import{s as AT,n as QT,o as wT}from"../chunks/scheduler.f3b1e791.js";import{S as mT,i as RT,e as O,s as t,c as M,h as DT,a as y,d as e,b as U,f as iT,g as I,j as A,k as aT,l as kT,m as J,n,t as s,o as i,p as a}from"../chunks/index.023a9934.js";import{C as rT}from"../chunks/CopyLLMTxtMenu.ee636735.js";import{C as Q}from"../chunks/CodeBlock.5aa52fca.js";import{H as m,E as pT}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.9b009efe.js";function cT(Fl){let w,Ul,Jl,Ol,R,Ml,D,yl,k,Hl="Use <code>kernels benchmark</code> to run benchmark scripts shipped with a kernel repository.",Il,r,Ll="The command:",nl,p,zl="<li>Downloads the kernel repo at a specific <strong>branch</strong> or <strong>version</strong></li> <li>Runs all <code>benchmarks/benchmark*.py</code> scripts</li> <li>Times each <code>benchmark_*</code> workload and prints a results table</li> <li>Optionally saves results as JSON</li>",sl,c,il,g,Pl="<code>kernels benchmark</code> requires extra dependencies:",al,C,Al,o,Ql,u,wl,j,Kl="Example output:",ml,f,Rl,b,Dl,h,ql="You must specify which revision to benchmark, either via flags or with <code>@...</code> in the repo id:",kl,$,rl,d,pl,x,lT="Benchmark a tagged kernel version:",cl,N,gl,v,TT="Equivalent shorthand:",Cl,B,ol,W,eT="Benchmark a branch:",ul,V,jl,E,JT="Tune warmup and iteration count:",fl,Z,bl,G,tT="Save results to a file (JSON):",hl,Y,$l,X,UT="Benchmark a local kernel checkout (must contain <code>benchmarks/</code>):",dl,S,xl,_,Nl,F,OT="<li>By default, a table is printed (timings in ms).</li> <li><code>--output &lt;file&gt;.json</code> writes a JSON payload to disk.</li>",vl,H,Bl,L,MT=`Benchmark scripts must live under <code>benchmarks/</code> in the kernel repository and match <code>benchmark*.py</code>.
Each script should define one or more subclasses of <code>kernels.benchmark.Benchmark</code>.`,Wl,z,yT="Minimal example (<code>benchmarks/benchmark_activation.py</code>):",Vl,P,El,K,IT="The runner will:",Zl,q,nT="<li>Call <code>setup()</code> once per workload (or <code>setup_&lt;workload&gt;()</code> if present)</li> <li>Warm up (<code>--warmup</code>)</li> <li>Time <code>benchmark_&lt;workload&gt;()</code> for <code>--iterations</code></li> <li>If <code>verify_&lt;workload&gt;()</code> exists, check that outputs match (<code>torch.allclose(..., atol=1e-2)</code>) and show a speedup vs the reference computation</li>",Gl,ll,Yl,Tl,sT="<li>If the repo does not contain a <code>benchmarks/</code> directory (or no <code>benchmark*.py</code> files), the command exits with an error.</li> <li>If a benchmark script defines no <code>Benchmark</code> subclasses, the command exits with an error.</li> <li>If <code>verify_&lt;workload&gt;()</code> exists and the outputs do not match, the command exits with an error.</li>",Xl,el,Sl,tl,_l;return R=new rT({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),D=new m({props:{title:"kernels benchmark",local:"kernels-benchmark",headingTag:"h1"}}),c=new m({props:{title:"Installation",local:"installation",headingTag:"h2"}}),C=new Q({props:{code:"dXYlMjBwaXAlMjBpbnN0YWxsJTIwJ2tlcm5lbHMlNUJiZW5jaG1hcmslNUQnJTIwJTIzJTIwb3IlMjBwaXAlMjBpbnN0YWxsJTIwJ2tlcm5lbHMlNUJiZW5jaG1hcmslNUQn",highlighted:'uv pip install <span class="hljs-string">&#x27;kernels[benchmark]&#x27;</span> <span class="hljs-comment"># or pip install &#x27;kernels[benchmark]&#x27;</span>',wrap:!1}}),o=new m({props:{title:"Example",local:"example",headingTag:"h2"}}),u=new Q({props:{code:"a2VybmVscyUyMGJlbmNobWFyayUyMGtlcm5lbHMtY29tbXVuaXR5JTJGYWN0aXZhdGlvbiUyMC0tdmVyc2lvbiUyMDE=",highlighted:"kernels benchmark kernels-community/activation --version 1",wrap:!1}}),f=new Q({props:{code:" ",highlighted:`Downloading kernels-community/activation@v1...
Running benchmark.py...

  GPU      Apple M3 Max (30 cores)
  CPU      Apple M3 Max
  OS       Darwin 25.2.0
  PyTorch  2.10.0

  Running SiluWorkloads on mps

┌───────────────┬────────────┬─────┬───────────┬────────────┬───────────┬───────────┬───────────┬───────────┬────────────┬───────────┬─────────┐
│ Benchmark     │ Workload   │   N │ Speedup   │   Mean(ms) │   Std(ms) │   Min(ms) │   Max(ms) │   IQR(ms) │   Outliers │   Ref(ms) │ Match   │
├───────────────┼────────────┼─────┼───────────┼────────────┼───────────┼───────────┼───────────┼───────────┼────────────┼───────────┼─────────┤
│ SiluWorkloads │ large      │ 100 │ 1.72x     │     6.5153 │    0.4343 │    6.2883 │    8.4699 │    0.1701 │          8 │   11.2048 │ ✓       │
│ SiluWorkloads │ medium     │ 100 │ 2.48x     │     1.1813 │    0.3976 │    1.04   │    4.2146 │    0.0698 │          5 │    2.9332 │ ✓       │
│ SiluWorkloads │ small      │ 100 │ 1.96x     │     0.4909 │    0.2175 │    0.4407 │    2.6438 │    0.0085 │         16 │    0.9622 │ ✓       │
└───────────────┴────────────┴─────┴───────────┴────────────┴───────────┴───────────┴───────────┴───────────┴────────────┴───────────┴─────────┘

  large: 1.72x faster (95% CI: 6.4302-6.6004ms vs ref 11.2048ms) ✓ significant
  medium: 2.48x faster (95% CI: 1.1034-1.2592ms vs ref 2.9332ms) ✓ significant
  small: 1.96x faster (95% CI: 0.4483-0.5335ms vs ref 0.9622ms) ✓ significant

Kernel: 2385e44  Benchmark: 5b53516`,wrap:!1}}),b=new m({props:{title:"Usage",local:"usage",headingTag:"h2"}}),$=new Q({props:{code:"a2VybmVscyUyMGJlbmNobWFyayUyMCUzQ3JlcG9faWQlM0UlMjAtLXZlcnNpb24lMjAlM0NOJTNFJTBBa2VybmVscyUyMGJlbmNobWFyayUyMCUzQ3JlcG9faWQlM0UlMjAtLWJyYW5jaCUyMCUzQ25hbWUlM0UlMEFrZXJuZWxzJTIwYmVuY2htYXJrJTIwJTNDcmVwb19pZCUzRSU0MHYlM0NOJTNFJTBBa2VybmVscyUyMGJlbmNobWFyayUyMCUzQ3JlcG9faWQlM0UlNDAlM0NicmFuY2glM0U=",highlighted:`kernels benchmark &lt;repo_id&gt; --version &lt;N&gt;
kernels benchmark &lt;repo_id&gt; --branch &lt;name&gt;
kernels benchmark &lt;repo_id&gt;@v&lt;N&gt;
kernels benchmark &lt;repo_id&gt;@&lt;branch&gt;`,wrap:!1}}),d=new m({props:{title:"Examples",local:"examples",headingTag:"h2"}}),N=new Q({props:{code:"a2VybmVscyUyMGJlbmNobWFyayUyMGtlcm5lbHMtY29tbXVuaXR5JTJGYWN0aXZhdGlvbiUyMC0tdmVyc2lvbiUyMDE=",highlighted:"kernels benchmark kernels-community/activation --version 1",wrap:!1}}),B=new Q({props:{code:"a2VybmVscyUyMGJlbmNobWFyayUyMGtlcm5lbHMtY29tbXVuaXR5JTJGYWN0aXZhdGlvbiU0MHYx",highlighted:"kernels benchmark kernels-community/activation@v1",wrap:!1}}),V=new Q({props:{code:"a2VybmVscyUyMGJlbmNobWFyayUyMGtlcm5lbHMtY29tbXVuaXR5JTJGYWN0aXZhdGlvbiUyMC0tYnJhbmNoJTIwbWFpbg==",highlighted:"kernels benchmark kernels-community/activation --branch main",wrap:!1}}),Z=new Q({props:{code:"a2VybmVscyUyMGJlbmNobWFyayUyMGtlcm5lbHMtY29tbXVuaXR5JTJGYWN0aXZhdGlvbiU0MHYxJTIwLS13YXJtdXAlMjAyMCUyMC0taXRlcmF0aW9ucyUyMDIwMA==",highlighted:"kernels benchmark kernels-community/activation@v1 --warmup 20 --iterations 200",wrap:!1}}),Y=new Q({props:{code:"a2VybmVscyUyMGJlbmNobWFyayUyMGtlcm5lbHMtY29tbXVuaXR5JTJGYWN0aXZhdGlvbiU0MHYxJTIwLS1vdXRwdXQlMjByZXN1bHRzLmpzb24=",highlighted:"kernels benchmark kernels-community/activation@v1 --output results.json",wrap:!1}}),S=new Q({props:{code:"a2VybmVscyUyMGJlbmNobWFyayUyMC4lMkZteV9rZXJuZWw=",highlighted:"kernels benchmark ./my_kernel",wrap:!1}}),_=new m({props:{title:"Output",local:"output",headingTag:"h2"}}),H=new m({props:{title:"Writing Benchmark Scripts",local:"writing-benchmark-scripts",headingTag:"h2"}}),P=new Q({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch

<span class="hljs-keyword">from</span> kernels.benchmark <span class="hljs-keyword">import</span> Benchmark


<span class="hljs-keyword">class</span> <span class="hljs-title class_">ActivationBenchmark</span>(<span class="hljs-title class_ inherited__">Benchmark</span>):
    seed = <span class="hljs-number">0</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">setup</span>(<span class="hljs-params">self</span>):
        self.x = torch.randn(<span class="hljs-number">128</span>, <span class="hljs-number">1024</span>, device=self.device, dtype=torch.float16)
        self.out = torch.empty(<span class="hljs-number">128</span>, <span class="hljs-number">512</span>, device=self.device, dtype=torch.float16)

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">benchmark_silu_and_mul</span>(<span class="hljs-params">self</span>):
        self.kernel.silu_and_mul(self.out, self.x)

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">verify_silu_and_mul</span>(<span class="hljs-params">self</span>):
        <span class="hljs-comment"># Return reference tensor; runner compares with self.out</span>
        <span class="hljs-keyword">return</span> torch.nn.functional.silu(self.x[..., :<span class="hljs-number">512</span>]) * self.x[..., <span class="hljs-number">512</span>:]`,wrap:!1}}),ll=new m({props:{title:"Troubleshooting",local:"troubleshooting",headingTag:"h2"}}),el=new 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