PyTorch

{
  "run": {
    "kind": "BenchmarkRun",
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      "status": "passed",
      "timing": {
        "latencyNs": {
          "median": 23052032
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      "observedAt": "2024-09-04T12:59:48.000Z",
      "measurements": [
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          "statistic": "p20"
        },
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      "sourceNative": {
        "source": "liger-kernel-bench",
        "metrics": {
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        "benchmark": "kl_div/full",
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      "workloadDigest": "sha256:1132a095e1d9986df016e0176a61e301b849bee6a81f96affd8361823cc3863f",
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      "implementationDigest": "sha256:9d1db6030b44918b1cda264dd2cd4e22a87e96c0b0e342ebd47437ce292a270c"
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    "metadata": {
      "name": "liger-kl-div-full-torch-nvidia-h100-pcie-tokens16384-vocab32768-fp32-2024-09-04-12-59-48",
      "title": "KL-divergence loss · torch · full",
      "labels": {
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    },
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  },
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        "repository": "https://github.com/linkedin/Liger-Kernel"
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      "comparability": {
        "notes": "Timed: the forward pass plus the backward 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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        "architecture": "sm_90"
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      "title": "Liger benchmark host · H100 PCIe"
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}