PyTorch

{
  "run": {
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      "status": "passed",
      "timing": {
        "latencyNs": {
          "median": 10601536
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        "primaryStatistic": "median"
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      "measurements": [
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      "sourceNative": {
        "source": "liger-kernel-bench",
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        "benchmark": "jsd/full",
        "externalId": "jsd/full/torch/nvidia-h100-pcie/tokens8192-vocab8192-fp32/2024-10-02-16-21-43"
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      "name": "liger-jsd-full-torch-nvidia-h100-pcie-tokens8192-vocab8192-fp32-2024-10-02-16-21-43",
      "title": "Jensen-Shannon divergence loss · torch · full",
      "labels": {
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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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      "title": "Liger benchmark host · H100 PCIe"
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}