[kimik3][ROCm][Perf] Fuse MLA chunked-context gather on AITER

[nsusanto]

Purpose

The dense-MLA prefill spends four kernels per context chunk in
MLACommonBaseImpl._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.

MLACommonBaseImpl._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 DeepSeek-shaped MLA dims that kernel asserts (STD_TORCH_CHECK on
128-wide NoPE / 64-wide rope). Measured at 3867 tokens, H=16:

Test Plan

lm_eval \
  --model local-completions \
  --model_args "model=/data/Kimi-K3/,base_url=http://localhost:8002/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

Server command:

#!/bin/bash

export VLLM_ROCM_USE_AITER=1
export SAFETENSORS_FAST_GPU=1
export AITER_DISABLE_FMHA_OPUS=1
# AITER_SITUV2_A8W4=1 forces fp8 MoE activations (a8w4). K3 is hidden_act=situ
# -> Situv2, and its checkpoint is weight-only mxfp4 ("input_activations": null).
# With this on, gsm8k 5-shot = 0.00 (degenerate output); off = 1.00. Keep it off.
# export AITER_SITUV2_A8W4=1
export VLLM_ROCM_USE_AITER_MOE_SITUV2_A8W4=1
export AITER_BF16_FP8_MOE_BOUND=0
export VLLM_USE_BREAKABLE_CUDAGRAPH=0
vllm serve /data/Kimi-K3/ \
  --trust-remote-code \
  --port 8001 \
  --moe-backend auto \
  --tensor-parallel-size 8 \
  --load-format auto \
  --gpu-memory-utilization 0.95 \
  --mm-encoder-tp-mode data \
  --max-num-batched-tokens 4096 \
  --enable-auto-tool-choice \
  --tool-call-parser kimi_k3 \
  --reasoning-parser kimi_k3 \
  --max-num-seqs 128 --enable-prefix-caching \
  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY","custom_ops":["+fused_rms_norm_gated"]}' \
  --profiler-config '{"profiler":"torch","torch_profiler_dir":"/app/traces_fused"}' \
  "$@"

# Optional extras -- add to the serve args above when you want them:
#
#   --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY","custom_ops":["+fused_rms_norm_gated"]}'
#       (was in the original docker run, not in the serve template)

Bench Command:

vllm bench serve --backend vllm --model /data/Kimi-K3/ \
  --host localhost --port ${PORT:-8001} --endpoint /v1/completions \
  --dataset-name random --random-input-len 1024 --random-output-len 1024 \
  --random-range-ratio 0 --random-prefix-len 0 \
  --num-prompts 640 --max-concurrency 64 --ignore-eos --trust-remote-code \

Test Results

|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|

Before:

============   Serving Benchmark Result ============
--
Successful requests:                     640
Benchmark duration (s):                  474.10
Total input tokens:                      655360
Total generated tokens:                  655360
Request throughput (req/s):              1.35
Output token throughput (tok/s):         1382.33
Total token throughput (tok/s):          2764.67
---------------Time to First   Token----------------
Mean TTFT (ms):                          1167. 22
Median TTFT (ms):                        879.07
P99 TTFT (ms):                           5851.73
-----Time per Output Token (excl. 1st   token)------
Mean TPOT (ms):                          45.17
Median TPOT (ms):                        45.30
P99 TPOT (ms):                           46.24
---------------Inter-token   Latency----------------
Mean ITL (ms):                           45.17
Median ITL (ms):                        42.03
P99 ITL (ms):                            244.00
==================================================

After:

============ Serving Benchmark Result ============
--
Successful requests:                     640
Failed requests:                         0
Maximum request concurrency:             64
Benchmark duration (s):                  434.37
Total input tokens:                      655360
Total generated tokens:                  655360
Request throughput (req/s):              1.47
Output token throughput (tok/s):         1508.75
Peak output token throughput (tok/s):    1792.00
Peak concurrent requests:                125.00
Total token throughput (tok/s):          3017.50
---------------Time to First Token----------------
Mean TTFT (ms):                          1092.85
Median TTFT (ms):                        1006.88
P50 TTFT (ms):                           1006.88
P99 TTFT (ms):                           2508.16
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          41.36
Median TPOT (ms):                        41.47
P50 TPOT (ms):                           41.47
P99 TPOT (ms):                           45.57
---------------Inter-token Latency----------------
Mean ITL (ms):                           41.36
Median ITL (ms):                         38.50
P50 ITL (ms):                            38.50
P99 ITL (ms):                            206.39
----------------End-to-end Latency----------------
Mean E2EL (ms):                          43408.55
Median E2EL (ms):                        43434.35
P50 E2EL (ms):                           43434.35
P99 E2EL (ms):                           48900.55
==================================================

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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.