[Mamba] NVIDIA GB10 and B200 tuned selective_state_update configs and benchmark tooling by bananighosh · Pull Request #41398 · vllm-project/vllm

bananighosh · 2026-04-30T18:06:35Z

Summary

Adds an auto-tuning framework for selective_state_update that loads per-GPU JSON configs at runtime, falling back to the existing hard-coded heuristics when no config is present
Adds tuned dstate configs (16/32/64/128/256) for NVIDIA GB10 and B200 GPUs under vllm/model_executor/layers/mamba/configs/<device_name>/
Adds benchmarks/kernels/benchmark_selective_state_update.py with a --validate flag to generate and verify configs on any GPU
Adds config loader unit tests (tests/kernels/mamba/test_mamba_ssm_configs.py)

Benchmark Results — NVIDIA B200 (bfloat16)

dstate	Batch	Heuristic (µs)	Tuned (µs)	Speedup
16	1024	143.94	71.89	2.00x
32	1024	281.23	131.35	2.14x
64	1024	579.86	236.13	2.46x ← peak
128	1024	670.24	487.34	1.38x
256	256	465.50	290.42	1.60x

Validation

NVIDIA B200: 55/55 configs passed (11 batch sizes × 5 dstates, atol=0.01)
NVIDIA GB10: 55/55 configs passed (11 batch sizes × 5 dstates, atol=0.01)

Test plan

pytest tests/kernels/mamba/test_mamba_ssm_configs.py -v — 6/6 passed
Benchmark and config generation validated on NVIDIA GB10 and B200 hardware

Notes

Config lookup mirrors the fused_moe pattern (respects VLLM_TUNED_CONFIG_FOLDER env override)
No behaviour change on GPUs without a config file — falls back to original heuristic
The benchmark script can generate configs for any GPU, not just B200/GB10

Closes #33034

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github-actions · 2026-04-30T18:07:03Z

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Code Review

This pull request introduces a tuning and benchmarking framework for the Mamba selective_state_update kernel, enabling optimized launch configurations based on GPU type, dstate, and batch size. It includes a new benchmarking script, pre-tuned configurations for NVIDIA B200 and GB10 GPUs, and logic in the model executor to load these configurations at runtime with a fallback to existing heuristics. Feedback focuses on improving the robustness of the JSON configuration loader by adding type checks and handling empty configuration files to prevent potential runtime crashes.

… and B200 configs - Add benchmark and --validate script for generating tuned configs on any GPU - Add tuned dstate configs (16/32/64/128/256) for NVIDIA GB10 and B200 - Update mamba_ssm.py ops to support config-driven kernel selection - Add per-GPU subfolder config structure under vllm/model_executor/layers/mamba/configs/ - Add config loader unit tests with edge case coverage - Add type guard for non-dict JSON config files (prevents AttributeError) - Fix empty config dict crash path in _get_ssm_launch_config (prevents ValueError) Signed-off-by: Banani Ghosh <bg2502@nyu.edu>

bananighosh · 2026-04-30T19:40:33Z

@danisereb This PR implements the feature requested in #33034 — tuning script and JSON configs for selective_state_update.

I have tested the implementation on NVIDIA B200 (achieving up to 2.46x speedup) and GB10.

Could you please add the ready label to trigger CI? I am happy to address any feedback.

tomeras91 · 2026-05-05T12:53:14Z

General comment about config search space

I'd like to push for a more careful framing of what we tune over. The kernel grid is (cdiv(headdim, BLOCK_SIZE_M), batch, nheads), with BLOCK_SIZE_DSTATE = next_pow2(dstate) auto-set (the kernel processes the full dstate dimension per program - no chunked-K-style accumulator, so BLOCK_SIZE_DSTATE isn't a tunable knob). Each of headdim, nheads, dstate, batch, and the SSM-cache dtype affects the optimal (BLOCK_SIZE_M, num_warps), but for distinct reasons:

dstate sets the upper bound on BLOCK_SIZE_M (register-pressure ceiling). Per-program tile is BLOCK_SIZE_M × next_pow2(dstate) elements; at large dstate the tile pressure forces BLOCK_SIZE_M down. This is what the existing heuristic captures (32→16→8→4 as dstate grows).
headdim caps BLOCK_SIZE_M at headdim itself - BLOCK_SIZE_M > headdim is wasted (the kernel mask zeros out elements past headdim). With headdim=64 (universal in deployed Mamba2), this caps BLOCK_SIZE_M at 64.
batch × nheads × cdiv(headdim, BLOCK_SIZE_M) is the total grid count, which sets another upper bound on BLOCK_SIZE_M via wave-quantization. To saturate the SMs we need enough programs (cdiv(headdim, BLOCK_SIZE_M) × effective_batch ≥ saturation_threshold), which translates to BLOCK_SIZE_M ≤ headdim × effective_batch / threshold. This bound is tight at small effective_batch (forces BLOCK_SIZE_M small to keep program count high) and loose at large effective_batch (lets BLOCK_SIZE_M grow until the dstate ceiling binds instead).

Optimal BLOCK_SIZE_M is then under all three upper bounds — dtype/dstate (register pressure), headdim (mask cap), and saturation (wave-quantization) — bounded below only by per-program scheduling overhead.

batch and nheads enter the grid symmetrically - (batch=1, nheads=128) saturates the GPU the same as (batch=128, nheads=1). So tuning per effective_batch = batch × nheads is sufficient and portable across models with the same (headdim, dstate, dtype) but different nheads. Tuning per raw batch silently bakes in whichever nheads was used at tuning time.

Concrete proposal:

File key: (headdim, dstate) - both are model-constant and set the BLOCK_SIZE_M bounds. Filename mirrors MoE convention: headdim=<H>,dstate=<D>,device_name=<device>.json.
In-file key: effective_batch = batch × nheads - captures the wave-quantization axis that varies across runtime (batch) and across models (nheads).
Tunable params: (BLOCK_SIZE_M, num_warps) only.

Representative values for the sweep, based on currently-deployed Mamba2-family models:

Model	nheads	headdim	dstate
Codestral Mamba 7B	128	64	128
Bamba 9B	128	64	128
Nemotron3 Nano 30B A3B	64	64	128
Nemotron3 Super 120B A12B	128	64	128
Granite-4 H-Tiny	48	64	128
Granite-4 H-Small	128	64	128

So:

headdim = 64 covers all current models using Mamba2 I'm aware of.
dstate = 128 covers all current models using Mamba2 I'm aware of.
This basically means we have only 1 file for each hardware. Still good to keep headdim and dstate in filename for future-proofing, or for when we want to add older Mamba1 models using smaller dstate.
nheads ∈ {48, 64, 128} is a 3× spread - confirms effective_batch keying matters in practice.
batch_size ∈ {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024} - reasonable batch sizes
I'd suggest to have the tuning script accept both nheads and batch_size as inputs (the natural model-and-deployment parameterization), and compute effective_batch as set[b*n for b,n in itertools.product(batch_size, nheads)]. With the values above, that produces 23 unique effective_batch points.

As for candidate ranges for tunable BLOCK_SIZE_M and num_warps, I'd suggest:

BLOCK_SIZE_M ∈ {4, 8, 16, 32, 64} - (same as you already have) lower bound from per-program overhead, upper bound from headdim
num_warps ∈ {1, 2, 4, 8} (same as you already have)

That's 1(headdim) × 1(dstate) × 23(effective_batch) = 23 entries per GPU × 20 candidate (BLOCK_SIZE_M, num_warps) combos = ~460 timing runs.

Note: In some cases, a different SSM state dtype is used (FP32, FP8), which can also affect the optimal config. We can expand and add config files per dtype in the future.

tomeras91

Thanks for this! This looks good overall, but I do have som suggestions for improvement:

config search space
See dedicated comment: #41398 (comment)
config file layout
I suggest to match the convention of the MoE configs, and use a flat layout. So don't group configs into folders by hardware, but rather use a flat layout like headdim=<H>,dstate=<D>,device_name=<device>.json
config loader code
see inline comments
triton version logging
Similar to how it's done for MoE configs, I think it's worth adding triton_version metadata to the config jsons (and strip them when loading). Worth documenting this as different triton versions can result in different optimal configs.
Tuning script architecture
Generally, I would like to better match the MoE tuning script architecture. We can add:
5.1. @ray.remote class BenchmarkWorker for multi-GPU parallel tuning
5.2. _distribute(method, inputs) helper
5.3. tqdm progress bar
5.4. [optional] A BenchmarkConfig TypedDict to hold the tunable params
The biggest gap is enabling multi-GPU parallelism via ray
config override mechanism
You're current implementation of the benchmarking script uses unittest.mock.patch.object to override the config when calling selective_state_update. It's probably not best to not use testing mechanisms in this code. MoE solves this issue with an override_config context manager. Can we add something similar here?
correctness validation
You added a validation step to the benchmarking script, to make sure the selected config doesn't result with any accuracy issues. This is nice! Yet, you added another reference implementation for selective state update. Such reference implementations already exist in the repo (for example, in tests/kernels/mamba/test_mamba_ssm.py). I would prefer we re-use existing code instead of adding duplications.

Overall: many of the comments above are different angles on the same suggestion - let's mirror the FusedMoE config conventions where we can. It's the closest existing analog and aligning with it pays off in maintainability.

tomeras91 · 2026-05-05T07:23:30Z

+    config_file_paths: list[str] = []
+
+    # User-supplied override (same env-var as fused_moe)
+    user_dir = os.environ.get("VLLM_TUNED_CONFIG_FOLDER")


use envs.VLLM_TUNED_CONFIG_FOLDER instead of direct os.environ access

tomeras91 · 2026-05-05T09:46:11Z

+                raw = json.load(f)
+                if isinstance(raw, dict):
+                    return {int(k): v for k, v in raw.items()}
+


In case no config was found, can we add a logger.warning_once("Using default config. Performance might be sub-optimal!"), similar to what's done for MoE configs?

tomeras91 · 2026-05-05T09:50:38Z

+        cfg = configs[closest]
+        return cfg["BLOCK_SIZE_M"], cfg["num_warps"]
+
+    # ---- original hard-coded heuristic (unchanged) ----


nit: maybe worth wrapping this defaults logic in its own function

tomeras91 · 2026-05-05T09:50:41Z

+    return None
+
+
+def _get_ssm_launch_config(


Can we rename this function? Currently the name is too similar to get_ssm_configs and it causes confusion.. Maybe something like try_get_optimal_ssm_config, mimicking the MoE design?

I think this function should be @lru_cache-ed as well.. we can save finding the optimal config per batch size for each block, as well as the default config logic.

danisereb · 2026-05-19T11:21:07Z

Hey @bananighosh
Thanks for the PR!

We want to merge this soon, so I'll finish the remaining work in another PR:
#43083

My PR has your commit/changes and my changes.

@tomeras91 is aware and will review my new PR.

bananighosh · 2026-05-20T16:05:24Z

@danisereb Thank you so much for carrying this forward and integrating my changes into #43083!

@tomeras91 Really appreciate the detailed reviews and feedback — the suggestions were very insightful. Looking forward to contributing more in this area.

mergify · 2026-05-24T17:38:38Z

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

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

bananighosh · 2026-06-04T18:27:40Z

Closing this as this implementation is pulled and merged via #43083

bananighosh requested review from WoosukKwon, mgoin, tdoublep, tlrmchlsmth, tomeras91 and yewentao256 as code owners April 30, 2026 18:06

claude Bot reviewed Apr 30, 2026

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mergify Bot added performance Performance-related issues nvidia labels Apr 30, 2026

github-project-automation Bot added this to NVIDIA Apr 30, 2026

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Comment thread vllm/model_executor/layers/mamba/ops/mamba_ssm.py Outdated

Comment thread vllm/model_executor/layers/mamba/ops/mamba_ssm.py Outdated

bananighosh force-pushed the mamba-ssm-b200-configs branch from c67b313 to a038fca Compare April 30, 2026 19:12

tomeras91 reviewed May 5, 2026

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danisereb mentioned this pull request May 19, 2026

Tuning script and configs for Triton Mamba SSU kernel #43083

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mergify Bot added the needs-rebase label May 24, 2026

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Summary

Benchmark Results — NVIDIA B200 (bfloat16)

Validation

Test plan

Notes

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