[Kernel][ROCm] Use naive block assignment for WNA16 MoE

[ciru-ai]

Summary

ROCm GPTQ/AWQ WNA16 MoE currently always calls moe_align_block_size, even
when a very small decode batch activates only a sparse fraction of the expert
pool. vLLM's unquantized Triton MoE path already avoids that overhead with a
naive assignment mode in this regime.

This PR:

• enables the same existing sparsity heuristic for the ROCm WNA16 Triton path;
• adds direct route-list handling to fused_moe_kernel_gptq_awq;
• keeps CUDA, expert-parallel mappings, and non-sparse cases on the existing
  aligned path; and
• adds focused INT4 and INT8 routing coverage.

In naive mode, each program handles one routed token/expert pair. Lane zero is
valid and the remaining BLOCK_SIZE_M - 1 lanes are masked. The change is
model-agnostic: it does not dispatch on model name, hidden size, expert count,
or other Ling-specific geometry.

Performance evidence

A fresh matched current-main A/B on AMD Radeon 8060S / Strix Halo (gfx1151)
compared upstream 75231eff2 with this PR's df4c60d38. Three 32-token decode
rows per side improved median throughput from 9.212774 to 10.363958 tok/s
(+12.50%). Response text and usage were identical across all six rows.
The CPU-isolation gates were 92.026% and 92.029% idle, respectively.

Both servers used VLLM_ROCM_USE_SKINNY_GEMM=0 to prevent the separate known
gfx1151 skinny-GEMM issue from obscuring this assignment-only comparison. JIT
warnings ended before either measured interval; no post-warmup JIT/autotuning,
preemption, HSA fault, device fault, or engine fault was observed.

The base and candidate intervals ran from 15:54:39.381–15:54:50.521 and
15:56:39.219–15:56:49.120 EDT, respectively, on August 8, 2026. They do not
overlap the documented August 7 host-contamination window.

Earlier matched Ling 3.0 Flash INT4/BF16 evidence changed only the assignment
route and improved median TG32 from 13.841172 to 17.000919 tok/s (+22.83%).
Seeded packed-INT4 W1/W2 fixtures were bitwise identical for unique and
duplicate expert IDs. The historical control has one retained row versus four
candidate rows, so the fresh current-main +12.50% result above is the primary
performance claim. A later 19.713926 tok/s result also included a separate
finalize-reduction change and is intentionally excluded from this PR's gain.

Duplicate search

GitHub issue/PR searches on August 8, 2026 for WNA16 naive block assignment,
WNA16 small token MoE, and fused_moe_kernel_gptq_awq alignment found no
direct duplicate. Adjacent work includes the merged generic unquantized naive
assignment PR #29354, the gfx1100-only native HIP W4A16 PR #44075, and CUDA
small-batch alignment PR #44167. None enables this ROCm Triton WNA16 route.

Tests

Current-main base: 75231eff2f3873e2bce7cc9558bb5227ea70b808.

AMD Radeon 8060S / Strix Halo (gfx1151), ROCm 7.15, Triton 3.8:

test_quantized_naive_block_assignment
  2 passed

test_fused_moe_wn16 focused GPU cases
  INT4 naive route: passed
  INT8 naive route: passed
  INT4 zero-point naive route: passed
  aligned fallback route: passed

pre-commit ruff-check: passed
pre-commit ruff-format: passed
pre-commit typos: passed
pre-commit mypy-3.10: passed
pre-commit check-spdx-header: passed
pre-commit check-root-lazy-imports: passed
pre-commit check-filenames: passed
pre-commit check-forbidden-imports: passed
pre-commit check-torch-cuda-call: passed
git diff --check: passed

fresh matched current-main A/B
  base 75231eff2:      9.212774 tok/s median
  candidate df4c60d38: 10.363958 tok/s median
  improvement:        +12.50%
  response text and usage: identical
  CPU isolation: passed
  measured JIT/autotuning: none

The aggregate pre-commit command could not bootstrap its unrelated
Markdown/Node environment because the host Python CA chain rejected the Node
download. Every hook applicable to the changed Python files was run
individually and passed; certificate verification was not disabled.

Model evaluation

The patch changes assignment mechanics, not routing decisions, weights, or
numerics. Focused INT4, INT8, zero-point, and aligned-fallback GPU tests match
the existing reference path, and the retained full-model A/B produced
identical outputs.

AI assistance

AI-assisted. The human submitter reviewed and understands every changed line.
CIRU is the sole author, committer, and DCO signatory.

PR readiness checklist

• Purpose and platform scope documented.
• Duplicate search completed.
• Focused routing and gfx1151 GPU tests completed.
• Applicable Python pre-commit hooks completed.
• Human line-by-line review completed.
• Fresh matched current-main end-to-end A/B completed.

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