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[Bugfix][Sampler] Fix split top-p masking every token for huge logits (thinking_token_budget + spec decode emits token 0) - #58231
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The split-row top-p pipeline (batch <= 64, top-p without top-k) found its pivot in probability space but masked in logit space with `x > log(pivot) + log(Z) + M`. For a row whose max logit M is huge, fp32 rounds that threshold to M itself, so no token passes the strict `>` and the whole row becomes -inf. Softmax then yields NaN and sampling returns token 0. thinking_token_budget forces the end-of-reasoning token by writing 1e9 into its logit. With speculative decoding (MTP) and a top_p < 1, the forced draft positions go through apply_top_k_top_p on both the V1 rejection sampler and the Model Runner V2 verifier, so instead of </think> the model emitted token 0 (<unk>) forever. Mask with the same probability-space predicate the step kernels used (`exp(x - M) / Z > pivot`), which also makes the tie handling reuse the same value, and always keep the row max. Signed-off-by: Gilad Zamoscinski <gilad.z@taboola.com> Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Confirming this in production on a different model, speculator, and GPU. Posting it in case a second repro helps with review. Setup
Symptom This matches the root cause described here. In Workaround we deployed until this lands: at container start we patch (Related, maybe the same root cause: #58390.) |
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@njhill gentle ping for a review when you have a moment, since this touches the small-batch top-p path from #54651. Short version: with There are now two independent production repros: ours (Nemotron-3.5-Lightning + MTP5, A100, v0.30.0) and @jorado's above (DeepSeek-V4-Flash + DSpark, 2× H200). We've been running the patch in production on v0.30.0 since Sep 23, and it cherry-picks cleanly onto v0.31.0. |
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Third independent repro, this time on v0.31.0 with a different model, speculator and GPU. This PR fixes it there as well. Setup
Symptom. With Kernel level. Stock
The 1e8 / 1e9 boundary matches your fp32-spacing analysis: spacing is 8 vs 64, against an offset of about −7.7. No regression on ordinary rows. On sm_110 the kept set differs from stock in 3 of 24 random batches (8 rows each, Follow-up 2 from your list (mask-and-bump) is running in our production. It is a defensive change to the MRV2 Diff against v0.31.0--- a/vllm/v1/sample/thinking_budget_state.py
+++ b/vllm/v1/sample/thinking_budget_state.py
@@ -575,8 +575,10 @@
force_tokens = async_tensor_h2d(
force_tokens_cpu, dtype=torch.long, device=device
)
- # Avoid CPU->GPU sync.
- fill = logits.new_full((len(active_indices_cpu),), 1e9)
+ # Avoid CPU->GPU sync. Mask the row instead of a 1e9 logit: with top_p < 1 the
+ # top-k/top-p kernel drops every token of a 1e9 row -> NaN -> token 0 (Thor port 0013).
+ logits.index_fill_(0, active_indices, float("-inf"))
+ fill = logits.new_zeros((len(active_indices_cpu),))
logits.index_put_((active_indices, force_tokens), fill)
return logits
--- a/vllm/v1/worker/gpu/sample/thinking_budget.py
+++ b/vllm/v1/worker/gpu/sample/thinking_budget.py
@@ -266,9 +266,11 @@
reasoning_start_token_ids_ptr,
natural_reasoning_end_token_ids_ptr,
reasoning_end_token_ids_ptr,
+ vocab_size,
START_LEN: tl.constexpr,
NATURAL_END_LEN: tl.constexpr,
END_LEN: tl.constexpr,
+ BLOCK_V: tl.constexpr,
):
token_idx = tl.program_id(0).to(tl.int64)
req_state_idx = tl.load(expanded_idx_mapping_ptr + token_idx)
@@ -362,7 +364,13 @@
end_prefix_len = prefix_len
force_token_id = tl.load(reasoning_end_token_ids_ptr + end_prefix_len)
- tl.store(logits_ptr + token_idx * logits_stride + force_token_id, 1.0e9)
+ # Force by masking the whole row (forced token 0, the rest -inf) instead of one 1e9 logit: with top_p < 1 the
+ # Triton top-k/top-p kernel drops every token of a row holding 1e9 -> NaN probs -> token 0 sampled forever
+ # (GLM-5.3: "!!!!" until max_tokens). One write per element: no intra-program write-write race.
+ row_ptr = logits_ptr + token_idx * logits_stride
+ for v0 in tl.range(0, vocab_size, BLOCK_V):
+ offs = v0 + tl.arange(0, BLOCK_V)
+ tl.store(row_ptr + offs, tl.where(offs == force_token_id, 0.0, float("-inf")), mask=offs < vocab_size)
def apply_thinking_budget(
@@ -418,7 +426,9 @@
reasoning_start_token_ids,
natural_reasoning_end_token_ids,
reasoning_end_token_ids,
+ logits.shape[1],
START_LEN=start_len,
NATURAL_END_LEN=natural_end_len,
END_LEN=end_len,
+ BLOCK_V=1024,
)Investigated with the help of an AI coding agent (Claude Code). Every number above was measured on our deployment. |
| assert (result[:, forced_token] == forced_logit).all() | ||
| assert ((result != float("-inf")).sum(-1) == 1).all() | ||
| probs = result.softmax(dim=-1) | ||
| assert not probs.isnan().any() | ||
| assert (probs.argmax(dim=-1) == forced_token).all() |
There was a problem hiding this comment.
This test also runs on CPU CI: CPU-Kernel Tests runs TestTritonTopkTopp with triton-cpu (.buildkite/hardware_tests/cpu.yaml). On CPU use_split is false (logits.device.type == "cuda"), so these rows go through the monolithic kernel. There the 1e9 logit overflows sum_exp, the not (final_pivot < max_logit) guard skips masking, and the whole row stays unmasked, so the exactly-one-kept assertion would fail.
The other three assertions hold on both paths and are the property we actually care about (the forced token still wins), so I'd keep them unconditional and gate only the split-specific one. is_cuda_alike() rather than is_cuda(), since ROCm also takes the split path:
| assert (result[:, forced_token] == forced_logit).all() | |
| assert ((result != float("-inf")).sum(-1) == 1).all() | |
| probs = result.softmax(dim=-1) | |
| assert not probs.isnan().any() | |
| assert (probs.argmax(dim=-1) == forced_token).all() | |
| assert (result[:, forced_token] == forced_logit).all() | |
| probs = result.softmax(dim=-1) | |
| assert not probs.isnan().any() | |
| assert (probs.argmax(dim=-1) == forced_token).all() | |
| if current_platform.is_cuda_alike(): | |
| # Split path only; the monolithic kernel leaves this row unmasked. | |
| assert ((result != float("-inf")).sum(-1) == 1).all() |
This is similar to what's done in test_large_batch_int64_row_offset in this file that wants to test just the split kernel.
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Thanks, applied your suggestion in daebd0f. Verified on CPU with triton-cpu (vLLM CPU build): the old test fails 4/4 at the exactly-one-kept assertion; with the change the test passes, and the full TestTritonTopkTopp class passes (97 passed, 1 skipped).
…uge_logit CPU CI runs TestTritonTopkTopp with triton-cpu, where the split top-p pipeline is not used (it requires a CUDA-alike device). The monolithic kernel leaves the huge-logit row unmasked there, so the exactly-one-kept assertion only holds on the split path. Keep the forced-token, no-NaN and argmax assertions unconditional and gate the split-specific one on current_platform.is_cuda_alike(). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Gilad Zamoscinski <gilad.z@taboola.com>
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This pull request has merge conflicts that must be resolved before it can be |
Resolve the test conflict with vllm-project#62046 by keeping both new tests (test_topp_keeps_forced_huge_logit and test_topp_ties_across_blocks). The kernel merged cleanly: the probability-space mask from this PR plus vllm-project#62046's num_preceding carry across blocks. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Gilad Zamoscinski <gilad.z@taboola.com>
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/ci run |
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✅ @zamog, CI is now available for this PR.
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✅ Triggered Buildkite CI #95714 for commit |
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/ci retry |
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✅ Queued 2 failed job(s) for retry in Buildkite CI #95714. |
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/ci run |
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✅ Triggered Buildkite CI #95736 for commit ℹ️ This PR is 2 commits behind upstream |
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/ci retry |
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✅ Triggered Buildkite CI #95842 for commit |
… (thinking_token_budget + spec decode emits token 0) (vllm-project#58231) Signed-off-by: Gilad Zamoscinski <gilad.z@taboola.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Co-authored-by: tomeras91 <57313761+tomeras91@users.noreply.github.com>
Purpose
This affects
thinking_token_budgetwith speculative decoding (MTP, on both the V1 runner and Model Runner V2) and anytop_p < 1, including a model'sgeneration_configdefault. When the budget is reached, the request emits token id 0 (<unk>for NemotronH) untilmax_tokensinstead of the forced</think>. Greedy requests andtop_p=1.0are not affected.Root cause. The bug is in the split-row top-p pipeline (#54651), not in the thinking-budget code.
_topp_sb_mask_kernelinvllm/v1/sample/ops/topk_topp_triton.pyruns on CUDA for batches of 64 rows or fewer, for rows with top-p and without an active top-k. It finds its pivot in probability space but masks in logit space:Thinking-budget forcing writes
1e9into the end-of-reasoning logit. The V1ThinkingBudgetStateHolderand the MRV2_thinking_budget_kernelboth do this, soM = 1e9. fp32 spacing at 1e9 is 64. The termlog(pivot) + log(Z)(about -7.7) is lost in the addition, sopivot_logit == M. No token passes the strict>, and the whole row becomes-inf. From there:softmaxgives NaN, the draft is rejected, andsample_recovered_tokens_kernelfalls back torecovered_id = 0._resample_kernel), argmax over an all-infrow returns index 0.Token 0 is sampled, the budget is still exceeded, forcing runs again, and the loop never ends. Throughput also drops about 3x because every draft is rejected.
Spec decode exposes the bug because the forced verification rows go through
apply_top_k_top_p: V1 viaapply_sampling_constraints, MRV2 viaSamplingStates.apply_top_k_top_p. Non-spec decoding is safe only in the default configuration, where the fused FlashInfer sampler is used. If processed-logprobs mode is on, or the FlashInfer sampler is unavailable, non-spec sampling goes through the same Triton path and hits the same bug.The monolithic
_topk_topp_kernel(batches over 64) is left alone on purpose. Itsmax_sampleestimate (avg + 10·std of a sample block) is far below 1e9, sosum_expoverflows to inf and the pivot becomes NaN. Itsnot (final_pivot < max_logit)guard then disables top-p for that row. Sampling stays correct because the 1e9 logit dominates the softmax.Fix. Mask with the same probability-space predicate the step kernels used to measure the mass:
exp(x - M) / Z > pivot. This matchespivotanddupbitwise against the values the search produced. Tie handling reuses the sameprob; it already compared in probability space for exactly this precision reason. The row max is also kept as a backstop. For ordinary logits the kept set does not change: 24 of 24 randomized rows were identical before and after underTRITON_INTERPRET=1.This is not a duplicate of #54906 or #39573. Those were reported on v0.28.1 and v0.19, which predate the split pipeline (#54651, 2026-09-03), and they describe the budget being ignored rather than token-0 output. No open PR or issue covers split top-p masking with large logits. #55414 touches the same kernel, but only to precompile variants.
Known related follow-ups (out of scope)
_topk_topp_kernelhas the same logit-space round trip (final_pivot = log(p_pivot * sum_exp) + max_*,duplicate_logit = ...). Today it fails open through the guard described above, so it does not produce wrong tokens.1e9sentinel logit. Masking every other token to-infand keeping the forced logit ("mask-and-bump") would stop producing extreme values that downstream sampling ops must tolerate.nomask = pivot * Z >= 1guard in_topp_sb_combineis stale: pivots are always strictly belowhi = 1/Z.abs(a - b) < 1e-9in the step/combine kernels versus exact equality in the mask kernel.Test Plan
New regression test
TestTritonTopkTopp::test_topp_keeps_forced_huge_logitintests/v1/sample/test_topk_topp_sampler.py:1e9and1e9/0.6(the latter models temperature 0.6);p = 0.95.It asserts that the forced logit survives, that every other token is masked (exactly one kept per row), that the softmax has no NaN, and that the argmax is the forced token.
pytest tests/v1/sample/test_topk_topp_sampler.py -vCPU kernel repro under the Triton interpreter:
topp_split_interp_harness.py. It loads the kernel file with vLLM imports stubbed and drives_topp_sb_stats_kernel,_topp_sb_step_kerneland_topp_sb_mask_kernelexactly as_apply_topp_splitdoes, withnum_sm=108for A100.End-to-end repro setup:
--reasoning-parser nemotron_v3 --speculative-config '{"method":"mtp","num_speculative_tokens":5}';thinking_token_budget: 300,temperature: 1.0,logprobs: true,skip_special_tokens: false.Related issues
top_k=20set. Rows with an active top-k go through the monolithic_topk_topp_kernel, not the split top-p path fixed here, so this PR is not expected to fix [Bug]: thinking_token_budget ignored by Model Runner V2 with Qwen3.8 NVFP4 + MTP #54906.Test Result
TRITON_INTERPRET=1(vocab 131072, p=0.95):forced 1e9giveskept=0: the whole row is-inf, and sampling returns token 0 via NaN.kept=1(the forced token), and the sampled token is 13 (</think>).top_p=0.95: the budget is reached, then every remaining token is<unk>.top_p=1.0ortop_k=50:</think>is forced at the budget and a normal answer follows.--noconftest):pytest tests/v1/sample/test_topk_topp_sampler.py -k test_topp_keeps_forced_huge_logit -v: 4 passed (splitandsplit-boundary, each with1e9and1e9_over_temp0.6).pytest tests/v1/sample/test_topk_topp_sampler.py: 135 passed, 2 skipped, 0 failed. This includes the existing Triton and FlashInfer distribution and reference tests;scipyis needed for the FlashInfer distribution tests.num_speculative_tokens=5, 1x A100, default samplingtop_p=0.95):/v1/completions,thinking_token_budget=300,skip_special_tokens=false: 0<unk>tokens.</think>is forced at the budget and a valid JSON answer follows (finish_reason=stop). Checked on BF16 weights and on FP8 weights.thinking_token_budget=1000: 212 tok/s (the unpatched run collapsed to 65 tok/s),finish_reason=stop, valid answer.AI assistance
This change was investigated and drafted with AI assistance (Claude). The submitter reviewed every changed line and ran the tests listed above.
Signed-off-by: Gilad Zamoscinski gilad.z@taboola.com
🤖 Generated with Claude Code