fix(platform): stub torch.distributed._symmetric_memory for torch < 2.8

[tengqm]

Summary

vllm.distributed.parallel_state imports torch.distributed._symmetric_memory
at module level (line 42), but that module only exists in PyTorch 2.8+.
Vendor torch builds on 2.7.x — iluvatar corex ships torch 2.7.1 — therefore
crash with ImportError before the engine core starts, in both the API-server
and spawned worker processes.

This PR pre-registers an empty stub module (plus a mirror of the name on the
torch.distributed parent package, since a bare sys.modules entry does not
set the parent attribute) whenever torch.distributed._symmetric_memory is
absent. All of vllm's runtime uses of the module are lazy (inside functions),
so the stub only needs to satisfy the module-level import gate; it is never
consulted on the verified greedy / sampling paths. The capability check keeps
PyTorch 2.8+ builds untouched.

This mirrors the existing module-top sentinel pattern already used for
torch.float4_e2m1fn_x2 (vllm_fl/__init__.py).

Notes

• Root-caused on-node: iluvatar-corex4.4.0 (torch 2.7.1 + corex), vllm 0.24.0
  app image, serve-gate crash at vllm/distributed/parallel_state.py:42.
  A container-level try/except around the import made serve fully green
  (greedy decode and temperature>0 sampling both clean).
• Deliberately not gated on a specific vendor: any torch build < 2.8 lacks the
  module, and the stub is a no-op where the module exists.

Verification

Pending on-node: iluvatar-corex4.4.0 app image serve E2E with Qwen3-4B,
built from the PR-head wheel.

---

This PR was written in part with the assistance of generative AI.