[big-yellow-duck]

Purpose

Add generalized Triton SplitKV paged decode for RDNA3 and RDNA4, with
in-tree FlyDSL kernels providing an opt-in optimized path on RDNA4.

The native BF16/FP16 Triton path is gated by on_gfx1x(), which includes
RDNA3. FP8 KV-cache SplitKV requires on_gfx12x(), and all FlyDSL routes
require on_rdna4(). RDNA3 therefore uses Triton for eligible BF16/FP16
workloads, even when the FlyDSL flag is enabled. The results below validate
RDNA4; RDNA3 correctness and performance have not been tested in this run.

The FlyDSL selector exposes eight routes that were promoted only after passing
unrestricted-value accuracy checks. Unsupported shapes, unavailable FlyDSL, or
disabled FlyDSL continue through Triton. The implementation supports BF16 and
FP16 queries, native and E4M3/E4M3FNUZ KV caches, head dimensions 128 and 256,
GQA ratios 1 through 16, ragged batches, and non-standard physical page sizes.

The FlyDSL path is opt-in through
VLLM_ROCM_USE_RDNA4_SPLITKV_FLYDSL=1.

Note: a custom build of FlyDSL is needed for FlyDSL SplitKV ROCm/FlyDSL#1109

Related work / duplicate check

This is related to and overlaps with #45916, which adds the original Triton
SplitKV fallback for native BF16/FP16, head dimension 256 on gfx1x. It is not a
duplicate: this PR extends that fallback to FP8/FNUZ, head dimension 128,
irregular physical pages, pointer block tables, workspace-manager allocation,
and CUDA/HIP graph replay, and adds the separate fast path RDNA4 FlyDSL implementation.

Test Plan

Tests run on Radeon AI PRO R9700 (gfx1201) GPUs. The dtype/TP-shape matrix
explicitly selects Triton-only or FlyDSL-enabled execution; the Triton-only
cases fail if anything attempts to load FlyDSL kernels.

export PYTORCH_ROCM_ARCH=gfx1201

# Force Triton SplitKV for the complete dtype and TP-shape matrix.
VLLM_ROCM_USE_RDNA4_SPLITKV_FLYDSL=0 \
  .venv/bin/python -m pytest -q \
  tests/kernels/attention/test_splitkv_paged_decode.py \
  -k 'dispatch_boundaries and triton' --junitxml=triton.xml

# Exercise FlyDSL routes and their intended Triton fallbacks on the same cases.
VLLM_ROCM_USE_RDNA4_SPLITKV_FLYDSL=1 \
  .venv/bin/python -m pytest -q \
  tests/kernels/attention/test_splitkv_paged_decode.py \
  -k 'dispatch_boundaries and flydsl' --junitxml=flydsl.xml

# Remaining route-specific, graph replay, integration, and split-count tests.
VLLM_ROCM_USE_RDNA4_SPLITKV_FLYDSL=0 \
  .venv/bin/python -m pytest -q \
  tests/kernels/attention/test_splitkv_paged_decode.py \
  tests/kernels/attention/test_splitkv_num_splits.py \
  -k 'not dispatch_boundaries' --junitxml=remaining.xml

The remaining tests include backend-specific checks that explicitly set their
own mode; that group is reported separately from the forced-Triton matrix.

Validation results

1,310 attention tests passed in the latest run, with no failures or skips:
548 forced-Triton cases, 548 FlyDSL-enabled cases, and 214 remaining tests.
This comprises 1,209 paged-decode tests and 101 split-count/eligibility tests.
The implementation uses FlyDSL 9b022dee and Torch 2.11.0+rocm7.14.0.

Kernel cases tested

The kernel validation covers a broad matrix of attention shapes and dtypes.
The same 548 configurations ran with forced Triton and with FlyDSL enabled.
The matrix consists of the following groups.

Case group	Configurations per backend mode	Coverage
Dispatch boundaries	224	D128/D256; every GQA ratio 1–16; batches 1, 3, 7, 8, 9, 16, 33; KV-head counts 1, 2, 4; rotating dtype, page-size, split-count, and attention-profile combinations.
Specialized-route shapes	144	Eight (head dimension, GQA, batch) shapes, each crossed with six query/KV dtype pairs and three attention profiles.
Large batches	36	D128/GQA16 and D256/GQA6; batches 64, 128, 256; all six query/KV dtype pairs.
Additional D256 layouts and lengths	144	Q/KV head counts 24/4, 12/2, 6/1; four KV dtypes; four layout/length patterns; three attention profiles.
Total	548	All passed in both modes.

The eight specialized-route shapes (D, GQA, batch) are
(128,16,8), (128,16,16), (128,16,1), (128,8,3),
(256,4,3), (256,16,3), (256,6,3), and (128,3,3).
The six query/KV dtype pairs are BF16/BF16, FP16/FP16,
BF16/FP8-E4M3, FP16/FP8-E4M3, BF16/FP8-E4M3FNUZ, and
FP16/FP8-E4M3FNUZ.

Across these groups, the cases exercise:

• Physical page sizes 16, 32, 528, 784, and 1568, with padded cache strides
  and noncontiguous query/output strides.
• Split counts 2, 4, 8, and 16.
• Ragged sequences, lengths immediately below/at/above page boundaries,
  lengths 4095–4102, and long sequences of 32,767 tokens.
• Ordinary, peaked, and nearly uniform attention profiles, masked NaN cache
  tails, preservation of untouched output storage, and FP8 scales 0.73/1.27.

Kernel results by head dimension

Head dimension	Forced Triton SplitKV	Actual FlyDSL route	Intended Triton fallback with FlyDSL enabled
D128	220/220 passed	91/91 passed	129/129 passed
D256	328/328 passed	310/310 passed	18/18 passed
Total	548/548 passed	401/401 passed	147/147 passed

Kernel results by KV-cache dtype

KV-cache dtype	Forced Triton SplitKV	Actual FlyDSL route	Intended Triton fallback with FlyDSL enabled
BF16	104/104 passed	86/86 passed	18/18 passed
FP16	104/104 passed	87/87 passed	17/17 passed
FP8 E4M3	170/170 passed	118/118 passed	52/52 passed
FP8 E4M3FNUZ	170/170 passed	110/110 passed	60/60 passed
Total	548/548 passed	401/401 passed	147/147 passed

The same 548 configurations were tested in both modes. The FlyDSL-enabled
run used FlyDSL for 401 cases and Triton for 147 cases, as recorded per test;
fallback passes are not counted as FlyDSL kernel passes. Native BF16/FP16
KV caches use matching query dtypes; FP8 coverage includes BF16 and FP16
queries. Coverage includes head dimensions 128/256, GQA ratios 1–16,
ragged batches, irregular cache pages/strides, and varied attention profiles.

Earlier validation additionally passed 28 FlyDSL compiler tests, 36 long-context
checks reaching 131,071 tokens, head-partition checks with 50 graph replays per
format, and 648 accuracy-checked measurements in the old/new FlyDSL benchmark
sweeps. Those checks were not rerun as part of this backend-matrix run.

Serving and model accuracy

A subset of the GSM8K benchmark is run comparing the baseline and the implemented SplitKv kernel

The comparative GSM8K smoke evaluation used the same 32 zero-shot questions per configuration, a brief-calculation instruction, and a 384-token cap:

TP	Old / new correct	Same final answers	Serving configuration
2	31/32 / 31/32	32/32	8K context, 8 sequences
4	31/32 / 31/32	32/32	32K context, 16 sequences

Online serving performance

NCCL_PROTO=Simple VLLM_ROCM_USE_RDNA4_SPLITKV_FLYDSL=1 \
vllm serve Qwen/Qwen3.8-27B-FP8 \
  -tp 4 \
  --load-format fastsafetensors \ 
  --max-num-seqs 32 \
  --max-model-len auto \
  --kv-cache-dtype fp8

Serving workload benchmark comparison

• Streams: 1, 2, 4, and 8
• Synthetic prompt: 2,048 tokens per turn
• Output: 512 tokens per turn
• Turns per conversation: 8

Streams	Output tok/s, baseline → SplitKV FP8	TPS change	Mean TTFT (ms), baseline → SplitKV FP8	TTFT change	Mean E2E (s), baseline → SplitKV FP8	E2E change
1	10.03 → 39.46	+293.5%	2,340 → 1,853	−20.8%	65.14 → 13.03	−80.0%
2	9.53 → 34.71	+264.1%	3,066 → 2,524	−17.7%	67.86 → 14.87	−78.1%
4	9.26 → 28.68	+209.6%	3,391 → 3,989	+17.6%	69.17 → 18.18	−73.7%
8	8.32 → 26.60	+219.7%	5,226 → 4,566	−12.6%	75.93 → 20.25	−73.3%

AI assistance disclosure

OpenAI Codex assisted with implementation, testing, benchmarking, and this history cleanup.

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[mergify]

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

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[big-yellow-duck]

#59132 superseeds the the perf stated here