kernels/pr_672/en/_app/immutable/nodes/3.55486e9e.js

import{s as Xs,o as Ns,n as V}from"../chunks/scheduler.f3b1e791.js";import{S as As,i as Qs,e as c,s as r,c as u,h as Fs,a as m,d as o,b as a,f as j,g as M,j as b,k as J,l as p,m as i,n as f,t as h,o as w,p as g}from"../chunks/index.023a9934.js";import{C as Ls,H as $,E as zs}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.6f5bdbef.js";import{D as _,E}from"../chunks/ExampleCodeBlock.d296bfa3.js";import{C as W}from"../chunks/CodeBlock.4080e84a.js";function Hs(k){let l,U="Example:",y,s,d;return s=new W({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> use_kernelized_func
<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>

<span class="hljs-comment"># Use on a function (converts the function to \`nn.Module\`). The function</span>
<span class="hljs-comment"># can then be replaced with a layer mapping for \`MyCustomLayer\`.</span>
<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">def</span> <span class="hljs-title function_">identity</span>(<span class="hljs-params">x: torch.Tensor</span>) -&gt; torch.Tensor:
    <span class="hljs-keyword">return</span> x

<span class="hljs-meta">@use_kernelized_func(<span class="hljs-params">identity</span>)</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">LayerUsingIdentity</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:
        <span class="hljs-keyword">return</span> identity(x)
`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function Ys(k){let l,U="Example:",y,s,d;return s=new W({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>`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function Ds(k){let l,U="Example:",y,s,d;return s=new W({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> use_kernelized_func
<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> Mode, kernelize

<span class="hljs-comment"># Use on a function (converts the function to \`nn.Module\`). The function</span>
<span class="hljs-comment"># can then be replaced with a layer mapping for \`MyCustomLayer\`.</span>
<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">def</span> <span class="hljs-title function_">identity</span>(<span class="hljs-params">x: torch.Tensor</span>) -&gt; torch.Tensor:
    <span class="hljs-keyword">return</span> x

<span class="hljs-meta">@use_kernelized_func(<span class="hljs-params">identity</span>)</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">LayerUsingIdentity</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:
        <span class="hljs-keyword">return</span> identity(x)

model = LayerUsingIdentity()

<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>`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function Ps(k){let l,U="Example:",y,s,d;return s=new W({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>)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function qs(k){let l,U="Example:",y,s,d;return s=new W({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>,
            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>`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function Ks(k){let l,U="Example:",y,s,d;return s=new W({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/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;kernels-community/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;kernels-community/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)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function Os(k){let l,U="Example:",y,s,d;return s=new W({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, use_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>,
            version=<span class="hljs-number">1</span>,
        )
    }
}

<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>
<span class="hljs-keyword">with</span> use_kernel_mapping(mapping):
    kernelized_model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function er(k){let l,U="Example:",y,s,d;return s=new W({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>)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function tr(k){let l,U="Example:",y,s,d;return s=new W({props:{code:"ZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBDVURBUHJvcGVydGllcyUyQyUyMERldmljZSUwQSUwQSUyMyUyMERlZmluZSUyMENVREElMjBwcm9wZXJ0aWVzJTIwZm9yJTIwbW9kZXJuJTIwR1BVcyUyMChjb21wdXRlJTIwY2FwYWJpbGl0eSUyMDcuNSUyMHRvJTIwOS4wKSUwQWN1ZGFfcHJvcHMlMjAlM0QlMjBDVURBUHJvcGVydGllcyhtaW5fY2FwYWJpbGl0eSUzRDc1JTJDJTIwbWF4X2NhcGFiaWxpdHklM0Q5MCklMEElMEElMjMlMjBDcmVhdGUlMjBhJTIwZGV2aWNlJTIwd2l0aCUyMHRoZXNlJTIwcHJvcGVydGllcyUwQWRldmljZSUyMCUzRCUyMERldmljZSh0eXBlJTNEJTIyY3VkYSUyMiUyQyUyMHByb3BlcnRpZXMlM0RjdWRhX3Byb3BzKQ==",highlighted:`<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> CUDAProperties, Device

<span class="hljs-comment"># Define CUDA properties for modern GPUs (compute capability 7.5 to 9.0)</span>
cuda_props = CUDAProperties(min_capability=<span class="hljs-number">75</span>, max_capability=<span class="hljs-number">90</span>)

<span class="hljs-comment"># Create a device with these properties</span>
device = Device(<span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;cuda&quot;</span>, properties=cuda_props)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function lr(k){let l,U="Example:",y,s,d;return s=new W({props:{code:"ZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBST0NNUHJvcGVydGllcyUyQyUyMERldmljZSUwQSUwQSUyMyUyMERlZmluZSUyMFJPQ00lMjBwcm9wZXJ0aWVzJTIwZm9yJTIwbW9kZXJuJTIwR1BVcyUyMChjb21wdXRlJTIwY2FwYWJpbGl0eSUyMDcuNSUyMHRvJTIwOS4wKSUwQXJvY21fcHJvcHMlMjAlM0QlMjBST0NNUHJvcGVydGllcyhtaW5fY2FwYWJpbGl0eSUzRDc1JTJDJTIwbWF4X2NhcGFiaWxpdHklM0Q5MCklMEElMEElMjMlMjBDcmVhdGUlMjBhJTIwZGV2aWNlJTIwd2l0aCUyMHRoZXNlJTIwcHJvcGVydGllcyUwQWRldmljZSUyMCUzRCUyMERldmljZSh0eXBlJTNEJTIycm9jbSUyMiUyQyUyMHByb3BlcnRpZXMlM0Ryb2NtX3Byb3BzKQ==",highlighted:`<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> ROCMProperties, Device

<span class="hljs-comment"># Define ROCM properties for modern GPUs (compute capability 7.5 to 9.0)</span>
rocm_props = ROCMProperties(min_capability=<span class="hljs-number">75</span>, max_capability=<span class="hljs-number">90</span>)

<span class="hljs-comment"># Create a device with these properties</span>
device = Device(<span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;rocm&quot;</span>, properties=rocm_props)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function nr(k){let l,U="Example:",y,s,d;return s=new W({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>,
    revision=<span class="hljs-string">&quot;main&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>
)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function sr(k){let l,U="Example:",y,s,d;return s=new W({props:{code:"ZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBMYXllclJlcG9zaXRvcnklMEElMEElMjMlMjBSZWZlcmVuY2UlMjBhJTIwc3BlY2lmaWMlMjBsYXllciUyMGJ5JTIwdmVyc2lvbiUwQWxheWVyX3JlcG8lMjAlM0QlMjBMYXllclJlcG9zaXRvcnkoJTBBJTIwJTIwJTIwJTIwcmVwb19pZCUzRCUyMmtlcm5lbHMtY29tbXVuaXR5JTJGYWN0aXZhdGlvbiUyMiUyQyUwQSUyMCUyMCUyMCUyMGxheWVyX25hbWUlM0QlMjJTaWx1QW5kTXVsJTIyJTJDJTBBJTIwJTIwJTIwJTIwdmVyc2lvbiUzRDElMkMlMEEp",highlighted:`<span class="hljs-keyword">from</span> kernels <span class="hljs-keyword">import</span> LayerRepository

<span class="hljs-comment"># Reference a specific layer by version</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>,
)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function rr(k){let l,U="Example:",y,s,d;return s=new W({props:{code:"ZnJvbSUyMHBhdGhsaWIlMjBpbXBvcnQlMjBQYXRoJTBBJTBBZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBMb2NhbEZ1bmNSZXBvc2l0b3J5JTBBJTBBJTIzJTIwUmVmZXJlbmNlJTIwYSUyMHNwZWNpZmljJTIwbGF5ZXIlMjBieSUyMHJldmlzaW9uJTBBbGF5ZXJfcmVwbyUyMCUzRCUyMExvY2FsRnVuY1JlcG9zaXRvcnkoJTBBJTIwJTIwJTIwJTIwcmVwb19wYXRoJTNEUGF0aCglMjIlMkZob21lJTJGZGFuaWVsJTJGa2VybmVscyUyRmFjdGl2YXRpb24lMjIpJTJDJTBBJTIwJTIwJTIwJTIwZnVuY19uYW1lJTNEJTIyc2lsdV9hbmRfbXVsJTIyJTJDJTBBKQ==",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>),
    func_name=<span class="hljs-string">&quot;silu_and_mul&quot;</span>,
)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function ar(k){let l,U="Example:",y,s,d;return s=new W({props:{code:"ZnJvbSUyMHBhdGhsaWIlMjBpbXBvcnQlMjBQYXRoJTBBJTBBZnJvbSUyMGtlcm5lbHMlMjBpbXBvcnQlMjBMb2NhbExheWVyUmVwb3NpdG9yeSUwQSUwQSUyMyUyMFJlZmVyZW5jZSUyMGElMjBzcGVjaWZpYyUyMGxheWVyJTIwYnklMjByZXZpc2lvbiUwQWxheWVyX3JlcG8lMjAlM0QlMjBMb2NhbExheWVyUmVwb3NpdG9yeSglMEElMjAlMjAlMjAlMjByZXBvX3BhdGglM0RQYXRoKCUyMiUyRmhvbWUlMkZkYW5pZWwlMkZrZXJuZWxzJTJGYWN0aXZhdGlvbiUyMiklMkMlMEElMjAlMjAlMjAlMjBsYXllcl9uYW1lJTNEJTIyU2lsdUFuZE11bCUyMiUyQyUwQSk=",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>),
    layer_name=<span class="hljs-string">&quot;SiluAndMul&quot;</span>,
)`,lang:"python",wrap:!1}}),{c(){l=c("p"),l.textContent=U,y=r(),u(s.$$.fragment)},l(t){l=m(t,"P",{"data-svelte-h":!0}),b(l)!=="svelte-11lpom8"&&(l.textContent=U),y=a(t),M(s.$$.fragment,t)},m(t,T){i(t,l,T),i(t,y,T),f(s,t,T),d=!0},p:V,i(t){d||(h(s.$$.fragment,t),d=!0)},o(t){w(s.$$.fragment,t),d=!1},d(t){t&&(o(l),o(y)),g(s,t)}}}function or(k){let l,U,y,s,d,t,T,ol,je,pl,$e,il,x,_e,Yl,Tt,On="Decorator factory that makes a layer extensible using the specified layer name.",Dl,bt,es=`This decorator which prepares a layer class to use kernel layers from the Hugging
Face Hub.`,Pl,Ut,ts=`When applied to a function, the function is converted into a layer (<code>nn.Module</code>),
made extensible using the given layer name, and then the class is instantiated.
Since <code>nn.Module</code> also implements the <code>__call__</code> method, the module can still be
used as if it was a function. Note that a decorated function is only visible to
<a href="/docs/kernels/pr_672/en/api/layers#kernels.kernelize">kernelize()</a> if it is attached to a module using <a href="/docs/kernels/pr_672/en/api/layers#kernels.use_kernelized_func">use_kernelized_func()</a>.`,ql,re,cl,Ce,ml,C,Ie,Kl,kt,ls="Decorator that makes a function extensible using the specified function name.",Ol,Jt,ns=`This is a decorator factory that returns a decorator which prepares a function to use kernels from the
Hugging Face Hub.`,en,jt,ss=`The function will be exposed as an instance of <code>torch.nn.Module</code> in which
the function is called in <code>forward</code>. For the function to be properly
kernelized, it <strong>must</strong> be a member of another <code>torch.nn.Module</code> that is
part of the model (see the example).`,tn,ve,rs=`<p><code>use_kernel_func_from_hub</code> is deprecated and will be removed in kernels 0.17.
Use <a href="/docs/kernels/pr_672/en/api/layers#kernels.use_kernel_forward_from_hub">use_kernel_forward_from_hub()</a> instead.</p>`,ln,ae,dl,xe,yl,D,Be,nn,$t,as=`This decorator attaches the target function within the module as a plain
attribute (not as a submodule). This makes the function visible to
<a href="/docs/kernels/pr_672/en/api/layers#kernels.kernelize">kernelize()</a>.`,sn,oe,ul,Ze,Ml,S,Re,rn,_t,os="Function that prepares a layer class to use kernels from the Hugging Face Hub.",an,Ct,ps=`It is recommended to use <a href="/docs/kernels/pr_672/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.`,on,pe,fl,Ge,hl,Ee,wl,X,Ve,pn,It,is="Context manager that sets a kernel mapping for the duration of the context.",cn,vt,cs=`This function allows temporary kernel mappings to be applied within a specific context, enabling different
kernel configurations for different parts of your code.`,mn,ie,gl,We,Tl,N,Se,dn,xt,ms="Register a global mapping between layer names and their corresponding kernel implementations.",yn,Bt,ds=`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_672/en/api/layers#kernels.kernelize">kernelize()</a>.`,un,ce,bl,Xe,Ul,Ne,kl,A,Ae,Mn,Zt,ys="Replace layer forward methods with optimized kernel implementations.",fn,Rt,us=`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_672/en/api/layers#kernels.register_kernel_mapping">register_kernel_mapping()</a> or <a href="/docs/kernels/pr_672/en/api/layers#kernels.use_kernel_mapping">use_kernel_mapping()</a>.`,hn,me,Jl,Qe,jl,Fe,$l,B,Le,wn,Gt,Ms="Represents a compute device with optional properties.",gn,Et,fs=`This class encapsulates device information including device type and optional device-specific properties
like CUDA capabilities.`,Tn,de,bn,ye,ze,Un,Vt,hs="Run class validators on the instance.",_l,He,Cl,I,Ye,kn,Wt,ws="CUDA-specific device properties for capability-based kernel selection.",Jn,St,gs=`This class defines CUDA compute capability constraints for kernel selection, allowing kernels to specify
minimum and maximum CUDA compute capabilities they support.`,jn,ue,$n,Xt,Ts=`Note:
CUDA compute capabilities are represented as integers where the major and minor versions are concatenated.
For example, compute capability 7.5 is represented as 75, and 8.6 is represented as 86.`,_n,Me,De,Cn,Nt,bs="Run class validators on the instance.",Il,Pe,vl,v,qe,In,At,Us="ROCM-specific device properties for capability-based kernel selection.",vn,Qt,ks=`This class defines ROCM compute capability constraints for kernel selection, allowing kernels to specify
minimum and maximum ROCM compute capabilities they support.`,xn,fe,Bn,Ft,Js=`Note:
ROCM compute capabilities are represented as integers where the major and minor versions are concatenated.
For example, compute capability 7.5 is represented as 75, and 8.6 is represented as 86.`,Zn,he,Ke,Rn,Lt,js="Run class validators on the instance.",xl,Oe,Bl,Q,et,Gn,zt,$s="Kernelize mode",En,Ht,_s=`The <code>Mode</code> flag is used by <a href="/docs/kernels/pr_672/en/api/layers#kernels.kernelize">kernelize()</a> to select kernels for the given mode. Mappings can be registered for
specific modes.`,Vn,Yt,Cs=`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>.`,Zl,tt,Rl,F,lt,Wn,Dt,Is="Repository and name of a function for kernel mapping.",Sn,nt,vs=`<p><code>FuncRepository</code> is deprecated and will be removed in kernels 0.17.
Use <a href="/docs/kernels/pr_672/en/api/layers#kernels.LayerRepository">LayerRepository</a> instead.</p>`,Xn,we,Gl,st,El,P,rt,Nn,Pt,xs="Repository and name of a layer for kernel mapping.",An,ge,Vl,at,Wl,L,ot,Qn,qt,Bs="Repository and function name from a local directory for kernel mapping.",Fn,pt,Zs=`<p><code>LocalFuncRepository</code> is deprecated and will be removed in kernels 0.17.
Use <a href="/docs/kernels/pr_672/en/api/layers#kernels.LocalLayerRepository">LocalLayerRepository</a> instead.</p>`,Ln,Te,Sl,it,Xl,q,ct,zn,Kt,Rs="Repository from a local directory for kernel mapping.",Hn,be,Nl,mt,Al,z,dt,Yn,Ot,Gs="Repository and name of a function.",Dn,el,Es=`In contrast to <code>FuncRepository</code>, this class uses repositories that
are locked inside a project.`,Pn,yt,Vs=`<p><code>LockedFuncRepository</code> is deprecated and will be removed in kernels 0.17.
Use <a href="/docs/kernels/pr_672/en/api/layers#kernels.LockedLayerRepository">LockedLayerRepository</a> instead.</p>`,Ql,ut,Fl,K,Mt,qn,tl,Ws="Repository and name of a layer.",Kn,ll,Ss=`In contrast to <code>LayerRepository</code>, this class uses repositories that
are locked inside a project.`,Ll,ft,zl,al,Hl;return d=new Ls({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),T=new $({props:{title:"Layers API Reference",local:"layers-api-reference",headingTag:"h1"}}),je=new $({props:{title:"Making layers kernel-aware",local:"making-layers-kernel-aware",headingTag:"h2"}}),$e=new $({props:{title:"use_kernel_forward_from_hub",local:"kernels.use_kernel_forward_from_hub",headingTag:"h3"}}),_e=new _({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_672/kernels/src/kernels/layer/layer.py#L270",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>
`}}),re=new E({props:{anchor:"kernels.use_kernel_forward_from_hub.example",$$slots:{default:[Hs]},$$scope:{ctx:k}}}),Ce=new $({props:{title:"use_kernel_func_from_hub",local:"kernels.use_kernel_func_from_hub",headingTag:"h3"}}),Ie=new _({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_672/kernels/src/kernels/layer/func.py#L196",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>
`}}),ae=new E({props:{anchor:"kernels.use_kernel_func_from_hub.example",$$slots:{default:[Ys]},$$scope:{ctx:k}}}),xe=new $({props:{title:"use_kernelized_func",local:"kernels.use_kernelized_func",headingTag:"h3"}}),Be=new _({props:{name:"kernels.use_kernelized_func",anchor:"kernels.use_kernelized_func",parameters:[{name:"*args",val:": Callable"}],parametersDescription:[{anchor:"kernels.use_kernelized_func.*args",description:`<strong>*args</strong> (<code>Callable</code>) &#x2014;
Kernel functions to attach to the module.`,name:"*args"}],source:"https://github.com/huggingface/kernels/blob/vr_672/kernels/src/kernels/layer/layer.py#L342",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>Callable</code></p>
`}}),oe=new E({props:{anchor:"kernels.use_kernelized_func.example",$$slots:{default:[Ds]},$$scope:{ctx:k}}}),Ze=new $({props:{title:"replace_kernel_forward_from_hub",local:"kernels.replace_kernel_forward_from_hub",headingTag:"h3"}}),Re=new _({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_672/kernels/src/kernels/layer/layer.py#L247"}}),pe=new E({props:{anchor:"kernels.replace_kernel_forward_from_hub.example",$$slots:{default:[Ps]},$$scope:{ctx:k}}}),Ge=new $({props:{title:"Registering kernel mappings",local:"registering-kernel-mappings",headingTag:"h2"}}),Ee=new $({props:{title:"use_kernel_mapping",local:"kernels.use_kernel_mapping",headingTag:"h3"}}),Ve=new _({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_672/kernels/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>
`}}),ie=new E({props:{anchor:"kernels.use_kernel_mapping.example",$$slots:{default:[qs]},$$scope:{ctx:k}}}),We=new $({props:{title:"register_kernel_mapping",local:"kernels.register_kernel_mapping",headingTag:"h3"}}),Se=new _({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_672/kernels/src/kernels/layer/kernelize.py#L97"}}),ce=new E({props:{anchor:"kernels.register_kernel_mapping.example",$$slots:{default:[Ks]},$$scope:{ctx:k}}}),Xe=new $({props:{title:"Kernelizing a model",local:"kernelizing-a-model",headingTag:"h2"}}),Ne=new $({props:{title:"kernelize",local:"kernels.kernelize",headingTag:"h3"}}),Ae=new _({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_672/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_672/kernels/src/kernels/layer/kernelize.py#L175",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>
`}}),me=new E({props:{anchor:"kernels.kernelize.example",$$slots:{default:[Os]},$$scope:{ctx:k}}}),Qe=new $({props:{title:"Classes",local:"classes",headingTag:"h2"}}),Fe=new $({props:{title:"Device",local:"kernels.Device",headingTag:"h3"}}),Le=new _({props:{name:"class kernels.Device",anchor:"kernels.Device",parameters:[{name:"type",val:": str"},{name:"properties",val:": kernels.layer.device.CUDAProperties | kernels.layer.device.ROCMProperties | 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> (<a href="/docs/kernels/pr_672/en/api/layers#kernels.CUDAProperties">CUDAProperties</a>, <em>optional</em>) &#x2014;
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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>) &#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</code>, <em>optional</em>) &#x2014;
The kernel version to download. Cannot be used together with <code>revision</code>.
Either <code>version</code> or <code>revision</code> must be specified.`,name:"version"}],source:"https://github.com/huggingface/kernels/blob/vr_672/kernels/src/kernels/layer/func.py#L26"}}),we=new E({props:{anchor:"kernels.FuncRepository.example",$$slots:{default:[nr]},$$scope:{ctx:k}}}),st=new $({props:{title:"LayerRepository",local:"kernels.LayerRepository",headingTag:"h3"}}),rt=new _({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 | None = None"},{name:"trust_remote_code",val:": bool | list[str] = False"}],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>) &#x2014;
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The kernel version to download. Cannot be used together with <code>revision</code>.
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Whether to allow loading kernels from untrusted organisations. A list
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The local repository containing the layer.`,name:"repo_path"},{anchor:"kernels.LocalFuncRepository.func_name",description:`<strong>func_name</strong> (<code>str</code>) &#x2014;
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The local repository containing the layer.`,name:"repo_path"},{anchor:"kernels.LocalLayerRepository.layer_name",description:`<strong>layer_name</strong> (<code>str</code>) &#x2014;
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