[Perf][ROCm][Attention] Fuse Kimi-K3 MLA chunked-context gather on AITER

[nsusanto]

Purpose

The dense-MLA prefill spends four kernels per context chunk in
_compute_prefill_context:

• vllm::gather_and_maybe_dequant_cache<bf16,bf16,(0),576,64>
• Cijk_…MT192x160x64 (kv_b_proj expansion)
• at::native direct_copy (k[..., :128] = k_nope)
• at::native direct_copy (k[..., 128:] = k_pe)

AITER ships gather_kv_b_proj, which does all four in one launch. AiterMLAImpl._compute_prefill_context
now calls it per context chunk, with the per-token KV indices built once per step in the metadata builder.
The fused gather runs only without decode context parallelism (dcp_world_size == 1); DCP > 1 keeps
main's DCP prefill path.

AiterMLAImpl._concat_k_nope_k_pe now calls the already-merged fused_kimi_k3_mla_kv_concat (#51772)
instead of two slice-assign copies, gated on the MLA dims that kernel asserts (STD_TORCH_CHECK on
128-wide NoPE / 64-wide rope).

Test Plan

Unit tests: tests/kernels/attention/test_rocm_aiter_mla_prefill_fusions.py (11 cases: fused vs unfused
prefill context, KV indices vs the block table, declined inputs, k concat), all pass on MI355X.

Accuracy:

lm_eval \
  --model local-completions \
  --model_args "model=/data/Kimi-K3/,base_url=http://localhost:8001/v1/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,tokenizer_backend=None,trust_remote_code=True" \
  --tasks gsm8k \
  --num_fewshot 5 \
  --seed 42 \
  --output_path /app/lm_eval_out --log_samples

Performance on the InferenceX Kimi-K3 MXFP4 agentic recipe (8x MI355X, image
vllm/vllm-openai-rocm:nightly-rocm100-36768d1bf, vLLM 0.30.1rc1.dev312+g36768d1bf, AITER v0.1.23).
Before = the image; After = the image with this PR's rocm_aiter_mla.py. One run per point after a warm-up,
10 x concurrency prompts.

Server command (conc 14: DCP 1, DSpark k=3)

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
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":[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112]}' \
  --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}}'

Server command (conc 70: DCP 8, no draft model)

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
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 140 \
  --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_AND_PIECEWISE","custom_ops":["+fused_rms_norm_gated"],"cudagraph_capture_sizes":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,256,512,1024,2048,4096,8192]}' \
  --kv-transfer-config '{"kv_connector":"SimpleCPUOffloadConnector","kv_role":"kv_both","kv_connector_extra_config":{"cpu_bytes_to_use_per_rank":224875000000,"lazy_offload":false}}' \
  --decode-context-parallel-size 8 \
  --dcp-comm-backend a2a

The conc-70 DCP 1 rows below use the same command without --decode-context-parallel-size 8 --dcp-comm-backend a2a.

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 <ISL> --random-output-len <OSL> --random-range-ratio 0 \
  --request-rate=inf --random-prefix-len 0 --num-prompts <10 x CONC> --max-concurrency <CONC> \
  --ignore-eos --seed 43 --percentile-metrics ttft,tpot,itl,e2el

Test Results

gsm8k:

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9591|±  |0.0055|
|     |       |strict-match    |     5|exact_match|↑  |0.9591|±  |0.0055|

Conc 14, (DCP 1, gather_kv_b_proj on)

Used "rejection_sample_method":"synthetic","synthetic_acceptance_length":1.9 to keep speculative decode static

ISL/OSL	Metric	Before	After	Delta
1k/1k	Output throughput (tok/s)	681.60	692.60	+1.6%
1k/1k	Mean TTFT (ms)	342.00	339.00	-0.9%
1k/1k	Mean TPOT (ms)	19.46	19.23	-1.2%
8k/1k	Output throughput (tok/s)	292.10	299.04	+2.4%
8k/1k	Mean TTFT (ms)	3083.97	3069.45	-0.5%
8k/1k	Mean TPOT (ms)	44.94	43.81	-2.5%

Conc 70, DCP 8 (production recipe; gather_kv_b_proj is gated off)

Minor change since gather_kv_b_proj is disabled through DCP 8

ISL/OSL	Metric	Before	After	Delta
1k/1k	Output throughput (tok/s)	1730.18	1728.03	-0.1%
1k/1k	Mean TTFT (ms)	1111.72	1116.27	+0.4%
1k/1k	Mean TPOT (ms)	39.40	39.44	+0.1%
8k/1k	Output throughput (tok/s)	1085.92	1085.19	-0.1%
8k/1k	Mean TTFT (ms)	3064.91	3068.07	+0.1%
8k/1k	Mean TPOT (ms)	61.42	61.46	+0.1%

Conc 70, DCP 1 (gather_kv_b_proj on)

ISL/OSL	Metric	Before	After	Delta
1k/1k	Output throughput (tok/s)	1843.78	1848.36	+0.2%
1k/1k	Mean TTFT (ms)	1111.11	1113.53	+0.2%
1k/1k	Mean TPOT (ms)	36.90	36.81	-0.2%
8k/1k	Output throughput (tok/s)	1103.27	1118.97	+1.4%
8k/1k	Mean TTFT (ms)	3167.12	3103.83	-2.0%
8k/1k	Mean TPOT (ms)	60.31	59.48	-1.4%

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[ChuanLi1101]

Nice speedup — the index math checks out, and gather_kv_b_proj is already in AITER v0.1.19 (our pin) with a matching signature. Three things though:

1. The AITER import at rocm_aiter_mla.py:9 is at module scope. Every other AITER import in this file is deferred, even flash_attn_varlen_func inside AiterMLAImpl.__init__. As written, anyone on an AITER without this op gets a hard ImportError at backend selection instead of just falling back. Can you make it lazy (or go through rocm_aiter_ops, already imported at line 11) and fold the availability check into _can_fuse_context_gather?

2. _can_fuse_context_gather looks at q.dtype, but the base path branches on prefill_metadata.q_data_type and casts q/kv_nope/k_pe to fp8 afterwards. So a bf16 q with q_data_type == fp8 passes your guard, takes the fused path, and comes back with bf16 k/v and an unconverted q. It's masked today because AITER's fp8 prefill metadata is only built when chunked_context is None, but determine_prefill_query_data_type is inherited and can still return fp8 — worth gating on q_data_type directly.

3. _use_fused_mla_kv_concat uses is_cuda_alike(), so it fires for any qk_nope=128 / qk_rope=64 MLA model, not just K3. On NVIDIA the flashinfer branch only wins at num_heads == 128, which means at TP8 this quietly takes over DeepSeek's prefill. Probably fine since the kernel is shape-generic and already used for K3 on NVIDIA, but for a [kimik3][ROCm] PR I'd either narrow it to is_rocm() or call it out with one DeepSeek datapoint.

[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, @nsusanto.

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

[azaidy]

1. Is it possible to create unit tests to verify the functionality?
2. AITER's _triton_gather_kv_b_proj_flat starts with assert KV_CDim == 4 * ScaleKGranularity, and ScaleKGranularity is 128. So kv_lora_rank must be 512. Any other value raises a Triton CompilationError. Can you add this check to ensure kv_lora_rank is 512 is we take the triton kernel path - I think you can add it to _can_fuse_context_gather.

[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

[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]

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