fix(moe): forward permute fusion to HybridEP

[HuiyingLi]

What does this PR do ?

Forward the existing moe_permute_fusion setting through AutoModel's HybridEP
dispatch/combine autograd wrappers to DeepEP's fuse_permute_dispatch and
fuse_unpermute_combine flags.

Without this propagation, enabling moe_permute_fusion changes the local
permute/unpermute path but silently leaves the HybridEP DeepEP path unfused.

Changelog

• Pass permute_fusion from _HybridEPManager into HybridEP dispatch and
  combine.
• Preserve the setting on the autograd context and use the matching DeepEP
  fusion flag in backward.
• Add unit coverage for all four DeepEP dispatch/combine forward/backward calls
  and for manager-level knob propagation.

Before your PR is "Ready for review"

Pre checks:

• The change is focused on the existing HybridEP permute-fusion knob.
• Unit tests cover forward and backward propagation.
• No documentation change is required; this fixes an existing setting.
• The commit includes a DCO sign-off.

Validation

• ruff format --check on all four changed files: passed.
• ruff check on the changed source files: passed.
• pytest -q tests/unit_tests/moe/test_fused_a2a.py tests/unit_tests/moe/test_token_dispatcher.py: 13 passed.
• H100 qualification with DeepEP 421443037f64e9d422814bec9341c0559749c215:
  • 1 node / 8 GPUs: aggregate valid TGS/GPU improved from 6550.50 to
    6936.18 (+5.89%).
  • 16 nodes / 128 GPUs: aggregate valid TGS/GPU improved from 7453.16 to
    7904.66 (+6.06%); all 40/40 steps completed with finite loss and gradient
    norm.

Additional Information

Current main (bb0fe53a) still omits this propagation and pins the same
DeepEP revision used for the H100 qualification. The relevant HybridEP code
path has no behavioral change between the qualification baseline and current
main.

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