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submission 551567

idanbeck · python · License unknown
Kernel source · 25 lines ↓

Use it

Vendorable · source mirrored · license unknownView source →

No package. Vendor the mirrored source: 25 lines, June 9 Researcher Reciprocity License v1.0.

submission.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-grayscale-v2-551567?include=source"
interfacepython
Compatibility
measured onNVIDIA A100
declared hardwareNVIDIA A100
architecturessm_80
dtypesfp32

Benchmark evidence

1 measurement across 1 GPU, fastest first.

Operation / workload
Hardware
Latency
Rank
Observed
RGB to grayscalesuite of 6 cases
NVIDIA A100
8.08ms
#50 of 137
2026-03-14
Run →

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:ee93c7228ab95ae0c1160c5dce3631aeb69602e0207c25e9fb243aff769368a2
license declaredunknown
license concludedunknown
authorsidanbeck
imported2026-08-15

Techniques

Extracted from the mirrored source by pattern, never inferred. Each row cites its line.

autotunereturn torch.compile(f, fullgraph=True, mode="max-autotune")

Kernel source

submission.py25 lines
#!POPCORN leaderboard grayscale_v2
#!POPCORN gpu A100
import torch
_F = None


def _build():
    def f(x):
        return x[..., 0] * 0.2989 + x[..., 1] * 0.5870 + x[..., 2] * 0.1140

    try:
        return torch.compile(f, fullgraph=True, mode="max-autotune")
    except Exception:
        return f


@torch.inference_mode()
def custom_kernel(data):
    global _F
    rgb, out = data
    if _F is None:
        _F = _build()
    out[...] = _F(rgb)
    return out

Source code from GPU Mode and the KernelBot dataset · June 9 Researcher Reciprocity License v1.0

Changes from previous submission

Against this author's previous submission submission 551534.

  #!POPCORN leaderboard grayscale_v2
  #!POPCORN gpu A100
  import torch
- _W=None
- _F=None
+ _F = None
   
- def _w(device,dtype):
-     global _W
-     if _W is None or _W.device!=device or _W.dtype!=dtype:
-         _W=torch.tensor([0.2989,0.5870,0.1140],device=device,dtype=dtype)
-     return _W
   
- def _build(dtype):
+ def _build():
      def f(x):
-         return x[...,0]*0.2989 + x[...,1]*0.5870 + x[...,2]*0.1140
+         return x[..., 0] * 0.2989 + x[..., 1] * 0.5870 + x[..., 2] * 0.1140
+  
      try:
-         return torch.compile(f, fullgraph=True, mode='max-autotune')
+         return torch.compile(f, fullgraph=True, mode="max-autotune")
      except Exception:
          return f
   
+  
  @torch.inference_mode()
  def custom_kernel(data):
      global _F
-     rgb,out=data
+     rgb, out = data
      if _F is None:
-         _F = _build(rgb.dtype)
+         _F = _build()
      out[...] = _F(rgb)
      return out
scrolls · 36 diff lines total

Best evidence level for this revision: reported

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