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# Building a compatible model backend for inference

Transformers models are compatible with inference engines like [vLLM](https://github.com/vllm-project/vllm) and [SGLang](https://docs.sglang.ai). Use the same Transformers model anywhere and avoid reimplementing a model from scratch for each inference engine. This also helps with models that an engine does not natively reimplement, as long as the model supports the attention backend interface described below.

This guide shows you how to implement a model in Transformers that works as a backend for any inference engine.

## Model implementation

1. Follow the model [contribution guidelines](./add_new_model) or the [custom model contribution guidelines](./custom_models). The model must have a valid `config.json` in its directory and a valid `auto_map` field pointing to the model class in the config.

2. Use the [`AttentionInterface`] class for custom and optimized attention functions. This interface unlocks each inference engine's performance features. 

   Use `ALL_ATTENTION_FUNCTIONS` when defining the attention layer and propagate `**kwargs` from the base `MyModel` class to the attention layers. Set `_supports_attention_backend` to `True` in [`PreTrainedModel`].
   
   Expand the code below for an example.

    <details>
    <summary>modeling_my_model.py</summary>

    ```python
    from transformers import PreTrainedModel
    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
    from torch import nn

    class MyAttention(nn.Module):

        def forward(self, hidden_states, **kwargs):
            ...
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
            attn_output, attn_weights = attention_interface(
                self,
                query_states,
                key_states,
                value_states,
                **kwargs,
            )
            ...

    class MyModel(PreTrainedModel):
        _supports_attention_backend = True
    ```

    </details>

3. Enable optional tensor or pipeline parallelism by adding the following keys to [`PreTrainedConfig`].

    * `base_model_tp_plan` enables [tensor parallelism](./perf_infer_gpu_multi) by mapping fully qualified layer name patterns to tensor parallel styles. Supports only the `"colwise"` and `"rowwise"` partitioning strategies.
    * `base_model_pp_plan` enables pipeline parallelism by mapping direct child layer names to tuples of lists of strings. The first element of the tuple contains the names of the input arguments. The last element contains the variable names of the layer outputs in the modeling code.

    Expand the code below for an example.

    <details>
    <summary>configuration_my_model.py</summary>

    ```python

    from transformers import PreTrainedConfig

    class MyConfig(PreTrainedConfig):
        base_model_tp_plan = {
            "layers.*.self_attn.k_proj": "colwise",
            "layers.*.self_attn.v_proj": "colwise",
            "layers.*.self_attn.o_proj": "rowwise",
            "layers.*.mlp.gate_proj": "colwise",
            "layers.*.mlp.up_proj": "colwise",
            "layers.*.mlp.down_proj": "rowwise",
        }
        base_model_pp_plan = {
            "embed_tokens": (["input_ids"], ["inputs_embeds"]),
            "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
            "norm": (["hidden_states"], ["hidden_states"]),
        }
    ```

    </details>

## Multimodal models

Multimodal models require additional changes beyond the [vision language model contribution checklist](./contributing#vision-language-model-contribution-checklist). These changes ensure multimodal inputs are properly processed.

1. The [`ProcessorMixin`] class must include the `self.image_token` and `self.image_token_ids` attributes. These placeholder tokens indicate image positions in the input. The same token appears in the input prompt for images and in the model code to scatter image features.

2. The [`ProcessorMixin`] class must include a `self._get_num_multimodal_tokens` method. This method computes the number of placeholder tokens required for multimodal inputs with given sizes. It returns a [`MultiModalData`] object. Placeholders between `<image>` tokens, such as row or column tokens, don't count as image placeholders. Count only tokens replaced by image features later in the modeling code.

3. The [`ProcessorMixin`] class must check the value of `return_mm_token_type_ids` and return `mm_token_type_ids`. This indicates whether each position is a text token (`0`), image placeholder token (`1`), or a video placeholder token (`2`). Multimodal token type id sequences must be contiguous with no breaks between consecutive tokens. Treat special tokens for beginning, ending, row, and column tokens as placeholders.

Expand the code below for an example.

<details>
<summary>modeling_my_multimodal_model.py</summary>

```python
class MyMultimodalProcessor(ProcessorMixin):

    def __call__(self, images=None, text=None, **kwargs):
        if return_mm_token_type_ids:
            mm_token_type_ids = np.zeros_like(input_ids)
            mm_token_type_ids[input_ids == self.image_token_id] = 1
            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)

    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
        """
        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
        Args:
            image_sizes (`list[list[int]]`, *optional*):
                The input sizes formatted as (height, width) per each image.
        Returns:
            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
            input modalities, along with other useful data.
        """
        vision_data = {}
        if image_sizes is not None:
            num_image_tokens = [256] * len(image_sizes) # 256 placeholder tokens for each image always
            num_image_patches = [1] * len(image_sizes) # no patching, thus each image is processed as a single base image
            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
        return MultiModalData(**vision_data)
```

</details>

## Resources

* Read the [Transformers backend integration in vLLM](https://blog.vllm.ai/2025/04/11/transformers-backend.html) blog post for more details.
* Read the [Transformers backend integration in SGLang](https://huggingface.co/blog/transformers-backend-sglang) blog post for more details.
