[FIX] multi-GPU quantization OOM by canonicalizing get_supported_kwargs cache keys

[ZX-ModelCloud]

Summary

During multi-GPU quantization, the parallel replay path inspects module.forward on per-device replicas.

When get_supported_kwargs caches the bound method object itself, the cache key keeps a strong reference to the owning module replica. That prevents those replicas and their device tensors from being reclaimed after replay,
so VRAM usage grows across layers/subsets and eventually hits OOM.

Single-GPU runs are mostly unaffected because they usually stay on the serial path and do not repeatedly inspect per-replica bound forwards.

What Changed

• canonicalize get_supported_kwargs cache keys to avoid retaining bound callables
• cache Python bound methods by __func__ instead of the bound method object

fix #2805
fix #2810