Shark backend integration

🚀 Feature

Integrate Shark as an accelerator backend for Lightning.

Motivation

Lightning users would benefit from a "performance first" backend option for running their models. Shark users would benefit from Lightning's clean UX-focused API and clean HW abstraction. Integration seems like a win-win.

Pitch

I'd like to integrate shark as an accelerator similar to how IPUs are implemented. A WIP PR should be incoming soon.

Additional context

Warning- not user friendly (hence the motivation for this issue)

SHARK repo: https://github.com/NodLabs/SHARK/tree/PerformanceDevBranch
Benchmarks: https://github.com/powderluv/transformer-benchmarks
SHARK examples: https://github.com/NodLabs/shark-samples/tree/main/examples

Examples of using torch mlir as a backend for pytorch can be found at:
https://github.com/llvm/torch-mlir/blob/main/examples
resnet jupyter notebook
plain python
Other models like bert can be generated using the heavydep testing instructions at top-level torch-mlir.

cc @borda @akihironitta @rohitgr7

Start by reviewing the SHARK repository and its examples, then compare the torch-mlir examples linked in the issue, including the ResNet notebook and plain Python example. The issue is complete when SHARK is integrated as a Lightning accelerator backend with a user-friendly path and demonstrated model usage.