PyTorch
Jensen-Shannon divergence loss · vocab = 8192 · tokens = 8192 · fp32 · run 01a016ae-6fb5…
●passedReported evidence
Not re-observed recently.
Primary measurement
4.85msmedian
Rank 2 in its comparison group · source-native comparison · observed 2024-10-02
ms 20 4.851238250732422 · ms 50 4.8528642654418945 · ms 80 4.854745864868164
Identity
implementationPyTorch
projectPyTorch
revisionunknown
workloadvocab = 8192 · tokens = 8192 · fp32
comparison keysha256:f2a76c7130f34e3a…
sourceLiger-Kernel benchmarks
external idjsd/forward/torch/nvidia-h100-pcie/tokens8192-vocab8192-fp32/2024-10-02-16-21-38
sha256:8615de7b363281a81e61215…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
vocab8192
tokens8192
student_log_probsfp32 [8192, 8192]
teacher_log_probsfp32 [8192, 8192]
definition comparatornot_asserted
Measurements
latency · median4.85 ms
latency · p204.85 ms
latency · p804.85 ms
Protocol
harnessLiger-Kernel benchmark scripts
timercuda_events
primaryStatisticmedian
comparabilityFamilyliger_kernel_bench
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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"notes": "Timed: the module forward pass, 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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"title": "Liger benchmark host · H100 PCIe"
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}
}Cite this record (permalink, digest, access date)
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Published 2026-08-18 · Liger-Kernel benchmarks · BSD-2-ClauseAll results for Jensen-Shannon divergence loss →JSON