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[https://nvbugs/6739116][fix] Reserve full-batch logits memory in KV cache estimation - #20817
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…cache estimation The KV cache capacity estimation profiles a context-only dummy request, which produces a single logits row. A serving step that runs eagerly (outside CUDA graphs) produces one row per scheduled request, and LogitsProcessor holds both the model-dtype logits and their float32 copy at that point. That vocab-sized working set was never part of the measured peak, so the KV cache was sized into memory the step later needs and the step failed with a CUDA OOM at logits.float(). Add the logits working set of a full-batch step to the profiled peak before the KV cache budget is computed, as the multimodal encoder reserve already does for its unprofiled memory. Signed-off-by: trtllm-agent <296075020+trtllm-agent@users.noreply.github.com>
WalkthroughKV cache capacity estimation now includes a reserve for eager-step logits memory. The estimate accounts for token limits, beam width, speculative decoding, padded vocabulary size, and pipeline rank. ChangesLogits memory reserve
Priority: ➖ Normal Estimated code review effort: 2 (Simple) | ~10 minutes Change: Bug fix Suggested reviewers: Merge Risk: 🔵 Low · up to
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| Check name | Status | Explanation | Resolution |
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Docstring coverage is 45.45% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 11 functions across 2 files. | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Title check The title follows the required NVBugs and type format and clearly describes the main fix: reserving full-batch logits memory during KV cache estimation. Description check The description clearly explains the issue, root cause, implementation, test coverage, verification results, tradeoffs, and scope. It uses different headings from the template, but it provides the req… Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Full details: Description check
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Explanation
The description clearly explains the issue, root cause, implementation, test coverage, verification results, tradeoffs, and scope. It uses different headings from the template, but it provides the required substantive information and is mostly complete.
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Review comments at @tensorrt_llm/_torch/pyexecutor/_util.py:
- Around line 1632-1633: Add a capacity-sizing test case with nonzero
`vocab_size_padded` so `_get_logits_memory_reserve()` returns a nonzero value,
and assert that `_cal_max_memory` receives the increased `peak_memory` or that
the resulting KV budget includes the reserve.
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tensorrt_llm/_torch/pyexecutor/_util.pytests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py
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| logits_memory_reserve = self._get_logits_memory_reserve() | ||
| peak_memory += logits_memory_reserve |
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rg -n 'configure_kv_cache_capacity|_get_logits_memory_reserve|_cal_max_memory|vocab_size_padded' tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py
sed -n '1220,1330p' tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py
sed -n '1620,1655p' tensorrt_llm/_torch/pyexecutor/_util.pyRepository: NVIDIA/TensorRT-LLM
Length of output: 7408
Test capacity sizing with a nonzero logits reserve.
The capacity-sizing test sets vocab_size_padded = None, so _get_logits_memory_reserve() returns zero. The test cannot detect removal of peak_memory += logits_memory_reserve. Add a nonzero-vocabulary case that asserts _cal_max_memory receives the increased peak or that the resulting KV budget includes the reserve.
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Review comment at @tensorrt_llm/_torch/pyexecutor/_util.py around lines 1632 -
1633:
Add a capacity-sizing test case with nonzero `vocab_size_padded` so
`_get_logits_memory_reserve()` returns a nonzero value, and assert that
`_cal_max_memory` receives the increased `peak_memory` or that the resulting KV
budget includes the reserve.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
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Reopening: #21177 hasn't merged yet, so this stays open until the owner decides between the two approaches. |
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bug closed |
Summary
KV cache capacity estimation now reserves the logits working set of a full-batch serving step, which the profiling run never materializes. Without it, the KV cache is sized into memory that an eager (non-CUDA-graph) step needs later, and that step fails with a CUDA OOM at
logits.float().Fixes https://nvbugs/6739116
Root cause
KvCacheCreator.configure_kv_cache_capacitymeasures peak memory with a context-only dummy request. That forward produces a single logits row.LogitsProcessorholds the model-dtype logits and their float32 copy at the same time, so the step needsrows * vocab * (dtype + 4)bytes that the measured peak never included.max_batch_size=512, that is about 0.71 GiB per rank. On H100 withfree_gpu_memory_fraction=0.9, the KV pool takes that memory. Once the pool fills (~90% utilization, ~400 scheduled requests), the 390 MiBlogits.float()allocation fails with only ~377 MiB free.Change
KvCacheCreator._get_logits_memory_reserve(): on the last PP rank,min(max_num_tokens, max_batch_size * max_beam_width * tokens_per_gen_step) * vocab_size_padded * (dtype_size + 4)bytes.configure_kv_cache_capacityadds it to the profiled peak before computing the KV budget, the same way the existing multimodal encoder reserve is applied.max_num_tokensbound, beams and draft tokens, and non-last PP ranks. One existing test mock now setsvocab_size_paddedexplicitly (a bareMockattribute is not a number).Verification
H100 80GB, 4 GPUs (EP4/TP4), release/1.3 CI image (PyTorch 26.09),
perf/test_perf.py::test_perf[qwen3.5_122b_a10b-bench-pytorch-bfloat16-input_output_len:500,2000-ep:4-tp:4-gpus:4]and[...input_output_len:128,128-ep:4-tp:4-gpus:4], 512 requests,max_batch_size=512:logits.float()(390 MiB requested, 377 MiB free)With the change, the log shows
Reserving 0.71 GiB for the logits of a full-batch step, and the KV budget drops from 12.72 to 12.08 GiB per rank.Unit tests:
tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py: 72 passed before the change, 76 passed with it. The 4 new tests fail without the product change.test_mla_workspace_reserve.py,test_py_executor_creator_mla_cache_reuse_sync.py,test_kv_cache_manager_v2_helix_superblock.py,test_dual_pool_kv_cache.py,test_quantization_for_cold_page.py,test_kv_cache_compression_manager.py): 189 passed before and after.Known effect and limits
qwen3.5_397b_a17b_fp8 ... 500,2000-ep:8-tp:8-gpus:8case from the original report was not re-run with this change. It no longer fails in recent QA runs; the current failures are the two 4-GPU 122B cases above.Dev Engineer Review
_get_logits_memory_reserveadds a reserve for eager-step logits to the profiled peak before KV capacity is calculated. The reserve accounts for model-dtype logits and their float32 copy. It returns zero when the rank does not have the LM head or the padded vocabulary size is unavailable.logits.float(). The eight-GPU Qwen3.5-397B case was not re-run. The report also notes thatmaindoes not include this reserve.QA Engineer Review
tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py. New tests cover row-count limits, beam width, speculative tokens, and zero reserve on pipeline ranks without the LM head.Per-File QA Perspective
tensorrt_llm/_torch/pyexecutor/_util.py: Verify the reserve uses the correct padded vocabulary size, model dtype, speculative token count, and full scheduled-row limit. Also verify that only the final pipeline rank reserves logits memory and that the added reserve does not reduce KV capacity incorrectly.tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py: Covers reserve calculation and its key conditions, including pipeline ranks without the LM head. It is not an integration test and is not listed in the integration test lists.