PyTorch
Multi-token attention · k = 3 · seq = 64 · batch = 2 · heads_in = 4 · heads_out = 4 · bf16 · run 01a0202b-219b…
●passedReported evidence
Not re-observed recently.
Primary measurement
63.5µsmedian
Rank 1 in its comparison group · source-native comparison · observed 2025-04-28
ms 20 0.062463998794555664 · ms 50 0.06348799914121628 · ms 80 0.06348799914121628
Identity
implementationPyTorch
projectPyTorch
revisionunknown
workloadk = 3 · seq = 64 · batch = 2 · heads_in = 4 · heads_out = 4 · bf16
comparison keysha256:aaf496aa235cc668…
sourceLiger-Kernel benchmarks
external idmulti_token_attention/backward/torch/nvidia-geforce-rtx-3090/batch2-heads_in4-heads_out4-k3-seq64-bf16/2025-04-28-04-46-15
sha256:2d73a5e262a4408014db8b8…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
k3
seq64
batch2
heads_in4
heads_out4
biasbf16 [4]
scoresbf16 [2, 4, 64, 64]
weightbf16 [4, 4, 3, 3]
definition comparatornot_asserted
Measurements
latency · median63.5 µs
latency · p2062.5 µs
latency · p8063.5 µs
Protocol
harnessLiger-Kernel benchmark scripts
timercuda_events
primaryStatisticmedian
comparabilityFamilyliger_kernel_bench
Environment
gpuNVIDIA GeForce RTX 3090 (sm_86)
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.
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Canonical manifest
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"notes": "Timed: the backward pass of the module output given a random upstream gradient, via triton.testing.do_bench with quantiles 0.5/0.2/0.8 (median with p20/p80 spread). The upstream CSV records no CUDA, driver, or torch version; the Liger release rides each run's labels. Comparable only within one kernel, workload, timed pass, and GPU.",
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}
}Cite this record (permalink, digest, access date)
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Published 2026-08-20 · Liger-Kernel benchmarks · BSD-2-ClauseAll results for Multi-token attention →JSON