[Bugfix][MoE] Deterministic expert selection in the grouped_topk Python fallback - #55514
[Bugfix][MoE] Deterministic expert selection in the grouped_topk Python fallback#55514davidcanar wants to merge 1 commit into
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…on fallback The Python `grouped_topk` fallback selects experts with `torch.topk(..., sorted=envs.VLLM_BATCH_INVARIANT)`, i.e. `sorted=False` by default. `sorted=False` licenses the backend to return the k results in any order, and above 256 columns `torch.topk` takes a multi-pass path whose order is not merely unsorted but differs between calls on identical input: 20 identical `torch.topk(x, k=8, sorted=False)` calls on a 64xE tensor give 1 distinct result for E <= 256 and 20 distinct for E >= 257 (gfx1151, ROCm 10.0, torch 2.11; the same split holds for any k >= 4). Two consequences for MoE routing: 1. The routing weights are permuted to match the returned order, so the order the expert outputs are summed in changes run to run and the MoE output is not reproducible. On a real 740x288 GLM-5.3 router score tensor this affected 740/740 rows on every call. 2. On exact fp32 ties at the k-boundary the selected set itself flips. `sorted=True` does not fix that, and `torch.use_deterministic_algorithms(True)` neither raises, warns, nor changes the behaviour. Select with a stable descending sort instead, giving a total order of value descending, then expert index ascending. This is not a new convention: it is what the fused CUDA kernel already implements (`moeTopKFuncs.cuh` packs `65535 - idx` into the comparison key, and the multi-group path prefers the lower index on equal values), what `test_grouped_topk_single_group_stable_ties` already asserts, and what `_single_group_reference` in that same test file already computes with `argsort(..., descending=True, stable=True)`. The Python fallback was the only piece that disagreed -- and it is the path ROCm takes, as well as the path CUDA takes whenever `e_score_correction_bias is None` or the shape falls outside the fused kernel's tier table. Because it needs more than 256 routed experts to appear at all, this has been easy to miss: DeepSeek-V3/R1 has exactly 256 and sits on the safe side of the boundary, while GLM-5.3's 288 is over it. End to end on GLM-5.3-Flash (TP=2, 2x gfx1151), 5 byte-identical greedy requests per prompt length, distinct completions before -> after: 244 tok 5 -> 1, 614 tok 5 -> 1, 1196 tok 3 -> 1, 1421 tok 5 -> 1, 1797 tok 5 -> 1. With a hash tap after every decoder layer, all 45 layers become bit-identical across repeated forwards on both TP ranks. Cost is negative: 71.6us vs 97.8us for the selection on the real 740x288 tensor. The new tests are deliberately not CUDA-gated, unlike the rest of that file, because the pure-PyTorch fallback is the code under test. They use 288 experts because the same tests written at 256 pass against unfixed code: 16 of the 18 new cases fail before this change and all 18 pass after. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: David Canar <davidcanar@gmail.com>
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📝 SummarySummary by CodeRabbit
WalkthroughThe Python grouped-topk fallback now uses stable descending selection with lower-index tie-breaking. New tests verify repeat determinism, score ordering, and expert and group tie behavior. ChangesGrouped top-k determinism
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: 🔵 Low · up to The fallback now provides deterministic group and expert ordering, but invalid routing cardinalities may silently select the wrong number of groups or experts instead of failing. Add bounds validation before merging. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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…ument Submitted as vllm-project/vllm#55514 (DCO passing). Findings posted to vllm-project/vllm#54521 as issuecomment-5555831371. PR-BODY.md gains what turned out to be the strongest point of all, found while merging the tests into upstream's file rather than shipping a standalone one: `_single_group_reference` in tests/kernels/moe/test_grouped_topk.py **already computes exactly the ordering this fix installs** -- `argsort(..., descending=True, stable=True)`. So the fused kernel, the test suite's own reference, and #55122's convention for the sparse indexer all agree on value-desc/index-asc, and the Python fallback was the lone dissenter. That is a much easier argument to accept than "ROCm is nondeterministic". CI note for whoever picks this up: `pre-run-check` fails by design, not on merit -- vLLM gates pre-commit runs behind a 'ready'/'verified' label or 4+ merged PRs from the author, and this is a first contribution. DCO passes and the diff is 2 files, +352/-9. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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In `@vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py`:
- Line 83: Update _deterministic_topk_indices to reject k values outside 0 <= k
<= x.size(-1) before slicing, preserving the prior torch.topk validation
behavior. Ensure GroupedTopKRouter validates both top_k and topk_group through
this path, and add regression coverage for invalid values of each.
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| irreproducible. A stable descending sort gives the same ordering the fused | ||
| kernel uses (value desc, then lower index first). | ||
| """ | ||
| return x.sort(dim=-1, descending=True, stable=True).indices[..., :k] |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
🔎 Supported by static analysis
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Preserve invalid-k rejection.
GroupedTopKRouter passes top_k and topk_group to _deterministic_topk_indices without validating their bounds. Python slicing accepts negative k and clamps oversized k, which can select the wrong number of experts or groups. The previous torch.topk calls rejected these values.
Reject values outside 0 <= k <= x.size(-1) before slicing, and add regression coverage for invalid topk and topk_group.
Proposed fix
def _deterministic_topk_indices(x: torch.Tensor, k: int) -> torch.Tensor:
+ if not 0 <= k <= x.size(-1):
+ raise RuntimeError("selected index k out of range")
return x.sort(dim=-1, descending=True, stable=True).indices[..., :k]📝 Committable suggestion
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| return x.sort(dim=-1, descending=True, stable=True).indices[..., :k] | |
| if not 0 <= k <= x.size(-1): | |
| raise RuntimeError("selected index k out of range") | |
| return x.sort(dim=-1, descending=True, stable=True).indices[..., :k] |
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In `@vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py` at line
83, Update _deterministic_topk_indices to reject k values outside 0 <= k <=
x.size(-1) before slicing, preserving the prior torch.topk validation behavior.
Ensure GroupedTopKRouter validates both top_k and topk_group through this path,
and add regression coverage for invalid values of each.
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The strongest argument, up front: the fallback does not match the kernel
This PR does not introduce a new ordering. Value descending, expert index
ascending is already the defined, implemented and tested output contract of
the fused CUDA kernel — the Python fallback simply does not match it.
The single-group fused kernel packs value and index into one comparison key
(
csrc/libtorch_stable/moe/moeTopKFuncs.cuh):The multi-group path is stable for the same reason
(
csrc/libtorch_stable/moe/grouped_topk_kernels.cu):And the contract is already asserted by an existing test,
tests/kernels/moe/test_grouped_topk.py::test_grouped_topk_single_group_stable_ties(all-zero logits → every score tied → the expected ids are ascending):
Most tellingly, the reference implementation in that same test file already
uses precisely the ordering this PR installs — a stable descending sort:
So the fused kernel, the test suite's reference, and #55122's convention for
the sparse indexer all agree on value-desc/index-asc. The Python fallback is
the only piece that does not, and it is the piece that actually runs on ROCm.
This PR makes it agree.
This PR makes the Python fallback select under the same total order
(value descending, index ascending on bitwise-equal scores), so the two
implementations of
grouped_topkfinally agree on ties. It also matches theconvention #55122 chose for the sparse indexer's
persistent_topk("ascending index order ... top-k by value desc, index asc").
The bug, for someone with no context
The Python
grouped_topkselects experts withtorch.topk(..., sorted=envs.VLLM_BATCH_INVARIANT)— i.e.sorted=Falsebydefault — at three sites in
vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py(group selection L135, expert selection L148 and L152-154 as of 8bf3963).
Measured on a real router score tensor from a GLM-5.3 prefill (740x288 fp32,
k=8; gfx1151, ROCm 10.0, torch 2.11), 20 identical calls of the raw ops:
sort(descending=True, stable=True)[:k](this PR)topk(sorted=False)(today's default)topk(sorted=True)Two independent defects:
sorted=Falsepermutes the returned order on every row, every call.The selected set is correct, but the order of the returned top-k differs
on 740/740 rows from call to call (a 20-call follow-up on other layers
measured ≥715/740 — essentially every row). The gathered routing weights
are permuted to match, so the downstream k-term summation order changes
and the MoE output differs run-to-run.
sorted=Truestill flips the selected set. The real tensors containbitwise-equal fp32 biased scores exactly at the k-boundary (rows 40 and
549: experts {12, 202} and {243, 250} — different logits and different
biases whose fp32 sums round to the same value), and
torch.topkguarantees no tie-break between equal values. On row 40, 20-call sampling
shows the set flipping between expert 202 (39/60) and expert 12 (21/60).
torch.use_deterministic_algorithms(True)does not help: neither inwarn_onlynorstrictmode does it raise, warn, or changetopk'sbehaviour on these tensors.
Why this has stayed invisible: the boundary is at 256 experts
sorted=Falselicenses any order, and above 256 columnstorch.topktakesa multi-pass path whose output order is not merely unsorted but differs
between calls. The boundary is exact. 20 identical
torch.topk(x, k=8, sorted=False)calls on a64 x Etensor, same stack asabove:
The same split holds for any
k >= 4(k <= 2is order-trivial), andsorted=Trueat E=288 gives 1/20 - consistent with defect 2 needing a realtie rather than mere width.
So the blast radius is models with more than 256 routed experts.
DeepSeek-V3/R1 has exactly 256 and sits on the safe side of the boundary,
which is presumably why this has gone unnoticed; GLM-5.3's 288 is over it. The
boundary is an implementation detail of one
topkon one backend, though -the fallback should not depend on unspecified ordering at any width.
The change
A module-level helper realising the kernel's total order, used at all three
selection sites:
group_idx = _deterministic_topk_indices(group_scores, topk_group)topk_ids = _deterministic_topk_indices(tmp_scores, topk)topk_ids = _deterministic_topk_indices(tmp_scores, topk)followed by
topk_weights = tmp_scores.gather(1, topk_ids)(weights keepcoming from
tmp_scores; the bias branch still gathers fromoriginal_scores)The now-unused
use_sorted = envs.VLLM_BATCH_INVARIANTlocal and itscomment are removed. The group-score reduction at L126
(
view(...).topk(2)[0].sum(-1)) is deliberately untouched: it reducesvalues only, and a tie at its k-boundary is between equal values, so the
sum is unaffected.
This is not ROCm-only
The fused
_moe_Ckernel is taken only when all of the following hold(
grouped_topk_router.pyL91-97):So CUDA also runs the Python path whenever
e_score_correction_bias is None(softmax-grouped models without a correction bias), orVLLM_USE_FUSED_MOE_GROUPED_TOPK=0, or a shape outside the kernel's tiertable (
num_expert_group > 32,topk > 32). The bug is ROCm-default, notROCm-only — it is latent on CUDA for the no-bias configs.
Performance
Same real 740x288 fp32 tensor, k=8, CUDA-event timing, N=50 after warmup
(gfx1151; the GPU is shared so absolute values are contention-inflated —
the relative ordering was reproduced three times):
topk(sorted=False)(today)topk(sorted=True)sort(stable=True)+ slice (this PR)Per prefill forward (42 MoE layers): 4.11 ms -> 3.01 ms, i.e. the fix
saves ~1.1 ms of a ~2.4 s prefill rather than costing anything. Cost in
memory: ~2.4 MiB transient per call at this shape (832 KiB values + 1.6 MiB
int64 indices, freed immediately; scales with tokens x experts — ~107 MiB at
32k tokens x 288 experts) vs ~69 KiB for
topk.Honest caveat: a full sort is O(E log E) vs
topk's O(E log k), so at muchlarger expert counts the trade could invert. It is also data-dependent: on
randnsynthetic scores at this shapetopkcosts ~78 us and the two armsare comparable — the measured win is on real router data, not universal.
An env-gated variant is possible, but
VLLM_BATCH_INVARIANTis documentedNVIDIA-SM90-only, so ROCm users cannot opt into a gated mitigation in a
supported way; correctness fixes should not be platform-flag-gated.
Behavioural delta
topkalreadyproduced (across all measured tensors: 0 rows where the ascending-index
selection was never returned by
topkin 60 calls) — the fix pins thewinner, it does not introduce a new selection.
topk(sorted=True)in both set and order. Relative to today's default (
sorted=False), thereturned order becomes fully sorted — that order change is the point of
the fix.
row 549 -> 243, layer-34 row 651 -> 58 — the lower index in every case.
Known gap (pre-existing, disclosed)
AITER's
biased_grouped_topk(taken on ROCm whenVLLM_ROCM_USE_AITER_MOE=1)is deterministic, but its tie-break is opaque and demonstrably not
ascending-index: on the real tie rows it picks 202/250/188 where the
ascending-index rule picks 12/243/58, and a 9-configuration synthetic sweep
found no index rule at all (
{5,200}->200but{0,279}->0). After this PRthe Python fallback and the CUDA
_moe_Ckernel agree on ties; the aiterkernel keeps its own (deterministic, kernel-defined) tie-break, so a
cross-path inconsistency remains on ROCm-with-aiter. That divergence is
pre-existing and not created by this patch; fixing it means teaching the
aiter kernel the same rule, which is out of scope here.
fused_topk_bias_router.py(L330-333) carries the sameuse_sorted = envs.VLLM_BATCH_INVARIANTpattern; it is reachable only viathe invalid-grouping fallback path and is left to a follow-up so this PR
stays minimal and reviewable.
Test plan
New regression tests in
tests/kernels/moe/test_grouped_topk.py(staged copy: they are not CUDA-gated, unlike the existing tests in
that file, because the Python fallback is the code under test — it must
also run on ROCm and CPU CI):
test_grouped_topk_repeat_determinism: 20 identical calls must returnbitwise-identical
topk_idsandtopk_weights. No tie required - this isdefect 1, the ordering one. Parametrized over sigmoid/softmax,
bias/no-bias, and 1-group/8-group.
test_grouped_topk_returns_value_descending_order: the selected expertscome back in descending score order, and the selected set equals
topk(..., sorted=True)'s. This is a single-call assertion - norepetition, so it cannot be flaky - and it is the one that pins the
fallback to the kernel's existing contract.
test_grouped_topk_tie_broken_by_lower_expert_index: a deliberate,bitwise-exact tie at the k-boundary (two experts with identical logit
bits, so the tie survives any elementwise rounding, eager or compiled) is
won by the ascending expert index.
test_grouped_topk_group_tie_broken_by_lower_group_index: abitwise-exact tie at the group boundary (
topk_group > 1) is broken byascending group index, covering the group-selection site at L135.
These fail on
mainand pass with this PR: 16 of 18 cases fail beforethe change, all 18 pass after, same GPU and same command. The 2 that
already passed are the group-tie pair, whose top-k is over
num_expert_group= 4 values and therefore never crosses the 256-columnthreshold; they are kept as contract tests for the L135 site.
The tests use 288 experts on purpose. Per the table above,
topk'sordering only degrades past 256 columns, so the same tests written at
DeepSeek-V3's 256 - or at the 32 experts the existing
test_grouped_topk_single_group_stable_tiesuses - pass against theunfixed code and demonstrate nothing. This was checked the wrong way round
first: an earlier draft at 32 experts passed on stock and had to be
rewritten.
End-to-end on the affected deployment (GLM-5.3, 2x TP, gfx1151, greedy,
740-token prefill, repeated N times, comparing first-token and full
completion bit-identity before/after):
Validated on the affected deployment (GLM-5.3-Flash AWQ-W4A16, TP=2 over
2x gfx1151, ROCm 10.0, torch 2.11,
--enforce-eager, speculative decodingoff, prefix caching off, engine warmed). 5 byte-identical greedy requests
per prompt length, comparing full completions:
Per-layer verification: with a hash tap after every one of the 45 decoder
layers, all layers are bit-identical across repeated forwards on both TP
ranks at 740 prompt tokens (they were not before). Op-level, the patched
grouped_topkgives 1 distinct result in 30 identical calls where a stockcontrol in the same process gives 30 — so the test is sensitive, not merely
passing.
No regressions: 4 chat prompts return correct, coherent answers; tool
calling still yields
finish_reason: tool_callswith valid-JSON arguments,streaming and non-streaming; prefill throughput 340 / 328 / 317 tok/s at
2K / 8K / 32K prompts, inside the 284-334 tok/s pre-patch baseline.
Scope of the claim — this fixes the router, not the platform. Above
index_topk(2048 for this checkpoint) a separate defect remains, and thisPR does not address it: the DSA sparse-attention indexer's own top-k. A
six-point tap inside the first sparse-attention layer, both ranks, one boot,
isolates it cleanly — at 719 prompt tokens all six points are deterministic;
at 2497 the divergence enters at the attention output
(
self.self_attn(...), 6 of 6 distinct) with its input bit-identical, andeverything downstream merely cascades. That is [Bug]: Qwen3.8-Flash-Next: greedy decoding is non-deterministic from persistent_topk in prefill when prompt length nears indexer_budget (sm121/GB10) #54521's original diagnosis
and is being fixed in [Kernel] Make persistent_topk deterministic #55122. So: greedy decoding becomes reproducible below
index_topk, and the remaining nondeterminism above it is tracked elsewhere.Other nondeterministic ops are tracked in [RFC]: Logprobs/Logits Semantics and Determinism Across the vLLM Ecosystem #42259.
Cross-references
gfx1151 GLM-5.3 arm); the router tie is the missing explanation for the
layer-21/34 divergence there.
selection would belong in its table (it currently tracks MoE combine
[Bugfix] Deterministic MoE combine (reduce_scatterv) under VLLM_BATCH_INVARIANT #45683 and sampler top-k ties [Bugfix][Sampler] Keep exactly k tokens in top-k with tied logits #50979, but not router selection).
sparse indexer's
persistent_topk; same bug class, different op.DCO / attribution
This PR was prepared with AI assistance (analysis of the nondeterminism,
the fix, the tests and this description); every line has been reviewed and
validated by the human submitter, per the AI Assisted Contributions policy.
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