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[TRTLLM-5059][feat] Enable KV-cache reuse and add E2E tests for llava-next #7349
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📝 WalkthroughWalkthroughIntroduces a preprocessing step in LLaVA-Next’s non-DISAGG forward path: after obtaining multimodal embeddings, applies find_uncached_mm_embeds to mm_embeds based on multimodal_params, then proceeds to fuse_input_embeds. Imports are updated accordingly. DISAGG path is unchanged. No public API signatures are modified. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor Caller
participant LLaVA_Next as LLaVA-Next Model
participant MM_Embed as multimodal_embedding / mm_encoder
participant Uncached as find_uncached_mm_embeds
participant Fuse as fuse_input_embeds
Caller->>LLaVA_Next: forward(input_ids, multimodal_params, ...)
alt DISAGG == false and multimodal_params present
LLaVA_Next->>MM_Embed: compute mm_embeds
MM_Embed-->>LLaVA_Next: mm_embeds
LLaVA_Next->>Uncached: find_uncached_mm_embeds(mm_embeds, multimodal_params[:num_context_requests])
Uncached-->>LLaVA_Next: filtered/transformed mm_embeds
LLaVA_Next->>Fuse: fuse_input_embeds(input_embeds, mm_embeds, ...)
Fuse-->>LLaVA_Next: fused_embeds
else DISAGG == true or no multimodal_params
LLaVA_Next->>Fuse: fuse_input_embeds(input_embeds, mm_embeds, ...)
Fuse-->>LLaVA_Next: fused_embeds
end
LLaVA_Next-->>Caller: outputs
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Suggested reviewers
✨ Finishing Touches
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tensorrt_llm/_torch/models/modeling_llava_next.py(2 hunks)
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Signed-off-by: Chang Liu (Enterprise Products) <9713593+chang-l@users.noreply.github.com>
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LGTM
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PR_Github #18101 [ run ] completed with state |
Signed-off-by: Chang Liu (Enterprise Products) <9713593+chang-l@users.noreply.github.com>
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PR_Github #18257 [ reuse-pipeline ] triggered by Bot |
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PR_Github #18257 [ reuse-pipeline ] completed with state |
…-next (NVIDIA#7349) Signed-off-by: Chang Liu (Enterprise Products) <9713593+chang-l@users.noreply.github.com>
This PR is related to #7628 and #7563, and should be kept in sync.
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