Add end-to-end tests for M-FSDP and ND-Parallel

[shjwudp]

What does this PR do ?

Changes in this PR:

1. Add end-to-end test for Megatron-FSDP (MCore + ND-Parallel) integration.
2. Fix optim and optim_grads sharding compatibility issues that occur with certain in-place input modules.

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[cspades]

Code review finished, post-backward refactor looks very good to me!

Have not checked unit tests yet, will read later!

[cspades]

This is a neat PyTorch native hook, I wonder if it is more tightly integrated with gradient computation streams in general, maybe it will fix many bugs that were not caught by a simple post-backward hook? 🙏🏻

[shjwudp]

Yes, our customized post-backward mechanism becomes ineffective for modules that perform in-place input modifications, so its applicability is limited — it can works for the FSDP modules we’ve specified not every torch module. The register_post_accumulate_grad_hook function nicely compensates for this limitation by providing a more accurate trigger right after gradient accumulation.

[shjwudp]

/ok to test 451c17d