trunk/4404a9185466a66a3a9dc96a3034305fc5c35abc: Make Dtensor have consistent cache key in compile (#173526)

The PyTorch team has resolved a significant technical bug in its distributed tensor (Dtensor) subsystem with pull request #173526, authored using Claude AI. The core issue was that during the compilation phase, the system was pickling (serializing) storage memory addresses to generate cache keys. Because these memory addresses differ across processes in a distributed computing environment, each process would compute a unique cache key for the same logical operation. This inconsistency defeated the purpose of caching, causing redundant compilations and slowing down distributed training jobs. The fix, which was initially reverted internally before being properly merged, changes the serialization protocol used by the AOTAutogradCache.

Technically, the solution shifts from using a `dispatch_table` protocol—which would require manually enumerating all tensor subclass types—to implementing the `_reduce_override` protocol. This more elegant approach allows Dtensor to define its own serialization method, ensuring it produces a consistent, process-agnostic identifier for caching. This patch is a deep but crucial optimization for developers using PyTorch for large-scale model training across multiple GPUs or nodes. It eliminates a hidden performance bottleneck, making distributed training workflows more efficient and predictable. The fix underscores the ongoing refinement of PyTorch's compilation stack (TorchDynamo/Inductor) for production AI workloads.