[yucshen]

Purpose

Add an explicit AITER Kimi-K3 prefill backend on ROCm:

• use AITER's fused BF16 QKV causal Conv1D kernel;
• expose AITER FlashKDA through --kda-prefill-backend flashkda;
• preserve the existing Triton implementation as the fallback and explicit
  --kda-prefill-backend triton path;
• stage the AITER Conv weight while loading the checkpoint; and
• warm the AITER kernels with a bounded representative profile when FlashKDA
  is selected.

The default policy remains native-fused-first. On ROCm, auto selects the
native fused backend whenever it is supported, otherwise FlashKDA when AITER is
enabled, and otherwise Triton. The existing Kimi-K3 GSM8K configuration remains
on auto; this PR does not make FlashKDA the default on gfx950.

Relation to existing PRs

This does not duplicate #54038. That PR adds the vLLM-native gfx950 HIP
implementation of the KDA prologue and recurrence, which remains the default
and is faster on gfx950. This PR adds AITER FlashKDA as an explicit alternative
and as the auto fallback when the native fused backend is unavailable. It
adds no KDA C++ kernel to vLLM.

#54055 changes the generic KDA operator's packed-QKV API for an external
backend; it does not provide the ROCm AITER Conv/FlashKDA execution path added
here.

This PR uses the AITER API already present in the version pinned by vLLM. It
does not require or change the global AITER version pin.

Test Plan

On an 8x MI355X system:

pytest -q \
  tests/models/kimi_k3/test_amd_kda_prefill.py \
  tests/models/kimi_k3/test_kda.py \
  tests/models/kimi_k3/test_kda_metadata.py \
  tests/models/kimi_k3/test_amd_kda_chunk.py

Run the complete 1319-question GSM8K evaluation with the existing
Kimi-K3-pruned75-DSpark-AITER-TP4.yaml configuration using its default auto
KDA selection.

Run serving benchmarks with Kimi-K3, TP8, 100 prompts, concurrency 32, OSL 1,
and ISL 4096 and 16384. Compare fused, flashkda, and triton on the same
node. FlashKDA measurements use the explicit
--kda-prefill-backend flashkda option.

Run pre-commit on every changed file, including the manual Python 3.12 mypy
hook.

Test Result

• Kimi-K3 tests: 95 passed, 27 skipped. The skipped cases require CUDA or
  native fused-decode kernels unavailable in the ROCm test container.
• AITER upstream selected FlashKDA tests: 8 passed.
• GSM8K, 1319 questions, 5-shot greedy:
  • strict match: 97.04%
  • flexible extract: 96.97%
• Same-node prefill measurements are reported below.

Each result is the arithmetic mean of three runs. Since these are
prefill-focused OSL=1 tests, input throughput is calculated for each run as
total token throughput - output token throughput. All runs completed 100
requests with zero failures.

Backend	Input length	Input throughput (tok/s)	Median TTFT (ms)
Triton	4K	15,088.06	8,672.46
FlashKDA, AITER v0.1.21.post2	4K	15,459.96	8,464.19
FlashKDA, AITER #5436 experimental build	4K	15,670.53	8,351.33
Native fused	4K	15,845.68	8,258.32
Triton	16K	14,680.45	35,688.11
FlashKDA, AITER v0.1.21.post2	16K	15,022.81	34,871.61
FlashKDA, AITER #5436 experimental build	16K	15,218.27	34,425.96
Native fused	16K	15,390.05	34,043.83

On gfx950, native fused remains the fastest backend and therefore remains the
default. Compared with native fused, the AITER version currently pinned by
vLLM has 2.43% lower input throughput at 4K and 2.39% lower input throughput at
16K. This PR therefore does not claim an end-to-end performance improvement
over current main on gfx950.

The experimental AITER build containing ROCm/aiter#5436 improves FlashKDA over
the currently pinned AITER by 1.36% at 4K and 1.30% at 16K, but remains about
1.1% behind native fused. These #5436 numbers are forward-looking validation;
this PR does not update vLLM's global AITER pin.

• All pre-commit hooks for the changed files passed.
• Manual Python 3.12 mypy hook passed.

AI assistance disclosure

OpenAI Codex was used to assist with code investigation, implementation,
review, and test orchestration. The human contributor selected the target
implementation, directed the integration and review decisions, and is
responsible for the submitted changes.

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR is described, including related open PRs.
• The test plan includes the commands and workloads used.
• Test, model-evaluation, and performance results are included.
• User-facing CLI help is updated, while the existing AMD evaluation
  configuration retains its default backend selection.

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[claude]

Claude Code Review

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[mergify]

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

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

[yucshen]

Thanks for the review. I noticed that #54038 has been merged, so I updated the
validation results and added a comparison against its native fused KDA prefill
backend. All measurements below were collected on the same 8×MI355X node.

Functional regression

Test suite	Result
Tests introduced by #54038 (test_amd_kda_chunk.py)	66 passed
AITER KDA prefill tests from this PR (test_amd_kda_prefill.py)	10 passed
ROCm AITER MoE tests	32 passed
GSM8K, 1319 questions, 20-shot greedy — flexible extract	96.89%
GSM8K, 1319 questions, 20-shot greedy — strict match	96.97%

During testing, we identified and fixed an AITER SiTU A8W4 environment-forwarding issue that caused corrupted model output.

Performance setup

All measurements used the same code, image, node, model, and server options.
Only --kda-prefill-backend was changed between fused, flashkda, and
triton. The benchmark parameters were aligned with the conditions used in
#54038.

VLLM_ROCM_USE_AITER=1 \
VLLM_ROCM_USE_AITER_MOE_SITUV2_A8W4=1 \
AITER_BF16_FP8_MOE_BOUND=0 \
VLLM_USE_BREAKABLE_CUDAGRAPH=0 \
vllm serve moonshotai/Kimi-K3 \
  --trust-remote-code \
  --gpu-memory-utilization 0.95 \
  --load-format auto \
  --mm-encoder-tp-mode data \
  --max-num-seqs 128 \
  --reasoning-parser kimi_k3 \
  --max-num-batched-tokens 4096 \
  --compilation-config \
  '{"cudagraph_mode":"FULL_DECODE_ONLY","custom_ops":["+fused_rms_norm_gated"]}' \
  --tensor-parallel-size 8 \
  --no-enable-prefix-caching \
  --kda-prefill-backend <fused|flashkda|triton>

triton was used as the unoptimized baseline. Omitting the option is not a
valid baseline on this branch because auto selects FlashKDA when AITER is
enabled.

Every benchmark was run three times and the tables report the arithmetic mean.

Comparison with #54038

vllm bench serve \
  --model moonshotai/Kimi-K3 \
  --backend vllm \
  --trust-remote-code \
  --dataset-name random \
  --random-input-len 8192 \
  --random-output-len 1024 \
  --max-concurrency 32 \
  --num-prompts 128 \
  --temperature 0 \
  --ignore-eos \
  --seed 1234

Metric	#54038, Config=PR	This run, 3-run mean	Difference
Total tokens/s	6,335.72	6,770.48	+6.86%
Requests/s	0.6875	0.733	approximately +6.7%
Output tokens/s	703.97	752.27	+6.86%
Mean TTFT	3,254.10 ms	3,262.91 ms	+0.27%
Mean TPOT	42.12 ms	39.21 ms	-6.90%
Mean ITL	42.12 ms	39.21 ms	-6.90%

The current result is consistent with #54038: mean TTFT is effectively
unchanged, while throughput and decode latency are better in the current
environment.

Prefill comparison

vllm bench serve \
  --backend openai \
  --model moonshotai/Kimi-K3 \
  --trust-remote-code \
  --dataset-name random \
  --random-input-len <4096|16384> \
  --random-output-len 1 \
  --random-range-ratio 0.0 \
  --num-prompts 100 \
  --num-warmups 10 \
  --max-concurrency 32 \
  --ignore-eos \
  --seed 42

ISL	OSL	Backend	Input tokens/s	vs. Triton	Median TTFT	vs. Triton
4K	1	Triton	15,203.90	—	8,602.55 ms	—
4K	1	FlashKDA	15,605.67	+2.64%	8,384.29 ms	-2.54%
4K	1	Fused	16,054.23	+5.59%	8,150.70 ms	-5.25%
16K	1	Triton	14,814.81	—	35,359.78 ms	—
16K	1	FlashKDA	15,181.24	+2.47%	34,492.00 ms	-2.45%
16K	1	Fused	15,596.54	+5.28%	33,590.68 ms	-5.00%

All benchmark runs completed without failed requests.

[ChuanLi1101]

auto should not select FlashKDA. On AITER, this PR makes auto → FlashKDA even when fused (#54038) is available. Your same-node table already shows fused is faster:

4K: FlashKDA +2.64% vs Triton, fused +5.59%
16K: FlashKDA +2.47%, fused +5.28%
So default AITER serving would lose ~3% prefill vs current main. Keep FlashKDA as --kda-prefill-backend flashkda; auto should keep fused.

Please also:

Don’t treat the 8k1k +6.86% vs #54038 as this PR’s win — different environment, TTFT flat. These benches are not AgentX.
ROCm warmup only runs for flashkda; fused/triton now warm nothing.
The resolve test stubs fused off, so it never hits gfx950 + AITER + fused.
tjtanaa’s version-check comments look addressed.

[yucshen]

Thanks for the review. I have updated the implementation accordingly.

auto should not select FlashKDA. On AITER, this PR makes auto → FlashKDA even when fused (#54038) is available. Your same-node table already shows fused is faster:

4K: FlashKDA +2.64% vs Triton, fused +5.59% 16K: FlashKDA +2.47%, fused +5.28% So default AITER serving would lose ~3% prefill vs current main. Keep FlashKDA as --kda-prefill-backend flashkda; auto should keep fused.

The updated selection logic is:

1. auto selects fused whenever it is available,
2. if not, selects flashkda when aiter is enabled,
3. otherwise, defaults to trition.

Please also:

Don’t treat the 8k1k +6.86% vs #54038 as this PR’s win — different environment, TTFT flat. These benches are not AgentX. ROCm warmup only runs for flashkda; fused/triton now warm nothing. The resolve test stubs fused off, so it never hits gfx950 + AITER + fused. tjtanaa’s version-check comments look addressed.

The 8k1k data listed above are never intended to be treated as some PR's win. I'd like to clarify here that the 8k1k data are supposed to show consistency with #54038 results, which is, we tried our best to reproduce their numbers, though they are still not exactly the same.

[mergify]

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

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

[yucshen]

Thanks again for the reviews. I have removed both the explicit --kda-prefill-backend flashkda option and the FlashKDA-only segment override from the Kimi-K3 GSM8K YAML and auto remains native-fused-first. The evaluation job therefore keeps the same default behavior as current main on gfx950. I also updated the description to position FlashKDA as an explicit backend and as the fallback when native fused is unavailable. In the latest performance results, native fused remains fastest. The currently pinned AITER FlashKDA is 2.43% and 2.39% slower than native fused at 4K and 16K respectively, so this PR does not claim an end-to-end gain over current main on gfx950.

[mergify]

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

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