[Kernel][Kimi K3] Fuse mixed-batch KDA boundary operations

[kimikimiki]

Purpose

This reduces launch and intermediate-tensor overhead in the NVIDIA Kimi K3 KDA
mixed speculative/non-speculative branch.

• A direct custom op stably packs QKV, g1, and beta for both partitions in
  one launch rather than six index_select calls.
• A second direct custom op scatters the two grouped outputs back to scheduler
  order and applies the K3 FP32 sigmoid-gated RMSNorm directly into the
  caller-owned output buffer, rather than two index_copy_ calls followed by
  a normalization operation.

The fast path is deliberately limited to K3's validated FP16/BF16,
head-dimension-128 geometry and compatible layouts. Other cases use the native
PyTorch fallback. Pure speculative and pure non-speculative paths are
unchanged. The benchmark reports the static boundary count (six to one for
input packing, three to one for output handling); it does not claim a measured
speedup.

Duplicate-work check

I checked open Kimi K3/KDA mixed-batch and scatter/RMSNorm PRs before opening
this PR. In particular, #50649 fixes ROCm KDA NaNs and a ROCm autotune race;
it changes neither this NVIDIA mixed-batch pack path nor indexed
scatter-plus-norm fusion. This PR is therefore materially distinct.

Test Plan

On a supported Linux CUDA host:

.venv/bin/python -m pytest \
  tests/models/kimi_k3/test_kda.py \
  tests/models/kimi_k3/test_kda_metadata.py -v

.venv/bin/python benchmarks/kernels/benchmark_kimi_k3_kda_mixed.py \
  --num-tokens 128 --num-heads 12 --dtype bfloat16

The intended end-to-end follow-up runs Kimi K3 on TP8 and TP16 with a scheduler
step that genuinely mixes prefill/decode and speculative tokens, across
EAGER/PIECEWISE/FULL CUDA-graph modes. It should compare outputs/logprobs,
acceptance rate, profiler launch counts, and latency/throughput before and
after the change.

Test Result

• Passed after the final no-conflict rebase: git diff --check.
• Passed after the final rebase: SKIP=update-dockerfile-graph pre-commit run --files <all five changed files> (ruff, mypy, SPDX, lazy imports,
  forbidden-import, CUDA-API, and configuration hooks passed). The Dockerfile
  graph hook was skipped because this Windows host has no /bin/bash.
• The CUDA KDA tests, opcheck, and Triton benchmark were not run. The required
  Triton distribution has no Windows wheel, and vLLM's test entry point also
  depends on uvloop==0.22.1, whose build reports that Windows is unsupported.
  No dynamic-test pass/fail result is claimed.
• Kimi K3 model evaluation was not run: this host has one 6 GB RTX 3060 and
  cannot run the required K3 TP8/TP16 configuration.

No public documentation update is needed for this internal optimization.

AI assistance and accountability

This PR was developed with assistance from OpenAI Codex. I, Jiahua Chen,
reviewed every changed line, understand the implementation and its remaining
Linux multi-GPU validation risks, and take responsibility for this submission.

---

Essential Elements of an Effective PR Description Checklist

• Purpose and duplicate-work check included.
• Test plan included.
• Test commands and their actual results included.
• Model-evaluation status disclosed.
• AI assistance disclosed.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @kimikimiki.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork