torch.compile (inductor)
ScaledDotProductAttention · num_heads = 32 · batch_size = 32 · sequence_length = 512 · embedding_dimension = 1024 · fp32 · run 01a03f0e-f6e4…
●passedReported evidence
Not re-observed recently.
Primary measurement
8.15ms±0.03 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 8.98 · min ms 8.02 · std ms 0.176 · mean ms 8.15
Identity
implementationtorch.compile (inductor)
projectPyTorch
revisionunknown
workloadnum_heads = 32 · batch_size = 32 · sequence_length = 512 · embedding_dimension = 1024 · fp32
comparison keysha256:e5b5cec6150b4f8d…
sourceKernelBench baseline timings
external idH100_Modal/torch-compile-inductor/level1/97_ScaledDotProductAttention.py
sha256:c71b9e8b8cbd22e775d2de3…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
num_heads32
batch_size32
sequence_length512
embedding_dimension1024
kfp32 [32, 32, 512, 1024]
qfp32 [32, 32, 512, 1024]
vfp32 [32, 32, 512, 1024]
definition comparatornot_asserted
Measurements
latency · max8.98 ms · n=100
latency · mean8.15 ms · n=100
latency · min8.02 ms · n=100
latency · std176.0 µs
Protocol
harnessKernelBench timing scripts
timercuda_events
primaryStatisticmean
comparabilityFamilykernelbench_baseline_timing
Environment
gpuNVIDIA H100 (sm_90)
Artifacts
No artifacts published with this run.
Replications and notes
No attestations yet.
Community attestations. They never change the evidence level; only a KernelIndex-controlled rerun does.
Add a reproduction or note
Canonical manifest
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{
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"timing": {
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"latencyNs": {
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]
},
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},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
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"statistic": "std"
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"metadata": {
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}
}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for ScaledDotProductAttention →JSON