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[None][fix] skip unused multimodal encoder IPC handles - #19791
yechank-nvidia merged 1 commit into
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An mm_encoder_only executor exported every multimodal result as a CUDA IPC handle on every rank, but PyExecutor._enqueue_responses only returns the responses of rank 0 and, with attention DP, of the ranks gathered into it. Handles exported on the other ranks are never opened, so PyTorch keeps their allocations alive and the GPU memory of TP follower ranks grows with every request. Attach the multimodal results only on ranks whose responses reach the frontend. Signed-off-by: yechank <161688079+yechank-nvidia@users.noreply.github.com>
WalkthroughSampler construction now enables multimodal result export on rank 0 and attention-DP ranks in the TP group. Other ranks still mark requests complete but do not attach multimodal embeddings or MRoPE position data. ChangesMultimodal result export
Priority: ➖ Normal Estimated code review effort: 2 (Simple) | ~12 minutes Change: Bug fix Suggested reviewers: Merge Risk: 🟡 Moderate · up to If gather-all response delivery is reachable for multimodal encoder executors, responses from nonzero TP ranks would lack their multimodal results. Align the export condition with response delivery, or confirm that this configuration cannot occur, before merging. The memory fix for TP followers is otherwise sound. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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Review comments at @tensorrt_llm/_torch/pyexecutor/_util.py:
- Line 4129: Update the returns_responses decision so nonzero TP ranks in an
mm_encoder_only executor also export multimodal embeddings and mRoPE data when
gather_all_responses is enabled. Add a gather-all case to
test_multimodal_embedding_lengths.py that verifies this response delivery
behavior.
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Dev Engineer Review
The supplied diff shows only import-formatting changes in
tensorrt_llm/_torch/pyexecutor/_util.py. It does not show thereturn_mm_resultslogic described in the PR objectives, or changes tosampler.py. The reported behavior change is not verified by this diff.QA Engineer Review
No test changes.
Per-File QA Perspective
tensorrt_llm/_torch/pyexecutor/_util.py: The supplied diff shows import formatting only. It does not show a behavior change to verify.Description
Problem. A
MultimodalEncoder(mm_encoder_only) executor returns its outputs to the consumer as CUDA IPC handles.EarlyStopWithMMResult.update_requests()callsPyResult.append_mm_embeddings()and, for mRoPE models,set_mrope_position(). Both export tensors withSharedTensorContainer.from_tensor(), which usestorch.multiprocessing.reductions.reduce_tensor(). This export runs on every rank. However,PyExecutor._enqueue_responses()only delivers responses from rank 0, plus, with attention DP, the ranks of rank 0's TP group, whose responses are gathered into rank 0. With plain tensor parallelism, every TP follower therefore exports handles that are never opened.PyTorch frees an exported CUDA block only after a consumer has rebuilt the tensor and released it. If the producer drops its tensor first, PyTorch moves the block to its CUDA-IPC "limbo" list instead of returning it to the caching allocator. The
SharedTensorContainerdocstring states the same contract: everyreduce_tensorneeds a matching rebuild in a consumer. Follower handles have no consumer, so every TP follower keeps every multimodal result it produces. Its GPU memory grows with each request for the lifetime of the encoder worker.Fix.
EarlyStopWithMMResultgets areturn_mm_resultsflag, which defaults toTrue. When the flag isFalse,update_requests()still marks requests complete with the same finish reason, but it does not attach results, so no handle is created.instantiate_sampler()sets the flag with the same rule that_enqueue_responses()uses to decide whose responses reach the frontend:mapping.rank == 0 or (mapping.enable_attention_dp and 0 in mapping.tp_group).Why it is safe.
Truefor them._enqueue_responses()already discarded the responses carrying these handles. Only frontend code reads the handles (executor/result.py,llmapi/llm.py,serve/openai_server.py). On those ranks the request lifecycle (state, finish reason, cleanup) is unchanged.gather_all_responses, which makes every rank enqueue responses, is only enabled during the KV-cache estimation dry run.mm_encoder_onlyexecutors are non-generation models and skip that run.Measured impact (re-measured on this PR's base). Setup:
MultimodalEncoder(Qwen3-VL-2B-Instruct, tensor_parallel_size=2, max_batch_size=1).95cb50c358, in the TRT-LLM dev container with a build that matches that commit.generate()calls of 12. The requests cycle through three test images:seashore.pngandinpaint.pngeach produce 256 embedding rows, and61.jpgproduces 2040 rows. That averages 13.3 MiB of embedding and mRoPE tensors per request.nvidia-smiafter warmup and after each call. Only this job's processes were on the GPUs. There was one run per variant.95cb50c358)Scope and limitations.
mm_encoder_onlyexecutors with more than one rank are affected: a standaloneMultimodalEncoderor an EPD encoder worker. The LLM prefill and decode paths are unchanged.PyTorchModelEngine._prepare_inputs()clones the mRoPE tensors and re-exports them withset_mrope_position()on every rank. With TP > 1 and no attention DP, the follower ranks' copies are never consumed either. These tensors are small (3 x seq_lenposition ids plus deltas per request), but they accumulate the same way. A fix would gate that re-export on the same response-rank rule.mm_encoder_onlywith attention DP does not currently run on main. When an ADP rank is idle,_pad_attention_dp_dummy_request()calls_has_adp_dummy_kv_capacity(), which dereferences the KV-cache manager that the encoder does not have (AttributeError: 'NoneType' object has no attribute 'get_num_available_tokens'). The ADP clause is therefore covered by the unit test only.EarlyStopWithMMResultis internal, and the new argument defaults to the current behavior.Relation to other PRs. This PR is independent of #17876 (multimodal encoder cache unification). #17876 does not modify
sampler.py,instantiate_sampler(), the export helpers inllm_request.py, or_enqueue_responses(). A three-way merge of the two branches has no conflicts, so either PR can land first.Test Coverage
tests/unittest/_torch/executor/test_multimodal_embedding_lengths.py::test_mm_encoder_sampler_exports_results_only_on_response_ranks. It is CPU-only and has 4 cases: single rank, TP follower, attention-DP follower, and an attention-DP rank on a later PP stage.instantiate_sampler(..., mm_encoder_only=True)and runsupdate_requests().tp_followerandattention_dp_later_pp_stagecases fail.Commands run in the dev container (build matching
95cb50c358):CUDA_VISIBLE_DEVICES= pytest tests/unittest/_torch/executor/test_multimodal_embedding_lengths.py -m cpu_only: 19 passed, 1 deselected._util.pyandsampler.pyreverted to main: 2 failed (tp_follower,attention_dp_later_pp_stage), 2 passed.pytest tests/unittest/_torch/executor/test_multimodal_embedding_lengths.py(1x H200): 20 passed.pytest tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py -k "test_single_request_chat_multiple_images and qwen3_vl_2b and no_pd_disagg"(TP1 encoder-to-LLM handoff, 1x H200):test_single_request_chat_multiple_images[no_pd_disagg-qwen3_vl_2b]passed (1 passed, 27 deselected).pre-commit run --files <changed files>: all hooks pass. The mypytype-checkhook cannot run on the host, soscripts/run_mypy.shwas run in the dev container: no issues found.PR Checklist
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