PyTorch

{
  "run": {
    "kind": "BenchmarkRun",
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      "status": "passed",
      "timing": {
        "latencyNs": {
          "median": 13523457
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        "primaryStatistic": "median"
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      "measurements": [
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        },
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      "sourceNative": {
        "source": "liger-kernel-bench",
        "metrics": {
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        "benchmark": "sparsemax/full",
        "externalId": "sparsemax/full/torch/nvidia-geforce-rtx-3090/features16384-tokens2048-fp32/2025-04-28-00-38-20"
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      "implementationDigest": "sha256:e496948f60e3e1a1ed83175ec6976b730d22c5a670f0b3813918a5a72833bb23"
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    "metadata": {
      "name": "liger-sparsemax-full-torch-nvidia-geforce-rtx-3090-features16384-tokens2048-fp32-2025-04-28-00-38-20",
      "title": "Sparsemax · torch · full",
      "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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      "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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        "product": "NVIDIA GeForce RTX 3090",
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      "title": "Liger benchmark host · GeForce RTX 3090"
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    "apiVersion": "kernelindex.dev/v1alpha1"
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}