perf(models): load three GLiClass models with bucketed CUDA graphs - #372
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gliclass-base-v1.0, gliclass-large-v1.0 and opir-multitask-large-v1.0 now
load with adapter_options.loadtime.cuda_graphs: bucketed. On CUDA their
forwards replay as CUDA graphs over sequence lengths padded to buckets.
This changes their scores. Probabilities can differ from eager execution by
up to 0.0049 (gliclass-base-v1.0), 0.0056 (gliclass-large-v1.0) and 0.0144
(opir-multitask-large-v1.0). The top label was unchanged in all 11,538
answers per model:
- 384 CVE descriptions from examples/typed-decisions, three questions each,
asked one at a time, as separate groups and as joint groups;
- 65 long documents;
- each input sent three times, long documents twice.
Usage and billing do not change.
On an L4, median latency of one call, eager -> graphs:
| Model | flat call | separate-groups call |
|---|---|---|
| gliclass-base-v1.0 | 17.7 -> 3.2 ms | 21.2 -> 8.9 ms |
| gliclass-large-v1.0 | 32.5 -> 8.4 ms | 36.2 -> 20.9 ms |
| opir-multitask-large-v1.0 | 32.2 -> 8.7 ms | 35.7 -> 20.3 ms |
The three models' graphs held about 1.4 GB together on one L4. Each model's
graphs are capped at 4% of device memory, so about 2.7 GB for the three at
most.
To revert a model to eager execution, set cuda_graphs: off in its profile
(quoted or not). A request can also send options={"cuda_graphs": "off"}.
The other DeBERTa-v3 GLiClass models stay eager: gliclass-small-v1.0,
gliclass-base-v3.0, gliclass-large-v3.0 and the instruct models each changed
a near-tie top label on the same inputs. So do the mDeBERTa and ModernBERT
models.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Included review availability: Your plan provides up to 10 included reviews per hour; 2 remain after this review. 📝 WalkthroughWalkthroughThree model profiles now enable bucketed CUDA graphs by default. Documentation reports comparisons with eager execution and describes eager opt-out. A test checks the shipped profile settings and opt-out. ChangesCUDA graph defaults
Suggested reviewers: Priority: ➖ Normal Merge Risk: ⚪ Minimal · up to The three profiles enable bucketed graphs, requests can opt out, and both READMEs report the same probability bound. No concrete issue remains that should block merging. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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In `@packages/sie_server/README.md`:
- Line 113: Update the `opir-multitask-large-v1.0` probability bound to include
the measured maximum, using `0.0144` or rounding upward to `0.015`. In
`packages/sie_server/README.md` lines 113-113 and `packages/sie_sdk/README.md`
lines 157-158, apply the same corrected bound.
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packages/sie_sdk/README.mdpackages/sie_server/README.mdpackages/sie_server/models/knowledgator__gliclass-base-v1.0.yamlpackages/sie_server/models/knowledgator__gliclass-large-v1.0.yamlpackages/sie_server/models/knowledgator__opir-multitask-large-v1.0.yamlpackages/sie_server/tests/adapters/test_gliclass_cuda_graphs.py
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The README table rounded each model's largest probability change to three decimals, so 0.0144 on opir-multitask-large-v1.0 read as 0.014 and was no longer an upper bound. Both READMEs now give the measured values. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Summary
This changes scores.
gliclass-base-v1.0,gliclass-large-v1.0andopir-multitask-large-v1.0now load withadapter_options.loadtime.cuda_graphs: bucketed(#367). On CUDA, their forwards replay as CUDA graphs over sequence lengths padded to buckets. Their probabilities can differ from eager execution:The top label was unchanged in all 11,538 answers per model on the evaluated sets. Usage and billing do not change.
Revert: to run one of these models eagerly again, set
cuda_graphs: offin its profile (quoted or not). A single request can sendoptions={"cuda_graphs": "off"}.Unchanged: every other GLiClass model stays eager.
gliclass-large-v3.0stays eager.Why these three
The rule: enable a DeBERTa-v3 model only if
bucketedchanges no top label and moves no probability by more than 0.02 against eager main. I checked each model's encoder in its config at the pinned revision.Evaluated sets.
examples/typed-decisions, three questions each, asked three ways: one at a time (flat), as separate label groups, and as joint label groups. Plus the 65-document long-text set (1.4 KB to 1 MB).Each cell is max |Δp| (top labels changed):
Flips come in threes because each input was sent three times: gliclass-small-v1.0, gliclass-base-v3.0 and gliclass-large-v3.0 each have one near-tie input whose top label changes.
Usage matched eager main in every call for every model.
The table was measured on an earlier revision of feat(server): replay GLiClass forwards as CUDA graphs #367 (149e7a6). I measured the three enabled models again on feat(server): replay GLiClass forwards as CUDA graphs #367 at 395be4c, with this branch's profiles, and got the same maxima and no top-label change. feat(server): replay GLiClass forwards as CUDA graphs #367's later commits change only error handling and validation.
Excluded without a new measurement:
gliclass-multilang-miniandopir-multitask-multilang-v1.0use mDeBERTa. gliclass-multilang-mini moved up to 0.023 and changed 3 of 900 top labels in feat(server): replay GLiClass forwards as CUDA graphs #367's measurements.gliclass-multilang-edgeand the Opir edge models use ModernBERT, where graphs do not apply.examples/typed-decisionsrunsgliclass-large-v1.0through a server, so a re-run against these profiles gets bucketed scores. Top labels matched on its inputs. I did not re-check its prior-weighted decision rules, which can turn on smaller score differences than a top-label change.Speed
Measured in-process on one L4, in a container with 10 vCPUs. Median / p90, ms, over 384 records, including the first pass, which records every graph:
Through the server,
mise run serve -m knowledgator/gliclass-large-v1.0with the shipped profile, 40 records overSIEClient:cuda_graphs: off;cuda_graphs: "off"ran eagerly, and one with"bucketed"got a 400.Memory
The driver also keeps a copy of each graph: about 8 MB for a DeBERTa-large forward.
Tests
test_shipped_profiles_enable_bucketed_graphs_only_where_measuredpins the set of shipped GLiClass profiles that enable graphs. It also checks that those profiles build adapters inbucketedmode that still accept a per-requestoff.mise run lintandmise run typecheckpass.mise run test: 8,251 passed, 256 skipped.🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
gliclass-base-v1.0,gliclass-large-v1.0, andopir-multitask-large-v1.0now use bucketed CUDA graphs by default. Each can still be run with eager execution.Documentation