[PyTorch][duplicate][CI] FP8 cuda graphs

[ksivaman]

This PR adds the following features (high-level):

• make_graphed_callables API similar to the PyTorch API with some additional arguments for FP8 usage. Support for fp8 weight caching via existing is_first_microbatchargument is also retained.
• Restructuring and amax reduction logic with a simpler design and handling of various parallelisms with minimal book-keeping compared to the previous approach.
• Forward and backward amaxes are reduced within the scope of current iteration, solving numerous bugs w.r.t. checkpointing and removing the need to save global buffers.
• Support for nested/multiple FP8 autocast contexts with different recipes and distributed groups.
• Amax reductions are module independent and happen at at autocast level. This also resolves numerous bugs and allows for support for MoE/LoRA like models.
• Redesign of transposes for Float8Tensor that makes the transposes persistent for graph capture. Also fixes use cases for the vanilla optimizers (non fp8-distopt).
• The scaling inverses for weight tensors are no longer frozen when caching weights across microbatches.

[ksivaman]

/te-ci pytorch