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# Writing kernels

This guide explains how to write kernels that go beyond a stateless `forward` replacement. It covers two capabilities the extended `KernelConfig` API supports:

1. Parameter transformation: the kernel expects weights in a different layout than the original model (for example, renamed or merged parameters).
2. Module fusion: the kernel replaces multiple adjacent modules with a single fused implementation.

For basic kernels (stateless `forward` replacements with no parameter changes), see the [kernels](https://github.com/huggingface/kernels) library documentation.

## Two-class pattern

Any kernel that carries its own parameters follows a two-class pattern.

- `KernelName`: contains only the `forward` pass. The `kernels` library uses this class to kernelize the model because it does not allow stateful kernel classes.
- `KernelNameLayout`: an `nn.Module` that holds the parameters and monkey-patches the original module before the checkpoint is loaded. At runtime, `kernelize` replaces its `forward` with the `forward` from `KernelName`'. You do not need to define `forward`. Transformers injects one automatically with the same signature as `KernelName.forward`.

> [!IMPORTANT]

The naming convention is strict. The layout class must be named `{KernelName}Layout` and defined in the same module as `KernelName`.

## Parameter transformation

Use this pattern when the kernel expects weights under different names or in a different shape than the original model checkpoint.

The `KernelNameLayout` class has the same `__init__` signature as the module it replaces and declares a `conversion_mapping` class attribute that tells Transformers how to remap checkpoint keys to the new parameter names (see [Dynamic weight loading](../weightconverter) for more details).

```python
import torch
import torch.nn as nn

class CustomRMSNormLayout(nn.Module):
    conversion_mapping = [...]  # rules that remap checkpoint keys to the new parameter names

    def __init__(self, hidden_size: int, eps: float = 1e-6):
        super().__init__()
        self.scale = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps


class CustomRMSNorm(nn.Module):
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.scale * hidden_states.to(input_dtype)


class layers:
    CustomRMSNorm = CustomRMSNorm
```

> [!NOTE]
> The `layers` class is required by the `kernels` library to expose the kernel entry point.

Load this kernel by passing the repo and class name to [`KernelConfig`]. The key is the original module class name from the model. The value points to the `KernelName` class (not the `Layout`) in the repo.

```python
from transformers import AutoModelForCausalLM, KernelConfig

kernel_config = KernelConfig({"RMSNorm": "owner/my-kernel:CustomRMSNorm"})
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-0.6B",
    use_kernels=True,
    kernel_config=kernel_config,
    device_map="cuda",
)
```

When the model loads, Transformers:
1. Loads `CustomRMSNorm` from the repo and looks for `CustomRMSNormLayout` in the same module.
2. Monkey-patches every `RMSNorm` in the model with `CustomRMSNormLayout`.
3. Remaps checkpoint weights using `conversion_mapping` so they load into the new parameter names.
4. Calls `kernelize`, which replaces `CustomRMSNormLayout.forward` with `CustomRMSNorm.forward`.

## Module fusion

Use this pattern when a kernel replaces multiple adjacent modules with a single fused implementation. Because the fused module combines parameters from several original modules, the `KernelNameLayout.__init__` receives the instantiated child modules rather than their constructor arguments.

```python
import torch
import torch.nn as nn

class RMSNormMLPLayout(nn.Module):
    conversion_mapping = [...]  # rules that remap checkpoint keys to the fused parameter names

    def __init__(self, norm, mlp):
        super().__init__()
        self.variance_epsilon = norm.variance_epsilon
        self.scale = nn.Parameter(torch.empty_like(norm.weight))
        self.gate_up_proj = nn.Linear(
            mlp.gate_proj.in_features,
            mlp.gate_proj.out_features + mlp.up_proj.out_features,
            bias=mlp.gate_proj.bias is not None,
            device=mlp.gate_proj.weight.device,
            dtype=mlp.gate_proj.weight.dtype,
        )
        self.down_proj = nn.Linear(
            mlp.down_proj.in_features,
            mlp.down_proj.out_features,
            bias=mlp.down_proj.bias is not None,
            device=mlp.down_proj.weight.device,
            dtype=mlp.down_proj.weight.dtype,
        )
        self.act_fn = mlp.act_fn


class RMSNormMLP(nn.Module):
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        hidden_states = self.scale * hidden_states.to(input_dtype)
        gate, up = self.gate_up_proj(hidden_states).chunk(2, dim=-1)
        return self.down_proj(self.act_fn(gate) * up)


class layers:
    RMSNormMLP = RMSNormMLP
```

To fuse modules, pass a tuple of `(class_name, path_pattern)` pairs as the key in `KernelConfig` instead of a plain string. All patterns must share the same parent module (Transformers fuses the children in that parent). The `*` wildcard matches any single path segment.

```python
from transformers import AutoModelForCausalLM, KernelConfig

kernel_config = KernelConfig(
    {
        (
            ("RMSNorm", "model.layers.*.post_attention_layernorm"),
            ("MLP",     "model.layers.*.mlp"),
        ): "owner/my-kernel:RMSNormMLP",
    }
)
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-0.6B",
    use_kernels=True,
    kernel_config=kernel_config,
    device_map="cuda",
)
```

When the model loads, Transformers:
1. Loads `RMSNormMLP` from the repo and finds `RMSNormMLPLayout` in the same module.
2. Matches every decoder layer at `model.layers.*` and builds a fused parent class whose `__init__` calls `RMSNormMLPLayout(post_attention_layernorm, mlp)`.
3. Replaces the remaining child (`mlp`) with `nn.Identity()` to preserve the parent module's interface.
4. Remaps checkpoint weights using `conversion_mapping`.
5. Calls `kernelize`, which replaces `RMSNormMLPLayout.forward` with `RMSNormMLP.forward`.

> [!TIP]
> The order of pairs in the fusion tuple determines the argument order passed to `KernelNameLayout.__init__`.
