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hf-doc-build/doc-dev / kernels /pr_628 /en /_app /immutable /nodes /3.b80eef3f.js
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import{s as Ms,o as us,n as N}from"../chunks/scheduler.f3b1e791.js";import{S as fs,i as hs,e as d,s as a,c as M,h as ws,a as y,d as r,b as o,f as k,g as u,j as b,k as $,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 gs}from"../chunks/CopyLLMTxtMenu.a1b0882e.js";import{D as _,E as X}from"../chunks/ExampleCodeBlock.50164384.js";import{C as F}from"../chunks/CodeBlock.bc671b5b.js";import{H as j,E as Us}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.fdc6aa4c.js";function bs(T){let n,J="Example:",m,s,c;return s=new F({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>`,lang:"python",wrap:!1}}),{c(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function Js(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function Ts(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function ks(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function $s(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function js(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function _s(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function Cs(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function vs(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function Is(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function xs(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function Bs(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function Rs(T){let n,J="Example:",m,s,c;return s=new F({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(){n=d("p"),n.textContent=J,m=a(),M(s.$$.fragment)},l(t){n=y(t,"P",{"data-svelte-h":!0}),b(n)!=="svelte-11lpom8"&&(n.textContent=J),m=o(t),u(s.$$.fragment,t)},m(t,U){i(t,n,U),i(t,m,U),f(s,t,U),c=!0},p:N,i(t){c||(h(s.$$.fragment,t),c=!0)},o(t){w(s.$$.fragment,t),c=!1},d(t){t&&(r(n),r(m)),g(s,t)}}}function Zs(T){let n,J,m,s,c,t,U,Pt,ge,qt,Ue,Kt,Z,be,Gn,pt,Rl="Decorator factory that makes a layer extensible using the specified layer name.",Sn,it,Zl=`This is a decorator factory that returns a decorator which prepares a layer class to use kernels from the
Hugging Face Hub.`,Wn,le,Ot,Je,en,I,Te,Vn,ct,El="Decorator that makes a function extensible using the specified function name.",An,mt,Gl=`This is a decorator factory that returns a decorator which prepares a function to use kernels from the
Hugging Face Hub.`,Qn,dt,Sl=`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).`,Xn,se,tn,ke,nn,E,$e,Nn,yt,Wl="Function that prepares a layer class to use kernels from the Hugging Face Hub.",Fn,Mt,Vl=`It is recommended to use <a href="/docs/kernels/pr_628/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.`,Ln,re,ln,je,sn,_e,rn,G,Ce,Dn,ut,Al="Context manager that sets a kernel mapping for the duration of the context.",zn,ft,Ql=`This function allows temporary kernel mappings to be applied within a specific context, enabling different
kernel configurations for different parts of your code.`,Hn,ae,an,ve,on,S,Ie,Yn,ht,Xl="Register a global mapping between layer names and their corresponding kernel implementations.",Pn,wt,Nl=`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_628/en/api/layers#kernels.kernelize">kernelize()</a>.`,qn,oe,pn,xe,cn,Be,mn,W,Re,Kn,gt,Fl="Replace layer forward methods with optimized kernel implementations.",On,Ut,Ll=`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_628/en/api/layers#kernels.register_kernel_mapping">register_kernel_mapping()</a> or <a href="/docs/kernels/pr_628/en/api/layers#kernels.use_kernel_mapping">use_kernel_mapping()</a>.`,el,pe,dn,Ze,yn,Ee,Mn,x,Ge,tl,bt,Dl="Represents a compute device with optional properties.",nl,Jt,zl=`This class encapsulates device information including device type and optional device-specific properties
like CUDA capabilities.`,ll,ie,sl,ce,Se,rl,Tt,Hl="Run class validators on the instance.",un,We,fn,C,Ve,al,kt,Yl="CUDA-specific device properties for capability-based kernel selection.",ol,$t,Pl=`This class defines CUDA compute capability constraints for kernel selection, allowing kernels to specify
minimum and maximum CUDA compute capabilities they support.`,pl,me,il,jt,ql=`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.`,cl,de,Ae,ml,_t,Kl="Run class validators on the instance.",hn,Qe,wn,v,Xe,dl,Ct,Ol="ROCM-specific device properties for capability-based kernel selection.",yl,vt,es=`This class defines ROCM compute capability constraints for kernel selection, allowing kernels to specify
minimum and maximum ROCM compute capabilities they support.`,Ml,ye,ul,It,ts=`Note:
ROCM compute capabilities are represented as integers where the major and minor versions are concatenated.
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specific modes.`,Ul,Zt,rs=`Note:
Different modes can be combined. For instance, <code>INFERENCE | TORCH_COMPILE</code> should be used for layers that
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are locked inside a project.`,xn,nt,Bn,P,lt,xl,Qt,ds="Repository and name of a layer.",Bl,Xt,ys=`In contrast to <code>LayerRepository</code>, this class uses repositories that
are locked inside a project.`,Rn,st,Zn,Yt,En;return c=new gs({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),U=new j({props:{title:"Layers API Reference",local:"layers-api-reference",headingTag:"h1"}}),ge=new j({props:{title:"Making layers kernel-aware",local:"making-layers-kernel-aware",headingTag:"h2"}}),Ue=new j({props:{title:"use_kernel_forward_from_hub",local:"kernels.use_kernel_forward_from_hub",headingTag:"h3"}}),be=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;
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`}}),ae=new X({props:{anchor:"kernels.use_kernel_mapping.example",$$slots:{default:[ks]},$$scope:{ctx:T}}}),ve=new j({props:{title:"register_kernel_mapping",local:"kernels.register_kernel_mapping",headingTag:"h3"}}),Ie=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;
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<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;.
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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_628/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>
`}}),pe=new X({props:{anchor:"kernels.kernelize.example",$$slots:{default:[js]},$$scope:{ctx:T}}}),Ze=new j({props:{title:"Classes",local:"classes",headingTag:"h2"}}),Ee=new j({props:{title:"Device",local:"kernels.Device",headingTag:"h3"}}),Ge=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;
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Device-specific properties. Currently only <a href="/docs/kernels/pr_628/en/api/layers#kernels.CUDAProperties">CUDAProperties</a> is supported for CUDA devices.`,name:"properties"}],source:"https://github.com/huggingface/kernels/blob/vr_628/kernels/src/kernels/layer/device.py#L106"}}),ie=new X({props:{anchor:"kernels.Device.example",$$slots:{default:[_s]},$$scope:{ctx:T}}}),Se=new _({props:{name:"validate",anchor:"kernels.Device.validate",parameters:[],source:"https://github.com/huggingface/kernels/blob/vr_628/kernels/src/huggingface_hub/dataclasses.py#L247"}}),We=new j({props:{title:"CUDAProperties",local:"kernels.CUDAProperties",headingTag:"h3"}}),Ve=new _({props:{name:"class kernels.CUDAProperties",anchor:"kernels.CUDAProperties",parameters:[{name:"min_capability",val:": int"},{name:"max_capability",val:": int"}],parametersDescription:[{anchor:"kernels.CUDAProperties.min_capability",description:`<strong>min_capability</strong> (<code>int</code>) &#x2014;
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Maximum CUDA compute capability supported (e.g., 90 for compute capability 9.0).`,name:"max_capability"}],source:"https://github.com/huggingface/kernels/blob/vr_628/kernels/src/kernels/layer/device.py#L8"}}),me=new X({props:{anchor:"kernels.CUDAProperties.example",$$slots:{default:[Cs]},$$scope:{ctx:T}}}),Ae=new _({props:{name:"validate",anchor:"kernels.CUDAProperties.validate",parameters:[],source:"https://github.com/huggingface/kernels/blob/vr_628/kernels/src/huggingface_hub/dataclasses.py#L247"}}),Qe=new j({props:{title:"ROCMProperties",local:"kernels.ROCMProperties",headingTag:"h3"}}),Xe=new _({props:{name:"class kernels.ROCMProperties",anchor:"kernels.ROCMProperties",parameters:[{name:"min_capability",val:": int"},{name:"max_capability",val:": int"}],parametersDescription:[{anchor:"kernels.ROCMProperties.min_capability",description:`<strong>min_capability</strong> (<code>int</code>) &#x2014;
Minimum ROCM compute capability required (e.g., 75 for compute capability 7.5).`,name:"min_capability"},{anchor:"kernels.ROCMProperties.max_capability",description:`<strong>max_capability</strong> (<code>int</code>) &#x2014;
Maximum ROCM compute capability supported (e.g., 90 for compute capability 9.0).`,name:"max_capability"}],source:"https://github.com/huggingface/kernels/blob/vr_628/kernels/src/kernels/layer/device.py#L57"}}),ye=new X({props:{anchor:"kernels.ROCMProperties.example",$$slots:{default:[vs]},$$scope:{ctx:T}}}),Ne=new _({props:{name:"validate",anchor:"kernels.ROCMProperties.validate",parameters:[],source:"https://github.com/huggingface/kernels/blob/vr_628/kernels/src/huggingface_hub/dataclasses.py#L247"}}),Fe=new j({props:{title:"Mode",local:"kernels.Mode",headingTag:"h3"}}),Le=new _({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_628/kernels/src/kernels/layer/mode.py#L4"}}),De=new j({props:{title:"FuncRepository",local:"kernels.FuncRepository",headingTag:"h3"}}),ze=new _({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 | None = None"},{name:"trust_remote_code",val:": bool | list[str] = False"}],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;
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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>.
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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;
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</code>, <em>optional</em>) &#x2014;
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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