[nsusanto]

Purpose

On the ROCm/AITER decode path every Kimi-K3 MLA layer spends two launches on the query
projection (M = 56 rows at the InferenceX c14 point, 12 heads per rank at TP8):

Kernel	What it does	Before (µs)	After (µs)
hgemm_bf16_t16x32x128x5	q_b_proj: x[56,1536] → q[56,12,192]	6.76	–
_batched_gemm_a16wfp4_kernel	absorb-in BMM: q_nope × W_UK (fp4) → ql_nope[56,12,512]	5.49	–
_gemm_a16_w16_kernel (aiter Triton gemm_a16w16)	x[56,1536] × W_fold[1536,6912] → [ql_nope | q_pe]	–	9.20
per layer		12.25	9.20

q_b_proj and the W_UK are consecutive linear maps, so
ql_nope[h] = x · (W_UK[h] · W_qb_nope[h])ᵀ. This PR builds once at load, from the bf16
checkpoint weights,

W_fold[h] = [ W_UK[h] · W_qb_nope[h] ; W_qb_pe[h] ]        # [512 + 64, 1536] per head

Decode only: the W_UK absorb exists only on the MQA decode path. Prefill uses the
un-absorbed form and keeps q_b_proj, so any step with prefill tokens takes the original path.

Small M win: the fold reads 21 MB instead of 7.5 MB and does 2.5x the FLOPs. The save shrinks as M grows and would turn negative at prefill sizes, which this path never sees.

We do additional 1.9Gb for the extra weight fold for all 93 layers of Kimi k3.

We're actively testing the prefill shape such that it cannot be much worse than non w-fold so that we don't have to pay for extra 1.9Gb.

Server command:

export VLLM_ROCM_AITER_MLA_ASM_PADDING=asm VLLM_ROCM_USE_AITER=1 SAFETENSORS_FAST_GPU=1
export VLLM_ROCM_USE_AITER_MOE_SITUV2=1 AITER_BF16_FP8_MOE_BOUND=0 VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=INT4
export HSA_NO_SCRATCH_RECLAIM=1 VLLM_ENGINE_READY_TIMEOUT_S=7200 PYTHONNOUSERSITE=1 VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=1200
export VLLM_USE_DIRECT_DCP_A2A=0 VLLM_USE_DIRECT_DCP_Q_GATHER=0 VLLM_USE_DIRECT_DCP_KV_GATHER=0
export VLLM_USE_BREAKABLE_CUDAGRAPH=0 PYTHONHASHSEED=42 HF_HUB_OFFLINE=1
# InferenceX agentic.yaml c14 point (vllm/vllm-openai-rocm:nightly-rocm100-36768d1bf, 8x MI355X)
vllm serve /data/Kimi-K3 --served-model-name moonshotai/Kimi-K3 --host 0.0.0.0 --port 8002 \
  --trust-remote-code --moe-backend auto --tensor-parallel-size 8 --load-format fastsafetensors \
  --gpu-memory-utilization 0.9 --language-model-only --max-num-seqs 28 --enable-auto-tool-choice \
  --tool-call-parser kimi_k3 --reasoning-parser kimi_k3 --max-model-len 1048576 --enable-prefix-caching \
  --kv-cache-dtype fp8 --max-num-batched-tokens 8192 \
  --attention-config '{"mla_prefill_backend":"ROCM_AITER_FA"}' \
  --compilation-config '{"mode":3,"cudagraph_mode":"FULL","max_cudagraph_capture_size":112,"custom_ops":["+fused_rms_norm_gated"],"cudagraph_capture_sizes":[2,3,...,112]}' \
  --speculative-config '{"model":"Inferact/Kimi-K3-DSpark","num_speculative_tokens":3,"method":"dspark","attention_backend":"ROCM_AITER_MLA","kv_cache_dtype":"fp8","draft_sample_method":"probabilistic","rejection_sample_method":"block"}' \
  --kv-transfer-config '{"kv_connector":"SimpleCPUOffloadConnector","kv_role":"kv_both","kv_connector_extra_config":{"cpu_bytes_to_use_per_rank":224875000000,"lazy_offload":false}}'

Bench Command:

vllm bench serve --backend openai-chat --model moonshotai/Kimi-K3 --tokenizer /data/Kimi-K3 --trust-remote-code \
  --host localhost --port 8002 --endpoint /v1/chat/completions \
  --dataset-name random --random-input-len 1024 --random-output-len 1024 --random-range-ratio 0 \
  --request-rate=inf --random-prefix-len 0 --num-prompts 140 --max-concurrency 14 --ignore-eos --seed 43 \
  --percentile-metrics ttft,tpot,itl,e2el

Test Results

Conc 14 (InferenceX c14, DSpark k=3, 140 prompts)

Metric	Before	After	Delta
Output throughput (tok/s)	685.62	693.13	+1.1%
Mean TPOT (ms)	19.47	19.27	-1.0%
Mean TTFT (ms)	340.08	336.53	-1.0%

Conc 48 (InferenceX c48, no draft model, DCP 8, 480 prompts)

Metric	Before	After	Delta
Output throughput (tok/s)	1412.23	1418.68	+0.5%
Mean TPOT (ms)	33.01	32.86	-0.5%
Mean TTFT (ms)	1026.51	1020.92	-0.5%

Conc 70 (InferenceX c70, no draft model, DCP 8, 700 prompts)

Metric	Before	After	Delta
Output throughput (tok/s)	1680.50	1705.97	+1.5%
Mean TPOT (ms)	40.58	39.96	-1.5%
Mean TTFT (ms)	1145.53	1127.79	-1.5%

Baseline:

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9583|±  |0.0055|
|     |       |strict-match    |     5|exact_match|↑  |0.9575|±  |0.0056|
spec decode over gsm8k: acceptance 78.67%  acceptance length 3.360  (accepted 96693 / draft tokens 122913, 40971 drafts)

After:

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9613|±  |0.0053|
|     |       |strict-match    |     5|exact_match|↑  |0.9613|±  |0.0053|
spec decode over gsm8k: acceptance 78.87%  acceptance length 3.366  (accepted 95978 / draft tokens 121698, 40566 drafts)

GPQA-diamond (CoT, zero-shot, chat endpoint, 198 questions) and MMLU (generative, 5-shot, 14042
questions) on the same c14 server, baseline image vs image + this change:

Eval	Baseline	Change	Stderr
GPQA-diamond CoT zero-shot (flexible-extract)	0.7424	0.7475	±0.031
MMLU generative 5-shot	0.9047	0.9049	±0.0024
- humanities	0.8589	0.8593	±0.0050
- other	0.9115	0.9115	±0.0048
- social sciences	0.9405	0.9405	±0.0042
- stem	0.9315	0.9315	±0.0044

# GPQA-diamond: chat endpoint so the reasoning parser applies; 8192 generation tokens
lm_eval --model local-chat-completions \
  --model_args "model=moonshotai/Kimi-K3,base_url=http://localhost:8002/v1/chat/completions,num_concurrent=32,max_retries=3,timeout=3600,max_length=40960,tokenizer=/data/Kimi-K3,tokenizer_backend=huggingface,trust_remote_code=True" \
  --apply_chat_template --tasks gpqa_diamond_cot_zeroshot --gen_kwargs '{"max_gen_toks":8192}' --seed 42
# MMLU generative (prompt logprobs are not usable on this server, so the loglikelihood variant is not run)
lm_eval --model local-completions \
  --model_args "model=moonshotai/Kimi-K3,base_url=http://localhost:8002/v1/completions,tokenizer=/data/Kimi-K3,num_concurrent=64,max_retries=3,tokenized_requests=False,trust_remote_code=True" \
  --tasks mmlu_generative --num_fewshot 5 --seed 42

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[mergify]

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

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

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[simondanielsson]

Thanks for the fix! Couple of comments from my end

[simondanielsson]

Question: can we double check ql_nope will always be contiguous here already, or if it's fine that it's not? IIUC y[:, : N * L].view(B, N, L) will be strided but I might be wrong

[nsusanto]

doing .contiguous() here would launch an extra copy kernel which losses the perf benefits. Also It isn't contiguous and doesn't need to be. fused_qk_rope_concat_and_cache_mla takes the token and head strides for q_nope/q_pe

[simondanielsson]

If it's not required to be contiguous, can we drop ql_nope = ql_nope.contiguous() on line 206?

[nsusanto]

@simondanielsson resolved your suggestions, can you code check it again? thanks!

[simondanielsson]

This LGTM, but would be great with another pair of eyes.

[simondanielsson]

Minor: can we do set_random_seed instead?

[simondanielsson]

I think these tests are good, my only question is if we can rather re-use what the actual implementation is using rather than redefining it here (as they might eventually diverge). IIUC what we are doing here is basically

           y = gemm_a16w16(q, self._w_fold)
            ql_nope = y[:, : N * L].view(B, N, L)
            q_pe = y[:, N * L :].view(B, N, self.qk_rope_head_dim)

so perhaps better to extract that to a helper in the core code and test that helper instead

[mergify]

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

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