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# Monkey patching (experimental feature)

Monkey patching allows you to replace model components globally without modifying the original model code. Once registered, patches are automatically applied when loading any model with [`~PreTrainedModel.from_pretrained`] or [`~PreTrainedModel.from_config`]. This enables you to restructure models for specific requirements like quantization compatibility, apply optimizations, or experiment with architectural variants.

> [!WARNING]
> **Monkey patching should be used as a last resort** when you need to change the layout and structure of a module and or its weights. For many customization and optimization needs, try using the [Attention interface](./attention_interface), [Experts interface](./experts_interface), or [Kernels registry](./kernel_doc/overview) instead. Only use monkey patching when you need structural changes that can't be achieved through custom forward implementations alone (e.g., for quantization library compatibility, fusing layers, or architectural experiments).

## Quick start

Here's a simple example showing how to replace a model component:

```python
from transformers import AutoModelForCausalLM
from transformers.models.llama.modeling_llama import LlamaAttention
from transformers.monkey_patching import register_patch_mapping


# Define your replacement class (must inherit from nn.Module)
class CustomLlamaAttention(LlamaAttention):
    def forward(self, *args, **kwargs):
        # Your custom implementation
        print("Using custom attention!")
        return super().forward(*args, **kwargs)


# Register the patch globally (only applies to transformers modeling modules)
register_patch_mapping(mapping={"LlamaAttention": CustomLlamaAttention})

# Load a model - the patch is automatically applied during initialization
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")

# All LlamaAttention layers in the model are now CustomLlamaAttention instances
print(type(model.model.layers[0].self_attn))  # <class '__main__.CustomLlamaAttention'>
```

## How it works

Monkey patches work through a two-step process:

1. **Registration**: Call [`register_patch_mapping`] to add mappings to a global registry.

2. **Application**: Patches are automatically applied during model initialization:
   - **`from_pretrained` / `from_config`**: Patches are **automatically** applied via an internal context manager. No additional action needed!
   - **Manual construction** (e.g., `Model(config)`): You must use the [`apply_patches`] context manager manually.

Once patches are registered, they persist and affect all subsequent model loads until you clear them with [`clear_patch_mapping`].

**Important limitations**:

- Only classes in `transformers` modeling modules are allowed to be patched (e.g., `LlamaAttention`, `LlamaMLP`).
- The mapping keys can be either exact class names or regular expression patterns (see [Pattern matching](#pattern-matching) below).

## Global registration

Use [`register_patch_mapping`] to register mappings globally:

```python
from transformers.monkey_patching import register_patch_mapping

# Register a single patch
register_patch_mapping(
    mapping={"Qwen2MoeExperts": SequentialExperts}
)

# Register multiple patches at once
register_patch_mapping(
    mapping={
        "Qwen2MoeExperts": SequentialExperts,
        "Qwen2MoeAttention": CustomAttention,
    },
    # Overwrite existing patches if they exist
    overwrite=True,
)
```

## Pattern matching

You can use regular expressions to match multiple classes with a single pattern:

```python
from transformers.monkey_patching import register_patch_mapping

# Match all classes containing "Attention"
register_patch_mapping(
    mapping={".*Attention": CustomAttention}
)

# More examples
register_patch_mapping(
    mapping={
        ".*MoeExperts$": CustomExperts,           # Ends with "MoeExperts"
        "^Llama\\d+Attention$": CustomAttention,  # Llama2Attention, Llama3Attention, etc.
    }
)
```

**Important**: Exact matches take precedence over patterns. If you register both `"LlamaAttention"` and `".*Attention"`, classes named `LlamaAttention` will use the exact-match replacement, while other matching classes will use the pattern-match replacement.

> [!WARNING]
> **Regex patterns can silently break models.** A broad pattern like `".*Attention"` will match *every* class whose name contains "Attention" — including container classes that wrap the attention you actually want to replace. For example, BERT has three attention-related classes: `BertSelfAttention` and `BertCrossAttention` (the inner attention implementations) and `BertAttention` (an outer module that *contains* one of those inner classes). Patching all three with the same custom attention layer produces a broken model because the outer `BertAttention` no longer wraps the inner one — it *is* one, eliminating expected sub-modules like `self` and `output`. Prefer narrow patterns (e.g., `".*SelfAttention$"`) or exact class names to avoid unintended matches.

To unregister patches, use [`unregister_patch_mapping`]:

```python
from transformers.monkey_patching import unregister_patch_mapping

# Unregister a single patch (use exact name or pattern from registration)
unregister_patch_mapping(keys=["Qwen2MoeExperts"])

# Unregister multiple patches at once
unregister_patch_mapping(keys=["Qwen2MoeExperts", "Qwen2MoeAttention"])

# Unregister a pattern
unregister_patch_mapping(keys=[".*Attention"])
```

To clear all registered patches, use [`clear_patch_mapping`]:

```python
from transformers.monkey_patching import clear_patch_mapping

clear_patch_mapping()
```

To view currently registered patches, use [`get_patch_mapping`]:

```python
from transformers.monkey_patching import get_patch_mapping

current_patches = get_patch_mapping()
print(current_patches)
```

## Manual model construction

The [`apply_patches`] context manager is only needed when you're constructing models **manually** (e.g., `Model(config)`) without using `from_pretrained` or `from_config`:

```python
from transformers import LlamaModel, LlamaConfig
from transformers.monkey_patching import register_patch_mapping, apply_patches

# Register patch globally
register_patch_mapping(mapping={"LlamaAttention": CustomAttention})

# For manual construction, you need the context manager
with apply_patches():
    model = LlamaModel(LlamaConfig())  # Uses CustomAttention

# Without the context manager, manual construction uses original classes
model = LlamaModel(LlamaConfig())  # Uses LlamaAttention

# But from_pretrained and from_config will always apply registered patches
model = LlamaModel.from_pretrained("meta-llama/Llama-3.2-1B")  # Uses CustomAttention
```

## Important notes

- **Weight handling**: Monkey patching only replaces classes, not weights. If your patched class has a different weights layout, you'll need to handle [weight conversions](./weightconverter) separately to ensure compatibility with pretrained weights. See the [Complete example](#complete-example) below for how to combine monkey patches with weight conversion mappings.

- **Global effect**: Patches registered with [`register_patch_mapping`] are applied globally to all models loaded after registration. Always use [`clear_patch_mapping`] to clean up when done, especially in tests, notebooks, or long-running applications.

- **Class validation**: The API automatically validates that replacement classes are `nn.Module` subclasses. If you pass an invalid class, you'll get a clear error message.

- **Thread safety**: All patching operations are thread-safe. You can safely register, unregister, and apply patches from multiple threads.

- **Matching behavior**: When using exact class names, they must exactly match the original class names as they appear in the model's source code (case-sensitive). When using regex patterns, they are matched against class names using `re.search()`.

## Troubleshooting

### My patch isn't being applied

**Check class name or pattern**: Ensure the class name or pattern in your mapping is correct:

```python
# For exact names - must match exactly (case-sensitive)
register_patch_mapping(mapping={"LlamaAttention": CustomAttention})

# For patterns - use valid regex
register_patch_mapping(mapping={".*Attention": CustomAttention})
```

**Verify registration**: Use [`get_patch_mapping`] to confirm your mapping is registered:

```python
print(get_patch_mapping())
# Shows all registered mappings: {'LlamaAttention': <class 'CustomAttention'>, '.*MLP': <class 'CustomMLP'>}
```

**Check model source**: Find the exact class name in the model's source:

```python
from transformers.models.llama import modeling_llama
print(dir(modeling_llama))  # Look for the class name
```

### How do I know if my patch is working?

Inspect the loaded model to verify the patch:

```python
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")

# Check the type of a specific module
print(type(model.model.layers[0].self_attn))  # Should show your custom class

# Or iterate through all modules
for name, module in model.named_modules():
    if 'attention' in name.lower():
        print(f"{name}: {type(module)}")
```

### Weight shape mismatch errors

If your patched class has different weight shapes, register a weight conversion:

```python
from transformers.conversion_mapping import register_checkpoint_conversion_mapping, WeightConverter
from transformers.monkey_patching import register_patch_mapping

register_patch_mapping(
    mapping={
        "LlamaAttention": LlamaFusedAttention,
    }
)

register_checkpoint_conversion_mapping(
    model_type="llama",
    mapping=[
        WeightConverter(
            source_patterns=["q_proj", "k_proj", "v_proj"],
            target_patterns=["qkv_proj"],
            operations=[
                Concatenate(dim=0),
            ],
        )
    ],
    overwrite=True,
)
```

### Cleaning up patches

Always clean up patches when you're done to avoid affecting other code:

```python
from transformers.monkey_patching import register_patch_mapping, clear_patch_mapping

try:
    register_patch_mapping(mapping={"LlamaAttention": CustomAttention})
    model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
    # ... use model ...
finally:
    clear_patch_mapping()  # Always clean up
```

## Complete example

Here's a comprehensive example showing how to restructure both the experts and attention modules in a Mixture-of-Experts model (`qwen2_moe`) for optimization and quantization compatibility. This demonstrates:

1. Creating custom replacement classes that maintain the same interface
2. Registering monkey patches for multiple components
3. Handling weight conversions for the new structure

```python
from typing import Unpack

import torch
import torch.nn as nn

from transformers import AutoModelForCausalLM, Concatenate, WeightConverter
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache
from transformers.conversion_mapping import register_checkpoint_conversion_mapping
from transformers.integrations.sdpa_attention import sdpa_attention_forward
from transformers.models.qwen2_moe.modeling_qwen2_moe import apply_rotary_pos_emb
from transformers.monkey_patching import register_patch_mapping
from transformers.utils.generic import TransformersKwargs


class MoeMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.num_experts = config.num_experts
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.moe_intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


# Adapted from the original Qwen2MoeExperts
class ModuleListExperts(nn.ModuleList):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.num_experts = config.num_experts
        for _ in range(self.num_experts):
            self.append(MoeMLP(config))

    def forward(
        self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor
    ) -> torch.Tensor:
        final_hidden_states = torch.zeros_like(hidden_states)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
            expert_mask = expert_mask.permute(2, 1, 0)

        for expert_idx in range(self.num_experts):
            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
            current_state = hidden_states[token_idx]
            current_hidden_states = self[expert_idx](current_state)
            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))
        return final_hidden_states


# Adapted from the original Qwen2MoeAttention
class FusedQKVAttention(nn.Module):
    def __init__(self, config, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.qkv_proj = nn.Linear(config.hidden_size, 3 * config.num_attention_heads * self.head_dim, bias=True)
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)

        if self.config.layer_types[layer_idx] == "sliding_attention":
            self.sliding_window = config.sliding_window

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_mask: torch.Tensor | None = None,
        past_key_values: Cache | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states, key_states, value_states = self.qkv_proj(hidden_states).chunk(3, dim=-1)

        query_states = query_states.view(hidden_shape).transpose(1, 2)
        key_states = key_states.view(hidden_shape).transpose(1, 2)
        value_states = value_states.view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_values is not None:
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)

        attn_output, attn_weights = sdpa_attention_forward(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


# Registering monkey patches for the new attention and experts modules.
register_patch_mapping(
    mapping={
        "Qwen2MoeExperts": ModuleListExperts,
        "Qwen2MoeAttention": FusedQKVAttention,
    }
)

# Registering weight conversion mappings adapted for the new modules. This registration will:
# - Override the original conversion mapping for qwen2_moe which concatenated the experts into a single parameter format.
# - Concatenate the q_proj, k_proj, v_proj weights/biases into a single qkv_proj weight/bias for the new fused attention module.
register_checkpoint_conversion_mapping(
    model_type="qwen2_moe",
    mapping=[
        WeightConverter(
            source_patterns=["q_proj", "k_proj", "v_proj"],
            target_patterns=["qkv_proj"],
            operations=[Concatenate(dim=0)],
        ),
    ],
    overwrite=True,
)

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B")
```

## API reference

[[autodoc]] transformers.monkey_patching.register_patch_mapping

[[autodoc]] transformers.monkey_patching.unregister_patch_mapping

[[autodoc]] transformers.monkey_patching.clear_patch_mapping

[[autodoc]] transformers.monkey_patching.get_patch_mapping

[[autodoc]] transformers.monkey_patching.apply_patches
