[Perf][MoE] Share FlashInfer B12x MoE workspaces across layers

[lucifer1004]

What

Share the FlashInfer B12x MoE workspaces across layers within each worker process.

Today every MoE layer constructs its own B12xMoEWrapper with its own static workspace, dynamic workspace, and output buffer. For large-expert-count models this is a substantial per-layer GPU memory cost (e.g. ~640 MiB per layer on a 384-expert NVFP4 shape — 60+ GiB across layers). The workspaces are identically shaped for every layer and layers execute sequentially, so all layers can share the buffers allocated by the first wrapper.

Dependency

Depends on flashinfer-ai/flashinfer#4603, which adds the shared_static_workspace / shared_dynamic_workspace / shared_output parameters to B12xMoEWrapper. This PR is opened as a draft until that lands and the FlashInfer version pin is updated; with an older FlashInfer the new keyword arguments would raise TypeError at model init.

Notes

• Sharing is keyed on (global_num_experts, topk, hidden_dim, intermediate_size_per_partition, max_num_tokens, activation, device); layers with a different MoE config allocate their own buffers.
• No numerical change: the wrapper writes into the shared buffers on every call, and layers do not execute concurrently within a worker.