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[Bugfix][GDN] Reclassify a partial final speculative group as prefill at the max-model-len boundary - #55516

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[Bugfix][GDN] Reclassify a partial final speculative group as prefill at the max-model-len boundary#55516
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jgavinray:gdn-partial-final-spec-group

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@jgavinray jgavinray commented Sep 6, 2026

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Problem

A completion that ends at exactly max-model-len yields one incomplete speculative token group (fewer than num_speculative_tokens + 1 tokens). GDNAttentionMetadataBuilder.build() still classifies the batch as pure spec decode and hands the fused GDN kernels a batch that violates their complete-group invariant (spec_token == num_spec_decodes * (num_speculative_tokens + 1)), so the final tokens of an exact full-context generation can never complete.

Repro: MTP4 on a GDN-hybrid Qwen checkpoint — a 131,072-token completion stalled at 124/128 outputs because the last step produced a 4-token group instead of 5.

Fix

In the pure-spec branch, detect the truncated final group and reclassify it as a stateful non-spec prefill (the prefill path already handles initial state). Complete groups are untouched. The classification lives in the shared builder, so every backend benefits; the kernel-side complete-group TORCH_CHECK stays intact as a live guard.

Sizing invariants of the reclassified batch, N = reclassified spec rows — sized by N alone, never the padded batch size (which may carry trailing zero-length sequences): num_spec_decodes=0, spec fields None, num_prefills=N, non_spec_query_start_loc(_cpu) = query_start_loc(_cpu)[:N + 1], non_spec_state_indices = block_table[:N, 0], has_initial_state = (computed_tokens > 0)[:N], num_accepted_tokens=None.

Tests (3 new)

  • test_gdn_build_classification[partial_final_spec_group_at_max_len_uses_full_group] — truncated step classifies as num_prefills=2, num_spec_decodes=0.
  • test_partial_final_spec_group_reclassified_as_prefill — no spec metadata leaks; non_spec_query_start_loc.tolist() == [0, 3, 5].
  • test_partial_final_spec_group_padded_batch_shapes — with a trailing zero-length padded sequence, asserts the fused-op sizing above (values [0, 3, 5], [True, True]) — the padded row is sliced out, not leaked.

Verification

CPU-only (no GPU) in vllm/vllm-openai-xpu@sha256:f01e24f6c7ff01f1e0662234255a1372297d1dbd89d003cf13c8fad3eab1ba4f (vllm 0.27.2rc1.dev77+gac7509e2b, torch 2.13.0+xpu): pytest -p no:cacheprovider -v tests/v1/attention/test_gdn_metadata_builder.py.

Regression-first — padded test against a build without the [:N] slices (padded row leaks [0, 3, 5, 5]):

E       AssertionError: assert 4 == ((2 + 0) + 1)
================== 1 failed, 11 passed, 22 warnings in 47.33s ==================

With the slices: 12 passed, 22 warnings in 43.27s, exit 0.

Kernel-contract checks (TORCH_CHECK(spec_token == num_spec_decodes * (num_speculative_tokens + 1)) etc.) verified against vllm-xpu-kernels gdn_attn_interface.cpp; on-device evidence follows in a comment.

Not #391 (vllm-xpu-kernels)

#391 (open, 2026-06-03, @ehartford/QuixiAI, fixes #389) relaxes metadata shape checks for graph-padded buffers whose groups are still complete, leaving the uniform-stride invariant intact. This PR handles a genuinely ragged final group by reclassifying it out of the spec path upstream of the kernel.

Related, none claiming this fix (checked 2026-09-05)

#50623 (cudagraph padding branch, different condition) · #50021 (accepted-token bounds, kernel-side) · #37052 (block_table OOB, different bug) · #55404 (merged; rewrote build_for_cudagraph_capture above this region — diff regenerated after it, applies at exact hunk positions).

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@mergify mergify Bot added the bug Something isn't working label Sep 6, 2026
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No actionable comments were generated in the recent review. 🎉

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📥 Commits

Reviewing files that changed from the base of the PR and between 6535e7f and 414c9ef.

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  • vllm/v1/attention/backends/gdn_attn.py

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📝 Summary

Summary by CodeRabbit

  • Bug Fixes
    • Corrected handling of truncated final speculative groups at the maximum sequence length.
    • Such groups are now processed as stateful prefills with accurate token counts and metadata.
    • Prevented invalid speculative metadata from being retained in this edge case.
    • Preserved correct initial-state information for continuing sequences.
    • Improved reliability when requests reach the maximum supported sequence length during speculative processing.

Walkthrough

The GDN metadata builder converts a truncated final speculative group into a stateful non-spec prefill. It clears speculative metadata, sizes non-spec tensors to the reclassified rows, aligns initial-state flags, and adds targeted tests.

Changes

GDN partial speculative group handling

Layer / File(s) Summary
Reclassify truncated speculative groups
vllm/v1/attention/backends/gdn_attn.py
The builder detects incomplete final speculative groups, clears speculative tensors, creates row-sized non-spec metadata, conditionally filters accepted tokens, and slices has_initial_state.
Validate reclassified metadata
tests/v1/attention/test_gdn_metadata_builder.py
Tests verify prefill counts, token counts, cleared speculative fields, and tensor shapes when a zero-length padded row is present.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Merge Risk: ⚪ Minimal · up to 414c9

Truncated final speculative groups at the model-length boundary are now handled as stateful non-speculative prefills, including padded batches, with no current merge-blocking risk identified.

Sequence Diagram(s)

sequenceDiagram
  participant GDNMetadataBuilder
  participant SpecMetadata
  participant NonSpecMetadata
  GDNMetadataBuilder->>GDNMetadataBuilder: Detect actual spec tokens below expected group size
  GDNMetadataBuilder->>SpecMetadata: Clear speculative metadata
  GDNMetadataBuilder->>NonSpecMetadata: Set prefill counts and row-sized tensors
  GDNMetadataBuilder->>NonSpecMetadata: Slice has_initial_state to reclassified rows
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Suggested reviewers: njhill, xyang16, yewentao256

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name Status Explanation Resolution
Docstring Coverage ⚠️ Warning Docstring coverage is 75.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 4 functions across 2 files. Write docstrings for the functions missing them to satisfy the coverage threshold.
✅ Passed checks (4 passed)
Check name Status Explanation
Linked Issues check ✅ Passed Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check ✅ Passed Check skipped because no linked issues were found for this pull request.
Title check ✅ Passed The title clearly and concisely describes the main fix: reclassifying a partial final speculative group as prefill at the max-model-len boundary.
Description check ✅ Passed The description directly explains the bug, the reclassification fix, metadata sizing requirements, tests, and verification results.
  • Fix all pre-merge checks with AI
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🛠️ Fix failing CI checks 💡
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… at the max-model-len boundary

When the scheduler hands the final speculative step fewer than num_speculative_tokens + 1 query tokens per sequence at the max-model-len boundary, the shared GDN metadata builder reclassified the batch as pure spec decode while the trailing partial group left the request unable to complete without padding. The builder now reclassifies the partial final spec group as a stateful non-spec prefill and slices the non-spec metadata (state indices, query-start-loc CPU views, initial-state masks) to the reclassified rows only. Regression tests pin the reclassified metadata shapes and content on CPU tensors, including zero-length padded batches.

Strip hardware-name marker from reclassification comment (no behavior change; AST-identical).

Signed-off-by: J. Gavin Ray <git@jgavinray.com>
@jgavinray
jgavinray force-pushed the gdn-partial-final-spec-group branch from 6535e7f to 414c9ef Compare September 6, 2026 01:15
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End-to-end verification on real hardware (Intel Arc Pro B70, 32 GB) — same request, both sides, single A/B variable:

Setup. Pinned image vllm/vllm-openai-xpu@sha256:f01e24f6c7ff… (vLLM 0.27.2rc1.dev77+gac7509e2b, torch 2.13.0+xpu, vllm-xpu-kernels 0.1.12.3 wheel — kernel .so md5 e7fb22f8… identical in both arms, no source builds). Qwen3.8-27B GDN-hybrid INT4-GPTQ with native MTP head, --speculative-config {"method":"mtp","num_speculative_tokens":4} (MTP4, the repro family), champion serve flags; --max-model-len 4096 so the boundary is reachable in-window (the defect is a max-sequence-boundary property, not a 131K property). Request: 70-token prompt, ignore_eos=true, max_tokens=4026 → final sequence lands exactly on max-model-len, making the last MTP group ragged (4/5).

BEFORE (image as-is, unpatched builder): at the boundary step the engine dies with the kernel's own invariant:

RuntimeError: Expected spec_token == num_spec_decodes * (num_speculative_tokens + 1) to be true, but got false.
EngineDeadError: EngineCore encountered an issue.
POST /v1/completions HTTP/1.1" 500 Internal Server Error

Completion never delivered — same signature as the field failure (a 131,072-token MTP4 generation that stalled at 124/128 outputs).

AFTER (single-file overlay of this PR's gdn_attn.py, diff sha256 2c6d613b… verified at apply time):

BOUNDARY prompt_len=70 requested_max_tokens=4026 completion_tokens=4026 finish_reason=length total_sequence_len=4096 (max_model_len=4096)
B70_MTP_PARTIAL_FINAL_GROUP FIRED: reclassified 2 partial spec rows to the non-spec prefill path (final group truncated at the max-sequence boundary)   # x33 across runs

Full-length completion, engine healthy (/health 200 afterwards). The log line confirms the new branch actually executed — not merely "did not crash".

Kernel-level, direct torch.ops._xpu_C.gdn_attention calls on device: (i) ragged group (spec_token = num_spec_decodes·5 − 1) → rejected by the same TORCH_CHECK (invariant live on device); (ii) the reclassified batch this PR emits (num_spec_decodes=0, spec fields None, query_start_loc[:N+1], non_spec_state_indices[:N,0], has_initial_state[:N], with padded RAW tensors sliced PR-style) → executes cleanly, outputs finite, at production GDN shapes. The fused op's sizing checks (non_spec_query_start_loc.size(0) == num_prefills + num_decodes + 1 etc.) accept the sliced tensors.

Fast path unaffected: ordinary non-boundary generation on the patched tree: 70.12 tok/s on the reference complex prompt — inside the pre-fix band for this exact config (62.3–62.7 baseline with the same async adapter + GPU-prep + draft-INT4 layers, identical md5s on both sides; the 82.1 figure is a different, fp16-draft config and explicitly not the comparison basis). Zero reclassification log lines during the timing run — the branch is dead code off the boundary.

Caveats, stated honestly: wheel 0.1.12.3 predates the kernels repo's split ops (#537/#544), so all runs used the fused prebuilt kernel (verified same binary both arms); the prebuilt binary's TORCH_CHECK messages carry no file:line context; kernels-repo mini-scope numerics have 4 known-flaky failures upstream-FIXME-skips (core_attn_out compare, upstream commit 196943fc) — CI-scope on the era-matched tag: 420 passed; neither touches this PR's verdict.

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What's the meaning of LLM class initialization, which will produce a worker and do a prediction with torch.zeros([max_num_batched_tokens]) as input ids?

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