trunk/082a5225ea63e1eba3c997f1e0982111af07f105: [FSDP2] Remove dynamo tracing support from fully_shard (#174863)

The PyTorch development team has merged a pivotal commit (082a5225ea63) that fundamentally changes the relationship between two of its core technologies: the FSDP2 distributed training system and the Dynamo graph compiler. The commit, titled "[FSDP2] Remove dynamo tracing support from fully_shard," strips out all code that previously allowed Dynamo to trace into FSDP2's internal hooks. This integration was an experimental path for applying TorchDynamo's just-in-time (JIT) compilation and optimization to the complex operations of sharding model parameters across multiple GPUs. By removing this support and unconditionally applying `torch._dynamo.disable` to FSDP2 hooks, the team is signaling a strategic simplification, likely due to the technical complexity and maintenance burden of making the two systems interoperate seamlessly.

This technical decision has immediate implications for AI engineers training large language models (LLMs) and other massive neural networks. Developers can no longer rely on Dynamo to automatically trace and compile the graph segments within FSDP2's communication and synchronization hooks. This may affect debugging workflows and could limit certain graph optimizations that were previously attempted. The move suggests the PyTorch team is prioritizing a stable, predictable FSDP2 core over a potentially fragile, integrated compilation path. For the ecosystem, it clarifies the boundary between distributed training logic and graph compilation, pushing developers toward more explicit optimization strategies and potentially paving the way for a cleaner, more robust alternative in the future, such as a redesigned integration or a focus on other compilation backends like AOTAutograd.