kernels/pr_150/en/_app/immutable/nodes/3.b0fa65ac.js

import{s as Pl,o as Kl,n as Te}from"../chunks/scheduler.7da89386.js";import{S as Ol,i as en,g as w,s as a,r as M,A as ln,h as U,f as s,c as o,j as X,u as d,x as J,k as V,y as i,a as p,v as y,d as u,t as h,w as f}from"../chunks/index.0b7befd3.js";import{D as S,E as Ue}from"../chunks/ExampleCodeBlock.a2c7df44.js";import{C as ge}from"../chunks/CodeBlock.c5b6371f.js";import{H as x,E as nn}from"../chunks/getInferenceSnippets.dcce2733.js";function tn(k){let t,g="Example:",m,r,c;return r=new ge({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(){t=w("p"),t.textContent=g,m=a(),M(r.$$.fragment)},l(l){t=U(l,"P",{"data-svelte-h":!0}),J(t)!=="svelte-11lpom8"&&(t.textContent=g),m=o(l),d(r.$$.fragment,l)},m(l,T){p(l,t,T),p(l,m,T),y(r,l,T),c=!0},p:Te,i(l){c||(u(r.$$.fragment,l),c=!0)},o(l){h(r.$$.fragment,l),c=!1},d(l){l&&(s(t),s(m)),f(r,l)}}}function sn(k){let t,g="Example:",m,r,c;return r=new ge({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(){t=w("p"),t.textContent=g,m=a(),M(r.$$.fragment)},l(l){t=U(l,"P",{"data-svelte-h":!0}),J(t)!=="svelte-11lpom8"&&(t.textContent=g),m=o(l),d(r.$$.fragment,l)},m(l,T){p(l,t,T),p(l,m,T),y(r,l,T),c=!0},p:Te,i(l){c||(u(r.$$.fragment,l),c=!0)},o(l){h(r.$$.fragment,l),c=!1},d(l){l&&(s(t),s(m)),f(r,l)}}}function rn(k){let t,g="Example:",m,r,c;return r=new ge({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> 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>,
        )
    }
}

<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(){t=w("p"),t.textContent=g,m=a(),M(r.$$.fragment)},l(l){t=U(l,"P",{"data-svelte-h":!0}),J(t)!=="svelte-11lpom8"&&(t.textContent=g),m=o(l),d(r.$$.fragment,l)},m(l,T){p(l,t,T),p(l,m,T),y(r,l,T),c=!0},p:Te,i(l){c||(u(r.$$.fragment,l),c=!0)},o(l){h(r.$$.fragment,l),c=!1},d(l){l&&(s(t),s(m)),f(r,l)}}}function an(k){let t,g="Example:",m,r,c;return r=new ge({props:{code:"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",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/activation&quot;</span>,
            layer_name=<span class="hljs-string">&quot;RmsNorm&quot;</span>,
            revision=<span class="hljs-string">&quot;layers&quot;</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>
            ),
            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>
            ),
        }
    }
}
register_kernel_mapping(advanced_mapping)`,wrap:!1}}),{c(){t=w("p"),t.textContent=g,m=a(),M(r.$$.fragment)},l(l){t=U(l,"P",{"data-svelte-h":!0}),J(t)!=="svelte-11lpom8"&&(t.textContent=g),m=o(l),d(r.$$.fragment,l)},m(l,T){p(l,t,T),p(l,m,T),y(r,l,T),c=!0},p:Te,i(l){c||(u(r.$$.fragment,l),c=!0)},o(l){h(r.$$.fragment,l),c=!1},d(l){l&&(s(t),s(m)),f(r,l)}}}function on(k){let t,g="Example:",m,r,c;return r=new ge({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;LayerNorm&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(){t=w("p"),t.textContent=g,m=a(),M(r.$$.fragment)},l(l){t=U(l,"P",{"data-svelte-h":!0}),J(t)!=="svelte-11lpom8"&&(t.textContent=g),m=o(l),d(r.$$.fragment,l)},m(l,T){p(l,t,T),p(l,m,T),y(r,l,T),c=!0},p:Te,i(l){c||(u(r.$$.fragment,l),c=!0)},o(l){h(r.$$.fragment,l),c=!1},d(l){l&&(s(t),s(m)),f(r,l)}}}function pn(k){let t,g="Example:",m,r,c;return r=new ge({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>)`,wrap:!1}}),{c(){t=w("p"),t.textContent=g,m=a(),M(r.$$.fragment)},l(l){t=U(l,"P",{"data-svelte-h":!0}),J(t)!=="svelte-11lpom8"&&(t.textContent=g),m=o(l),d(r.$$.fragment,l)},m(l,T){p(l,t,T),p(l,m,T),y(r,l,T),c=!0},p:Te,i(l){c||(u(r.$$.fragment,l),c=!0)},o(l){h(r.$$.fragment,l),c=!1},d(l){l&&(s(t),s(m)),f(r,l)}}}function cn(k){let t,g="Example:",m,r,c;return r=new ge({props:{code:"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",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>,
)

<span class="hljs-comment"># Reference a layer by version constraint</span>
layer_repo_versioned = 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-string">&quot;&gt;=0.0.3,&lt;0.1&quot;</span>
)`,wrap:!1}}),{c(){t=w("p"),t.textContent=g,m=a(),M(r.$$.fragment)},l(l){t=U(l,"P",{"data-svelte-h":!0}),J(t)!=="svelte-11lpom8"&&(t.textContent=g),m=o(l),d(r.$$.fragment,l)},m(l,T){p(l,t,T),p(l,m,T),y(r,l,T),c=!0},p:Te,i(l){c||(u(r.$$.fragment,l),c=!0)},o(l){h(r.$$.fragment,l),c=!1},d(l){l&&(s(t),s(m)),f(r,l)}}}function mn(k){let t,g,m,r,c,l,T,Se,P,Ne,b,K,il,Je,El="Decorator factory that makes a layer extensible using the specified layer name.",cl,ke,Al=`This is a decorator factory that returns a decorator which prepares a layer class to use kernels from the
Hugging Face Hub.`,ml,N,Qe,O,Fe,$,ee,Ml,je,Rl="Function that prepares a layer class to use kernels from the Hugging Face Hub.",dl,be,Gl=`It is recommended to use <a href="/docs/kernels/pr_150/en/api/layers#kernels.use_kernel_forward_from_hub">use_kernel_forward_from_hub()</a> decorator instead.
This function should only be used as a last resort to extend third-party layers,
it is inherently fragile since the member variables and <code>forward</code> signature
of usch a layer can change.`,yl,Q,ze,le,De,ne,Le,C,te,ul,$e,Wl="Context manager that sets a kernel mapping for the duration of the context.",hl,Ce,Xl=`This function allows temporary kernel mappings to be applied within a specific context, enabling different
kernel configurations for different parts of your code.`,fl,F,Ye,se,He,_,re,wl,_e,Vl="Register a global mapping between layer names and their corresponding kernel implementations.",Ul,Ie,Sl=`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_150/en/api/layers#kernels.kernelize">kernelize()</a>.`,Tl,z,qe,ae,Pe,oe,Ke,I,pe,gl,ve,Nl="Replace layer forward methods with optimized kernel implementations.",Jl,Be,Ql=`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_150/en/api/layers#kernels.register_kernel_mapping">register_kernel_mapping()</a> or <a href="/docs/kernels/pr_150/en/api/layers#kernels.use_kernel_mapping">use_kernel_mapping()</a>.`,kl,D,Oe,ie,el,ce,ll,j,me,jl,xe,Fl="Represents a compute device with optional properties.",bl,Ze,zl=`This class encapsulates device information including device type and optional device-specific properties
like CUDA capabilities.`,$l,L,Cl,Y,Me,_l,Ee,Dl="Create an appropriate repository set for this device type.",nl,de,tl,v,ye,Il,Ae,Ll="Kernelize mode",vl,Re,Yl=`The <code>Mode</code> flag is used by <a href="/docs/kernels/pr_150/en/api/layers#kernels.kernelize">kernelize()</a> to select kernels for the given mode. Mappings can be registered for
specific modes.`,Bl,Ge,Hl=`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>.`,sl,ue,rl,Z,he,xl,We,ql="Repository and name of a layer for kernel mapping.",Zl,H,al,fe,ol,Ve,pl;return c=new x({props:{title:"Layers API Reference",local:"layers-api-reference",headingTag:"h1"}}),T=new x({props:{title:"Making layers kernel-aware",local:"making-layers-kernel-aware",headingTag:"h2"}}),P=new x({props:{title:"use_kernel_forward_from_hub",local:"kernels.use_kernel_forward_from_hub",headingTag:"h3"}}),K=new S({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_150/src/kernels/layer.py#L971",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>
`}}),N=new Ue({props:{anchor:"kernels.use_kernel_forward_from_hub.example",$$slots:{default:[tn]},$$scope:{ctx:k}}}),O=new x({props:{title:"replace_kernel_forward_from_hub",local:"kernels.replace_kernel_forward_from_hub",headingTag:"h3"}}),ee=new S({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_150/src/kernels/layer.py#L731"}}),Q=new Ue({props:{anchor:"kernels.replace_kernel_forward_from_hub.example",$$slots:{default:[sn]},$$scope:{ctx:k}}}),le=new x({props:{title:"Registering kernel mappings",local:"registering-kernel-mappings",headingTag:"h2"}}),ne=new x({props:{title:"use_kernel_mapping",local:"kernels.use_kernel_mapping",headingTag:"h3"}}),te=new S({props:{name:"kernels.use_kernel_mapping",anchor:"kernels.use_kernel_mapping",parameters:[{name:"mapping",val:": Dict[str, Dict[Union[Device, str], Union[LayerRepositoryProtocol, Dict[Mode, LayerRepositoryProtocol]]]]"},{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_150/src/kernels/layer.py#L574",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Context manager that handles the temporary kernel mapping.</p>
`}}),F=new Ue({props:{anchor:"kernels.use_kernel_mapping.example",$$slots:{default:[rn]},$$scope:{ctx:k}}}),se=new x({props:{title:"register_kernel_mapping",local:"kernels.register_kernel_mapping",headingTag:"h3"}}),re=new S({props:{name:"kernels.register_kernel_mapping",anchor:"kernels.register_kernel_mapping",parameters:[{name:"mapping",val:": Dict[str, Dict[Union[Device, str], Union[LayerRepositoryProtocol, Dict[Mode, LayerRepositoryProtocol]]]]"},{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[LayerRepositoryProtocol, Dict[Mode, LayerRepositoryProtocol]]]]</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_150/src/kernels/layer.py#L653"}}),z=new Ue({props:{anchor:"kernels.register_kernel_mapping.example",$$slots:{default:[an]},$$scope:{ctx:k}}}),ae=new x({props:{title:"Kernelizing a model",local:"kernelizing-a-model",headingTag:"h2"}}),oe=new x({props:{title:"kernelize",local:"kernels.kernelize",headingTag:"h3"}}),pe=new S({props:{name:"kernels.kernelize",anchor:"kernels.kernelize",parameters:[{name:"model",val:": 'nn.Module'"},{name:"mode",val:": Mode"},{name:"device",val:": Optional[Union[str, 'torch.device']] = 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_150/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;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_150/src/kernels/layer.py#L800",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>
`}}),D=new Ue({props:{anchor:"kernels.kernelize.example",$$slots:{default:[on]},$$scope:{ctx:k}}}),ie=new x({props:{title:"Classes",local:"classes",headingTag:"h2"}}),ce=new x({props:{title:"Device",local:"kernels.Device",headingTag:"h3"}}),me=new S({props:{name:"class kernels.Device",anchor:"kernels.Device",parameters:[{name:"type",val:": str"},{name:"properties",val:": Optional[CUDAProperties] = 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;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_150/src/kernels/layer.py#L84"}}),L=new Ue({props:{anchor:"kernels.Device.example",$$slots:{default:[pn]},$$scope:{ctx:k}}}),Me=new S({props:{name:"create_repo",anchor:"kernels.Device.create_repo",parameters:[],source:"https://github.com/huggingface/kernels/blob/vr_150/src/kernels/layer.py#L126"}}),de=new x({props:{title:"Mode",local:"kernels.Mode",headingTag:"h3"}}),ye=new S({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_150/src/kernels/layer.py#L46"}}),ue=new x({props:{title:"LayerRepository",local:"kernels.LayerRepository",headingTag:"h3"}}),he=new S({props:{name:"class kernels.LayerRepository",anchor:"kernels.LayerRepository",parameters:[{name:"repo_id",val:": str"},{name:"layer_name",val:": str"},{name:"revision",val:": Optional[str] = None"},{name:"version",val:": Optional[str] = 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>str</code>, <em>optional</em>) &#x2014;
The kernel version to download. This can be a Python version specifier, such as <code>&quot;&gt;=1.0.0,&lt;2.0.0&quot;</code>.
Cannot be used together with <code>revision</code>.`,name:"version"}],source:"https://github.com/huggingface/kernels/blob/vr_150/src/kernels/layer.py#L245"}}),H=new Ue({props:{anchor:"kernels.LayerRepository.example",$$slots:{default:[cn]},$$scope:{ctx:k}}}),fe=new 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