[ROCm][Spec Decode] Enable adaptive DSpark verification for MiniMax M3 by olka-amd · Pull Request #56451 · vllm-project/vllm

olka-amd · 2026-09-11T12:01:18Z

Summary

Enables adaptive DSpark verification for MiniMax M3 on ROCm. Part of the adaptive DSpark backend expansion (#51303): each model needs varlen-capable sparse kernels before adaptive verification can redistribute draft tokens across requests.

Scope: only the ROCm Triton indexer and Triton sparse-decode paths gain ragged decode support (validated on MI325/gfx942 and MI355X/gfx950). The CUDA backends, the AITER MSA indexer / AITER sparse PA, the ROCm NVFP4 sparse path and the indexer CP path keep reporting device/CPU query-length mismatch as unsupported, so the selector keeps adaptive verification off there. With adaptive verification off, the uniform decode path is unchanged.

Changes

MiniMax M3 indexer and sparse attention (ROCm Triton): device-side ragged decode metadata. Both AMD index-score launch policies, the fused top-k selector and sparse decode map packed query tokens to requests from device query_sta rt_loc, using upstream's compute_token_to_req_indices (a device-side binary search that doesn't depend on max_query_len and handles zero-length padding rows). Shared builder logic lives in models/minimax_m3/common/varlen_decode.py.
Cudagraphs: the builders stay UNIFORM_BATCH and advertise the bounded varlen contract (get_varlen_cudagraph_max _query_len = 1 + num_speculative_tokens) only when adaptive verification is enabled. Mixed prefill+decode batches
never replay FULL graphs.
supports_device_cpu_query_lens_mismatch() on the MiniMax indexer/sparse backends is true only when the base rule
holds and the builder has ragged decode kernels.
kernel_paged_attention_2d: return for query_len != 1 (previously > 1). FULL cudagraph replay pads the request dimension with zero-length rows, which fell into the decode path and read/wrote the row one past the last real token. ROCM_ATTN reports ALWAYS, so it replays the ragged decode graphs adaptive verification produces
Indexer CP (minimax_m3_indexer_cp) rejects adaptive verification explicitly; its decode scores uniform rows.

The DSpark confidence-head config fallback that was here earlier removed

Testing

tests/kernels/attention/test_minimax_m3.py (incl. ragged index top-k and ragged sparse decode with zero-length padding rows, TP1–TP8 head shapes): pass on MI325 (gfx942) and MI355X (gfx950). The ragged index top-k test covers every gfx950 indexer decode launch path (balanced, high-batch and default score launches; single- and 16-chunk topk=16 selector).
tests/kernels/attention/test_minimax_m3_varlen_cudagraph.py, test_minimax_m3_indexer_cp.py: pass.
test_prefix_prefill.py::test_contexted_kv_attention (now with a trailing zero-length row): pass on gfx942 and gfx950.
tests/v1/spec_decode/test_adaptive_verification.py, tests/v1/worker/test_gpu_rejection_sampler_chunking.py, tests/v1/worker/test_attn_utils.py: pass.
Stress test of the ragged index top-k and sparse decode kernels at serving-sized batches: 60 random batches of 380
–512 requests (1–8 query tokens each, up to 4K context, zero-length padding rows), reference-checked every 10th batch: all passed.

End-to-end results

MI325, nvidia/MiniMax-M3-DSpark drafter, num_speculative_tokens=7, FULL cudagraphs, default memory settings,
--max-model-len 4096 --max-num-seqs 512. Fixed vs adaptive verification on the same build. This drafter declares a confidence head but ships no confidence_head.* weights, so adaptive verification uses the online acceptance estimator (#52228).

GSM8K, full test set (1,319 questions, 5-shot, greedy, concurrency 64):

Checkpoint	TP	Fixed	Adaptive
`amd/MiniMax-M3-MXFP4`	2	0.8196	0.8226
`MiniMaxAI/MiniMax-M3-MXFP8`	4	0.8779	0.8711
`MiniMaxAI/MiniMax-M3` (BF16)	8	0.8704	0.8688

Repeated MXFP8 runs vary by about ±0.01 for both modes (fixed 0.860–0.878 over 6 runs, adaptive 0.867–0.879 over 9 runs).

MI355X (gfx950), amd/MiniMax-M3-MXFP4, TP=2, same drafter and K=7, AITER MXFP4 MoE, FULL cudagraphs, --max-model-len 32768. main (fixed verification) vs this PR (adaptive verification):

Eval	main (fixed K=7)	Adaptive
GPQA diamond, 0-shot CoT, flexible-extract	0.7929	0.7929
NIAH single 1/2/3 @ 4K/8K/16K, mean of 9	0.987	0.986

Sonnet 550 in / 150 out / 200 shared prefix, ignore EOS, output tok/s (adaptive gain vs fixed):

Concurrency	MXFP4, TP=2	MXFP8, TP=4	BF16, TP=8
128	649.96 → 674.59 (+3.8%)	2241.79 → 2166.80 (−3.3%)	3451.90 → 3327.16 (−3.6%)
256	882.77 → 930.58 (+5.4%)	2560.18 → 2605.40 (+1.8%)	4005.66 → 4063.85 (+1.5%)
320	965.59 → 1020.85 (+5.7%)	2643.30 → 2733.47 (+3.4%)	4099.15 → 4268.66 (+4.1%)
380	1013.94 → 1071.63 (+5.7%)	2686.83 → 2778.46 (+3.4%)	4190.48 → see below
420	1040.76 → 1102.05 (+5.9%)	2713.55 → 2692.00 (−0.8%)	4263.58 → see below
500	1089.06 → 1146.61 (+5.3%)	fixed OOM → 1929.71	4228.05 → see below

The gain shrinks as verification gets cheaper: TP=4 and TP=8 leave little verify cost for adaptive budgeting to saver MXFP8 fixed verification at concurrency 500 runs out of memory allocating the full verification logits (1.6 GB) at the default
--gpu-memory-utilization; adaptive verification bounds that allocation and completes all requests.

BF16, TP=8, after #62769 (latest build), c512, 1k OSL: adaptive 3,322.8 tok/s (acceptance length 2.28) vs fixed k=7 3,082.1 tok/s (acceptance length 3.3); GSM8K 0.961 vs 0.959. The config that previously hung with GPU Hang at concurrency ≥ 380 now passes.

Earlier results (before rebase)

MXFP4 target with the olka-fi/MiniMax-M3-MXFP4-DSpark drafter, TP=2, same Sonnet workload, 3–5 minute steady-state runs; 9,040 requests, none failed.

Concurrency	Fixed tok/s	Adaptive tok/s	Gain	Fixed median TPOT	Adaptive median TPOT
128	299.82	330.13	+10.1%	381.78 ms	342.55 ms
256	413.86	479.84	+15.9%	500.45 ms	406.09 ms
320	471.08	573.31	+21.7%	570.16 ms	446.81 ms
380	488.89	603.86	+23.5%	615.22 ms	478.64 ms
420	528.18	658.30	+24.6%	645.89 ms	489.64 ms
500	568.84	731.51	+28.6%	667.91 ms	521.15 ms

github-actions · 2026-09-11T12:02:45Z

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mergify · 2026-09-12T15:40:14Z

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

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

ChuanLi1101 · 2026-10-15T02:32:06Z

Please take 6.4–17.1% and 40.7% out of the summary. 6.4–17.1% is the NVIDIA default-head series, and both arms drop at c500 (fixed 568 → 459, adaptive 638 → 493). 40.7% is only the head-disabled c128 point (221 → 311); c256 in that same series is +10.6%.

GSM8K 0.8167 → 0.8467 on 300 questions is about 9 extra correct answers. Zero invalid outputs. That is not a quality win.

Two code points:

This enables the ROCm Triton indexer and sparse decode only. CUDA and AITER still reject the device/CPU query-length mismatch.
scatter_token_to_req_indices covers max_query_len tokens per request. Adaptive verification moves draft budget onto one request, so that value has to be the max query length after the move. If it is still the CPU value from before the move, the extra tokens keep a stale request id.
The query_len != 1 return in paged decode is the right fix for the zero-length FULL-graph padding rows. Keep it with the ALWAYS cudagraph change.

Also please convert to regular PR and report FP8/FP16 numbers.

mergify · 2026-10-18T08:22:13Z

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

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Signed-off-by: Oleksandr Kachur <oleksandr.kachur@amd.com>

olka-amd · 2026-10-19T07:53:18Z

Thanks for the review Richard. Updated:

Please take 6.4–17.1% and 40.7% out of the summary. 6.4–17.1% is the NVIDIA default-head series, and both arms drop at c500 (fixed 568 → 459, adaptive 638 → 493). 40.7% is only the head-disabled c128 point (221 → 311); c256 in that same series is +10.6%.

Removed the 6.4–17.1% and 40.7% figures. Since Nvidia confidence head is miscalibrated I expect better performance gains with OOB confidence estimator that was recently merged. Will add tests for this one as well

GSM8K 0.8167 → 0.8467 on 300 questions is about 9 extra correct answers. Zero invalid outputs. That is not a quality win.

Done, Rerun on full GSM8K task set and updated description now says only that accuracy is preserved, with no claim of a quality gain

Two code points:

This enables the ROCm Triton indexer and sparse decode only. CUDA and AITER still reject the device/CPU query-length mismatch.

Summary updated: now it states that only the ROCm Triton indexer and sparse decode support ragged decode; CUDA and AITER still reject the query-length mismatch, so adaptive verification stays off there.

scatter_token_to_req_indices covers max_query_len tokens per request. Adaptive verification moves draft budget onto one request, so that value has to be the max query length after the move. If it is still the CPU value from before the move, the extra tokens keep a stale request id.

PR no longer adds scatter_token_to_req_indices. It uses main's compute_token_to_req_indices, a device-side binary search over query_start_loc that doesn't depend on max_query_len. Separately, the max_query_len passed to attention metadata on the adaptive path is max(num_scheduled_tokens_upper_bound), the pre-compaction length including every scheduled draft, and reallocate_drafts caps each request at its scheduled draft count. So the post-move length can't exceed it.

The query_len != 1 return in paged decode is the right fix for the zero-length FULL-graph padding rows. Keep it with the ALWAYS cudagraph change.

Paged decode: kept the query_len != 1 return, together with the FULL cudagraph path.

Also please convert to regular PR and report FP8/FP16 numbers.

Done.

olka-amd · 2026-11-03T12:10:44Z

test	Adaptive	Fixed k=7
GSM8K	0.961	0.959
c512, 1k OSL	3,322.8 tok/s, acceptance len=2.28	3,082.1 tok/s acceptance len=3.3

The M3 BF16 adaptive run (TP8, nvidia drafter) is passing now, including the config that hung 3 of 3 times on the old build

shen-shanshan · 2026-11-03T13:25:52Z

/ci retry

github-actions · 2026-11-03T13:27:09Z

✅ Queued 1 failed job(s) for retry in Buildkite CI #99540.

simondanielsson

Thanks for the updates! Nice work

Suggestion: can we also run gpqa and niah accuracy vs main?

Kind reminder: Let's keep the PR description up to date, currently it looks quite outdated

simondanielsson · 2026-11-04T15:19:44Z


+    @classmethod
+    def supports_varlen_decode(cls) -> bool:
+        return current_platform.is_rocm()


Suggestion: can we make sure to test this also on cdna4, alt limit this to cdna3?

we may need to make the code change in MiniMaxM3IndexerMetadataBuilder

Instead of limiting MiniMaxM3IndexerTritonMetadataBuilder.supports_varlen_decode() to CDNA3, I extended test_decode_index_topk_varlen_with_padded_rows to cover each of those paths with ragged batches (random 1–8 query tokens per request plus zero-length padding rows) for topk 6 and 16: 16/16 pass on MI355X. The rest of test_minimax_m3.py, test_minimax_m3_varlen_cudagraph.py, test_minimax_m3_indexer_cp.py and test_prefix_prefill.py::test_contexted_kv_attention also pass on gfx950

If you'd still prefer a CDNA3 gate - happy to do that

simondanielsson · 2026-11-04T15:21:36Z

        x = (
            torch.cat([hidden, markov_embed], dim=-1) if self.with_markov else hidden
        ).float()
        return self.proj(x).squeeze(-1)


this PR touches confidence head config format by modelopt which

Can you remind me why we do this here? Or could that be a separate PR

simondanielsson · 2026-11-04T15:23:41Z

+        # Varlen decode replays decode-only batches of 1 to 1 + K tokens per
+        # request; mixed prefill+decode batches never replay FULL graphs.
+        if not cls._use_varlen_decode(vllm_config) or (
+            cls.get_cudagraph_support(vllm_config, kv_cache_spec)  # type: ignore[attr-defined]


Question: is this mypy skip safe?

simondanielsson · 2026-11-04T15:29:11Z

-        if cur_batch_query_len > 1:
+        # FULL graphs may pad the request dimension with zero-length rows.
+        # This kernel handles exactly one query token per request.
+        if cur_batch_query_len != 1:


Question: is this strictly related to this PR?

Yes. FULL graphs pad the request dimension with zero-length rows (query_start_loc[num_reqs+1:] = num_tokens). With > 1, those rows fall through to the decode path and that was the issue that popped up during implementation

simondanielsson · 2026-11-04T15:31:50Z


+    @classmethod
+    def supports_device_cpu_query_lens_mismatch(cls) -> bool:
+        return cls.get_builder_cls().supports_varlen_decode()


Suggestion: I don't think is backwards compatible (see the implementation we override here) and will break cuda, should we do

super().supports_device_cpu_query_lens_mismatch() or cls.get_builder_cls().supports_varlen_decode()

simondanielsson · 2026-11-04T15:35:48Z

                device=device,
            )
+        self.varlen_decode = self._use_varlen_decode(vllm_config)
+        self.token_to_req_buffer = torch.empty(


Minor: for these, should we only allocate it if using varlen decode? Currently it will allocate these uncondintionally

simondanielsson · 2026-11-04T15:36:45Z

+    module = sys.modules[__name__]
+    monkeypatch.setattr(module, "NUM_Q_HEADS", num_q_heads)
+    monkeypatch.setattr(module, "NUM_KV_HEADS", num_kv_heads)
+    torch.manual_seed(0)


Nit: set_random_seed is preferred here 👍

simondanielsson · 2026-11-04T15:38:24Z

+
+
+def test_aiter_msa_indexer_builder_does_not_claim_varlen():
+    indexer_msa = pytest.importorskip("vllm.models.minimax_m3.amd.indexer_msa")


Nit: not sure if we rather be explicit about when to skip this rather than using an importskip. E.g. require cuda-alike or similar

simondanielsson · 2026-11-04T15:38:51Z

+    indexer_msa = pytest.importorskip("vllm.models.minimax_m3.amd.indexer_msa")
+    builder = indexer_msa.MiniMaxM3IndexerMSAMetadataBuilder
+    assert not builder.supports_varlen_decode()
+    assert builder.get_varlen_cudagraph_max_query_len(_vllm_config(True), None) is None


Minor: can this and the test below be a single parametrized test?

mergify · 2026-11-07T16:51:47Z

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

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

… DSpark verification - Compose supports_device_cpu_query_lens_mismatch() with the base rule. - Allocate the token-to-request buffer only for varlen decode. - Drop the mypy ignore in VarlenDecodeBuilderMixin. - Move the DSpark confidence-head config fallback to its own PR. - Tests: set_random_seed, plain imports, one parametrized test for the uniform-only ROCm builders, backend-level query-lens gate test. Signed-off-by: Oleksandr Kachur <oleksandr.kachur@amd.com>

# Conflicts: # tests/kernels/attention/test_minimax_m3.py # vllm/models/minimax_m3/common/indexer.py # vllm/models/minimax_m3/common/sparse_attention.py

…_drafting Signed-off-by: Oleksandr Kachur <oleksandr.kachur@amd.com>

…tches The bf16 head_dim 128 score kernel picks the balanced, high-batch or default launch by request count on gfx950, and the topk=16 selector takes a short single chunk or 16 chunks. Parametrize the ragged index top-k test over batch shapes and topk so each of those paths sees varlen metadata. Signed-off-by: Oleksandr Kachur <oleksandr.kachur@amd.com>

olka-amd · 2026-11-11T14:59:57Z

/ci run

github-actions · 2026-11-11T15:00:27Z

✅ Triggered Buildkite CI #101830 for commit ddc78f508142.

ℹ️ This PR is 8 commits behind upstream main. Merged upstream main into this branch as ddc78f508142 before starting CI. Pull the branch before pushing more changes.

olka-amd · 2026-11-11T15:03:39Z

Thanks for the updates! Nice work

Suggestion: can we also run gpqa and niah accuracy vs main?

Kind reminder: Let's keep the PR description up to date, currently it looks quite outdated

Eval	main (fixed K=7)	PR (adaptive)
GPQA diamond, 0-shot CoT, flexible-extract (198 q)	0.7929 ± 0.029	0.7929 ± 0.029
NIAH single_1 @ 4K / 8K / 16K	0.998 / 1.000 / 1.000	1.000 / 1.000 / 1.000
NIAH single_2 @ 4K / 8K / 16K	0.996 / 0.994 / 0.994	0.994 / 0.996 / 0.988
NIAH single_3 @ 4K / 8K / 16K	0.984 / 0.954 / 0.964	0.966 / 0.962 / 0.964
NIAH mean (9 cells, 500 samples each)	0.987	0.986

olka-amd · 2026-11-11T15:50:18Z

/ci retry

github-actions · 2026-11-11T15:50:37Z

✅ Queued 2 failed job(s) for retry in Buildkite CI #101830.

gfx942 runs fp32 tl.dot on xf32 MFMA, so the indexer's block scores can differ from the exact fp32 reference by a few 1e-3. With topk=16 that can swap two blocks that tie the 16th-best score. Compare the kernel's selection against the reference block scores instead: a block may differ from the reference only when its score is within tolerance of the k-th best, and padding tokens must select nothing. Signed-off-by: Oleksandr Kachur <oleksandr.kachur@amd.com>

…n' into pr56451-review3

olka-amd · 2026-11-11T17:49:28Z

/ci run

github-actions · 2026-11-11T17:49:58Z

✅ Triggered Buildkite CI #101895 for commit 33cf319a196b.

ℹ️ This PR is 12 commits behind upstream main. Merged upstream main into this branch as 33cf319a196b before starting CI. Pull the branch before pushing more changes.

dllehr-amd

Thanks @olka-amd!

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[ROCm][Spec Decode] Enable adaptive DSpark verification for MiniMax M3

d02968e

Signed-off-by: Oleksandr Kachur <oleksandr.kachur@amd.com>

olka-amd force-pushed the amd/minimax-m3-dspark-varlen-main branch from 28489dd to d02968e Compare October 19, 2026 07:16

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olka-amd marked this pull request as ready for review October 19, 2026 07:53

olka-amd requested review from dllehr-amd, hongxiayang, mgoin, tdoublep, tjtanaa, tlrmchlsmth and vadiklyutiy as code owners October 19, 2026 07:53

Fangzhou-Ai mentioned this pull request Nov 3, 2026

[ROCm][Perf][MiniMax-M3.1] Pick the NVFP4 sparse decode splits per XCD, not per CU #65481

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olka-amd added 4 commits November 11, 2026 11:47

Merge branch 'main' into pr56451-review3

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# Conflicts: # tests/kernels/attention/test_minimax_m3.py # vllm/models/minimax_m3/common/indexer.py # vllm/models/minimax_m3/common/sparse_attention.py

[Test] Update MiniMax M3 varlen config stub for max_num_new_slots_for…

e630f08

…_drafting Signed-off-by: Oleksandr Kachur <oleksandr.kachur@amd.com>

mergify Bot removed the needs-rebase label Nov 11, 2026

Merge branch 'main' into amd/minimax-m3-dspark-varlen-main

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olka-amd added 2 commits November 11, 2026 16:16

Merge remote-tracking branch 'origin/amd/minimax-m3-dspark-varlen-mai…

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…n' into pr56451-review3

Merge branch 'main' into amd/minimax-m3-dspark-varlen-main

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		def test_aiter_msa_indexer_builder_does_not_claim_varlen():
		indexer_msa = pytest.importorskip("vllm.models.minimax_m3.amd.indexer_msa")

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