[Bugfix][Kimi-K3] Do not classify a stateless first chunk as a decode

[sashko-zakharchuk]

Purpose

Related to #51039.

A request being forwarded for the first time owns no KDA state, but the non-spec branch of the
Kimi-K3 KDA metadata builder classified any one-token row as a decode
(split_decodes_and_prefills(m, decode_threshold=1) with the default
treat_short_extends_as_decodes=True, nvidia/kda_metadata.py:405). When every row in a batch is one
token, num_prefills is 0, has_initial_state is never computed, and kda.py takes the
pure-decode route, which reads conv_state[slot] and recurrent_state[slot] unconditionally.
gather_initial_states is the only thing that masks a stateless slot, and it lives on the
prefill route. Mamba blocks are not zeroed on reallocation
(single_type_kv_cache_manager.py:86 restricts zero-on-allocate to the attention specs), so
such a request silently continues whatever KDA state the previous owner of its recycled block
left behind.

This builder already applies the correct rule on its spec path
(nvidia/kda_metadata.py:439-447, "Query length alone cannot distinguish a true decode from a
one-token prefill chunk"), and the Mamba2 builder does the same at mamba_attn.py:459-481
("First-token prefills have no prior Mamba state and must stay prefills").

Reachable on stock flags: a one-token prompt scheduled alongside ordinary decodes. Also
reachable when a request's first chunk is clipped to one token by the remaining token budget
(scheduler.py:990). Under the default mamba_cache_mode="align",
Scheduler._mamba_block_aligned_split often clips that chunk to zero and drops the request from
the step instead. Two cases get through: a block-aligned chunk when the mamba spec carries
prefill checkpoint blocks and no Eagle drafter is configured, which is how Kimi-K3 runs under
the FlashKDA prefill backend (nvidia/kda.py:598), and any chunk when the mamba block size
exceeds the prefill budget (scheduler.py:419-436). A batch containing any row longer than one
token is unaffected, since nvidia/kda.py:824 then routes everything through the packed prefill
with the correct has_initial_state.

Three changes:

1. Classify by state rather than by query length. no_prior_state is computed from
   seq_lens_cpu_upper_bound <= query_lens_cpu, which identifies rows with no computed tokens
   (the bound is documented as precise for prefill rows and optimistic only for async spec
   decode rows, backend.py:422-426; an optimistic bound can only miss a stateless row, never
   promote a real decode), and it is additionally masked with the runner's is_prefilling flag.
   The dummy batch used for cudagraph capture also has seq_len == query_len for every row,
   and without that mask the captured decode graph would record the prefill kernels.

   One-token chunks that resume a partially prefilled request own valid state and keep the
   decode path, so treat_short_extends_as_decodes=False alone would be wrong: it would move a
   whole decode batch onto chunk_kda_with_fused_gate whenever a chunked prefill ends on a
   one-token chunk.

2. Re-key the cudagraph staging guard. Decode-graph dispatch is shape based
   (_is_uniform_decode decides from token counts before build() runs), so a one-token batch
   can replay the captured decode graph even when a stateless first chunk makes it a prefill batch. Keyed on
   num_prefills == 0 the guard would go false, staging would be skipped, and the replay would
   read state indices staged in an earlier step. The new condition is the one the dispatcher
   uses. On base this is a strict no-op: num_prefills == 0 and max_query_len <= 1 are
   equivalent there, and the removed fill_(NULL_BLOCK_ID) is dead because entering the guard
   implied num_decodes == batch_size (NULL padding still comes from the runner's block table).

3. Exclude cudagraph padding from num_prefills. split_decodes_and_prefills returns
   num_reqs - num_decodes, the whole suffix after the first prefill, so trailing zero-length
   padding rows are counted as prefill requests once a stateless first chunk promotes a row ahead
   of them. The value is only ever compared against zero downstream and never crosses zero, so no
   routing decision changes. num_prefill_tokens is inflated separately, since it inherits
   num_actual_tokens, which a full cudagraph pads past the last real token; it is recomputed
   from the same real query boundary, and nothing on the KDA execution path reads it. The shared GDN
   builder has all three defects, and [Bugfix][GDN] Fix stateless first-chunk classification #51565 makes the same changes there.

Note on blast radius: kda.py does not split the non-spec group, so once num_prefills > 0 the
whole non-spec batch takes the prefill route for that step. A single freshly admitted request
therefore moves an otherwise all-decode batch off ops.fused_kda_decode in eager, PIECEWISE,
and uncaptured sizes. Both prefill backends handle all-length-1 cu_seqlens correctly.

What this does not do. It corrects the metadata, not the dispatch. With the default
FULL_AND_PIECEWISE an all-one-token batch is still dispatched to the captured decode graph on
shape alone, so a replayed graph is unchanged; Mamba2 has the same residual gap. Closing it needs
either worker-side zeroing of mamba pages on reallocation or making the dispatcher decline the
full graph when a mamba group has a stateless row, both core-runner scope, and I am happy to
follow up on either.

It is also a no-op on batches whose metadata carries no is_prefilling flag, which is the
microbatching and context-parallel path (ubatch_utils._make_metadata_with_slice,
cp_utils.py): without the flag the builder cannot tell a first chunk from a resumed one-token
chunk, so it conservatively keeps the base classification there rather than promote a
capture-shaped batch by mistake.

On #51039 specifically: this is a correctness bug that needs no NaN, and it is also one way a
poisoned state block can be inherited by a brand-new request. It does not explain the origin of
the first NaN at 240K, so I am not claiming it resolves that incident.

Coordination: #50855 (draft) touches the same two files and adds an all_initial_states_fresh
flag computed from the same fresh-versus-resumed distinction this changes. Whoever lands second should re-check that flag against the new classification.

Line references above are against d9fbe526c, main at the time of writing.

Test Plan

Ten tests in tests/models/kimi_k3/test_kda_metadata.py, driving the real builder:

• test_one_token_first_chunk_is_not_a_decode: seq_lens=[100, 50, 1], all query lengths 1,
  expects 2 decodes, 1 prefill, has_initial_state == [True, True, False].
• test_one_token_extend_chunk_stays_a_decode: seq_lens=[100, 50, 65], all query lengths 1,
  expects 3 decodes and no prefill, so the change cannot regress mid-prefill one-token chunks.
• test_zero_length_padding_row_is_not_a_prefill: a padded row is never promoted.
• test_one_token_first_chunk_excludes_trailing_padding: two real rows (an ordinary decode,
  then a stateless first chunk) followed by two zero-length padding rows, expects 1 decode and
  1 prefill, 1 token each.
• test_one_token_first_chunk_excludes_padded_tokens: the same batch with num_actual_tokens
  forced to 4, expects 2 decodes, 1 prefill, 1 prefill token.
• test_cudagraph_capture_batch_stays_decode_only: drives build_for_cudagraph_capture on a
  uniform dummy batch and asserts it stays decode-only.
• test_one_token_batch_stages_cudagraph_state_indices, parametrized over a first-token chunk
  and a pure decode, and test_all_stateless_one_token_batch_stages_cudagraph_state_indices
  for the num_decodes == 0 case: assert the returned tensor is the builder's persistent
  staging buffer and equals block_table_tensor[:, 0].
• test_multi_token_batch_does_not_stage_cudagraph_state_indices: a batch with a longer row is
  not a decode-graph batch, so the staging guard must not fire.
• test_first_chunk_without_prefill_flag_is_left_unclassified: with no is_prefilling flag the
  builder keeps the base classification rather than promote a possibly capture-shaped batch.

Test Result

• test_one_token_first_chunk_is_not_a_decode fails on unpatched main and passes with the
  change.
• Each guard is pinned by mutation. Removing the is_prefilling mask fails the capture test;
  keying the staging guard on num_decodes > 0 fails the all-stateless test; dropping the
  and m.max_query_len <= 1 clause fails the multi-token staging test; dropping the
  query_lens > 0 clause fails the zero-length padding test; removing the padding subtraction
  fails the trailing-padding test; dropping the num_prefill_tokens recompute fails the
  padded-token test. Making the flag-less fallback a no-op fails the unclassified
  test, and reverting the classification entirely fails three tests.
• tests/models/kimi_k3/test_kda_metadata.py: 29 passed.
• tests/models/kimi_k3/ (excluding test_latent_moe_tail.py, which needs ray, absent in my
  environment): 139 passed, 9 failed, 38 skipped, against 128 passed, 9 failed, 38 skipped on
  the same base (a3561ef8e). The difference is the eleven new cases. The nine failures are in
  test_mla_prefill_context.py and are present on the unmodified base.
• ruff check and ruff format --check clean on both changed files.
• Environment: RTX 5050 (sm_120), torch 2.13.0+cu130.

Tool assistance

Claude Code was used for the investigation, for reviewing this change, and for drafting this
description. All code was reviewed by me, and every claim above was verified against the tree
and by running the tests reported here.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

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[maxpla3]

#51508 might be related.

[sashko-zakharchuk]

Same state-bookkeeping layer, different defect (it took me a while to untangle these). This PR
is the non-spec branch: a stateless first chunk (one-token row, num_computed_tokens == 0)
gets classified as a decode, so the decode route reads a recycled state block that nothing
masked. #51508 is the spec path after a discarded async-scheduling step:
num_accepted_tokens == 0, and the state index is num_accepted_tokens - 1. The GDN-side
counterpart of this PR's bug is filed separately as #51562; the wider cluster
(#49918 / PR #47123) is mapped in
#51039 (comment).

Practical overlap: #51508 also touches kda_metadata.py and test_kda_metadata.py, so
whichever lands second needs a small rebase. I'll rebase this one if it goes second.

[sashko-zakharchuk]

@ZJY0516 you added kda_metadata.py in #50089. This has been open since 8 August with no
review of the code itself: vllm/models/kimi_k3/nvidia/ has no CODEOWNERS entry, so the
reviewers auto-requested here own /tests/models and came in through the test file rather than
the change.

The bug: in the non-spec branch of KimiK3KDAMetadataBuilder.build,
split_decodes_and_prefills(m, decode_threshold=1) classifies a request's first one-token chunk
as a decode. The decode route reads conv_state[slot] and recurrent_state[slot]
unconditionally, gather_initial_states masks a stateless slot only on the prefill route, and
mamba blocks are not zeroed on reallocation, so a fresh request continues whatever KDA state the
previous owner of its recycled block left behind. It came out of the #51039 investigation, and
it is a correctness bug on its own as well as a route by which a poisoned block can reach an
unrelated request.

The red pre-run-check is the gate that skips pre-commit for authors without four merged PRs,
not a test failure, so no CI has run at all yet; a ready label would let it run.
kda_metadata.py itself is untouched on main, so the diff still applies, but the branch is 268
commits behind, and I am happy to rebase and re-run the tests if you would rather review it
against current main.