[TRTLLM-10858][feat] Multi-image support for EPD disagg#11264
[TRTLLM-10858][feat] Multi-image support for EPD disagg#112642ez4bz wants to merge 1 commit intoNVIDIA:mainfrom
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📝 WalkthroughWalkthroughThe PR refactors multimodal embedding handling to support multiple multimodal items per request instead of a single item. Changes include updating model input processors to validate multiple handles, converting embedding storage from single dict to list-based structures, updating result handling to use disaggregated parameters, and adjusting tests and API surfaces accordingly. Changes
Estimated code review effort🎯 4 (Complex) | ⏱️ ~50 minutes Possibly related PRs
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⚠️ Outside diff range comments (3)
tensorrt_llm/serve/openai_server.py (1)
624-635:⚠️ Potential issue | 🟠 MajorDon’t drop additional multimodal handles in the MM‑encoder response.
Selecting index 0 silently loses embeddings for multi‑image requests and undercounts tokens. Please return all handles (and update the response schema accordingly) or explicitly reject multi‑image inputs. Also sum tokens across handles.🔧 Possible fix (return all handles and sum tokens)
- mm_embedding_handle = ( - promise.disaggregated_params.multimodal_embedding_handles[0] - if promise.disaggregated_params - and promise.disaggregated_params.multimodal_embedding_handles - else None - ) - if not mm_embedding_handle or "tensor_size" not in mm_embedding_handle: + mm_embedding_handles = ( + promise.disaggregated_params.multimodal_embedding_handles + if promise.disaggregated_params + else None + ) + if not mm_embedding_handles: return self.create_error_response( message="Multimodal embedding handle missing in response", err_type="InternalServerError", status_code=HTTPStatus.INTERNAL_SERVER_ERROR) - num_tokens = int(mm_embedding_handle["tensor_size"][0]) + if any("tensor_size" not in h for h in mm_embedding_handles): + return self.create_error_response( + message="Multimodal embedding handle missing tensor_size", + err_type="InternalServerError", + status_code=HTTPStatus.INTERNAL_SERVER_ERROR) + mm_embedding_handle = ( + mm_embedding_handles[0] + if len(mm_embedding_handles) == 1 + else mm_embedding_handles + ) + num_tokens = sum(int(h["tensor_size"][0]) for h in mm_embedding_handles)tensorrt_llm/_torch/models/modeling_qwen3vl.py (1)
355-388:⚠️ Potential issue | 🟡 MinorUpdate
mm_handlesdocstring for multi-handle support.The docstring still claims only a single handle is supported, but the loop now validates multiple handles. This is misleading for API users.
📝 Suggested docstring tweak
- mm_handles: List of multimodal embedding handles. Currently only a single handle is supported. + mm_handles: List of multimodal embedding handles, one per multimodal item.tensorrt_llm/_torch/models/modeling_llava_next.py (1)
170-201:⚠️ Potential issue | 🟡 MinorUpdate
mm_handlesdocstring for multi-handle support.The docstring still states that only a single handle is supported, but the code now validates all handles. Please update the description to match behavior.
📝 Suggested docstring tweak
- mm_handles: List of multimodal embedding handles. Currently only a single handle is supported. + mm_handles: List of multimodal embedding handles, one per multimodal item.
🧹 Nitpick comments (1)
tensorrt_llm/_torch/models/modeling_qwen2vl.py (1)
1055-1086: Update the docstring to reflect multi‑handle support.
The argument description still states a single handle, but the code now accepts multiple.✏️ Docstring update
- mm_handles: List of multimodal embedding handles. Currently only a single handle is supported. + mm_handles: List of multimodal embedding handles, one per multimodal item.
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| # requests. | ||
| cache_transceiver_cfg = CacheTransceiverConfig( | ||
| backend="DEFAULT") if pd_disagg else None | ||
| backend="DEFAULT", max_tokens_in_buffer=10240) if pd_disagg else None |
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Do you think it’s in general necessary to set max_tokens_in_buffer for EPD applications? If so, could we improve the error handling when it’s not explicitly set (e.g., provide a clearer error or default behavior)?
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I was actually going to ask you about this as well. If configured incorrectly, it seems to be causing issues in the decode worker - so how is this usually handled in P/D disagg for LLMs? Do we also need to tune this wrt to the maximum input sequence length?
I can certainly look into improving the error message, but do you mind if this is done in a subsequent PR?
* Why? Prior to this commit, we only supported a single multimodal input for E/P/D disaggregated serving. * What? This commit does a minor refactor of the multimodal embedding handles that cross process boundaries to enable this. Existing unit tests are updated accordingly to test this. Signed-off-by: William Zhang <133824995+2ez4bz@users.noreply.github.com>
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Summary by CodeRabbit
New Features
Bug Fixes
Tests
Description
Prior to this commit, we only supported a single multimodal input for E/P/D disaggregated serving.
This commit does a minor refactor of the multimodal embedding handles that cross process boundaries to enable this.
Existing unit tests are updated accordingly to test this.
Test Coverage
Adjusted existing unit tests to use multiple images in a single request.
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)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
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