[None][fix] Fix MTP illegal memory access #8161
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Signed-off-by: Mike Iovine <[email protected]>
mikeiovine
requested review from
sunnyqgg and
zheyuf
and removed request for
zheyuf
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/bot run |
📝 WalkthroughWalkthroughAdds a guarded exception in speculative decoding to bypass chunked-context handling for TRTLLM MLA when using MTP_EAGLE mode. Generalizes “Eagle-style” handling in the drafter to include MTP_EAGLE. Updates an integration test to invoke quickstart with 3 MTP modules instead of 1. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant Caller
participant SpecMode as SpeculativeDecodingMode
participant Attn as AttentionBackend
participant Drafter as ModelDrafter
Caller->>SpecMode: extend_ctx(request)
SpecMode->>Attn: check backend type
alt use_one_engine() is true
SpecMode-->>Caller: proceed with existing chunked-context flow
else If Attn is TrtllmAttention AND mode == MTP_EAGLE
Note over SpecMode: New guard: disable chunked-context
SpecMode-->>Caller: return False
else
SpecMode-->>Caller: existing behavior (incl. SM checks)
end
Caller->>Drafter: prepare_draft(spec_dec_mode, backend)
Drafter->>Drafter: is_eagle_style = Eagle3 OR MTP_EAGLE
alt backend is TRTLLM AND is_eagle_style
Drafter-->>Caller: handle Eagle-style path (broadened)
else
Drafter-->>Caller: handle non-Eagle path
end
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Pre-merge checks and finishing touches❌ Failed checks (1 warning)
✅ Passed checks (2 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
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🧬 Code graph analysis (3)tensorrt_llm/_torch/speculative/model_drafter.py (2)
tensorrt_llm/_torch/speculative/interface.py (1)
tests/integration/defs/test_e2e.py (2)
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PR_Github #20734 [ run ] completed with state |
3 participants
Description
Fix illegal memory access on MTP 2-model. The
extend_ctxmode does not work reliably with MLA (kernel issue). Thankfully, using the same kernels as 1-model will work without any changes.Test Coverage
Extended existing MTP test to cover the issue.
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
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CODEOWNERS updated if ownership changes
Documentation updated as needed
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Please check this after reviewing the above items as appropriate for this PR.
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