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Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Luca Motz <luca.motz@icloud.com>
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No actionable comments were generated in the recent review. 🎉 ℹ️ Recent review info⚙️ Run configurationConfiguration used: Repository UI Review profile: CHILL Plan: Team Run ID: 📒 Files selected for processing (4)
Included review availability: Your plan provides up to 10 included reviews per hour; 9 remain after this review. WalkthroughHybrid MLA alignment now uses a shared helper for cache-dtype-specific packed Mamba state sizes. MLA cache specification and platform alignment use the helper. A parametrized test covers ChangesHybrid MLA alignment
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: ⚪ Minimal · up to Hybrid MLA alignment now uses the packed physical cache width for supported FP8 and NVFP4 formats, with matching cache-spec and alignment behavior covered by tests. No current merge-blocking risk remains. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Luca Motz <luca.motz@icloud.com>
Purpose
Hybrid block alignment uses the semantic head size to estimate MLA pages, but packed FP8/NVFP4 caches have different physical byte widths. Use the same width lookup as MLA attention so padded Mamba pages match the final MLA pages. This fixes inconsistent cache sizing; the packed layouts themselves are unchanged.
Duplicate check: #55219 is a separate GLM layout refactor; no other open PR was found for this sizing calculation when this PR was opened. AI-assisted contribution.
Test Plan
HF_HUB_OFFLINE=1 .venv/bin/python -m pytest tests/v1/core/test_kv_cache_utils.py \ -k 'hybrid_mla_block_alignment or glm5 or mamba_hybrid' -q --tb=short HF_HUB_OFFLINE=1 .venv/bin/python -m pytest tests/v1/attention/test_mla_backends.py \ -k mla_kv_cache_spec_uses_layer_cache_dtype -q --tb=short HF_HUB_OFFLINE=1 .venv/bin/python -m pytest tests/v1/core/test_kv_cache_utils.py -q --tb=short .venv/bin/pre-commit run --files vllm/platforms/interface.py \ vllm/v1/kv_cache_interface.py \ vllm/model_executor/layers/attention/mla_attention.py \ tests/v1/core/test_kv_cache_utils.pyTest Result
13 alignment/GLM/Mamba tests and 2 existing MLA-spec tests passed. The alignment regression also checks that Mamba padding matches the spec built by MLA attention. Both packed-format cases fail on base
e473e90; ordinary FP8 passes. Full cache test file: 105 passedChecked the real GLM-5.3-Flash TP2/MTP5
fp8_ds_mlaconfiguration through the cache-spec and allocator APIs:e473e90a120883The PR makes early Mamba padding agree with the final MLA page.