PyTorch

{
  "run": {
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      "status": "passed",
      "timing": {
        "latencyNs": {
          "median": 32480
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        "primaryStatistic": "median"
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      "observedAt": "2026-06-10T21:03:38.000Z",
      "measurements": [
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        },
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      "sourceNative": {
        "source": "liger-kernel-bench",
        "metrics": {
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        "benchmark": "megatron_rms_norm/forward",
        "externalId": "megatron_rms_norm/forward/torch/nvidia-h100-80gb-hbm3/batch1-hidden4096-seq4096-bf16/2026-06-10-21-03-38"
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      "workloadDigest": "sha256:998e3bbc244509154ea2ea973d0657ac0b188aed764b12428012e635c4c3efcf",
      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:07486a893d05b3338eb1876548053826c12ab08c8afa12bcc829975f75e540c2"
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    "metadata": {
      "name": "liger-megatron-rms-norm-forward-torch-nvidia-h100-80gb-hbm3-batch1-hidden4096-seq4096-bf16-2026-06-1",
      "title": "RMSNorm, Megatron layout · torch · forward",
      "labels": {
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    },
    "apiVersion": "kernelindex.dev/v1alpha1"
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        "repository": "https://github.com/linkedin/Liger-Kernel"
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      "measurement": {
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        "primaryStatistic": "median"
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      "comparability": {
        "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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        "formFactor": "80GB HBM3",
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      "title": "Liger benchmark host · H100 80GB HBM3"
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    "apiVersion": "kernelindex.dev/v1alpha1"
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}