[Doc][V1] Update model implementation guide for V1 support

docs/source/contributing/model/basic.md

@@ -57,7 +57,17 @@ class MyModelForCausalLM(nn.Module):
 
 ### Computation Code
 
-Rewrite the {meth}`~torch.nn.Module.forward` method of your model to remove any unnecessary code, such as training-specific code. Modify the input parameters to treat `input_ids` and `positions` as flattened tensors with a single batch size dimension, without a max-sequence length dimension.
+- Add a `get_input_embeddings` method inside `MyModel` module that returns the text embeddings given `input_ids`. This is equivalent to directly calling the text embedding layer, but provides a unified interface in case `MyModel` is used within a composite multimodal model.
+
+```python
+class MyModel(nn.Module):
+        ...
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        ... 
+```
+
+- Rewrite the {meth}`~torch.nn.Module.forward` method of your model to remove any unnecessary code, such as training-specific code. Modify the input parameters to treat `input_ids` and `positions` as flattened tensors with a single batch size dimension, without a max-sequence length dimension.
 
 ```python
 def forward(

docs/source/contributing/model/multimodal.md

@@ -9,7 +9,78 @@ This document walks you through the steps to extend a basic model so that it acc
 It is assumed that you have already implemented the model in vLLM according to [these steps](#new-model-basic).
 Further update the model as follows:
 
-- Implement the {class}`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.
+- Reserve a keyword parameter in {meth}`~torch.nn.Module.forward` for each input tensor that corresponds to a multi-modal input, as shown in the following example:
+
+  ```diff
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+  +     pixel_values: torch.Tensor,
+    ) -> SamplerOutput:
+  ```
+
+  More conveniently, you can simply pass `**kwargs` to the {meth}`~torch.nn.Module.forward` method and retrieve the keyword parameters for multimodal inputs from it.
+
+- Implement {meth}`~vllm.model_executor.models.interfaces.SupportsMultiModal.get_multimodal_embeddings` that returns the embeddings from running the multimodal inputs through the multimodal tokenizer of the model. Below we provide a boilerplate of a typical implementation pattern, but feel free to adjust it to your own needs.
+
+    ```python
+    class YourModelForImage2Seq(nn.Module):
+        ...
+
+        def _process_image_input(self, image_input: YourModelImageInputs) -> torch.Tensor:
+
+            assert self.vision_encoder is not None
+            image_features = self.vision_encoder(image_input)
+            return self.multi_modal_projector(image_features)
+
+        def get_multimodal_embeddings(self, **kwargs: object) -> Optional[NestedTensors]:
+
+            # Validate the multimodal input keyword arguments
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            if image_input is None:
+                return None
+
+            # Run multimodal inputs through encoder and projector
+            vision_embeddings = self._process_image_input(image_input)
+            return vision_embeddings
+    ```
+
+    ```{important}
+    The returned `multimodal_embeddings` must be either a **3D {class}`torch.Tensor`** of shape `(num_items, feature_size, hidden_size)`, or a **list / tuple of 2D {class}`torch.Tensor`'s** of shape `(feature_size, hidden_size)`, so that `multimodal_embeddings[i]` retrieves the embeddings generated from the `i`-th multimodal data item (e.g, image) of the request.
+    ```
+
+- Implement {meth}`~vllm.model_executor.models.interfaces.SupportsMultiModal.get_input_embeddings` to merge `multimodal_embeddings` with text embeddings from the `input_ids`. If input processing for the model is implemented correctly (see sections below), then you can leverage the utility function we provide to easily merge the embeddings.
+
+    ```python
+    from .utils import merge_multimodal_embeddings
+
+    class YourModelForImage2Seq(nn.Module):
+        ...
+
+        def get_input_embeddings(
+            self,
+            input_ids: torch.Tensor,
+            multimodal_embeddings: Optional[NestedTensors] = None,
+        ) -> torch.Tensor:
+
+            # `get_input_embeddings` should already be implemented for the language 
+            # model as one of the requirements of basic vLLM model implementation.
+            inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+
+            if multimodal_embeddings is not None:
+                inputs_embeds = merge_multimodal_embeddings(
+                    input_ids=input_ids, 
+                    inputs_embeds=inputs_embeds, 
+                    multimodal_embeddings=multimodal_embeddings,
+                    placeholder_token_id=self.config.image_token_index)
+
+            return inputs_embeds
+    ```
+
+- Once the above steps are done, update the model class with the {class}`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.
 
   ```diff
   + from vllm.model_executor.models.interfaces import SupportsMultiModal
@@ -23,20 +94,6 @@ Further update the model as follows:
   Check out [the HuggingFace Transformers documentation](https://huggingface.co/docs/transformers/model_doc/auto#multimodal) for some examples.
   ```
 
-- If you haven't already done so, reserve a keyword parameter in {meth}`~torch.nn.Module.forward`
-  for each input tensor that corresponds to a multi-modal input, as shown in the following example:
-
-  ```diff
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-  +     pixel_values: torch.Tensor,
-    ) -> SamplerOutput:
-  ```
-
 ## 2. Specify processing information
 
 Next, create a subclass of {class}`~vllm.multimodal.processing.BaseProcessingInfo`