chore: import upstream snapshot with attribution

docs/source/en/kernel_doc/writing_kernels.md

@@ -0,0 +1,168 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# 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__`.