Value?