[https://nvbugs/6732123][fix] Correct V2 KV cache quota estimation - #18988
[https://nvbugs/6732123][fix] Correct V2 KV cache quota estimation#18988yizhang-nv wants to merge 9 commits into
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Signed-off-by: Yi Zhang <187001205+yizhang-nv@users.noreply.github.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. WalkthroughThe change unifies KV-cache sizing, preserves independent target and draft costs during budget splitting, adds V2 manager selection to benchmarks, and expands runtime-backed allocation coverage. ChangesKV-cache estimation
Priority: ⚪ Not assessed Estimated code review effort: 4 (Complex) | ~45 minutes Change: Bug fix Sequence Diagram(s)sequenceDiagram
participant CacheCostEstimator
participant KVCacheManagerV2
participant DraftCacheManager
CacheCostEstimator->>KVCacheManagerV2: request target cost with max_num_tokens
KVCacheManagerV2-->>CacheCostEstimator: return target slope and intercept
CacheCostEstimator->>DraftCacheManager: request draft cost with max_num_tokens
DraftCacheManager-->>CacheCostEstimator: return draft slope and intercept
CacheCostEstimator-->>CacheCostEstimator: split budgets using independent affine costs
Merge Risk: 🔵 Low · up to The sizing changes have broad focused validation, but two previously identified test-coverage concerns remain open around large-batch draft capacity and removed cache-mode agreement coverage. These are bounded validation risks rather than demonstrated runtime failures. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1⚔️ Resolve merge conflicts 💡
🧪 Generate unit tests (beta)
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Inline comments:
In `@tensorrt_llm/_torch/pyexecutor/_util.py`:
- Around line 810-814: Update the draft-manager call to _per_manager_cache_cost
in the budget-split estimation path to pass is_draft=True, preserving the fixed
DFlash SWA reservation for two-model draft costs. Add a regression test covering
self._draft_model_engine with DFlash and assert that the calculated draft cost
retains its fixed intercept.
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tensorrt_llm/_torch/pyexecutor/_util.pytensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.pytests/unittest/_torch/executor/kv_cache/test_kv_cache_budget_split.pytests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.pytests/unittest/_torch/executor/kv_cache/test_mamba_cache_manager.py
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Signed-off-by: Yi Zhang <187001205+yizhang-nv@users.noreply.github.com>
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Signed-off-by: Yi Zhang <187001205+yizhang-nv@users.noreply.github.com>
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Inline comments:
In `@tests/unittest/_torch/executor/kv_cache/test_kv_cache_budget_split.py`:
- Line 160: Strengthen the assertion in the large-batch test case around
max_batch_size 2048 to require at least one allocatable draft KV block, using
the draft cache cost and block geometry already exposed by the test setup.
Replace the weak positive max_gpu_total_bytes check while preserving the
existing test scenario.
In `@tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py`:
- Line 660: Extend the parameterized coverage around DFlashDecodingConfig and
PARDDecodingConfig to compare static cache cost against the runtime quota for
normal, draft, scratch-reuse, and multiple max_tokens scenarios. Restore the
removed cases or add equivalent assertions while preserving the existing
allocation coverage.
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Description
Fix three V2 KV cache sizing failures in one PR: PARD's separate draft pool can be too small to allocate or resume, GPT-OSS with finite window overrides can report hundreds of GiB of mandatory fixed cache, and layer-wise hybrid benchmarks can underallocate attention pages for their synthetic requests.
CacheCost(slope, intercept)model. Explicit nativefull_attentionlayers contribute to the slope even when a runtime window override bounds retention; native SWA and external draft pools retain fixed costs. Runtime windows are unchanged. No separate budget-estimation mode orfor_budget_splitparameter is needed. Configured target/draft quotas still sum to the input budget.avg_seq_len=max_seq_lenin the layer-wise benchmark, where every dummy request actually has that length. Keep this workload correction in the benchmark rather than changing the hybrid pool solver.PARD/DFlash allocation and resume regressions run in the GPU unit-test lane. The focused Nemotron/Qwen3-Next benchmark cases select V2 directly, without adding duplicate V1 CI cases. This PR incorporates the fixes from #18932 and #18954.
The native-full classification relies on explicit layer metadata; configs without it retain existing behavior. This is a static affine estimate, not an exact saturation curve for arbitrary all-SWA workloads.
The SWA context and boundary-page reservations also apply to ordinary target models with speculative decoding disabled. For a Gemma3-27B-shaped configuration (30 SWA layers at window 1024, 10 full-attention layers, 2048 bytes/token/layer, 64-token blocks, batch size 8, and
max_num_tokens=8192), the fixed cost with scratch reuse disabled increases from 480 MiB (~0.47 GiB) to 990 MiB (~0.97 GiB): 480 MiB for context capacity and 30 MiB for retained boundary pages. Lower estimated token capacity under the same GPU memory budget is an expected consequence of correcting the earlier underestimation. Static estimation reads scratch reuse fromKvCacheConfigand keeps it disabled for draft managers, matching runtime.Test Coverage
e13e487195; the related suite passed again on the merged revision: 142 passed. Main removed the Llama-3.1-8B accuracy suite in [None][test] clean Llama-3.1-8B accuracy test coverage #18809, including the old PARD overlap cases; this PR preserves that removal and retains the CUDA-backed PARD/DFlash quota regressions.cpu_onlyestimator file intotest_kv_cache_manager_v2.py. Existing H100/B300/GB300 executor entries cover that file with-m "not cpu_only"; adding a GPU marker alone would not cancel an inherited CPU marker. Validated the CI marker expressions on B200: 40 passed, 54 deselected with CUDA hidden and-m cpu_only; 54 passed, 3 deselected with-m "not cpu_only", including both PARD and DFlash regressions with no skips.resize(1035, 1024)); the current estimator passes.ptxas); Qwen3-Next validation is incomplete, with no runtime quota result from this run.PR Checklist
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