[ROCm][Perf] Fuse DSA indexer QK preprocessing with AITER

[sumin-hong]

Purpose

Every DeepSeek-Sparse-Attention layer (DeepSeek-V3.2, GLM-5.x, and their MTP drafts) runs a lightweight indexer whose pre-processing costs five kernel launches per layer per step:

1. LayerNorm on k
2. RoPE on the leading rope dims of q and k
3. per-token-group FP8 quantization of q
4. a pointwise fold of the q scale into the indexer weights
5. FP8 quantization of k plus the paged indexer K-cache write

At decode sizes this is launch-bound: the cost barely moves with token count. AITER already ships a kernel that does all five in one launch (indexer_qk_rope_quant_and_cache). This PR wires it in behind a new, default-off env flag VLLM_ROCM_USE_AITER_INDEXER_QK_FUSION.

The kernel writes the indexer K cache itself, so the sparse-attention indexer op skips its own insert (skip_k_cache_insert). Two data-movement optimizations ride along: the rope cos_sin_cache halves are registered once as strided views on the indexer's rotary embedding instead of being re-split every layer every step, and one zero-initialized q_fp8/weights output pair is shared by the indexer layers of a model (allocated next to topk_indices_buffer and sized the same way), with only the first indexer of a forward pass zero-filling it. The change is Python-only: the kernel is already in the AITER version the tree pins.

The fused path engages only when all of the following hold, and otherwise falls back silently to today's code:

• VLLM_ROCM_USE_AITER=1 and VLLM_ROCM_USE_AITER_INDEXER_QK_FUSION=1
• gfx942 / gfx950 (on_mi3xx()): AITER ships this kernel in its CK build, and the RDNA paths that SparseAttnIndexer.forward_hip now also serves have Triton-only AITER
• the in-place-RoPE indexer path, i.e. the rotary_embedding custom op is enabled
• DSA indexer shapes index_head_dim == 128 and qk_rope_head_dim == 64
• no context parallelism (decode_context_parallel_size == 1 and prefill_context_parallel_size == 1). The fused kernel is driven by slot_mapping, which is PAD_SLOT_ID on ranks that do not own a token, so those ranks would skip the row and never produce its quantized query — while every CP rank still needs the query to score its own KV shard.

Related work

#51315 is an ongoing effort in the same area and also integrates AITER's fused indexer QK preprocessing kernel. This PR was developed independently and covers additional integration details, including a default-off feature gate; MI3XX, shape, and context-parallel guards; shared output ownership and zeroing for skipped rows; MTP wiring; split RoPE cache registration; and corresponding correctness coverage.

We are opening this as a draft to get maintainer feedback on how best to coordinate the two efforts and which integration pieces should be carried forward.

Test Plan

• Image: vllm/vllm-openai-rocm:nightly-5a4c8d99242e9e069b604d0e9b969e77f7dd501d.
• 8× MI355X (gfx950) — GLM-5.2-FP8 and GLM-5.2-MXFP4: op test, kernel benchmark, GSM8K accuracy (speculative decoding off and on), serving throughput.
• Serve configuration: the official recipe for GLM-5.2, AMD selection (https://recipes.vllm.ai/zai-org/GLM-5.2), plus --no-enable-prefix-caching for the throughput sweep. Accuracy leaves prefix caching at its default, since GSM8K's few-shot prompts share prefixes; both arms are identical per workload.

Serve — GLM-5.2-FP8.

export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1
export VLLM_ROCM_USE_AITER_INDEXER_QK_FUSION=1   # the flag this PR adds

vllm serve /models/GLM-5.2-FP8 --port $PORT \
  --kv-cache-dtype fp8_e4m3 --tensor-parallel-size 8 \
  --linear-backend aiter --moe-backend aiter \
  --tool-call-parser glm47 --enable-auto-tool-choice --reasoning-parser glm45 \
  --no-enable-prefix-caching        # throughput sweep only; accuracy keeps the default

Serve — GLM-5.2-MXFP4.

export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1
export VLLM_ROCM_USE_AITER_INDEXER_QK_FUSION=1
export VLLM_USE_V2_MODEL_RUNNER=1
export VLLM_ROCM_USE_AITER_FP8BMM=0
export VLLM_ROCM_USE_AITER_FP4BMM=0

vllm serve /models/GLM-5.2-MXFP4 --port $PORT \
  --kv-cache-dtype fp8_e4m3 --tensor-parallel-size 8 \
  --linear-backend aiter --moe-backend aiter \
  --tool-call-parser glm47 --enable-auto-tool-choice --reasoning-parser glm45 \
  --no-enable-prefix-caching --trust-remote-code

Speculative decoding. Append this to either serve command:

--speculative-config '{"method":"mtp","num_speculative_tokens":5}'

Op test

.venv/bin/python -m pytest \
  tests/kernels/attention/test_rocm_aiter_indexer_qk_fusion.py -v

Kernel benchmark

.venv/bin/python benchmarks/kernels/benchmark_indexer_qk_fusion.py --repeat 5

Accuracy

lm_eval --model local-completions --tasks gsm8k \
  --model_args "model=<model>,base_url=http://127.0.0.1:$PORT/v1/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,timeout=600"

Serving throughput, for CONC in 4 8 16 32 64 128 256:

vllm bench serve --port $PORT --model <model> --dataset-name random \
  --random-input-len 8192 --random-output-len 1024 \
  --num-prompts $((3*CONC)) --max-concurrency $CONC \
  --num-warmups 8 --request-rate inf --ignore-eos \
  --percentile-metrics ttft,tpot,itl,e2el

Lint

uvx pre-commit run --files $(git diff --name-only HEAD~1 HEAD)

The op test scores the fused op and the unfused flow against an fp64 golden of the same math over 20 cases (num_tokens ∈ {1,7,32,257,1023} × block_size ∈ {1,64} × is_neox ∈ {True,False}): the fused path must be no less accurate than the unfused one, and the two must agree to within one fp8 code on every element above 1e-3 of the tensor maximum, for both q_fp8 * weights_out and the dequantized indexer K cache. Two further cases pin the rows the kernel skips to zero and opcheck the op's schema, for 22 in total.

Test Result

Current main rebase — the PR is based on vLLM 1eab6fef01 with one import-only conflict resolution. The full vLLM pre-commit set passes on the rebased commit (ruff, formatting, typos, mypy, SPDX, import checks, and configuration validation). The ROCm GPU results below were collected on the original test image; GPU tests have not been rerun after the rebase.

Op-level equivalence — 22 passed on 8× MI355X (gfx950).

Kernel speed — one indexer layer's pre-processing, median of --repeat 5 at block_size=64, n_head=32 (index_n_heads for both GLM-5.2 checkpoints) and the interleaved RoPE layout GLM-5.x selects, on 8× MI355X (gfx950):

tokens	unfused	fused	speedup
1	70.29 µs	11.14 µs	6.31×
8	74.10 µs	11.31 µs	6.55×
32	74.17 µs	11.16 µs	6.65×
64	74.51 µs	11.17 µs	6.67×
256	74.93 µs	11.36 µs	6.59×
1024	73.00 µs	25.06 µs	2.91×

pre-commit — all hooks pass (ruff check, ruff format, typos, markdownlint, mypy 3.10, SPDX headers, root lazy imports, forbidden imports, torch.cuda API check, configuration validation, …), exit code 0.

GSM8K (1319 questions, lm_eval gsm8k)

8× MI355X, TP8. Reported as strict-match with lm_eval's standard error, flexible-extract in brackets.

Model	MTP	Before	After
GLM-5.2-FP8	off	94.54% ±0.63 (94.54%)	94.16% ±0.65 (94.31%)
GLM-5.2-FP8	5 tokens	94.39% ±0.63 (94.39%)	94.39% ±0.63 (94.39%)
GLM-5.2-MXFP4	off	93.10% ±0.70 (93.10%)	93.40% ±0.68 (93.40%)
GLM-5.2-MXFP4	5 tokens	94.01% ±0.65 (94.01%)	93.63% ±0.67 (93.48%)

Serving throughput, ISL 8192 / OSL 1024

GLM-5.2-FP8, MTP off, 8× MI355X TP8:

Conc	Out tok/s before	Out tok/s after	Δ	Mean TPOT ms
4	154.6	157.2	+1.66%	24.8 → 24.3
8	273.2	278.3	+1.84%	27.4 → 26.8
16	461.2	469.9	+1.88%	31.5 → 30.9
32	700.2	705.2	+0.72%	41.0 → 40.8
64	989.9	995.2	+0.54%	57.9 → 57.1
128	1308.4	1311.7	+0.25%	85.8 → 86.0
256	1547.1	1547.8	+0.05%	144.2 → 144.0

GLM-5.2-MXFP4, MTP off, 8× MI355X TP8:

Conc	Out tok/s before	Out tok/s after	Δ	Mean TPOT ms
4	187.5	193.9	+3.41%	20.3 → 19.7
8	324.8	330.3	+1.71%	23.0 → 22.7
16	562.4	564.5	+0.37%	26.0 → 25.9
32	853.8	864.1	+1.21%	33.9 → 33.4
64	1249.8	1259.7	+0.79%	45.7 → 45.6
128	1642.5	1650.0	+0.46%	69.0 → 68.7
256	1975.0	1980.8	+0.30%	113.1 → 112.5

Note: AI assistance was used to rebase, review, and prepare this contribution.

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR is described, including related work.
• Test commands are provided.
• Test and model evaluation results are provided with their tested base.
• AI assistance is disclosed.

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