kernels/pr_321/en/_app/immutable/nodes/3.c81d995e.js

import{s as yl,o as Ml,n as P}from"../chunks/scheduler.f3b1e791.js";import{S as ul,i as fl,e as w,s as a,c as d,h as hl,a as T,d as s,b as o,f as $,g as y,j as U,k as j,l as i,m as p,n as M,t as u,o as f,p as h}from"../chunks/index.d8b6a549.js";import{C as wl}from"../chunks/CopyLLMTxtMenu.1edf0ddf.js";import{D as I,E as q}from"../chunks/ExampleCodeBlock.d686d227.js";import{C as K}from"../chunks/CodeBlock.05c913ee.js";import{H as b,E as Tl}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.a742978a.js";function Jl(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> torch.nn <span class="hljs-keyword">as</span> nn

<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> use_kernel_forward_from_hub
<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> Mode, kernelize

<span class="hljs-meta">@use_kernel_forward_from_hub(<span class="hljs-params"><span class="hljs-string">&quot;MyCustomLayer&quot;</span></span>)</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyCustomLayer</span>(nn.Module):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, hidden_size</span>):
        <span class="hljs-built_in">super</span>().__init__()
        self.hidden_size = hidden_size

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, x: torch.Tensor</span>):
        <span class="hljs-comment"># original implementation</span>
        <span class="hljs-keyword">return</span> x

model = MyCustomLayer(<span class="hljs-number">768</span>)

<span class="hljs-comment"># The layer can now be kernelized:</span>
<span class="hljs-comment"># model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE, device=&quot;cuda&quot;)</span>`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function gl(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> torch.nn <span class="hljs-keyword">as</span> nn

<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> use_kernel_func_from_hub
<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> Mode, kernelize

<span class="hljs-meta">@use_kernel_func_from_hub(<span class="hljs-params"><span class="hljs-string">&quot;my_custom_func&quot;</span></span>)</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">my_custom_func</span>(<span class="hljs-params">x: torch.Tensor</span>):
    <span class="hljs-comment"># Original implementation</span>
    <span class="hljs-keyword">return</span> x

<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyModel</span>(torch.nn.Module):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self</span>):
        <span class="hljs-built_in">super</span>().__init__()
        self.fn = my_custom_func

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, x</span>):
        <span class="hljs-keyword">return</span> self.fn(x)

model = MyModel()

<span class="hljs-comment"># The layer can now be kernelized:</span>
<span class="hljs-comment"># model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE, device=&quot;cuda&quot;)</span>`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function Ul(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"ZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjByZXBsYWNlX2tlcm5lbF9mb3J3YXJkX2Zyb21faHViJTBBaW1wb3J0JTIwdG9yY2gubm4lMjBhcyUyMG5uJTBBJTBBcmVwbGFjZV9rZXJuZWxfZm9yd2FyZF9mcm9tX2h1Yihubi5MYXllck5vcm0lMkMlMjAlMjJMYXllck5vcm0lMjIp",highlighted:`<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> replace_kernel_forward_from_hub
<span class="hljs-keyword">import</span> torch.nn <span class="hljs-keyword">as</span> nn

replace_kernel_forward_from_hub(nn.LayerNorm, <span class="hljs-string">&quot;LayerNorm&quot;</span>)`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function kl(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"aW1wb3J0JTIwdG9yY2glMEFpbXBvcnQlMjB0b3JjaC5ubiUyMGFzJTIwbm4lMEFmcm9tJTIwdG9yY2gubm4lMjBpbXBvcnQlMjBmdW5jdGlvbmFsJTIwYXMlMjBGJTBBJTBBZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjB1c2Vfa2VybmVsX2ZvcndhcmRfZnJvbV9odWIlMEFmcm9tJTIwa2VybmVscyUyMGltcG9ydCUyMHVzZV9rZXJuZWxfbWFwcGluZyUyQyUyMExheWVyUmVwb3NpdG9yeSUyQyUyMERldmljZSUwQWZyb20lMjBrZXJuZWxzJTIwaW1wb3J0JTIwTW9kZSUyQyUyMGtlcm5lbGl6ZSUwQSUwQSUyMyUyMERlZmluZSUyMGElMjBtYXBwaW5nJTBBbWFwcGluZyUyMCUzRCUyMCU3QiUwQSUyMCUyMCUyMCUyMCUyMlNpbHVBbmRNdWwlMjIlM0ElMjAlN0IlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJjdWRhJTIyJTNBJTIwTGF5ZXJSZXBvc2l0b3J5KCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHJlcG9faWQlM0QlMjJrZXJuZWxzLWNvbW11bml0eSUyRmFjdGl2YXRpb24lMjIlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBsYXllcl9uYW1lJTNEJTIyU2lsdUFuZE11bCUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHZlcnNpb24lM0QxJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwKSUwQSUyMCUyMCUyMCUyMCU3RCUwQSU3RCUwQSUwQSU0MHVzZV9rZXJuZWxfZm9yd2FyZF9mcm9tX2h1YiglMjJTaWx1QW5kTXVsJTIyKSUwQWNsYXNzJTIwU2lsdUFuZE11bChubi5Nb2R1bGUpJTNBJTBBJTIwJTIwJTIwJTIwZGVmJTIwZm9yd2FyZChzZWxmJTJDJTIweCUzQSUyMHRvcmNoLlRlbnNvciklMjAtJTNFJTIwdG9yY2guVGVuc29yJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwZCUyMCUzRCUyMHguc2hhcGUlNUItMSU1RCUyMCUyRiUyRiUyMDIlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjByZXR1cm4lMjBGLnNpbHUoeCU1Qi4uLiUyQyUyMCUzQWQlNUQpJTIwKiUyMHglNUIuLi4lMkMlMjBkJTNBJTVEJTBBJTBBbW9kZWwlMjAlM0QlMjBTaWx1QW5kTXVsKCklMEElMEElMjMlMjBVc2UlMjB0aGUlMjBtYXBwaW5nJTIwZm9yJTIwdGhlJTIwZHVyYXRpb24lMjBvZiUyMHRoZSUyMGNvbnRleHQuJTBBd2l0aCUyMHVzZV9rZXJuZWxfbWFwcGluZyhtYXBwaW5nKSUzQSUwQSUyMCUyMCUyMCUyMCUyMyUyMGtlcm5lbGl6ZSUyMHVzZXMlMjB0aGUlMjB0ZW1wb3JhcnklMjBtYXBwaW5nJTBBJTIwJTIwJTIwJTIwbW9kZWwlMjAlM0QlMjBrZXJuZWxpemUobW9kZWwlMkMlMjBtb2RlJTNETW9kZS5UUkFJTklORyUyMCU3QyUyME1vZGUuVE9SQ0hfQ09NUElMRSUyQyUyMGRldmljZSUzRCUyMmN1ZGElMjIpJTBBJTBBJTIzJTIwT3V0c2lkZSUyMHRoZSUyMGNvbnRleHQlMkMlMjBvcmlnaW5hbCUyMG1hcHBpbmdzJTIwYXJlJTIwcmVzdG9yZWQ=",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> torch.nn <span class="hljs-keyword">as</span> nn
<span class="hljs-keyword">from</span> torch.nn <span class="hljs-keyword">import</span> functional <span class="hljs-keyword">as</span> F

<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> use_kernel_forward_from_hub
<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> use_kernel_mapping, LayerRepository, Device
<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> Mode, kernelize

<span class="hljs-comment"># Define a mapping</span>
mapping = {
    <span class="hljs-string">&quot;SiluAndMul&quot;</span>: {
        <span class="hljs-string">&quot;cuda&quot;</span>: LayerRepository(
            repo_id=<span class="hljs-string">&quot;kernels-community/activation&quot;</span>,
            layer_name=<span class="hljs-string">&quot;SiluAndMul&quot;</span>,
            version=<span class="hljs-number">1</span>
        )
    }
}

<span class="hljs-meta">@use_kernel_forward_from_hub(<span class="hljs-params"><span class="hljs-string">&quot;SiluAndMul&quot;</span></span>)</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">SiluAndMul</span>(nn.Module):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, x: torch.Tensor</span>) -&gt; torch.Tensor:
        d = x.shape[-<span class="hljs-number">1</span>] // <span class="hljs-number">2</span>
        <span class="hljs-keyword">return</span> F.silu(x[..., :d]) * x[..., d:]

model = SiluAndMul()

<span class="hljs-comment"># Use the mapping for the duration of the context.</span>
<span class="hljs-keyword">with</span> use_kernel_mapping(mapping):
    <span class="hljs-comment"># kernelize uses the temporary mapping</span>
    model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE, device=<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># Outside the context, original mappings are restored</span>`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function bl(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"ZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBMYXllclJlcG9zaXRvcnklMkMlMjByZWdpc3Rlcl9rZXJuZWxfbWFwcGluZyUyQyUyME1vZGUlMEElMEElMjMlMjBTaW1wbGUlMjBtYXBwaW5nJTIwZm9yJTIwYSUyMHNpbmdsZSUyMGtlcm5lbCUyMHBlciUyMGRldmljZSUwQWtlcm5lbF9sYXllcl9tYXBwaW5nJTIwJTNEJTIwJTdCJTBBJTIwJTIwJTIwJTIwJTIyTGxhbWFSTVNOb3JtJTIyJTNBJTIwJTdCJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIyY3VkYSUyMiUzQSUyMExheWVyUmVwb3NpdG9yeSglMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjByZXBvX2lkJTNEJTIya2VybmVscy1jb21tdW5pdHklMkZsYXllcl9ub3JtJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwbGF5ZXJfbmFtZSUzRCUyMkxsYW1hUk1TTm9ybSUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHZlcnNpb24lM0QxJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwKSUyQyUwQSUyMCUyMCUyMCUyMCU3RCUyQyUwQSU3RCUwQXJlZ2lzdGVyX2tlcm5lbF9tYXBwaW5nKGtlcm5lbF9sYXllcl9tYXBwaW5nKSUwQSUwQSUyMyUyMEFkdmFuY2VkJTIwbWFwcGluZyUyMHdpdGglMjBtb2RlLXNwZWNpZmljJTIwa2VybmVscyUwQWFkdmFuY2VkX21hcHBpbmclMjAlM0QlMjAlN0IlMEElMjAlMjAlMjAlMjAlMjJNdWx0aUhlYWRBdHRlbnRpb24lMjIlM0ElMjAlN0IlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJjdWRhJTIyJTNBJTIwJTdCJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwTW9kZS5UUkFJTklORyUzQSUyMExheWVyUmVwb3NpdG9yeSglMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjByZXBvX2lkJTNEJTIydXNlcm5hbWUlMkZ0cmFpbmluZy1rZXJuZWxzJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwbGF5ZXJfbmFtZSUzRCUyMlRyYWluaW5nQXR0ZW50aW9uJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwdmVyc2lvbiUzRDElMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjApJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwTW9kZS5JTkZFUkVOQ0UlM0ElMjBMYXllclJlcG9zaXRvcnkoJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwcmVwb19pZCUzRCUyMnVzZXJuYW1lJTJGaW5mZXJlbmNlLWtlcm5lbHMlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBsYXllcl9uYW1lJTNEJTIyRmFzdEF0dGVudGlvbiUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHZlcnNpb24lM0QxJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwKSUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCU3RCUwQSUyMCUyMCUyMCUyMCU3RCUwQSU3RCUwQXJlZ2lzdGVyX2tlcm5lbF9tYXBwaW5nKGFkdmFuY2VkX21hcHBpbmcp",highlighted:`<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> LayerRepository, register_kernel_mapping, Mode

<span class="hljs-comment"># Simple mapping for a single kernel per device</span>
kernel_layer_mapping = {
    <span class="hljs-string">&quot;LlamaRMSNorm&quot;</span>: {
        <span class="hljs-string">&quot;cuda&quot;</span>: LayerRepository(
            repo_id=<span class="hljs-string">&quot;kernels-community/layer_norm&quot;</span>,
            layer_name=<span class="hljs-string">&quot;LlamaRMSNorm&quot;</span>,
            version=<span class="hljs-number">1</span>,
        ),
    },
}
register_kernel_mapping(kernel_layer_mapping)

<span class="hljs-comment"># Advanced mapping with mode-specific kernels</span>
advanced_mapping = {
    <span class="hljs-string">&quot;MultiHeadAttention&quot;</span>: {
        <span class="hljs-string">&quot;cuda&quot;</span>: {
            Mode.TRAINING: LayerRepository(
                repo_id=<span class="hljs-string">&quot;username/training-kernels&quot;</span>,
                layer_name=<span class="hljs-string">&quot;TrainingAttention&quot;</span>,
                version=<span class="hljs-number">1</span>,
            ),
            Mode.INFERENCE: LayerRepository(
                repo_id=<span class="hljs-string">&quot;username/inference-kernels&quot;</span>,
                layer_name=<span class="hljs-string">&quot;FastAttention&quot;</span>,
                version=<span class="hljs-number">1</span>,
            ),
        }
    }
}
register_kernel_mapping(advanced_mapping)`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function $l(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> torch.nn <span class="hljs-keyword">as</span> nn

<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> kernelize, Mode, register_kernel_mapping, LayerRepository
<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> use_kernel_forward_from_hub

<span class="hljs-meta">@use_kernel_forward_from_hub(<span class="hljs-params"><span class="hljs-string">&quot;SiluAndMul&quot;</span></span>)</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">SiluAndMul</span>(nn.Module):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, x: torch.Tensor</span>) -&gt; torch.Tensor:
        d = x.shape[-<span class="hljs-number">1</span>] // <span class="hljs-number">2</span>
        <span class="hljs-keyword">return</span> F.silu(x[..., :d]) * x[..., d:]

mapping = {
    <span class="hljs-string">&quot;SiluAndMul&quot;</span>: {
        <span class="hljs-string">&quot;cuda&quot;</span>: LayerRepository(
            repo_id=<span class="hljs-string">&quot;kernels-community/activation&quot;</span>,
            layer_name=<span class="hljs-string">&quot;SiluAndMul&quot;</span>,
        )
    }
}
register_kernel_mapping(mapping)

<span class="hljs-comment"># Create and kernelize a model</span>
model = nn.Sequential(
    nn.Linear(<span class="hljs-number">1024</span>, <span class="hljs-number">2048</span>, device=<span class="hljs-string">&quot;cuda&quot;</span>),
    SiluAndMul(),
)

<span class="hljs-comment"># Kernelize for inference</span>
kernelized_model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE)`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function jl(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> Device, CUDAProperties

<span class="hljs-comment"># Basic CUDA device</span>
cuda_device = Device(<span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># CUDA device with specific capability requirements</span>
cuda_device_with_props = Device(
    <span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;cuda&quot;</span>,
    properties=CUDAProperties(min_capability=<span class="hljs-number">75</span>, max_capability=<span class="hljs-number">90</span>)
)

<span class="hljs-comment"># MPS device for Apple Silicon</span>
mps_device = Device(<span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;mps&quot;</span>)

<span class="hljs-comment"># XPU device (e.g., Intel(R) Data Center GPU Max 1550)</span>
xpu_device = Device(<span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;xpu&quot;</span>)

<span class="hljs-comment"># NPU device (Huawei Ascend)</span>
npu_device = Device(<span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;npu&quot;</span>)`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function _l(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> FuncRepository

<span class="hljs-comment"># Reference a specific layer by revision</span>
layer_repo = FuncRepository(
    repo_id=<span class="hljs-string">&quot;kernels-community/activation&quot;</span>,
    func_name=<span class="hljs-string">&quot;silu_and_mul&quot;</span>,
)

<span class="hljs-comment"># Reference a layer by version</span>
layer_repo_versioned = FuncRepository(
    repo_id=<span class="hljs-string">&quot;kernels-community/relu&quot;</span>,
    func_name=<span class="hljs-string">&quot;relu&quot;</span>,
    version=<span class="hljs-number">1</span>
)`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function Il(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"ZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBMYXllclJlcG9zaXRvcnklMEElMEElMjMlMjBSZWZlcmVuY2UlMjBhJTIwc3BlY2lmaWMlMjBsYXllciUyMGJ5JTIwcmV2aXNpb24lMEFsYXllcl9yZXBvJTIwJTNEJTIwTGF5ZXJSZXBvc2l0b3J5KCUwQSUyMCUyMCUyMCUyMHJlcG9faWQlM0QlMjJrZXJuZWxzLWNvbW11bml0eSUyRmFjdGl2YXRpb24lMjIlMkMlMEElMjAlMjAlMjAlMjBsYXllcl9uYW1lJTNEJTIyU2lsdUFuZE11bCUyMiUyQyUwQSUyMCUyMCUyMCUyMHZlcnNpb24lM0QxJTJDJTBBKQ==",highlighted:`<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> LayerRepository

<span class="hljs-comment"># Reference a specific layer by revision</span>
layer_repo = LayerRepository(
    repo_id=<span class="hljs-string">&quot;kernels-community/activation&quot;</span>,
    layer_name=<span class="hljs-string">&quot;SiluAndMul&quot;</span>,
    version=<span class="hljs-number">1</span>,
)`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function Cl(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"ZnJvbSUyMHBhdGhsaWIlMjBpbXBvcnQlMjBQYXRoJTBBJTBBZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBMb2NhbEZ1bmNSZXBvc2l0b3J5JTBBJTBBJTIzJTIwUmVmZXJlbmNlJTIwYSUyMHNwZWNpZmljJTIwbGF5ZXIlMjBieSUyMHJldmlzaW9uJTBBbGF5ZXJfcmVwbyUyMCUzRCUyMExvY2FsRnVuY1JlcG9zaXRvcnkoJTBBJTIwJTIwJTIwJTIwcmVwb19wYXRoJTNEUGF0aCglMjIlMkZob21lJTJGZGFuaWVsJTJGa2VybmVscyUyRmFjdGl2YXRpb24lMjIpJTJDJTBBJTIwJTIwJTIwJTIwcGFja2FnZV9uYW1lJTNEJTIyYWN0aXZhdGlvbiUyMiUyQyUwQSUyMCUyMCUyMCUyMGZ1bmNfbmFtZSUzRCUyMnNpbHVfYW5kX211bCUyMiUyQyUwQSk=",highlighted:`<span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path

<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> LocalFuncRepository

<span class="hljs-comment"># Reference a specific layer by revision</span>
layer_repo = LocalFuncRepository(
    repo_path=Path(<span class="hljs-string">&quot;/home/daniel/kernels/activation&quot;</span>),
    package_name=<span class="hljs-string">&quot;activation&quot;</span>,
    func_name=<span class="hljs-string">&quot;silu_and_mul&quot;</span>,
)`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function vl(k){let l,g="Example:",m,r,c;return r=new K({props:{code:"ZnJvbSUyMHBhdGhsaWIlMjBpbXBvcnQlMjBQYXRoJTBBJTBBZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBMb2NhbExheWVyUmVwb3NpdG9yeSUwQSUwQSUyMyUyMFJlZmVyZW5jZSUyMGElMjBzcGVjaWZpYyUyMGxheWVyJTIwYnklMjByZXZpc2lvbiUwQWxheWVyX3JlcG8lMjAlM0QlMjBMb2NhbExheWVyUmVwb3NpdG9yeSglMEElMjAlMjAlMjAlMjByZXBvX3BhdGglM0RQYXRoKCUyMiUyRmhvbWUlMkZkYW5pZWwlMkZrZXJuZWxzJTJGYWN0aXZhdGlvbiUyMiklMkMlMEElMjAlMjAlMjAlMjBwYWNrYWdlX25hbWUlM0QlMjJhY3RpdmF0aW9uJTIyJTJDJTBBJTIwJTIwJTIwJTIwbGF5ZXJfbmFtZSUzRCUyMlNpbHVBbmRNdWwlMjIlMkMlMEEp",highlighted:`<span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path

<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> LocalLayerRepository

<span class="hljs-comment"># Reference a specific layer by revision</span>
layer_repo = LocalLayerRepository(
    repo_path=Path(<span class="hljs-string">&quot;/home/daniel/kernels/activation&quot;</span>),
    package_name=<span class="hljs-string">&quot;activation&quot;</span>,
    layer_name=<span class="hljs-string">&quot;SiluAndMul&quot;</span>,
)`,wrap:!1}}),{c(){l=w("p"),l.textContent=g,m=a(),d(r.$$.fragment)},l(n){l=T(n,"P",{"data-svelte-h":!0}),U(l)!=="svelte-11lpom8"&&(l.textContent=g),m=o(n),y(r.$$.fragment,n)},m(n,J){p(n,l,J),p(n,m,J),M(r,n,J),c=!0},p:P,i(n){c||(u(r.$$.fragment,n),c=!0)},o(n){f(r.$$.fragment,n),c=!1},d(n){n&&(s(l),s(m)),h(r,n)}}}function xl(k){let l,g,m,r,c,n,J,kn,ye,bn,Me,$n,C,ue,st,ze,Vt="Decorator factory that makes a layer extensible using the specified layer name.",rt,Ye,Nt=`This is a decorator factory that returns a decorator which prepares a layer class to use kernels from the
Hugging Face Hub.`,at,te,jn,fe,_n,_,he,ot,He,At="Decorator that makes a function extensible using the specified function name.",pt,De,Ft=`This is a decorator factory that returns a decorator which prepares a function to use kernels from the
Hugging Face Hub.`,it,qe,Lt=`The function will be exposed as an instance of <code>torch.nn.Module</code> in which
the function is called in <code>forward</code>. For the function to be properly
kernelized, it <strong>must</strong> be a member of another <code>torch.nn.Module</code> that is
part of the model (see the example).`,ct,le,In,we,Cn,v,Te,mt,Pe,zt="Function that prepares a layer class to use kernels from the Hugging Face Hub.",dt,Ke,Yt=`It is recommended to use <a href="/docs/kernels/pr_321/en/api/layers#kernels.use_kernel_forward_from_hub">use_kernel_forward_from_hub()</a> decorator instead.
This function should only be used as a last resort to extend third-party layers,
it is inherently fragile since the member variables and <code>forward</code> signature
of such a layer can change.`,yt,se,vn,Je,xn,ge,Bn,x,Ue,Mt,Oe,Ht="Context manager that sets a kernel mapping for the duration of the context.",ut,en,Dt=`This function allows temporary kernel mappings to be applied within a specific context, enabling different
kernel configurations for different parts of your code.`,ft,re,Zn,ke,Rn,B,be,ht,nn,qt="Register a global mapping between layer names and their corresponding kernel implementations.",wt,tn,Pt=`This function allows you to register a mapping between a layer name and the corresponding kernel(s) to use,
depending on the device and mode. This should be used in conjunction with <a href="/docs/kernels/pr_321/en/api/layers#kernels.kernelize">kernelize()</a>.`,Tt,ae,En,$e,Wn,je,Gn,Z,_e,Jt,ln,Kt="Replace layer forward methods with optimized kernel implementations.",gt,sn,Ot=`This function iterates over all modules in the model and replaces the <code>forward</code> method of extensible layers
for which kernels are registered using <a href="/docs/kernels/pr_321/en/api/layers#kernels.register_kernel_mapping">register_kernel_mapping()</a> or <a href="/docs/kernels/pr_321/en/api/layers#kernels.use_kernel_mapping">use_kernel_mapping()</a>.`,Ut,oe,Sn,Ie,Xn,Ce,Qn,R,ve,kt,rn,el="Represents a compute device with optional properties.",bt,an,nl=`This class encapsulates device information including device type and optional device-specific properties
like CUDA capabilities.`,$t,pe,Vn,xe,Nn,E,Be,jt,on,tl="Kernelize mode",_t,pn,ll=`The <code>Mode</code> flag is used by <a href="/docs/kernels/pr_321/en/api/layers#kernels.kernelize">kernelize()</a> to select kernels for the given mode. Mappings can be registered for
specific modes.`,It,cn,sl=`Note:
Different modes can be combined. For instance, <code>INFERENCE | TORCH_COMPILE</code> should be used for layers that
are used for inference <em>with</em> <code>torch.compile</code>.`,An,Ze,Fn,G,Re,Ct,mn,rl="Repository and name of a function for kernel mapping.",vt,ie,Ln,Ee,zn,S,We,xt,dn,al="Repository and name of a layer for kernel mapping.",Bt,ce,Yn,Ge,Hn,X,Se,Zt,yn,ol="Repository and function name from a local directory for kernel mapping.",Rt,me,Dn,Xe,qn,Q,Qe,Et,Mn,pl="Repository from a local directory for kernel mapping.",Wt,de,Pn,Ve,Kn,V,Ne,Gt,un,il="Repository and name of a function.",St,fn,cl=`In contrast to <code>FuncRepository</code>, this class uses repositories that
are locked inside a project.`,On,Ae,et,N,Fe,Xt,hn,ml="Repository and name of a layer.",Qt,wn,dl=`In contrast to <code>LayerRepository</code>, this class uses repositories that
are locked inside a project.`,nt,Le,tt,Un,lt;return c=new wl({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),J=new b({props:{title:"Layers API Reference",local:"layers-api-reference",headingTag:"h1"}}),ye=new b({props:{title:"Making layers kernel-aware",local:"making-layers-kernel-aware",headingTag:"h2"}}),Me=new b({props:{title:"use_kernel_forward_from_hub",local:"kernels.use_kernel_forward_from_hub",headingTag:"h3"}}),ue=new I({props:{name:"kernels.use_kernel_forward_from_hub",anchor:"kernels.use_kernel_forward_from_hub",parameters:[{name:"layer_name",val:": str"}],parametersDescription:[{anchor:"kernels.use_kernel_forward_from_hub.layer_name",description:`<strong>layer_name</strong> (<code>str</code>) &#x2014;
The name of the layer to use for kernel lookup in registered mappings.`,name:"layer_name"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/layer.py#L251",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A decorator function that can be applied to layer classes.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Callable</code></p>
`}}),te=new q({props:{anchor:"kernels.use_kernel_forward_from_hub.example",$$slots:{default:[Jl]},$$scope:{ctx:k}}}),fe=new b({props:{title:"use_kernel_func_from_hub",local:"kernels.use_kernel_func_from_hub",headingTag:"h3"}}),he=new I({props:{name:"kernels.use_kernel_func_from_hub",anchor:"kernels.use_kernel_func_from_hub",parameters:[{name:"func_name",val:": str"}],parametersDescription:[{anchor:"kernels.use_kernel_func_from_hub.func_name",description:`<strong>func_name</strong> (<code>str</code>) &#x2014;
The name of the function name to use for kernel lookup in registered mappings.`,name:"func_name"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/func.py#L167",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A decorator function that can be applied to layer classes.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Callable</code></p>
`}}),le=new q({props:{anchor:"kernels.use_kernel_func_from_hub.example",$$slots:{default:[gl]},$$scope:{ctx:k}}}),we=new b({props:{title:"replace_kernel_forward_from_hub",local:"kernels.replace_kernel_forward_from_hub",headingTag:"h3"}}),Te=new I({props:{name:"kernels.replace_kernel_forward_from_hub",anchor:"kernels.replace_kernel_forward_from_hub",parameters:[{name:"layer_name",val:": str"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/layer.py#L228"}}),se=new q({props:{anchor:"kernels.replace_kernel_forward_from_hub.example",$$slots:{default:[Ul]},$$scope:{ctx:k}}}),Je=new b({props:{title:"Registering kernel mappings",local:"registering-kernel-mappings",headingTag:"h2"}}),ge=new b({props:{title:"use_kernel_mapping",local:"kernels.use_kernel_mapping",headingTag:"h3"}}),Ue=new I({props:{name:"kernels.use_kernel_mapping",anchor:"kernels.use_kernel_mapping",parameters:[{name:"mapping",val:": dict[str, dict[Device | str, RepositoryProtocol | dict[Mode, RepositoryProtocol]]]"},{name:"inherit_mapping",val:": bool = True"}],parametersDescription:[{anchor:"kernels.use_kernel_mapping.mapping",description:`<strong>mapping</strong> (<code>dict[str, dict[Union[Device, str], Union[LayerRepositoryProtocol, dict[Mode, LayerRepositoryProtocol]]]]</code>) &#x2014;
The kernel mapping to apply. Maps layer names to device-specific kernel configurations.`,name:"mapping"},{anchor:"kernels.use_kernel_mapping.inherit_mapping",description:`<strong>inherit_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
When <code>True</code>, the current mapping will be extended by <code>mapping</code> inside the context. When <code>False</code>,
only <code>mapping</code> is used inside the context.`,name:"inherit_mapping"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/kernelize.py#L17",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Context manager that handles the temporary kernel mapping.</p>
`}}),re=new q({props:{anchor:"kernels.use_kernel_mapping.example",$$slots:{default:[kl]},$$scope:{ctx:k}}}),ke=new b({props:{title:"register_kernel_mapping",local:"kernels.register_kernel_mapping",headingTag:"h3"}}),be=new I({props:{name:"kernels.register_kernel_mapping",anchor:"kernels.register_kernel_mapping",parameters:[{name:"mapping",val:": dict[str, dict[Device | str, RepositoryProtocol | dict[Mode, RepositoryProtocol]]]"},{name:"inherit_mapping",val:": bool = True"}],parametersDescription:[{anchor:"kernels.register_kernel_mapping.mapping",description:`<strong>mapping</strong> (<code>dict[str, dict[Union[Device, str], Union[RepositoryProtocol, dict[Mode, RepositoryProtocol]]]]</code>) &#x2014;
The kernel mapping to register globally. Maps layer names to device-specific kernels.
The mapping can specify different kernels for different modes (training, inference, etc.).`,name:"mapping"},{anchor:"kernels.register_kernel_mapping.inherit_mapping",description:`<strong>inherit_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
When <code>True</code>, the current mapping will be extended by <code>mapping</code>. When <code>False</code>, the existing mappings
are erased before adding <code>mapping</code>.`,name:"inherit_mapping"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/kernelize.py#L97"}}),ae=new q({props:{anchor:"kernels.register_kernel_mapping.example",$$slots:{default:[bl]},$$scope:{ctx:k}}}),$e=new b({props:{title:"Kernelizing a model",local:"kernelizing-a-model",headingTag:"h2"}}),je=new b({props:{title:"kernelize",local:"kernels.kernelize",headingTag:"h3"}}),_e=new I({props:{name:"kernels.kernelize",anchor:"kernels.kernelize",parameters:[{name:"model",val:": 'nn.Module'"},{name:"mode",val:": Mode"},{name:"device",val:": str | 'torch.device' | None = None"},{name:"use_fallback",val:": bool = True"}],parametersDescription:[{anchor:"kernels.kernelize.model",description:`<strong>model</strong> (<code>nn.Module</code>) &#x2014;
The PyTorch model to kernelize.`,name:"model"},{anchor:"kernels.kernelize.mode",description:`<strong>mode</strong> (<a href="/docs/kernels/pr_321/en/api/layers#kernels.Mode">Mode</a>) &#x2014; The mode that the kernel is going to be used in. For example,
<code>Mode.TRAINING | Mode.TORCH_COMPILE</code> kernelizes the model for training with
<code>torch.compile</code>.`,name:"mode"},{anchor:"kernels.kernelize.device",description:`<strong>device</strong> (<code>Union[str, torch.device]</code>, <em>optional</em>) &#x2014;
The device type to load kernels for. Supported device types are: &#x201C;cuda&#x201D;, &#x201C;mps&#x201D;, &#x201C;npu&#x201D;, &#x201C;rocm&#x201D;, &#x201C;xpu&#x201D;.
The device type will be inferred from the model parameters when not provided.`,name:"device"},{anchor:"kernels.kernelize.use_fallback",description:`<strong>use_fallback</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use the original forward method of modules when no compatible kernel could be found.
If set to <code>False</code>, an exception will be raised in such cases.`,name:"use_fallback"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/kernelize.py#L179",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The kernelized model with optimized kernel implementations.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>nn.Module</code></p>
`}}),oe=new q({props:{anchor:"kernels.kernelize.example",$$slots:{default:[$l]},$$scope:{ctx:k}}}),Ie=new b({props:{title:"Classes",local:"classes",headingTag:"h2"}}),Ce=new b({props:{title:"Device",local:"kernels.Device",headingTag:"h3"}}),ve=new I({props:{name:"class kernels.Device",anchor:"kernels.Device",parameters:[{name:"type",val:": str"},{name:"properties",val:": kernels.layer.device.CUDAProperties | None = None"}],parametersDescription:[{anchor:"kernels.Device.type",description:`<strong>type</strong> (<code>str</code>) &#x2014;
The device type (e.g., &#x201C;cuda&#x201D;, &#x201C;mps&#x201D;, &#x201C;npu&#x201D;, &#x201C;rocm&#x201D;, &#x201C;xpu&#x201D;).`,name:"type"},{anchor:"kernels.Device.properties",description:`<strong>properties</strong> (<code>CUDAProperties</code>, <em>optional</em>) &#x2014;
Device-specific properties. Currently only <code>CUDAProperties</code> is supported for CUDA devices.`,name:"properties"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/device.py#L95"}}),pe=new q({props:{anchor:"kernels.Device.example",$$slots:{default:[jl]},$$scope:{ctx:k}}}),xe=new b({props:{title:"Mode",local:"kernels.Mode",headingTag:"h3"}}),Be=new I({props:{name:"class kernels.Mode",anchor:"kernels.Mode",parameters:[{name:"value",val:""},{name:"names",val:" = None"},{name:"module",val:" = None"},{name:"qualname",val:" = None"},{name:"type",val:" = None"},{name:"start",val:" = 1"}],parametersDescription:[{anchor:"kernels.Mode.INFERENCE",description:"<strong>INFERENCE</strong> &#x2014; The kernel is used for inference.",name:"INFERENCE"},{anchor:"kernels.Mode.TRAINING",description:"<strong>TRAINING</strong> &#x2014; The kernel is used for training.",name:"TRAINING"},{anchor:"kernels.Mode.TORCH_COMPILE",description:"<strong>TORCH_COMPILE</strong> &#x2014; The kernel is used with <code>torch.compile</code>.",name:"TORCH_COMPILE"},{anchor:"kernels.Mode.FALLBACK",description:"<strong>FALLBACK</strong> &#x2014; In a kernel mapping, this kernel is used when no other mode matches.",name:"FALLBACK"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/mode.py#L4"}}),Ze=new b({props:{title:"FuncRepository",local:"kernels.FuncRepository",headingTag:"h3"}}),Re=new I({props:{name:"class kernels.FuncRepository",anchor:"kernels.FuncRepository",parameters:[{name:"repo_id",val:": str"},{name:"func_name",val:": str"},{name:"revision",val:": str | None = None"},{name:"version",val:": int | str | None = None"}],parametersDescription:[{anchor:"kernels.FuncRepository.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
The Hub repository containing the layer.`,name:"repo_id"},{anchor:"kernels.FuncRepository.func_name",description:`<strong>func_name</strong> (<code>str</code>) &#x2014;
The name of the function within the kernel repository.`,name:"func_name"},{anchor:"kernels.FuncRepository.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision (branch, tag, or commit) to download. Cannot be used together with <code>version</code>.`,name:"revision"},{anchor:"kernels.FuncRepository.version",description:`<strong>version</strong> (<code>int|str</code>, <em>optional</em>) &#x2014;
The kernel version to download as an integer. The <code>str</code> variant is deprecated and will be
removed in a future release. Cannot be used together with <code>revision</code>.`,name:"version"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/func.py#L27"}}),ie=new q({props:{anchor:"kernels.FuncRepository.example",$$slots:{default:[_l]},$$scope:{ctx:k}}}),Ee=new b({props:{title:"LayerRepository",local:"kernels.LayerRepository",headingTag:"h3"}}),We=new I({props:{name:"class kernels.LayerRepository",anchor:"kernels.LayerRepository",parameters:[{name:"repo_id",val:": str"},{name:"layer_name",val:": str"},{name:"revision",val:": str | None = None"},{name:"version",val:": int | str | None = None"}],parametersDescription:[{anchor:"kernels.LayerRepository.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
The Hub repository containing the layer.`,name:"repo_id"},{anchor:"kernels.LayerRepository.layer_name",description:`<strong>layer_name</strong> (<code>str</code>) &#x2014;
The name of the layer within the kernel repository.`,name:"layer_name"},{anchor:"kernels.LayerRepository.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision (branch, tag, or commit) to download. Cannot be used together with <code>version</code>.`,name:"revision"},{anchor:"kernels.LayerRepository.version",description:`<strong>version</strong> (<code>int|str</code>, <em>optional</em>) &#x2014;
The kernel version to download as an integer. The <code>str</code> variant is deprecated and will be
removed in a future release. Cannot be used together with <code>revision</code>.`,name:"version"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/layer.py#L32"}}),ce=new q({props:{anchor:"kernels.LayerRepository.example",$$slots:{default:[Il]},$$scope:{ctx:k}}}),Ge=new b({props:{title:"LocalFuncRepository",local:"kernels.LocalFuncRepository",headingTag:"h3"}}),Se=new I({props:{name:"class kernels.LocalFuncRepository",anchor:"kernels.LocalFuncRepository",parameters:[{name:"repo_path",val:": Path"},{name:"package_name",val:": str"},{name:"func_name",val:": str"}],parametersDescription:[{anchor:"kernels.LocalFuncRepository.repo_path",description:`<strong>repo_path</strong> (<code>Path</code>) &#x2014;
The local repository containing the layer.`,name:"repo_path"},{anchor:"kernels.LocalFuncRepository.package_name",description:`<strong>package_name</strong> (<code>str</code>) &#x2014;
Package name of the kernel.`,name:"package_name"},{anchor:"kernels.LocalFuncRepository.func_name",description:`<strong>func_name</strong> (<code>str</code>) &#x2014;
The name of the function within the kernel repository.`,name:"func_name"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/func.py#L110"}}),me=new q({props:{anchor:"kernels.LocalFuncRepository.example",$$slots:{default:[Cl]},$$scope:{ctx:k}}}),Xe=new b({props:{title:"LocalLayerRepository",local:"kernels.LocalLayerRepository",headingTag:"h3"}}),Qe=new I({props:{name:"class kernels.LocalLayerRepository",anchor:"kernels.LocalLayerRepository",parameters:[{name:"repo_path",val:": Path"},{name:"package_name",val:": str"},{name:"layer_name",val:": str"}],parametersDescription:[{anchor:"kernels.LocalLayerRepository.repo_path",description:`<strong>repo_path</strong> (<code>Path</code>) &#x2014;
The local repository containing the layer.`,name:"repo_path"},{anchor:"kernels.LocalLayerRepository.package_name",description:`<strong>package_name</strong> (<code>str</code>) &#x2014;
Package name of the kernel.`,name:"package_name"},{anchor:"kernels.LocalLayerRepository.layer_name",description:`<strong>layer_name</strong> (<code>str</code>) &#x2014;
The name of the layer within the kernel repository.`,name:"layer_name"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/layer.py#L109"}}),de=new q({props:{anchor:"kernels.LocalLayerRepository.example",$$slots:{default:[vl]},$$scope:{ctx:k}}}),Ve=new b({props:{title:"LockedFuncRepository",local:"kernels.LockedFuncRepository",headingTag:"h3"}}),Ne=new I({props:{name:"class kernels.LockedFuncRepository",anchor:"kernels.LockedFuncRepository",parameters:[{name:"repo_id",val:": str"},{name:"lockfile",val:": pathlib.Path | None = None"},{name:"func_name",val:": str"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/func.py#L222"}}),Ae=new b({props:{title:"LockedLayerRepository",local:"kernels.LockedLayerRepository",headingTag:"h3"}}),Fe=new I({props:{name:"class kernels.LockedLayerRepository",anchor:"kernels.LockedLayerRepository",parameters:[{name:"repo_id",val:": str"},{name:"lockfile",val:": Path | None = None"},{name:"layer_name",val:": str"}],source:"https://github.com/huggingface/kernels/blob/vr_321/src/kernels/layer/layer.py#L166"}}),Le=new 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