[big-yellow-duck]

Summary

Add an opt-in ROCM_SEGMENTED_ATTN attention backend with segmented Triton attention for paged decode, prefill, and multi-token verification. Startup tuning persists launch choices and shares a Triton cache across target and draft compilation.

• Supports RDNA3, RDNA3.5, and RDNA4 (gfx11/gfx12) with native FP16/BF16 KV; FP8 KV is gated to gfx12.
• Covers head sizes 64, 128, and 256, GQA ratios 1–16, causal and non-causal attention, sliding windows, and attention sinks.
• Includes focused kernel, backend-selection, workspace, tuning-cache, and Triton-cache tests.
• Uses the existing Triton/ROCm stack and adds no package dependency or custom compiler build.
• Startup autotuning is disabled by default to avoid the tuning sweep during engine startup. Set --kernel-config.enable_rocm_segmented_attn_autotune=True to enable it; skipping tuning may reduce serving performance.

Relationship to existing work

This overlaps with #55996, which adds generalized Triton SplitKV paged decode plus an optional RDNA4 FlyDSL path.The overlap is in terms of paged attention optimization. That optional path requires a custom FlyDSL build. ROCM_SEGMENTED_ATTN uses Triton already in vLLM, with no additional dependency, and covers paged decode, prefill, and multi-token verification across gfx11/gfx12. It also covers D=64, sliding windows, and attention sinks. The serving results below compare against ROCM_ATTN; they are not a direct performance comparison with #55996.

Validation

The three serving comparisons used ROCm 10, the same PyTorch version, and the same GuideLLM 0.7.3 simple-agent-multiturn-prefix-cache workload. Each run used eight turns, 2,048 configured prompt tokens per turn, 512 output tokens per request, seed 20260715, and 1/2/4/8 configured streams with 8/16/32/64 completed requests, respectively. All runs finished with zero errors or incomplete requests.

Each table compares ROCM_ATTN (baseline) with ROCM_SEGMENTED_ATTN (candidate). Values are arithmetic means across successful requests; output tokens/s is per-request. These are end-to-end serving measurements, including model execution and scheduling, rather than isolated kernel timings. Hardware, model, and serving options are listed for each comparison.

gfx1151 serving comparison

On gfx1151, the comparison used dbirks/Qwen3.8-27B-W4A16-AutoRound.

Streams	Output tokens/s: baseline → segmented	Change	TTFT (ms): baseline → segmented	Change	E2E (s): baseline → segmented	Change
1	5.49 → 10.79	+96.6%	8,424 → 6,878	−18.4%	102.33 → 47.49	−53.6%
2	4.22 → 8.54	+102.3%	18,215 → 10,549	−42.1%	135.15 → 60.12	−55.5%
4	3.12 → 7.76	+148.5%	52,347 → 18,142	−65.3%	182.52 → 69.52	−61.9%
8	2.77 → 5.22	+88.6%	63,995 → 34,987	−45.3%	218.37 → 105.57	−51.7%

The per-turn figures show that the segmented backend maintains a flatter per-request output rate as the conversation grows:

Serving commands

Baseline:

vllm serve dbirks/Qwen3.8-27B-W4A16-AutoRound \
    --max-num-seqs 8 \
    --gpu-memory-utilization 0.8 \
    --max-num-batched-tokens 8192 \
    --max-model-len auto \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_xml \
    --reasoning-parser qwen3 \
    --mm-encoder-tp-mode data \
    --load-format fastsafetensors \
    --attention-backend ROCM_ATTN

Candidate (same options, different backend):

vllm serve dbirks/Qwen3.8-27B-W4A16-AutoRound \
    --max-num-seqs 8 \
    --gpu-memory-utilization 0.8 \
    --max-num-batched-tokens 8192 \
    --max-model-len auto \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_xml \
    --reasoning-parser qwen3 \
    --mm-encoder-tp-mode data \
    --load-format fastsafetensors \
    --attention-backend ROCM_SEGMENTED_ATTN

gfx1100 serving comparison

On two Radeon RX 7900 XTX GPUs (gfx1100, TP2), the comparison used dbirks/Qwen3.8-27B-W4A16-AutoRound. Both backends ran the same earlier standalone checkout (4f20819329); only the attention backend changed.

Streams	Output tokens/s: baseline → segmented	Change	TTFT (ms): baseline → segmented	Change	E2E (s): baseline → segmented	Change
1	10.15 → 47.38	+366.7%	1,812 → 1,763	−2.7%	62.64 → 10.81	−82.7%
2	9.45 → 39.24	+315.2%	3,047 → 2,240	−26.5%	67.44 → 13.08	−80.6%
4	9.25 → 29.38	+217.5%	3,908 → 4,693	+20.1%	68.57 → 17.56	−74.4%
8	7.47 → 25.48	+241.2%	9,746 → 5,646	−42.1%	82.74 → 21.85	−73.6%

Output rate and E2E latency improve at all four stream settings. Mean TTFT rises at four streams.

Candidate serving command; the baseline used the same options with --attention-backend ROCM_ATTN:

vllm serve dbirks/Qwen3.8-27B-W4A16-AutoRound \
    --max-num-seqs 8 \
    --tensor-parallel-size 2 \
    --gpu-memory-utilization 0.9 \
    --max-num-batched-tokens 8192 \
    --max-model-len auto \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_xml \
    --reasoning-parser qwen3 \
    --mm-encoder-tp-mode data \
    --load-format fastsafetensors \
    --attention-backend ROCM_SEGMENTED_ATTN

gfx1201 serving comparison

On two Radeon AI PRO R9700 GPUs (gfx1201, TP2), the comparison used Qwen/Qwen3.8-27B-FP8 with FP8 KV. Both backends ran the same image, built from an earlier branch checkout (4f20819329); only the attention backend changed.

Streams	Output tokens/s: baseline → segmented	Change	TTFT (ms): baseline → segmented	Change	E2E (s): baseline → segmented	Change
1	4.19 → 6.53	+56.0%	1,790 → 2,113	+18.0%	127.62 → 78.37	−38.6%
2	4.21 → 6.63	+57.7%	1,708 → 884	−48.2%	126.68 → 77.19	−39.1%
4	7.35 → 20.28	+176.0%	3,488 → 1,794	−48.6%	85.90 → 34.81	−59.5%
8	7.84 → 22.03	+181.1%	7,604 → 3,317	−56.4%	78.71 → 23.71	−69.9%

Output rate and E2E latency improve at all four stream settings. Mean TTFT rises by 18.0% at one stream in this cold run, which logged inference-time Triton JIT compilation.

Candidate serving command; the baseline used the same options with --attention-backend ROCM_ATTN:

vllm serve Qwen/Qwen3.8-27B-FP8 \
    --max-num-seqs 8 \
    --gpu-memory-utilization 0.92 \
    --tensor-parallel-size 2 \
    --max-num-batched-tokens 8192 \
    --max-model-len auto \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_xml \
    --reasoning-parser qwen3 \
    --mm-encoder-tp-mode data \
    --kv-cache-dtype fp8 \
    --load-format safetensors \
    --attention-backend ROCM_SEGMENTED_ATTN

Both measured runs used --load-format safetensors; the supplied fastsafetensors launcher failed to start in this environment.

Speculative decoding: Qwen3.8-27B-FP8 + DFlash2 on gfx1201

Measured on two Radeon AI PRO R9700 GPUs (TP2), using Qwen/Qwen3.8-27B-FP8 with z-lab/Qwen3.8-27B-DFlash2, seven speculative tokens, FP8 KV, and concurrency 4. Both the target and draft use the backend selected for each case:

• A: ROCM_ATTN.
• B: ROCM_SEGMENTED_ATTN, autotuning disabled.
• C: ROCM_SEGMENTED_ATTN, autotuning enabled.

Accuracy checks first

GSM8K uses the same first 100 test questions and five worked training examples in every case. Sampling follows the model's thinking-mode recommendation: temperature 1.0, top-p 0.95, top-k 20, min-p 0, presence penalty 0, repetition penalty 1, and default xhigh reasoning effort. Each question uses a fixed seed.

Case	GSM8K correct / 100
A: baseline	98
B: segmented, static	98
C: segmented, tuned — pass 1	99
C: segmented, tuned — pass 2	98

A/B miss IDs 12 and 45; C misses ID 12 in pass 1 and IDs 12 and 45 in pass 2. C repeats the same 100 questions, rather than adding 100 new evaluation questions. This is a quick quality check, not a full canonical GSM8K evaluation.

The long-context inputs are coherent GSM8K worked problems, followed by a new question; no random-token prompts are used. Actual input lengths are 16,606–16,636 and 32,817–32,847 tokens, with four requests at each length. These speed checks use a 512-token output cap:

Input	A correct / 4	B correct / 4	C correct / 4, passes 1 / 2
16K	3	3	3 / 3
32K	3	3	3 / 3

The small sample and truncated outputs make the long-context results a serving check, not an accuracy claim.

A → B: speculative-decoding speed with segmented attention

The backend covers multi-token verification and non-causal draft attention. The PR also reserves attention scratch space before graph capture, initializes draft attention in its own workspace lane, and preserves a shared Triton cache across target/draft compilation.

Workload	A: baseline output tokens/s	B: segmented static output tokens/s	B / A
GSM8K, 100 questions	102.27	253.50	2.48×
16K coherent input, 4 requests	8.20	185.47	22.62×
32K coherent input, 4 requests	4.08	131.54	32.27×

These are aggregate final output tokens/s, calculated as returned completion tokens divided by batch wall time, including prefill and scheduling and excluding warmup and draft proposals. They differ from the per-request GuideLLM rates in the earlier tables.

Mean acceptance length (1 + accepted draft tokens / draft rounds) was approximately 4.05 → 4.05 for GSM8K, 1.04 → 3.92 at 16K, and 1.03 → 3.90 at 32K. The large long-context gains coincide with recovering draft acceptance from near one token per round in A. Both target and draft backends change, so this does not isolate a single kernel or establish the cause of the baseline acceptance collapse. The 22–32× figures describe this specific DFlash2 workload, not a general serving speedup.

B → C: additional performance with autotuning

The tuner selects launch configurations for the target and draft attention workloads. Updated C uses the original C run's saved 125 target and 42 draft tuning records, unchanged, with two warmed measured passes.

Workload	B: static output tokens/s	C: tuned mean output tokens/s	C passes 1 / 2	Observed C vs B
GSM8K, 100 questions	253.50	268.79	261.97 / 275.62	+6.0%
16K coherent input, 4 requests	185.47	195.80	186.04 / 205.56	+5.6%
32K coherent input, 4 requests	131.54	134.64	135.35 / 133.94	+2.4%

C is the arithmetic mean of both batch rates. These results suggest additional serving benefit from autotuning, but A/B are historical single runs and C uses extended warmup plus prefix-cache resets. The smaller B/C differences are observed comparisons, not an isolated or statistically established autotuning gain.

First engine startup cost

Autotuning adds work on the first startup with an empty matching tuning cache:

Configuration	Tuning time	Engine initialization time	Extra initialization vs B
B: autotuning disabled	—	253.88 s	—
C: cold autotuning (separate startup measurement)	410.02 s target + 88.44 s draft	750.34 s	496.46 s (~8.3 min)

These are logged engine initialization times (profiling, KV allocation, and model warmup), not full process launch-to-ready times. Matching cached startups loaded all 167 records and tuned zero workloads, avoiding the tuning sweep; model loading, compilation, and graph capture still have their own costs. Cache invalidation can require retuning. Set --kernel-config.enable_rocm_segmented_attn_autotune=False to skip tuning.

Speculative-decoding serving commands

Run one server at a time on the two-GPU setup above. These commands select the attention backend for both the target and the DFlash2 draft, with seven speculative tokens.

A — baseline (ROCM_ATTN):

vllm serve Qwen/Qwen3.8-27B-FP8 \
    --host 127.0.0.1 \
    --served-model-name Qwen/Qwen3.8-27B-FP8 \
    --tensor-parallel-size 2 \
    --gpu-memory-utilization 0.92 \
    --max-model-len 65536 \
    --max-num-batched-tokens 8192 \
    --max-num-seqs 4 \
    --kv-cache-dtype fp8 \
    --load-format safetensors \
    --attention-backend ROCM_ATTN \
    --speculative-config '{"method":"dflash","model":"z-lab/Qwen3.8-27B-DFlash2","num_speculative_tokens":7,"attention_backend":"ROCM_ATTN"}'

C — segmented attention with autotuning enabled:

vllm serve Qwen/Qwen3.8-27B-FP8 \
    --host 127.0.0.1 \
    --served-model-name Qwen/Qwen3.8-27B-FP8 \
    --tensor-parallel-size 2 \
    --gpu-memory-utilization 0.92 \
    --max-model-len 65536 \
    --max-num-batched-tokens 8192 \
    --max-num-seqs 4 \
    --kv-cache-dtype fp8 \
    --load-format safetensors \
    --attention-backend ROCM_SEGMENTED_ATTN \
    --kernel-config.enable_rocm_segmented_attn_autotune=True \
    --speculative-config '{"method":"dflash","model":"z-lab/Qwen3.8-27B-DFlash2","num_speculative_tokens":7,"attention_backend":"ROCM_SEGMENTED_ATTN"}'

For B — segmented attention without autotuning, omit the autotuning flag from the second command (the current default is disabled). Python callers enable tuning with kernel_config={"enable_rocm_segmented_attn_autotune": True}. The setting follows the existing FlashInfer autotuning pattern in KernelConfig and applies per engine to startup tuning, workspace sizing, and tuned-kernel selection.

The measured runs used cached model snapshots: target 017b9c7af6b5689d5dd426a76e0bc077eb5ca20a and draft 50307d4c4cde6860d4eee73e2547cd786fe8e8a4. For exact model-weight reproduction, substitute the corresponding local snapshot paths for the model IDs. Sampling settings and client concurrency are supplied by the benchmark client.

Reproducibility and validation scope

• Serving settings: --max-model-len 65536 --max-num-batched-tokens 8192 --max-num-seqs 4 --gpu-memory-utilization 0.92 --tensor-parallel-size 2 --kv-cache-dtype fp8 --load-format safetensors; seven DFlash2 speculative tokens. A/B disable autotuning; C enables autotuning explicitly in the current API. The historical measurements used the former environment variable; the commands above use the current KernelConfig API.
• PR Python source at 2f2ccc82515537f3a1add4d547b5aae60527fc76 was copied into the same ROCm 10 image, retaining compiled extensions from 4f20819329. C uses the source now committed as 0eb508cf96811c8c2b4f5d057305fb6aa5fa704c. This is not a clean image build from the PR head.
• Before C's measurements, each server warmed four full GSM8K requests and both long workloads. Prefix caches were reset before each batch, followed by the client's excluded 128-token warmup. A's historical 16K run had an extra cached prefix from an exploratory run, favoring its prefill.
• To hold tuning choices fixed across the source change, only the tuner-source hash in the saved cache identity was adapted; all launch records and the attention-kernel source hash were verified unchanged.
• 20 focused tuning tests passed (58 deselected). Repository pre-commit checks passed. The full focused suite suggested below has not been rerun.

Command used inside the GPU test container (PR worktree mounted at /src):

.venv/bin/python -m pytest /src/tests/kernels/attention/test_segmented_attention.py \
    -k tuning --confcutdir=/src/tests/kernels/attention -q

Source and test checks

• A previous independent checkout reported 83 passing focused segmented, cache, and workspace tests; these have not been rerun on the squashed commit.
• After syncing to current main and squashing to one commit, git diff --check, Ruff lint, and Ruff format checks pass on all 14 changed Python files. The final PR diff contains only source and tests.
• The full focused suite has not been rerun in this checkout. The GPU-container tuning tests and GSM8K/DFlash2 evaluations are recorded in the speculative-decoding section above. Earlier serving measurements from a separate combined RDNA4 stack are excluded from this standalone-branch result.

Suggested focused run before marking ready:

.venv/bin/python -m pytest tests/kernels/attention/test_segmented_attention.py tests/v1/attention/test_rocm_attention_backends_selection.py tests/v1/worker/test_workspace.py tests/compile/test_config.py -v

KernelConfig migration validation (2026-10-04)

On ROCm 10 / gfx1201, using the native rocm_workspaces/10/gfx1201 environment against the PR review checkout:

• 100 focused segmented-attention kernel and backend cases passed; 62 unrelated cases were deselected. Existing tests were adapted to the new setting; no configuration-only tests were added.
• Qwen3-0.6B eager generation with default-on tuning and explicit opt-out returned identical eight-token output. Default-on tuned ten workloads with zero failures; opt-out skipped startup tuning. This is a startup/generation smoke check, not a model-quality benchmark.
• Python configuration, dotted CLI and JSON CLI forms passed. Ruff lint, formatting and configuration validation passed.

.venv/bin/python -m pytest tests/kernels/attention/test_segmented_attention.py \
    tests/v1/attention/test_rocm_attention_backends_selection.py -k segmented -q

The review checkout reused native compiled extensions from the gfx1201 workspace; only Python source changed. Existing serving and model-quality results above remain historical measurements.

Shared kernel warmup migration (2026-10-04)

Segmented attention tuning now lives in vllm/model_executor/warmup/rocm_segmented_attn_autotune_warmup.py and is invoked by kernel_warmup() after KV allocation and before live graph capture. The post-load hook only retains each layer's engine configuration. Workload selection still respects target/draft model length and query limits; temporary scratch uses remaining free memory, while cache feasibility uses the allocated KV-cache budget. The KernelConfig.enable_rocm_segmented_attn_autotune setting now defaults to False following maintainer review and can be enabled with the CLI/Python options above. The moved source changes the tuning-cache identity, so old records will be regenerated.

• The focused suite passed 100 cases before removing the redundant default-on/opt-out test. The final backend run passed 14 cases (62 unrelated cases deselected); unchanged kernel correctness and tuning-cache coverage is retained. No configuration-only tests were added.
• On ROCm 10 / gfx1201, Qwen3-0.6B eager, FULL graph default-on, FULL graph opt-out, and FULL graph draft-model generation returned identical eight-token output. Each graph smoke generated twice and checked identical output. These are startup/generation checks, not model-quality benchmarks.
• At 90% GPU memory utilization, cold tuning used a 1 GiB scratch budget and the allocated 27 GiB KV cache for workload feasibility. It completed before live graph capture.
• A cached draft-model startup loaded ten records without retuning; generation proposed 12 speculative tokens and accepted all 12. The same cached Qwen3-0.6B model was used for target and draft, with three speculative tokens.
• Full repository pre-commit checks passed, including Ruff, mypy, and configuration validation. Python source reused the native gfx1201 workspace's compiled extensions.

Graph smoke configuration: BF16, max_model_len=128, max_num_seqs=1, max_num_batched_tokens=16, compilation_config={"mode": 0, "cudagraph_mode": "FULL", "cudagraph_capture_sizes": [1, 2, 4]}. Default/opt-out used gpu_memory_utilization=0.9; the draft smoke used 0.3.

Default-off startup validation (2026-10-04)

Following maintainer review, enable_rocm_segmented_attn_autotune defaults to False. Explicit opt-in still tunes the selected backend.

On native ROCm 10 / gfx1201 (Radeon AI PRO R9700, one GPU), Qwen3-0.6B BF16 passed FULL graph startup and two greedy eight-token prompts in every condition below, with identical tokens. Settings: context 128, one sequence, 16 batched tokens, 90% GPU utilization, compilation mode 0, FULL graph capture sizes [1, 2, 4]. These are single-run smoke measurements, not serving or model-quality benchmarks; model weights were already local.

Cache condition	Autotune	Engine initialization	Process-to-ready
Empty tuning and Triton caches	Enabled	61.38 s	80.73 s
Empty tuning and Triton caches	Disabled	23.17 s	42.93 s
Warm tuning and Triton caches	Enabled	4.80 s	17.65 s
Warm Triton cache	Disabled	4.53 s	17.39 s
Warm Triton cache, no kernel-config override	Default disabled	4.57 s	17.14 s

Cold tuning selected ten workloads with zero failures in 38.64 s. Cached tuning loaded ten records and tuned zero. Engine initialization covers profiling, KV allocation, and model warmup; process-to-ready also includes imports and model loading. The final default-path startup skipped tuning. Repository pre-commit checks passed; no configuration-only tests were added.

Reproduction: construct LLM(model="Qwen/Qwen3-0.6B", dtype="bfloat16", attention_config={"backend": "ROCM_SEGMENTED_ATTN"}, max_model_len=128, max_num_seqs=1, max_num_batched_tokens=16, gpu_memory_utilization=0.9, seed=42, compilation_config={"mode": 0, "cudagraph_mode": "FULL", "cudagraph_capture_sizes": [1, 2, 4]}), and generate "The capital of France is" twice with SamplingParams(max_tokens=8, temperature=0). For explicit cases, pass kernel_config={"enable_rocm_segmented_attn_autotune": True/False}; use fresh VLLM_CACHE_ROOT and TRITON_CACHE_DIR for the cold cases.

AI assistance

AI assistance was used for implementation, cleanup, and preparation of this draft. The human submitter will review every changed line and run relevant tests and model-quality evaluations before requesting review.

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CI selector (shadow): 185 test steps (261 jobs) instead of 86 (132 jobs)

Shadow mode: this changes nothing about what CI runs. It shows what the evidence-based selector would pick for this PR, next to today's rules. How it works.

Feedback welcome: reply here if it would skip a step this change needs, or runs something unrelated.

steps (jobs)	Today's rules	Selector	Would skip	Would add
NVIDIA, CPU and others	86 (132)	185 (261)	1 (1)	100 (130)
AMD mirrors	84 (130)	158 (222)	0 (0)	74 (92)

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• gemm-rs-ar-2xb200
• glm5next-unit-tests
• hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
• hybrid-ssm-nixlconnector-pd-prefix-cache-2-gpus
• inkling-unit-tests-b200
• jit-monitor-no-runtime-jit
• kernels-attention-diffkv-test-h100
• kernels-attention-test ×7
• kernels-b200 ×3
• kernels-core-operation-test ×3
• kernels-deepgemm-test-h100
• kernels-fla-ops-test-b200
• kernels-flashmla-test-h100
• kernels-fusedmoe-layer-test-2-b200s
• kernels-fusedmoe-layer-test-2-h100s
• kernels-helion-test ×5
• kernels-mamba-test
• kernels-mhc-test-b200
• kernels-minimax-reduce-rms-test-2-gpus
• kernels-moe-test ×5
• kernels-quantization-test ×6
• kernels-root-misc-test-b200
• kimi-k3-prefix-cache-4xb200 ×2
• kimi-k3-unit-tests-b200
• kv-offload-large
• kv-offload-medium
• kv-offload-small
• language-models-tests-extra-standard ×2
• language-models-tests-granite-l4-compatibility
• language-models-tests-hybrid ×2
• language-models-tests-standard
• lm-eval-dspark-watermark-2xh100
• lm-eval-small-models
• lm-eval-turboquant-k3v4nc
• lm-eval-turboquant-k8v4
• lm-eval-turboquant-t3nc
• lm-eval-turboquant-t4nc
• lm-eval-watermarking
• lora ×4
• lora-tp-distributed ×4
• metrics-tracing-2-gpus
• model-executor
• mooncake-ec-tcp-e2e-2-gpus
• mrcr-eval-small-models
• multi-modal-accuracy-eval-small-models
• multi-modal-models-standard-1-qwen2
• multi-modal-models-standard-2-qwen3-gemma
• multi-modal-models-standard-3-llava-qwen2-vl
• multi-modal-models-standard-4-other-whisper
• multi-modal-processor ×4
• multi-modal-processor-cpu ×4
• multiconnector-nixl-offloading-pd-accuracy-2-gpus
• multiconnector-nixl-offloading-pd-edge-cases-2-gpus
• nixlconnector-pd-edge-cases-2-gpus
• openai-api-correctness
• pipeline-context-parallelism-4-gpus
• platform-tests
• plugin-tests-2-gpus
• push-nixlconnector-pp-prefill-pd-accuracy-4-gpus
• pytorch-compilation-dynamic-shapes
• pytorch-compilation-passes-unit-tests
• pytorch-compilation-unit-tests
• pytorch-compilation-unit-tests-h100
• pytorch-fullgraph-cudagraph-l4-compatibility
• pytorch-fullgraph-test
• quantization ×4
• quantized-fusions
• quantized-models-test
• quantized-moe-test-b200
• qwen4-exp-unit-tests
• qwen4-exp-unit-tests-cpu
• rayexecutorv2-4-gpus
• regression
• replayssm-e2e
• rust-frontend-core-correctness
• rust-frontend-distributed
• rust-frontend-openai-coverage
• rust-frontend-serve-admin-coverage
• rust-frontend-tool-use
• samplers-multimodal-beam-search
• samplers-test
• scale-out-ec-e2e-2-gpus
• sharded-rdt-weight-transfer
• spec-decode-draft-model ×4
• spec-decode-eagle-1-deepseek-qwen
• spec-decode-eagle-2-llama3-qwen-vl-other
• spec-decode-mtp-deepseek-mimo
• spec-decode-mtp-gemma4
• spec-decode-mtp-qwen3-5
• spec-decode-ngram-suffix
• spec-decode-speculators
• v1-attention-b200 ×2
• v1-attention-h100-mi300 ×2
• v1-core
• v1-executor-worker
• v1-kv-connectors ×4
• v1-kv-offload
• v1-logits-oracle
• v1-metrics-lmeval
• v1-others-cpu
• v1-sample
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• vllm-ir-tests

Would skip (today's rules run them) (1)

• ascend-npu-test

Would add (today's rules do not run them) (100)

• amd-fp8-moe-kernels-mi355 (code map)
• amd-kernels-mi355 (code map)
• amd-native-quantization-kernels-mi355 (code map)
• arm-cpu-test ×3 (code map)
• cpu-distributed-tests-dp-tp (code map)
• cpu-distributed-tests-pp-tp (code map)
• cpu-kernel-tests ×2 (code map)
• cpu-multi-modal-model-tests-n ×4 (code map)
• cpu-quantization-model-tests (code map)
• cpu-qwen2-5-vl-multimodal-tests (code map)
• cpu-spec-decode-tests (code map)
• crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus (code map)
• cudagraph (code map)
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• deepseek-v4-kernel-test-h100 (code map)
• distributed-comm-ops (code map)
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• distributed-compile-unit-tests-2xh100 (code map)
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• distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus (code map)
• distributed-mooncakeconnector-pd-accuracy-4-gpus (code map)
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• distributed-tests-8xh100 (code map)
• distributed-torchrun-examples-4-gpus (code map)
• distributed-torchrun-shutdown-tests-2-gpus (code map)
• docker-build-metadata (code map)
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• extract-hidden-states-integration (code map)
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• fault-tolerance-e2e-2xh100 (code map)
• fusion-and-compile-unit-tests-2xb200 (code map)
• fusion-e2e-config-sweep-h100 (code map)
• fusion-e2e-tp2-ar-rms-config-sweep-h100 (code map)
• fusion-e2e-tp2-asynctp-config-sweep-h100 (code map)
• gemm-rs-ar-2xb200 (code map)
• glm5next-unit-tests (code map)
• hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus (code map)
• hybrid-ssm-nixlconnector-pd-prefix-cache-2-gpus (code map)
• inkling-unit-tests-b200 (code map)
• kernels-attention-diffkv-test-h100 (code map)
• kernels-core-operation-test ×3 (code map)
• kernels-deepgemm-test-h100 (code map)
• kernels-flashmla-test-h100 (code map)
• kernels-helion-test ×5 (code map)
• kernels-mamba-test (code map)
• kernels-minimax-reduce-rms-test-2-gpus (code map)
• kernels-quantization-test ×6 (code map)
• kimi-k3-prefix-cache-4xb200 ×2 (code map)
• kimi-k3-unit-tests-b200 (code map)
• kv-offload-large (code map)
• kv-offload-medium (code map)
• kv-offload-small (code map)
• lm-eval-small-models (code map)
• lm-eval-turboquant-k3v4nc (code map)
• lm-eval-turboquant-k8v4 (code map)
• lm-eval-turboquant-t3nc (code map)
• lm-eval-turboquant-t4nc (code map)
• lm-eval-watermarking (code map)
• lora ×4 (code map)
• lora-tp-distributed ×4 (code map)
• mooncake-ec-tcp-e2e-2-gpus (Python record)
• mrcr-eval-small-models (code map)
• multi-modal-accuracy-eval-small-models (Python record)
• multiconnector-nixl-offloading-pd-accuracy-2-gpus (code map)
• multiconnector-nixl-offloading-pd-edge-cases-2-gpus (code map)
• nixlconnector-pd-edge-cases-2-gpus (code map)
• openai-api-correctness (code map)
• platform-tests (code map)
• plugin-tests-2-gpus (code map)
• push-nixlconnector-pp-prefill-pd-accuracy-4-gpus (code map)
• quantization ×4 (code map)
• quantized-fusions (code map)
• quantized-models-test (code map)
• quantized-moe-test-b200 (code map)
• qwen4-exp-unit-tests (code map)
• qwen4-exp-unit-tests-cpu (code map)
• rayexecutorv2-4-gpus (code map)
• replayssm-e2e (code map)
• rust-frontend-core-correctness (code map)
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• rust-frontend-openai-coverage (code map)
• rust-frontend-serve-admin-coverage (code map)
• rust-frontend-tool-use (code map)
• samplers-multimodal-beam-search (code map)
• samplers-test (code map)
• scale-out-ec-e2e-2-gpus (code map)
• sharded-rdt-weight-transfer (code map)
• spec-decode-draft-model ×4 (code map)
• spec-decode-eagle-1-deepseek-qwen (code map)
• spec-decode-eagle-2-llama3-qwen-vl-other (code map)
• spec-decode-ngram-suffix (code map)
• vllm-ir-tests (code map)

AMD mirrors: would skip (0)

none

AMD mirrors: would add (74)

• crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus (code map)
• cudagraph (code map)
• deepseek-v4-kernel-test-h100 (code map)
• distributed-comm-ops (code map)
• distributed-compile-comm-4-gpus (code map)
• distributed-compile-rpc-tests-2-gpus (code map)
• distributed-compile-unit-tests-2xh100 (code map)
• distributed-dp-tests-2-gpus (code map)
• distributed-dp-tests-4-gpus (code map)
• distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus (code map)
• distributed-mooncakeconnector-pd-accuracy-4-gpus (code map)
• distributed-nixlconnector-pd-accuracy-4-gpus (code map)
• distributed-tests-8xh100 (code map)
• distributed-torchrun-examples-4-gpus (code map)
• distributed-torchrun-shutdown-tests-2-gpus (code map)
• docker-build-metadata (code map)
• dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus (code map)
• engine-1-gpu (code map)
• entrypoints-unit-tests (code map)
• eplb-algorithm (code map)
• eplb-execution (code map)
• examples (code map)
• extract-hidden-states-integration (code map)
• fusion-e2e-config-sweep-h100 (code map)
• fusion-e2e-tp2-asynctp-config-sweep-h100 (code map)
• gemm-rs-ar-2xb200 (code map)
• glm5next-unit-tests (code map)
• hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus (code map)
• hybrid-ssm-nixlconnector-pd-prefix-cache-2-gpus (code map)
• inkling-unit-tests-b200 (code map)
• kernels-attention-diffkv-test-h100 (code map)
• kernels-core-operation-test ×3 (code map)
• kernels-flashmla-test-h100 (code map)
• kernels-helion-test ×5 (code map)
• kernels-mamba-test (code map)
• kernels-minimax-reduce-rms-test-2-gpus (code map)
• kimi-k3-unit-tests-b200 (code map)
• kv-offload-large (code map)
• kv-offload-medium (code map)
• kv-offload-small (code map)
• lm-eval-turboquant-k3v4nc (code map)
• lm-eval-turboquant-k8v4 (code map)
• lm-eval-turboquant-t3nc (code map)
• lm-eval-turboquant-t4nc (code map)
• lm-eval-watermarking (code map)
• lora ×4 (code map)
• lora-tp-distributed ×4 (code map)
• mooncake-ec-tcp-e2e-2-gpus (Python record)
• multi-modal-accuracy-eval-small-models (Python record)
• multiconnector-nixl-offloading-pd-accuracy-2-gpus (code map)
• multiconnector-nixl-offloading-pd-edge-cases-2-gpus (code map)
• nixlconnector-pd-edge-cases-2-gpus (code map)
• nixlconnector-pd-spec-decode-acceptance-2-gpus (code map)
• plugin-tests-2-gpus (code map)
• push-nixlconnector-pp-prefill-pd-accuracy-4-gpus (code map)
• quantization ×4 (code map)
• quantized-fusions (code map)
• quantized-models-test (code map)
• qwen4-exp-unit-tests (code map)
• rayexecutorv2-4-gpus (code map)
• rust-frontend-core-correctness (code map)
• rust-frontend-distributed (code map)
• rust-frontend-openai-coverage (code map)
• rust-frontend-serve-admin-coverage (code map)
• rust-frontend-tool-use (code map)
• samplers-multimodal-beam-search (code map)
• samplers-test (code map)
• scale-out-ec-e2e-2-gpus (code map)
• sharded-rdt-weight-transfer (code map)
• spec-decode-draft-model ×4 (code map)
• spec-decode-eagle-1-deepseek-qwen (code map)
• spec-decode-eagle-2-llama3-qwen-vl-other (code map)
• spec-decode-ngram-suffix (code map)
• vllm-ir-tests (code map)

_{9 changed files · base 6ad2e06c4a · head 2d07478000 · Python record: build 92956 at 18c8a65edd · kernel record: table 18c8a65 (build 92956), map 18c8a65 · not counted: 10 build steps, 5 A100 steps the generator no longer emits, 136 optional steps the selector would also run}

[big-yellow-duck]

/ci retry

[github-actions]

✅ Queued 1 failed job(s) for retry in Buildkite CI #92809.

[big-yellow-duck]

/ci retry

[github-actions]

✅ Triggered Buildkite CI #93047 for commit 2d0747800098, running 1 failed step(s) from Buildkite CI #92809.

[ydawei]

Appreciation from the field: ROCM_SEGMENTED_ATTN rescues hybrid MoE decode on RDNA4. 8.8 → 68 t/s at 32K context on a single R9700.

If you run a hybrid linear-attention model (GDN/Mamba layers plus a few full-attention layers) on an AMD RDNA GPU, the regular ROCm decode path collapses at long context. The attention block size gets inflated to fit GDN state pages (my log line: Setting attention block size to 1056 tokens), the block is not a power of two, vLLM refuses the optimized paged_attention_rocm kernel and falls back to generic Triton paged decode, and decode degrades with context length until it is unusable. This is the same collapse described in #50264 .

With this backend, on llama-benchy (--pp 2048 --tg 512 --depth 0 32768 --runs 3, concurrency 1):

config	decode t/s	decode t/s @ 32K ctx	prefill @ 32K (t/s)	TTFT @ 32K
vllm/vllm-openai-rocm:latest (v0.31.0), backend auto	49.1 ± 0.1	8.8	2024	17.2 s
nightly (v0.31.1rc1.dev88+g81198e97b), backend auto	50.1	9.5	2152	14.7 s
nightly + --attention-backend ROCM_SEGMENTED_ATTN	73.8 ± 0.1	68.2 ± 0.2	4718	6.7 s

That is 7.8× faster decode at 32K context and 2.6× faster time-to-first-token, on the identical model, image, and flags. The auto rows were measured on the same machine minutes apart, so the comparison is a clean A/B. Note the backend only exists in builds newer than this PR's merge, so a latest image from before Oct 6 does not have it.

Environment:

• GPU: Radeon AI PRO R9700 32 GB (RDNA4), single card
• vLLM ROCm Docker nightly, v0.31.1rc1.dev88+g81198e97b
• Model: cyankiwi/Qwen3.6-35B-A3B-AWQ-4bit (hybrid GDN + attention MoE, AWQ INT4, head_dim 256)
• Flags: --gpu-memory-utilization 0.97 --max-num-seqs 16 --enable-chunked-prefill --max-num-batched-tokens 2048 --attention-backend ROCM_SEGMENTED_ATTN, prefix caching on, KV cache auto-fit to 239,712 tokens

Thank you @big-yellow-duck for building this and to @cadamcat whose profiling in #50264 laid out the mechanism. The hybrid-block-alignment plus generic-Triton-fallback path has degraded my Qwen3.6-35B-A3B slot repeatedly since v0.27 (0.27.0: 9.1 t/s at 32K, recovered by 0.29, 8.8 again on 0.31.0). This is the first fix that holds, and the 32K-context tier of my local serving stack is usable again.

[big-yellow-duck]

glad to hear the you're getting good gains. more good things coming soon