[Doc] Further update multi-modal impl doc (#33065)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/contributing/model/multimodal.md

@@ -43,9 +43,54 @@ Further update the model as follows:
                 )
     ```
 
-- Implement [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal] 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.
+- Remove the embedding part from the [forward][torch.nn.Module.forward] method:
+    - Move the multi-modal embedding to [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal].
+    - The text embedding and embedding merge are handled automatically by a default implementation of [embed_input_ids][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_input_ids]. It does not need to be overridden in most cases.
 
-    ??? code
+    ```diff
+      def forward(
+          self,
+          input_ids: torch.Tensor | None,
+    -     pixel_values: torch.Tensor,
+          positions: torch.Tensor,
+          intermediate_tensors: IntermediateTensors | None = None,
+          inputs_embeds: torch.Tensor | None = None,
+      ) -> torch.Tensor:
+    -     if inputs_embeds is None:
+    -         inputs_embeds = self.get_input_embeddings()(input_ids)
+    -
+    -     if pixel_values is not None:
+    -         image_features = self.get_image_features(
+    -             pixel_values=pixel_values,
+    -         )
+    -         special_image_mask = self.get_placeholder_mask(
+    -             input_ids,
+    -             inputs_embeds=inputs_embeds,
+    -             image_features=image_features,
+    -         )
+    -         inputs_embeds = inputs_embeds.masked_scatter(
+    -             special_image_mask,
+    -             image_features,
+    -         )
+
+           hidden_states = self.language_model(
+               input_ids,
+               positions,
+               intermediate_tensors,
+               inputs_embeds=inputs_embeds,
+           )
+         ...
+  
+    +  def embed_multimodal(
+    +      self,
+    +      pixel_values: torch.Tensor,
+    +  ) -> MultiModalEmbeddings | None:
+    +      return self.get_image_features(
+    +          pixel_values=pixel_values,
+    +      )
+    ```
+
+    Below we provide a boilerplate of a typical implementation pattern of [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal], but feel free to adjust it to your own needs.
 
     ```python
     def _process_image_input(self, image_input: YourModelImageInputs) -> torch.Tensor: