[None][doc] Add doc for multimodal feature support matrix (#6619) #6739
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Signed-off-by: Chang Liu <[email protected]>
📝 WalkthroughWalkthroughA new documentation file, "Multimodal Feature Support Matrix (PyTorch Backend)", has been added. This markdown document provides a table listing several multimodal models and indicates their support for specific features, such as CUDA Graph, Encoder IFB, KV Cache Reuse, and Chunked Prefill. Changes
Sequence Diagram(s)No sequence diagram generated as the change is documentation-only. Estimated code review effort🎯 1 (Trivial) | ⏱️ ~2 minutes Possibly related PRs
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✨ Finishing Touches🧪 Generate unit tests
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Actionable comments posted: 1
🔭 Outside diff range comments (1)
docs/source/reference/multimodal-feature-support-matrix.md (1)
1-14: Add the new matrix page to your docs navigationOur checks show that
multimodal-feature-support-matrix.mdis not referenced by any toctree, so it won’t appear in the rendered docs. Please add it under the Reference section. For example:• Create or update
docs/source/reference/index.rst:Reference ========= .. toctree:: :maxdepth: 1 multimodal-feature-support-matrix• Ensure your top-level
docs/source/index.rstincludes the Reference folder (if it doesn’t already):.. toctree:: :maxdepth: 2 reference/indexThese changes will make the multimodal feature support matrix reachable from your docs navigation.
🧹 Nitpick comments (4)
docs/source/reference/multimodal-feature-support-matrix.md (4)
1-2: Add a short scope note and recency to prevent misinterpretation and staleness.Consider inserting a brief note under the title to clarify scope and when this was last validated:
# Multimodal Feature Support Matrix (PyTorch Backend) +> Note: This matrix covers the PyTorch backend only. "Yes" reflects support in shipped example recipes; "No" means not supported or not yet validated. Status last reviewed: 2025-08-08.
3-4: Expand acronym and align terminology (“CUDA Graphs”).Use commonly adopted names and expand the acronym on first use:
-| Model | CUDA Graph | Encoder IFB | KV Cache Reuse | Chunked Prefill | +| Model | CUDA Graphs | Encoder IFB (Inflight Batching) | KV Cache Reuse | Chunked Prefill | | :----------------- | :--------- | :------------------ | :------------- | :-------------- |
3-13: Add a minimal legend to define each feature.This avoids ambiguity for new readers:
| Qwen2.5-VL | Yes | Yes | Yes | No | +## Legend +- CUDA Graphs: Using CUDA graph capture/replay to reduce kernel launch overhead. +- Encoder IFB (Inflight Batching): Batching encoder/prefill work across requests to improve throughput/latency. +- KV Cache Reuse: Reusing cached KV states across requests with identical prefixes/contexts. +- Chunked Prefill: Splitting long prefill sequences into chunks for streaming/latency improvements.
3-13: Clarify what “No” means and when “N/A” should be used.If some features are not applicable to a given model architecture (vs. simply not implemented/validated), consider either:
- Adding a “Notes” column with brief caveats, or
- Using “N/A” where the concept doesn’t apply, reserving “No” for unsupported/not yet validated.
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🔇 Additional comments (1)
docs/source/reference/multimodal-feature-support-matrix.md (1)
1-14: LGTM overall—useful snapshot for PyTorch backend.
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PR_Github #14577 [ skip ] triggered by Bot |
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PR_Github #14577 [ skip ] completed with state |
… (NVIDIA#6739) Signed-off-by: Chang Liu <[email protected]>
… (NVIDIA#6739) Signed-off-by: Chang Liu <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
… (NVIDIA#6739) Signed-off-by: Chang Liu <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
… (NVIDIA#6739) Signed-off-by: Chang Liu <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
4 participants
It's cherry-pick PR of #6619
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