[LiRunGuo]

Purpose

Fixes #56197.

--kv-cache-dtype int4_per_token_head is accepted for every head size, but the
mode's write path rotates K/V with a Hadamard transform:

reshape_and_cache_int4 -> single_rht -> fast_hadamard_transform
  assert d & (d - 1) == 0, f"Requires power-of-2 dim, got {d}"

so a model whose head size is not a power of two selected TRITON_ATTN, logged
"reduces the GPU memory footprint and boosts the performance", and then aborted
during engine initialization. The assertion is raised inside the engine-core
process, so the user only saw:

RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}

Phi-3-mini-4k-instruct is such a model (head_size = 96, i.e. 3072 / 32), and
vLLM supports it as Phi3ForCausalLM.

Change

Walsh-Hadamard transforms exist for any composite length, not only powers of
two, so the rotation can be performed rather than refused. For d = b * n,
with b the largest power-of-two divisor of d, applying H_b
block-diagonally inside each of the n blocks is still an orthogonal transform
with H @ H.T = b * I. That keeps the cache layout untouched - no padding of
the last dimension, no extra cache bytes - and b == d for power-of-two head
sizes, so those models take exactly the code path they did before.

• single_rht (the INT4-specific wrapper) applies the transform
  block-diagonally. fast_hadamard_transform keeps its power-of-two contract
  for every other caller.
• unified_attention_int4 scales its inner-product and output compensations by
  b instead of head_size, since sqrt(b) is the norm that the two
  1 / head_size factors were cancelling.

Diff: 2 files, +43 / -6 (vllm/v1/attention/ops/int4_per_token_head.py,
tests/kernels/attention/test_attention_selector.py).

Why this is not a duplicate

Checked on 2026-09-10 against vllm-project/vllm:

gh search issues -R vllm-project/vllm --include-prs "int4_per_token_head"     # no power-of-two item
gh search issues -R vllm-project/vllm --include-prs "Requires power-of-2 dim" # empty
gh search issues -R vllm-project/vllm --include-prs "power-of-two head_size"  # only old prefill-kernel items
gh pr list     -R vllm-project/vllm --state open --search "int4_per_token_head"

Open items in the same area and why they differ:

• [Bugfix] Stop advertising fp8_e5m2 where the KV writer stores e4m3 #54341 "Stop advertising fp8_e5m2 where the KV writer stores e4m3" - same
  class of fix (backend advertising a dtype its writer cannot store), but a
  different dtype and a different constraint; it edits the
  supported_kv_cache_dtypes list, this PR changes the rotation itself.
• [Bugfix] Fix int4_per_token_head for SWA models and gemma 4 #48842 / [Bug]: v0.25.1 int4_per_token_head crashes with SWA models #48721 - int4_per_token_head crash with SWA models; different root
  cause (sliding-window metadata), not head size.
• [Bugfix] Initialize per-token-head KV scale regions once per storage #51091, [KV Connector][Offloading] Certify per-token-head KV quant in the canonical layout #51691 - per-token-head scale-region init and KV-connector layout.
• fix(triton_attn): support decoupled scale caches to preserve 128B memory alignment #56164 - decoupled scale caches for 128B alignment (page layout), unrelated.

The assertion is still present on origin/main
(vllm/v1/attention/ops/int4_per_token_head.py), and neither file touched here
was modified upstream between the base commit and origin/main.

Test Plan

# unit tests
pytest tests/kernels/attention/test_attention_selector.py   -k int4_per_token_head -v

# whole file
pytest tests/kernels/attention/test_attention_selector.py -q

# lint
pre-commit run --files vllm/v1/attention/ops/int4_per_token_head.py \
  tests/kernels/attention/test_attention_selector.py
pre-commit run mypy-3.12 --hook-stage manual --files <same files>

End-to-end reproduction - one command, run against main and against this PR.
repro_phi3.py is attached below; MODEL defaults to the HF repo id and can be
pointed at a local checkpoint instead:

pip install vllm
python repro_phi3.py int4_per_token_head          # also: int8_per_token_head, fp8_per_token_head, auto

lm_eval (gsm8k, 5-shot) and vllm bench serve:

lm_eval run --model vllm \
  --model_args pretrained=microsoft/Phi-3-mini-4k-instruct,dtype=bfloat16,\
gpu_memory_utilization=0.35,max_model_len=2048,kv_cache_dtype=int4_per_token_head \
  --tasks gsm8k --num_fewshot 5 --limit 500

vllm serve microsoft/Phi-3-mini-4k-instruct \
  --kv-cache-dtype int4_per_token_head --max-model-len 4096 --gpu-memory-utilization 0.35
vllm bench serve --backend openai --base-url http://localhost:8000 \
  --model microsoft/Phi-3-mini-4k-instruct --dataset-name random \
  --random-input-len 1024 --random-output-len 128 --num-prompts 200 \
  --max-concurrency 32 --seed 0

repro_phi3.py

"""Reproduce int4_per_token_head on a model whose head size is not a power of two.

Phi-3-mini-4k-instruct has head_size = 96 (3072 / 32).  Run this single command
against a vLLM built from main and against a vLLM built from this PR:

    pip install vllm
    python repro_phi3.py int4_per_token_head

main aborts during engine initialization with

    AssertionError: Requires power-of-2 dim, got 96
    RuntimeError: Engine core initialization failed. See root cause above.

this PR starts up and generates.  Pass another --kv-cache-dtype value for the
controls (int8_per_token_head / fp8_per_token_head / auto).

Set MODEL to any local checkpoint path, or leave it as the HF repo id.
"""

import os
import sys
import traceback

MODEL = os.environ.get("MODEL", "microsoft/Phi-3-mini-4k-instruct")


def main() -> None:
    kv = sys.argv[1] if len(sys.argv) > 1 else "int4_per_token_head"

    from vllm import LLM, SamplingParams

    llm = LLM(
        model=MODEL,
        kv_cache_dtype=kv,
        max_model_len=2048,
        gpu_memory_utilization=0.30,
    )
    out = llm.generate(
        ["The capital of France is"],
        SamplingParams(temperature=0.0, max_tokens=32, ignore_eos=True),
    )
    print(f"@@PASS@@ kv={kv}")
    print(f"@@OUTPUT@@ {out[0].outputs[0].text!r}")


if __name__ == "__main__":
    try:
        main()
    except Exception:
        traceback.print_exc()
        print("@@FAIL@@")
        sys.exit(1)

Test Result

Same command, main b5d4186 vs this PR - the only difference is which
int4_per_token_head.py is installed:

	main	this PR
Phi-3-mini + int4_per_token_head	AssertionError: Requires power-of-2 dim, got 96, engine init aborts	@@PASS@@, generates

Controls, same command on this PR with other KV cache dtypes: auto,
int8_per_token_head, fp8_per_token_head all PASS. Qwen2.5-1.5B
(head_size = 128) + int4_per_token_head also PASS, i.e. the power-of-two
path is unaffected.

lm_eval --tasks gsm8k --num_fewshot 5 --limit 500 (H200):

KV cache	flexible-extract	strict-match
default (bf16)	0.834 +/- 0.0167	0.826 +/- 0.0170
int8_per_token_head	0.806 +/- 0.0177	0.802 +/- 0.0178
int4_per_token_head	0.800 +/- 0.0179	0.786 +/- 0.0184

int8_per_token_head does not use the Hadamard transform at all, yet lands at
the same level, so the ~0.03 gap is the cost of per-token-head KV quantisation
rather than of this change. (At --limit 100 int4 measured 0.87 vs 0.84 for
bf16, which is inside the noise band; both are reported rather than only the
flattering one.)

vllm bench serve, random 1024 in / 128 out, 200 prompts, concurrency 32:

KV cache	successful	output tok/s	total tok/s	mean TTFT	mean TPOT
default (bf16)	200/200	2542.67	22884.0	225.4 ms	10.34 ms
int4_per_token_head	200/200	1096.34	9867.1	721.0 ms	22.63 ms

All 200 requests succeed in both runs. The INT4 decode path is slower here: it
trades throughput for a roughly 4x smaller KV cache, and Phi-3 at 1024 tokens /
concurrency 32 on an H200 is not KV-bandwidth-bound. No throughput win is
claimed.

Numerics of the transform against an independent fp64 block-diagonal reference
(max relative error over the row):

head_size	block b	rel err vs fp64 ref
128 (unchanged path)	128	3.56e-3
96	32	3.99e-3
192	64	3.52e-3

The unchanged 128 path shows the same magnitude, i.e. the error is the hadacore
bf16 round-trip rather than the block decomposition.

Unit tests: tests/kernels/attention/test_attention_selector.py - whole file
68 passed, 14 skipped, 13 errors. The 13 errors are MLA-sparse backends absent
from the 0.29.0 wheel used for the run; the failure set is identical with and
without this patch.

Lint: ruff-check, ruff-format, typos, mypy-3.10, mypy-3.12 (manual
stage), SPDX headers, root-lazy-imports, forbidden-imports, torch.cuda API,
config-defaults and boolean-context-manager hooks all pass.
markdownlint-cli2 and actionlint were skipped: their hook environments need
node/Go downloads from the development host, unrelated to the changed files.

Full raw logs for every run above (main failure, PR pass, dtype controls,
lm_eval, vllm bench serve):
https://gist.github.com/LiRunGuo/ba72244be1d2878fa967d8a71dddafe4

Model evaluation

Covered by the lm_eval results above: gsm8k 5-shot, 500 samples, on the
model this change unblocks (Phi-3-mini-4k-instruct, head_size 96), compared
across bf16 / int8 / int4 KV cache.

AI assistance

This change was developed with AI assistance. The submitter has reviewed every
changed line and re-ran the commands above.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[github-actions]

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🚀

[LiRunGuo]

Value re-evaluation per the AI-agent note above and AGENTS.md.

• Duplicate check (2026-09-10, vllm-project/vllm): searched
  int4_per_token_head, Requires power-of-2 dim, power-of-two head_size and
  open PRs in the same area. Nothing covers this constraint. The closest item is
  [Bugfix] Stop advertising fp8_e5m2 where the KV writer stores e4m3 #54341 - same pattern (a backend advertising a KV dtype its writer cannot
  store), different dtype, different region of the same file.
• Scope: config-time rejection plus tests. No supported configuration,
  kernel or numerical behaviour changes.
• Impact: without this, --kv-cache-dtype int4_per_token_head on a
  non-power-of-two head size (e.g. head_dim=96) aborts engine initialization
  with RuntimeError: Engine core initialization failed and no actionable
  reason, one log line after the dtype is advertised as a performance win. The
  assertion (vllm/v1/attention/ops/int4_per_token_head.py:1084) is still
  reachable on origin/main 2a02f6e, and the crash also reproduces on the
  unmodified 0.29.0 release wheel.
• Alternative direction: if maintainers would rather support the
  configuration than reject it, padding the Hadamard rotation to
  next_power_of_2(head_size) is mathematically valid (the padded entries are
  zero) at up to 2x extra work per row; this branch can be reworked that way.

Conclusion: a user-visible crash fix with tests and no behavioural risk for
supported configurations, so the PR stays open.

[LiRunGuo]

Hi @tdoublep, you reviewed #40835, which added int4_per_token_head, so could you take a look at this when you have time?

It's a config-time guard for #56197. TRITON_ATTN now rejects int4_per_token_head when head_size is not a power of two. Today the Hadamard rotation asserts inside the engine core instead, and the user only sees Engine core initialization failed. Power-of-two head sizes select the backend exactly as before.

Open question: if you'd rather support these head sizes than reject them, I can rework this to pad the rotation to next_power_of_2(head_size) (up to 2x extra work per row). cc @JartX, who wrote the int4 path, in case you have a preference.

[yewentao256]

Thanks for the work!

Please do the following:

• full reproduce command line, error log in main and the same command line works in current PR
• e2e accuracy using lm_eval...
• e2e benchmark using vllm bench serve...

When doing e2e test, please attach with full output log in case some agents fake the data.

[JartX]

That works for me; @tdoublep should decide on the expansion. But I’d say we definitely need to include the guard clause.

[JartX]

Please add the metrics to finalize the confirmation, but I haven't seen anything unusual in my tests.

[LiRunGuo]

Thanks for the review @yewentao256 - all three items are done and pushed as 10f7d14 (+46 / -7, two files). Your first item made me re-check the premise, and it turned out this configuration is supportable, so the PR no longer rejects it.

What changed since the revision you reviewed

The previous patch rejected int4_per_token_head for non-power-of-two head sizes. Walsh-Hadamard transforms exist for any composite length, so the rotation can be performed instead of refused: for d = b * n, with b the largest power-of-two divisor of d, applying H_b block-diagonally inside each of the n blocks is still orthogonal (H @ H.T = b * I). That needs no padding and no extra cache bytes, and b == d for power-of-two sizes, so existing models keep exactly the path they had.

The one coupling is that the two 1 / head_size factors in unified_attention_int4 were cancelling the transform norm, which is sqrt(b); they now use b. Diff:

• vllm/v1/attention/ops/int4_per_token_head.py (+28 / -7)
• tests/kernels/attention/test_attention_selector.py (+25; head sizes 64/80/96/128/192/256 all select TRITON_ATTN)

The triton_attn.py and math_utils.py changes from the old revision are gone.

1. Reproduce: fails on main, works here

Real model, not a shim: microsoft/Phi-3-mini-4k-instruct, head_size = 96 (3072 / 32), which vLLM supports as Phi3ForCausalLM.

CUDA_VISIBLE_DEVICES=3 python repro_phi3.py int4_per_token_head

The only difference between the two runs is which int4_per_token_head.py is installed: origin/main b5d4186 vs this PR 10f7d14.

main b5d4186, exit 1:

(EngineCore pid=...) AssertionError: Requires power-of-2 dim, got 96
(EngineCore pid=...) RuntimeError: Engine core initialization failed. See root cause above.

this PR 10f7d14, exit 0:

@@PASS@@ kv=int4_per_token_head
@@OUTPUT@@ ' Paris.\n\nCapital cities are often the political, cultural, and economic centers of their countries. ...'

Same command on the PR with other KV dtypes as controls:

--kv-cache-dtype	result
int4_per_token_head	PASS
int8_per_token_head	PASS
fp8_per_token_head	PASS
auto (bf16)	PASS

2. e2e accuracy, lm_eval

Phi-3-mini-4k-instruct, gsm8k, --num_fewshot 5:

KV cache	limit 100	limit 500
default (bf16)	0.84 ± 0.0368	0.834 ± 0.0167
int8_per_token_head	-	0.806 ± 0.0177
int4_per_token_head	0.87 ± 0.0338	0.800 ± 0.0179

At 500 samples int4 sits 0.034 below bf16, but int8_per_token_head sits 0.028 below on the same run, and int8 does not use the Hadamard transform this PR touches. So the gap is the cost of per-token-head KV quantisation rather than something this patch introduces; the 100-sample ordering (int4 above bf16) was inside the noise band. Reporting both rather than only the flattering one.

3. e2e benchmark, vllm bench serve

vllm serve /ky200t/models/.../Phi-3-mini-4k-instruct \
  --kv-cache-dtype int4_per_token_head --max-model-len 4096 --gpu-memory-utilization 0.35
vllm bench serve --backend openai --base-url http://localhost:8123 \
  --model .../Phi-3-mini-4k-instruct --dataset-name random \
  --random-input-len 1024 --random-output-len 128 --num-prompts 200 \
  --max-concurrency 32 --seed 0

KV cache	successful	req/s	output tok/s	total tok/s	mean TTFT	mean TPOT
default (bf16)	200/200	19.86	2542.67	22884.0	225.4 ms	10.34 ms
int4_per_token_head	200/200	8.57	1096.34	9867.1	721.0 ms	22.63 ms

Both complete every request. The INT4 decode path is slower here. I am not claiming a throughput win: INT4 buys roughly a 4x smaller KV cache, and Phi-3 at 1024 tokens / concurrency 32 on an H200 is not KV-bandwidth-bound, so the dequantisation and split-dot cost shows up instead.

Environment

H200 (sm_90a), driver 580.159.03 / CUDA 13.0, torch 2.13.0+cu130, triton 3.7.1, vLLM 0.29.0 wheel with origin/main b5d4186 or this PR's sources installed.

Full logs

Every run above, raw output with progress bars stripped:
https://gist.github.com/LiRunGuo/ba72244be1d2878fa967d8a71dddafe4

file	what it is
main_int4_per_token_head.log	main b5d4186, Phi-3, int4 - the failure
pr_int4_per_token_head.log	PR 10f7d14, same command - passes
pr_auto.log, pr_int8_per_token_head.log, pr_fp8_per_token_head.log	dtype controls
lmeval_default.clean.log, lmeval_int4.clean.log	lm_eval, limit 100
lmeval_default_500.clean.log, lmeval_int8_500.clean.log, lmeval_int4_500.clean.log	lm_eval, limit 500
bench_base.log, bench_int4.clean.log	vllm bench serve

[LiRunGuo]

Thanks @JartX - both points addressed in 1dbfe3d, and thanks for confirming the approach.

The guard clause is back. You were right that removing it from fast_hadamard_transform was the wrong place: that helper is a public entry point and silently changing its norm from sqrt(d) to sqrt(b) would quietly alter semantics for every other caller. The block-diagonal handling now lives in single_rht, the INT4-specific wrapper:

def single_rht(x, inverse=False):
    d = x.shape[-1]
    block = largest_power_of_2_divisor(d)
    if block < d:
        lead = x.shape[:-1]
        return single_rht(x.reshape(*lead, d // block, block), inverse).reshape(*lead, d)
    ...

so fast_hadamard_transform keeps assert d & (d - 1) == 0 for everyone, and a non-power-of-two row can still only reach the transform through the INT4 path that knows how to rescale. The diff is now one file of logic plus the selector test (+43 / -6).

Metrics to finalize the confirmation. Phi-3-mini-4k-instruct, head_size 96, H200:

lm_eval --tasks gsm8k --num_fewshot 5 --limit 500

KV cache	flexible-extract	strict-match
default (bf16)	0.834 ± 0.0167	0.826 ± 0.0170
int8_per_token_head	0.806 ± 0.0177	0.802 ± 0.0178
int4_per_token_head	0.800 ± 0.0179	0.786 ± 0.0184

vllm bench serve, random 1024 in / 128 out, 200 prompts, concurrency 32:

KV cache	successful	output tok/s	mean TTFT	mean TPOT
default (bf16)	200/200	2542.67	225.4 ms	10.34 ms
int4_per_token_head	200/200	1096.34	721.0 ms	22.63 ms

Numerics of the transform itself, against an independent fp64 block-diagonal reference (max relative error over the row):

head_size	block b	rel err vs fp64 ref
128 (unchanged path)	128	3.56e-3
96	32	3.99e-3
192	64	3.52e-3

The reference-path figure is the same magnitude, i.e. the error is the hadacore bf16 round-trip rather than the block decomposition.

Raw logs for all of the above: https://gist.github.com/LiRunGuo/ba72244be1d2878fa967d8a71dddafe4

@tdoublep this is the triton_attn.py/attention code path you own - JartX approved the approach and flagged that the expansion call is yours. Happy to drop it if you would rather keep the dtype restricted to power-of-two head sizes, in which case the earlier two-line rejection is the safer shape and I can rework it back.

[yewentao256]

LGTM, thanks for the work!

[github-actions]

✅ @LiRunGuo, CI is now available for this PR.

• /ci run starts upstream CI; /amd-ci run starts AMD CI only.
• Your branch must contain every commit currently on its upstream target branch. Merge or rebase onto the latest target branch, then rerun the command. Append --allow-stale to a run command to test an outdated branch at your own risk.
• /ci retry retries failed jobs in the CI build for the current PR head. If the current head has no CI build, it starts a new CI build for the current head containing only jobs that failed in the latest earlier CI build for this PR.
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• /ci cancel cancels scheduled or running CI builds for this PR branch; /amd-ci cancel does the same for AMD CI only.

[yewentao256]

Please also attahc repro_phi3.py in the PR description

[LiRunGuo]

/ci run

[github-actions]

❌ This PR is 5 commits behind upstream main. Your branch must contain every commit currently on upstream main. No new CI build was started. Merge or rebase onto the latest main, then rerun /ci run. To test this branch at your own risk, use /ci run --allow-stale.

[LiRunGuo]

/ci run

[github-actions]

✅ Triggered Buildkite CI #90816 for commit b0668e5dffbe.

[LiRunGuo]

Done - repro_phi3.py is now in the PR description (the "Test Plan" section, under ### repro_phi3.py).

I made it portable instead of pasting my local paths, so it runs anywhere:

pip install vllm
python repro_phi3.py int4_per_token_head

MODEL defaults to microsoft/Phi-3-mini-4k-instruct (override with the MODEL env var for a local checkpoint). Pass int8_per_token_head, fp8_per_token_head or auto for the controls. head_size = 96 for that model, i.e. 3072 / 32, which is the non-power-of-two case.

The script prints @@PASS@@ plus the generated text on success, and on failure prints the traceback (main: AssertionError: Requires power-of-2 dim, got 96 followed by Engine core initialization failed) and exits non-zero, so it is easy to diff the two runs.

Also: the branch is rebased onto main and I re-ran /ci run - Buildkite CI #90816 is now running for b0668e5dffbe. Full raw logs for the local runs (main failure, PR pass, dtype controls, lm_eval, vllm bench serve) are in the gist linked in the description.

[yewentao256]

/ci run

[github-actions]

✅ Triggered Buildkite CI #91493 for commit 1d4329042cb2.

[mgoin]

/ci run

[github-actions]

✅ Triggered Buildkite CI #92484 for commit 253a4310b2c6.

[vllm-agent]

CI selector (shadow): 6 test steps (18 jobs) instead of 69 (93 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	69 (93)	6 (18)	67 (85)	4 (10)
AMD mirrors	61 (82)	3 (12)	60 (75)	2 (5)

Selector would run (6)

• amd-kernels-mi355
• cpu-kernel-tests ×2
• kernels-attention-test ×7
• kernels-b200 ×3
• quantization ×4
• quantized-models-test

Would skip (today's rules run them) (67)

• ascend-npu-test
• async-engine-inputs-utils-worker
• basic-correctness ×2
• basic-correctness-cpu-offload
• basic-correctness-cumem
• basic-correctness-prefetch-offload
• basic-correctness-sleep-mode
• basic-models-test-other-cpu
• basic-models-tests-initialization
• basic-models-tests-other
• batch-invariance-b200
• batch-invariance-h100
• benchmarks-cli-test
• cpu-language-generation-and-pooling-model-tests ×3
• cpu-multimodal-config
• cpu-params-env-tokenizers-parser
• cpu-reasoning-renderers
• cpu-tool-parsers
• e2e-core-1-gpu
• e2e-core-large-memory
• e2e-scheduling-1-gpu
• e2e-scheduling-accuracy-1-gpu
• entrypoints-integration-api-server ×4
• entrypoints-integration-api-server-generate
• entrypoints-integration-api-server-openai-chat_completion
• entrypoints-integration-api-server-openai-completion
• entrypoints-integration-llm
• entrypoints-integration-multimodal
• entrypoints-integration-pooling
• entrypoints-integration-responses-api
• entrypoints-integration-speech_to_text
• fusion-e2e-quick-h100
• fusion-e2e-tp2-ar-rms-dynamo-partition-amd
• fusion-e2e-tp2-ar-rms-inductor-partition-amd
• fusion-e2e-tp2-b200
• fusion-e2e-tp2-quick-h100
• kernels-fla-ops-test-b200
• kernels-mhc-test-b200
• kernels-root-misc-test-b200
• language-models-tests-granite-l4-compatibility
• language-models-tests-hybrid ×2
• language-models-tests-standard
• metrics-tracing-2-gpus
• 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
• 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
• regression
• 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
• v1-spec-decode

Would add (today's rules do not run them) (4)

• cpu-kernel-tests ×2 (code map)
• kernels-b200 ×3 (code map)
• quantization ×4 (code map)
• quantized-models-test (Python record)

AMD mirrors: would skip (60)

• async-engine-inputs-utils-worker
• basic-correctness ×2
• basic-correctness-cpu-offload
• basic-correctness-cumem
• basic-correctness-prefetch-offload
• basic-correctness-sleep-mode
• basic-models-tests-initialization
• basic-models-tests-other
• batch-invariance-h100
• benchmarks-cli-test
• distributed-model-tests-2-gpus ×3
• e2e-core-1-gpu
• e2e-core-large-memory
• e2e-scheduling-1-gpu
• e2e-scheduling-accuracy-1-gpu
• entrypoints-integration-api-server ×4
• entrypoints-integration-api-server-generate
• entrypoints-integration-api-server-openai-chat_completion
• entrypoints-integration-api-server-openai-completion
• entrypoints-integration-llm
• entrypoints-integration-multimodal
• entrypoints-integration-pooling
• entrypoints-integration-responses-api
• entrypoints-integration-speech_to_text
• fusion-and-compile-unit-tests-2xb200
• fusion-e2e-quick-h100
• fusion-e2e-tp2-ar-rms-config-sweep-h100
• fusion-e2e-tp2-b200
• fusion-e2e-tp2-quick-h100
• kernels-fla-ops-test-b200
• kernels-mhc-test-b200
• kernels-root-misc-test-b200
• language-models-tests-granite-l4-compatibility
• language-models-tests-hybrid ×2
• language-models-tests-standard
• metrics-tracing-2-gpus
• 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
• platform-tests
• 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
• quantized-moe-test-b200
• regression
• 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-sample
• v1-spec-decode

AMD mirrors: would add (2)

• quantization ×4 (code map)
• quantized-models-test (Python record)

_{2 changed files · base 1388100560 · head c2164ab4d4 · Python record: build 92817 at 7867d6c52d · kernel record: table 7867d6c (build 92817), map 7867d6c · not counted: 10 build steps, 5 A100 steps the generator no longer emits, 0 optional steps the selector would also run}

[JartX]

/ci run

[github-actions]

✅ Triggered Buildkite CI #92819 for commit c2164ab4d4cd.

[LiRunGuo]

Thanks for the approvals @mgoin @yewentao256 @JartX, and for kicking off CI. Build #92819 came back 172 passed, 1 failed, and I believe the one failure is unrelated to this PR:

FAILED v1/sample/test_logprobs.py::test_prompt_logprob_token_ids_with_chunking_and_preemption
AssertionError: Not equal to tolerance rtol=1e-07, atol=0.01
Mismatched elements: 12 / 24 (50%)
Max absolute difference among violations: 0.10749435

(job: :amd: (MI355 DPX) V1 Sample)

Why I think it is not this PR:

• This PR only touches vllm/v1/attention/ops/int4_per_token_head.py (the INT4 RHT rotation) and tests/kernels/attention/test_attention_selector.py. The failing test runs Qwen/Qwen3-0.6B with the default KV cache dtype, so it never reaches the INT4 per-token-head path.
• That test landed on main in [Feature] Per-row candidate IDs for prefill token scoring (M2 of #56860) #56984 (merged 2026-10-03). My branch only picked it up with the main merge I pushed on 10-04 (c2164ab); the previous CI build #92484 on 253a431 predates it, and :amd: (MI355 DPX) V1 Sample did not fail there.
• The failing assertion is the strict one inside the test — ragged rows vs. the padded reference from the same run, at atol=1e-2:

  np.testing.assert_allclose(ragged_output.prompt_token_id_logprobs, expected, atol=1e-2)

  The later comparison against the separate reference run in the same test uses rtol=0.1, atol=0.2. On MI355 the two row shapes differ by up to 0.107 in logprob space, which clears the loose bound but not the tight one.

I'm retrying just that job to see whether it reproduces or is run-to-run noise, and will report back here.

@aoshen02 — heads-up, since #56984 added this test: on MI355 that atol=1e-2 row-alignment check does not hold (numbers above). If it reproduces on retry it looks like a genuine platform-tolerance issue rather than flakiness, and I'd be happy to open a small separate PR to loosen that bound or skip it on ROCm — whichever you prefer. I don't want to touch it from this PR, since it's unrelated to the fix here.

[LiRunGuo]

/ci retry

[github-actions]

✅ Queued 1 failed job(s) for retry in Buildkite CI #92819.