Add fixed-order MoE shared/residual merge reference

[AsterWang]

Different addition orders and intermediate BF16 rounding can change the merged MoE token output. This adds shared_residual_merge_fwd(routed, shared, residual), an eager PyTorch reference that computes (routed + shared) + residual in FP32 and casts to BF16 once at the end.

The reference uses the existing MoeMergeOp module and semantic registry, exposes FP32 intermediate boundaries, and includes numerical fixtures, source-metadata validation helpers, sealed evidence tests, and owner-local EP replication tests.

Validation

• 4 × NVIDIA H100 80GB, PyTorch 2.9.1 / CUDA 12.9: python3 -m pytest -q tests/test_moe_merge.py tests/test_kernel_registry.py — 142 passed, 1 skipped, including 55 CUDA test cases and real NCCL EP=1/2/4 validation.
• Evidence: 13 artifact seals and the saved intermediate tensor checksums were reloaded and verified.
• After the public function rename: CPU regression checks passed (84 passed, 59 GPU cases skipped). Compared with the H100-tested sources, changes are limited to the function name and its references; arithmetic, validation logic and fixtures are unchanged.
• Black, isort, Ruff and diff whitespace checks passed for the relevant files.

The distributed tests use synthetic inputs and test-harness collectives; they do not establish model integration. The reference is forward-only and requires eager execution.

[coderabbitai]

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