warmup: add batch=3 to MTP uniform decode kernel warmup

[lennytinkeredapps]

Problem

When concurrency climbs to 3+ requests, 10 Triton JIT kernels in the sparse-MLA decode attention path compile mid-inference, causing ~20s latency spikes:

Attention/decode — 3+ batch:

• _build_combined_decode_valid_mask_kernel
• _finish_materialized_scores_with_sink_candidate_block_kernel
• _indexed_d512_split_score_kernel
• _indexed_d512_split_stats_kernel
• _indexed_d512_split_value_kernel
• _accumulate_fp8ds_global_slots_attention_chunk_multihead_kernel
• _finish_two_attention_states_with_sink_kernel
• _indexed_d512_chunked_merge_acc_kernel
• _indexed_d512_chunked_merge_state_kernel
• _accumulate_indexed_attention_chunk_multihead_kernel

Root Cause

_DEEPSEEK_V4_MTP_UNIFORM_DECODE_WARMUP_REQUESTS = (1, 2, 4, 8, 16, 24, 32) skips batch size 3. The _dummy_run(uniform_decode=True) warmup therefore never exercises the decode attention path at batch=3, so the kernels above are uncompiled until the first 3-concurrent request.

Fix

Add 3 to the warmup tuple: (1, 2, **3**, 4, 8, 16, 24, 32).

This ensures _deepseek_v4_sparse_mla_attention_warmup() calls runner._dummy_run(uniform_decode=True) with num_reqs=3, which triggers the full sparse-MLA decode attention forward path and compiles all listed kernel shapes during startup.

Verification

• Logic verified: _deepseek_v4_mtp_uniform_decode_warmup_requests() now returns (1, 2, 3, 4, 8, 16, 24, 32, 256) for the c256 case
• Updated test_deepseek_v4_kernel_warmup.py assertions to expect 3 in all tuples
• All existing test assertions still pass with updated expected values

Context

Observed on DeepSeek-V4-Flash EP=2 on dual GB10 (SM12x) with 1M context / 32.3GB KV recipe.

Supplements jasl PR vllm-project#41834 — extends warmup coverage to the 3-batch decode shapes.

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[jasl]

I need to validate it this Friday

[erictinkeredapps]

DeepSeek-V4-Flash-GB10.yaml

Including our recipe. Has comments / documentation included. We've put some mileage on it at this point - let us know if you need us to look at anything specific.

Appreciate your efforts jasl, Thank you!!

[jasl]

Thanks @lennytinkeredapps — good catch that the warmup tuple (1, 2, 4, 8, 16, 24, 32) skips 3, and the diff is clean (the union logic at kernel_warmup.py does produce (1, 2, 3, 4, 8, 16, 24, 32, 256) for the c256 case, so the test update is internally consistent).

I validated it on our dual-GB10 (SM121) box today with a JIT-coverage gate, and I want to share the result before merging, because it changes what the right fix is.

Gate result: with the baseline warmup (no batch-3), driving concurrency-3 MTP decode produced 0 in-inference JIT compiles of the decode kernels. So on our MTP2 config, batch=3 is not an uncovered shape — there's nothing left to warm.

Here's why, from the decode path:

• The decode kernels' Triton compile key is batch-independent. num_tokens only feeds the launch grid (grid=(num_tokens, …), token_idx = tl.program_id(0)); Triton does not specialize cubins on grid dims. Every shape constexpr (strides, num_heads, num_candidates, HEAD_DIM, BLOCK_C) is per-token / per-head.
• The one batch-driven constexpr is HEAD_BLOCK, from sparse_mla_decode_head_block_size(num_decode_tokens) (sparse_mla_kernels.py): 1 for ≤4, 2 for 4 < x < 16, 4 for ≥16. It keys off num_decode_tokens = num_reqs * query_len, not num_reqs. For MTP2 (query_len = 3): num_reqs=2 → 6 tok → HB=2, and num_reqs=3 → 9 tok → HB=2 — the same cubin that num_reqs=2 already warms. So the existing tuple already covers all three HEAD_BLOCK regimes (1/2/4) and both use_dot_finish branches (the ≤16 split).
• Also, 6 of the 10 kernels you listed (_indexed_d512_split_{score,stats,value}, _indexed_d512_chunked_merge_{acc,state}, _accumulate_indexed_attention_chunk_multihead) are actually prefill kernels — only reached from _forward_sparse_mla_prefill_triton (kv.shape[0] == 1) and warmed by _deepseek_v4_indexed_d512_split_prefill_warmup, not by this decode-tuple edit. So the decode-tuple change can't move those regardless.

So the change is safe and rebases cleanly, but on MTP2 it's a no-op — which means it wouldn't eliminate the ~20 s spike you saw, and I don't want to merge something that looks like a fix but isn't.

The deciding question is your exact config. Could you share:

1. Your spec-decode setting — num_speculative_tokens (MTP2? something else?), or non-spec decode?
2. The startup warmup log line (… MTP uniform decode requests=[…], or the equivalent) from your run — that tells us exactly which num_decode_tokens got warmed.

If you're on a query_len where batch=3 lands on a real HEAD_BLOCK boundary (i.e. num_reqs * query_len crosses 4 or 16 at num_reqs=3), then 3 genuinely helps and I'll merge it — though in that case the more robust fix is to make the warmup boundary-aware (guarantee HEAD_BLOCK {1, 2, 4} + the ≤16 dot-finish split for the deployed query_len), which I'm happy to put up as a follow-up. If you're on MTP2 like our gate, the spike is coming from somewhere else and we should chase that instead.

And thanks @erictinkeredapps for attaching DeepSeek-V4-Flash-GB10.yaml — we'll review the recipe and fold anything useful into our canonical GB10 serve config.

[lennytinkeredapps]

jasl — here is the warmup log from our dual-GB10 run:

kernel_warmup.py:583: Warming up DeepSeek V4 sparse MLA attention for
mixed tokens=16, prefill tokens=512, and MTP uniform decode requests=[1, 2, 4, 5].

kernel_warmup.py:642: Warming up DeepSeek V4 MTP spec-decode kernels
for request counts=[1, 2, 4, 5] and 2 draft tokens.

kernel_warmup.py:497: Warming up DeepSeek V4 D512-split sparse-MLA prefill kernels
for combined_topk widths=[256, 384, 512, 640] (heads=32, padded_q_heads=64).

Our warmup tuple is [1, 2, 4, 5], not your (1, 2, 4, 8, 16, 24, 32). At our MTP2 (query_len=3): all four warmup request counts land on HEAD_BLOCK ≤ 2 — we never reach HEAD_BLOCK=4. Your tuple hits it at reqs=6+. That alone could explain why your gate sees zero JIT and ours sees compiles.

The prefill side: our max_num_batched_tokens=512 means chunked prefill works in 512-token slices, and the warmup covers combined_topk widths at exactly that chunk size. Production requests arrive with different prompt lengths → different chunk counts, and we see JIT of _compute_prefill_metadata_kernel, _build_prefill_chunk_metadata_kernel, and the _indexed_d512_* family when the chunk geometry does not match the warmup shape. Your gate at 4096 mnbt gets one big prefill chunk per warmup request — different geometry, no surprise you see zero there either.

So our candidate for the real spike: the warmup tuple does not cover all HEAD_BLOCK regimes, and the 512-token prefill chunking creates shapes the warmup does not exercise. The boundary-aware warmup you offered (guarantee HEAD_BLOCK {1, 2, 4} + dot-finish split for the deployed query_len) plus extending the prefill warmup to cover the actual chunk counts we see in production would be the right fix — and we would be happy to test it.

[jasl]

Sorry, I forgot to ask you that I've improved the warmup in codex/ds4-sm120-min-enable branch, could you try it?

[lennytinkeredapps]

jasl — thanks you! We'll try the codex/ds4-sm120-min-enable branch on our dual-GB10 setup.

Will report back with JIT coverage and gen throughput numbers once we've got it running.

[jasl]

The upstream/main requires a specified NV-forked DeepGEMM dependency, for which you have to patch the Makefile and the unreleased FlashInfer, so it actually doesn't work.

[lennytinkeredapps]

JIT cleared at about 4 minutes after the first request. Gen steady at 37-39 tok/s single-stream, prefix cache at 65%. No new JIT warnings after that last _accumulate_indexed_attention_chunk_multihead_kernel at 13:45.

Four minutes from cold start to clean serving. The old container took 15+. Your expanded warmup plus the upstream fp8 kernel paths cut the JIT tax by nearly 75%.

Logs attached and thank you! Let us know if you want us to test anything else we push the GB10s pretty hard inference wise (we're probably good testers if you need us for future adventures).

startup-logs-through-JIT.txt

[jasl]

JIT cleared at about 4 minutes after the first request. Gen steady at 37-39 tok/s single-stream, prefix cache at 65%. No new JIT warnings after that last _accumulate_indexed_attention_chunk_multihead_kernel at 13:45.

Four minutes from cold start to clean serving. The old container took 15+. Your expanded warmup plus the upstream fp8 kernel paths cut the JIT tax by nearly 75%.

Logs attached and thank you! Let us know if you want us to test anything else we push the GB10s pretty hard inference wise (we're probably good testers if you need us for future adventures).

startup-logs-through-JIT.txt

Thank you! I'll dig your logs tomorrow

[lennytinkeredapps]

Closing — jasl incorporated the warmup feedback in codex/ds4-sm120-min-enable with expanded kernel coverage and updated warmup tuples. We just built and validated the new branch: 4 minutes from cold start to clean serving, 54-57 tok/s at 2 concurrent, JIT tax down 75% from prior builds. Thanks jasl for the collaboration — the merge from upstream/main plus the SM12x fixes is exactly what we needed.