[Doc] Update docs for MM model development with context usage (#32691)

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

docs/contributing/model/multimodal.md

@@ -23,29 +23,32 @@ Further update the model as follows:
                 raise ValueError("Only image modality is supported")
         ```
 
-- Reserve a keyword parameter in [forward][torch.nn.Module.forward] for each input tensor that corresponds to a multi-modal input, as shown in the following example:
+- Inside `__init__` method, initialize the language components of the model inside [_mark_language_model][vllm.model_executor.models.interfaces.SupportsMultiModal._mark_language_model], and the multimodal components of the model inside [_mark_tower_model][vllm.model_executor.models.interfaces.SupportsMultiModal._mark_tower_model], e.g.:
 
-  ```diff
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-  +     pixel_values: torch.Tensor,
-    ) -> SamplerOutput:
-  ```
-  
-  More conveniently, you can simply pass `**kwargs` to the [forward][torch.nn.Module.forward] method and retrieve the keyword parameters for multimodal inputs from it.
+    ```python
+        def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+            super().__init__()
+
+            config = vllm_config.model_config.hf_config
+
+            with self._mark_tower_model(vllm_config, "image"):
+                self.vision_encoder = ...
+                self.multi_modal_projector = ...
+
+            with self._mark_language_model(vllm_config):
+                self.language_model = init_vllm_registered_model(
+                    vllm_config=vllm_config,
+                    hf_config=config.text_config,
+                    prefix=maybe_prefix(prefix, "language_model"),
+                )
+    ```
 
 - 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.
 
     ??? code
 
         ```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)
 
@@ -71,18 +74,7 @@ Further update the model as follows:
     [PlaceholderRange][vllm.multimodal.inputs.PlaceholderRange] from input processing.
     This logic can be found at [embed_input_ids][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_input_ids].
 
-    You may override this method if additional logic is required for your model when merging embeddings. 
-
-- Implement [get_language_model][vllm.model_executor.models.interfaces.SupportsMultiModal.get_language_model] getter to provide stable access to the underlying language model.
-
-    ```python
-    class YourModelForImage2Seq(nn.Module):
-        ...
-
-        def get_language_model(self) -> torch.nn.Module:
-            # Change `language_model` according to your implementation.
-            return self.language_model
-    ```
+    You may override this method if additional logic is required for your model when merging embeddings.
 
 - Once the above steps are done, update the model class with the [SupportsMultiModal][vllm.model_executor.models.interfaces.SupportsMultiModal] interface.