#
Layers API Reference

##
Making layers kernel-aware

###
use_kernel_forward_from_hub

####
kernels.use_kernel_forward_from_hub

< source >

( layer_name: str ) →
`Callable`

Parameters

- layer_name (`str`) — The name of the layer to use for kernel lookup in registered mappings.

Returns

`Callable`

A decorator function that can be applied to layer classes.

Decorator factory that makes a layer extensible using the specified layer name.

This is a decorator factory that returns a decorator which prepares a layer class to use kernels from the Hugging Face Hub.

Example:

    import torch
    import torch.nn as nn

    from kernels import use_kernel_forward_from_hub
    from kernels import Mode, kernelize

    @use_kernel_forward_from_hub("MyCustomLayer")
    class MyCustomLayer(nn.Module):
        def __init__(self, hidden_size):
            super().__init__()
            self.hidden_size = hidden_size

        def forward(self, x: torch.Tensor):
            # original implementation
            return x

    model = MyCustomLayer(768)

    # The layer can now be kernelized:
    # model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE, device="cuda")

###
use_kernel_func_from_hub

####
kernels.use_kernel_func_from_hub

< source >

( func_name: str ) →
`Callable`

Parameters

- func_name (`str`) — The name of the function name to use for kernel lookup in registered mappings.

Returns

`Callable`

A decorator function that can be applied to layer classes.

Decorator that makes a function extensible using the specified function name.

This is a decorator factory that returns a decorator which prepares a function to use kernels from the Hugging Face Hub.

The function will be exposed as an instance of `torch.nn.Module` in which the function is called in `forward`. For the function to be properly kernelized, it must be a member of another `torch.nn.Module` that is part of the model (see the example).

Example:

    import torch
    import torch.nn as nn

    from kernels import use_kernel_func_from_hub
    from kernels import Mode, kernelize

    @use_kernel_func_from_hub("my_custom_func")
    def my_custom_func(x: torch.Tensor):
        # Original implementation
        return x

    class MyModel(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.fn = my_custom_func

        def forward(self, x):
            return self.fn(x)

    model = MyModel()

    # The layer can now be kernelized:
    # model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE, device="cuda")

###
replace_kernel_forward_from_hub

####
kernels.replace_kernel_forward_from_hub

< source >

( layer_name: str )

Function that prepares a layer class to use kernels from the Hugging Face Hub.

It is recommended to use use_kernel_forward_from_hub() 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 `forward` signature of such a layer can change.

Example:

    from kernels import replace_kernel_forward_from_hub
    import torch.nn as nn

    replace_kernel_forward_from_hub(nn.LayerNorm, "LayerNorm")

##
Registering kernel mappings

###
use_kernel_mapping

####
kernels.use_kernel_mapping

< source >

( mapping: dict[str, dict[Device | str, RepositoryProtocol | dict[Mode, RepositoryProtocol]]] inherit_mapping: bool = True )

Parameters

- mapping (`dict[str, dict[Union[Device, str], Union[LayerRepositoryProtocol, dict[Mode, LayerRepositoryProtocol]]]]`) — The kernel mapping to apply. Maps layer names to device-specific kernel configurations.
- inherit_mapping (`bool`, optional, defaults to `True`) — When `True`, the current mapping will be extended by `mapping` inside the context. When `False`, only `mapping` is used inside the context.

Context manager that sets a kernel mapping for the duration of the context.

This function allows temporary kernel mappings to be applied within a specific context, enabling different kernel configurations for different parts of your code.

Example:

    import torch
    import torch.nn as nn
    from torch.nn import functional as F

    from kernels import use_kernel_forward_from_hub
    from kernels import use_kernel_mapping, LayerRepository, Device
    from kernels import Mode, kernelize

    # Define a mapping
    mapping = {
        "SiluAndMul": {
            "cuda": LayerRepository(
                repo_id="kernels-community/activation",
                layer_name="SiluAndMul",
                version=1
            )
        }
    }

    @use_kernel_forward_from_hub("SiluAndMul")
    class SiluAndMul(nn.Module):
        def forward(self, x: torch.Tensor) -> torch.Tensor:
            d = x.shape[-1] // 2
            return F.silu(x[..., :d]) * x[..., d:]

    model = SiluAndMul()

    # Use the mapping for the duration of the context.
    with use_kernel_mapping(mapping):
        # kernelize uses the temporary mapping
        model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE, device="cuda")

    # Outside the context, original mappings are restored

###
register_kernel_mapping

####
kernels.register_kernel_mapping

< source >

( mapping: dict[str, dict[Device | str, RepositoryProtocol | dict[Mode, RepositoryProtocol]]] inherit_mapping: bool = True )

Parameters

- mapping (`dict[str, dict[Union[Device, str], Union[RepositoryProtocol, dict[Mode, RepositoryProtocol]]]]`) — 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.).
- inherit_mapping (`bool`, optional, defaults to `True`) — When `True`, the current mapping will be extended by `mapping`. When `False`, the existing mappings are erased before adding `mapping`.

Register a global mapping between layer names and their corresponding kernel implementations.

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 kernelize().

Example:

    from kernels import LayerRepository, register_kernel_mapping, Mode

    # Simple mapping for a single kernel per device
    kernel_layer_mapping = {
        "LlamaRMSNorm": {
            "cuda": LayerRepository(
                repo_id="kernels-community/layer_norm",
                layer_name="LlamaRMSNorm",
                version=1,
            ),
        },
    }
    register_kernel_mapping(kernel_layer_mapping)

    # Advanced mapping with mode-specific kernels
    advanced_mapping = {
        "MultiHeadAttention": {
            "cuda": {
                Mode.TRAINING: LayerRepository(
                    repo_id="kernels-community/training-kernels",
                    layer_name="TrainingAttention",
                    version=1,
                ),
                Mode.INFERENCE: LayerRepository(
                    repo_id="kernels-community/inference-kernels",
                    layer_name="FastAttention",
                    version=1,
                ),
            }
        }
    }
    register_kernel_mapping(advanced_mapping)

##
Kernelizing a model

###
kernelize

####
kernels.kernelize

< source >

( model: 'nn.Module' mode: Mode device: str | 'torch.device' | None = None use_fallback: bool = True ) →
`nn.Module`

Parameters

- model (`nn.Module`) — The PyTorch model to kernelize.
- mode (Mode) — The mode that the kernel is going to be used in. For example, `Mode.TRAINING | Mode.TORCH_COMPILE` kernelizes the model for training with `torch.compile`.
- device (`Union[str, torch.device]`, optional) — The device type to load kernels for. Supported device types are: “cuda”, “mps”, “npu”, “rocm”, “xpu”. The device type will be inferred from the model parameters when not provided.
- use_fallback (`bool`, optional, defaults to `True`) — Whether to use the original forward method of modules when no compatible kernel could be found. If set to `False`, an exception will be raised in such cases.

Returns

`nn.Module`

The kernelized model with optimized kernel implementations.

Replace layer forward methods with optimized kernel implementations.

This function iterates over all modules in the model and replaces the `forward` method of extensible layers for which kernels are registered using register_kernel_mapping() or use_kernel_mapping().

Example:

    import torch
    import torch.nn as nn

    from kernels import kernelize, Mode, use_kernel_mapping, LayerRepository
    from kernels import use_kernel_forward_from_hub

    @use_kernel_forward_from_hub("SiluAndMul")
    class SiluAndMul(nn.Module):
        def forward(self, x: torch.Tensor) -> torch.Tensor:
            d = x.shape[-1] // 2
            return F.silu(x[..., :d]) * x[..., d:]

    mapping = {
        "SiluAndMul": {
            "cuda": LayerRepository(
                repo_id="kernels-community/activation",
                layer_name="SiluAndMul",
                version=1,
            )
        }
    }

    # Create and kernelize a model
    model = nn.Sequential(
        nn.Linear(1024, 2048, device="cuda"),
        SiluAndMul(),
    )

    # Kernelize for inference
    with use_kernel_mapping(mapping):
        kernelized_model = kernelize(model, mode=Mode.TRAINING | Mode.TORCH_COMPILE)

##
Classes

###
Device

###
class kernels.Device

< source >

( type: str properties: kernels.layer.device.CUDAProperties | kernels.layer.device.ROCMProperties | None = None )

Parameters

- type (`str`) — The device type (e.g., “cuda”, “mps”, “npu”, “rocm”, “xpu”).
- properties (`CUDAProperties`, optional) — Device-specific properties. Currently only `CUDAProperties` is supported for CUDA devices.

Represents a compute device with optional properties.

This class encapsulates device information including device type and optional device-specific properties like CUDA capabilities.

Example:

    from kernels import Device, CUDAProperties

    # Basic CUDA device
    cuda_device = Device(type="cuda")

    # CUDA device with specific capability requirements
    cuda_device_with_props = Device(
        type="cuda",
        properties=CUDAProperties(min_capability=75, max_capability=90)
    )

    # MPS device for Apple Silicon
    mps_device = Device(type="mps")

    # XPU device (e.g., Intel(R) Data Center GPU Max 1550)
    xpu_device = Device(type="xpu")

    # NPU device (Huawei Ascend)
    npu_device = Device(type="npu")

####
validate

< source >

( )

Run class validators on the instance.

###
Mode

###
class kernels.Mode

< source >

( value names = None module = None qualname = None type = None start = 1 )

Parameters

- INFERENCE — The kernel is used for inference.
- TRAINING — The kernel is used for training.
- TORCH_COMPILE — The kernel is used with `torch.compile`.
- FALLBACK — In a kernel mapping, this kernel is used when no other mode matches.

Kernelize mode

The `Mode` flag is used by kernelize() to select kernels for the given mode. Mappings can be registered for specific modes.

Note: Different modes can be combined. For instance, `INFERENCE | TORCH_COMPILE` should be used for layers that are used for inference with `torch.compile`.

###
FuncRepository

###
class kernels.FuncRepository

< source >

( repo_id: str func_name: str revision: str | None = None version: int | None = None trust_remote_code: bool | list[str] = False )

Parameters

- repo_id (`str`) — The Hub repository containing the layer.
- func_name (`str`) — The name of the function within the kernel repository.
- revision (`str`, optional) — The specific revision (branch, tag, or commit) to download. Cannot be used together with `version`.
- version (`int`, optional) — The kernel version to download. Cannot be used together with `revision`. Either `version` or `revision` must be specified.

Repository and name of a function for kernel mapping.

Example:

    from kernels import FuncRepository

    # Reference a specific layer by revision
    layer_repo = FuncRepository(
        repo_id="kernels-community/activation",
        func_name="silu_and_mul",
        revision="main",
    )

    # Reference a layer by version
    layer_repo_versioned = FuncRepository(
        repo_id="kernels-community/relu",
        func_name="relu",
        version=1
    )

###
LayerRepository

###
class kernels.LayerRepository

< source >

( repo_id: str layer_name: str revision: str | None = None version: int | None = None trust_remote_code: bool | list[str] = False )

Parameters

- repo_id (`str`) — The Hub repository containing the layer.
- layer_name (`str`) — The name of the layer within the kernel repository.
- revision (`str`, optional) — The specific revision (branch, tag, or commit) to download. Cannot be used together with `version`.
- version (`int`, optional) — The kernel version to download. Cannot be used together with `revision`. Either `version` or `revision` must be specified.
- trust_remote_code (`bool | list[str]`, optional, defaults to `False`) — Whether to allow loading kernels from untrusted organisations. A list of signing identities can be provided for future verification support; until then it warns and falls back to the default trust check.

Repository and name of a layer for kernel mapping.

Example:

    from kernels import LayerRepository

    # Reference a specific layer by version
    layer_repo = LayerRepository(
        repo_id="kernels-community/activation",
        layer_name="SiluAndMul",
        version=1,
    )

###
LocalFuncRepository

###
class kernels.LocalFuncRepository

< source >

( repo_path: Path func_name: str )

Parameters

- repo_path (`Path`) — The local repository containing the layer.
- func_name (`str`) — The name of the function within the kernel repository.

Repository and function name from a local directory for kernel mapping.

Example:

    from pathlib import Path

    from kernels import LocalFuncRepository

    # Reference a specific layer by revision
    layer_repo = LocalFuncRepository(
        repo_path=Path("/home/daniel/kernels/activation"),
        func_name="silu_and_mul",
    )

###
LocalLayerRepository

###
class kernels.LocalLayerRepository

< source >

( repo_path: Path layer_name: str )

Parameters

- repo_path (`Path`) — The local repository containing the layer.
- layer_name (`str`) — The name of the layer within the kernel repository.

Repository from a local directory for kernel mapping.

Example:

    from pathlib import Path

    from kernels import LocalLayerRepository

    # Reference a specific layer by revision
    layer_repo = LocalLayerRepository(
        repo_path=Path("/home/daniel/kernels/activation"),
        layer_name="SiluAndMul",
    )

###
LockedFuncRepository

###
class kernels.LockedFuncRepository

< source >

( repo_id: str lockfile: pathlib.Path | None = None func_name: str trust_remote_code: bool | list[str] = False )

Repository and name of a function.

In contrast to `FuncRepository`, this class uses repositories that are locked inside a project.

###
LockedLayerRepository

###
class kernels.LockedLayerRepository

< source >

( repo_id: str lockfile: Path | None = None layer_name: str trust_remote_code: bool | list[str] = False )

Repository and name of a layer.

In contrast to `LayerRepository`, this class uses repositories that are locked inside a project.

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