initial

Signed-off-by: ShriKode <shrikode@gmail.com>

vllm/model_executor/models/gemma3n.py

@@ -730,29 +730,7 @@ class Gemma3nTextModel(nn.Module):
         return loaded_params
 
 
-class Gemma3nModel(nn.Module):
+class Gemma3nForCausalLM(nn.Module):
-
-    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        self.language_model = Gemma3nTextModel(vllm_config=vllm_config,
-                                               prefix=maybe_prefix(
-                                                   prefix, "language_model"))
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor],
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        return self.language_model(input_ids=input_ids,
-                                   positions=positions,
-                                   inputs_embeds=inputs_embeds,
-                                   **kwargs)
-
-
-class Gemma3nForConditionalGeneration(nn.Module):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -771,7 +749,7 @@ class Gemma3nForConditionalGeneration(nn.Module):
         del lora_config  # Unused.
         super().__init__()
         self.config = config
-        self.model = Gemma3nModel(vllm_config=vllm_config,
+        self.model = Gemma3nTextModel(vllm_config=vllm_config,
                                   prefix=maybe_prefix(prefix, "model"))
         self.logits_processor = LogitsProcessor(
             config.text_config.vocab_size,

vllm/model_executor/models/gemma3n_mm.py

@@ -0,0 +1,657 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Any, Literal, Optional, TypedDict
+
+import torch
+from torch import nn
+from transformers import BatchFeature, Gemma3nConfig, Gemma3nProcessor
+from transformers.models.gemma3n.processing_gemma3n import Gemma3nProcessorKwargs
+from transformers import AutoModel
+
+import vllm.envs as envs
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+# yapf: disable
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, BoundPromptUpdate,
+                                        PlaceholderFeaturesInfo,
+                                        PromptReplacement, PromptTargetMatch,
+                                        PromptUpdate, PromptUpdateDetails,
+                                        find_mm_placeholders,
+                                        replace_token_matches)
+# yapf: enable
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .siglip import SiglipVisionModel
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+logger = init_logger(__name__)
+
+
+class Gemma3nImagePixelInputs(TypedDict):
+    pixel_values: torch.Tensor
+    """Shape: `(batch_size * num_images, num_channels, height, width)`"""
+
+class Gemma3nAudioInputs(TypedDict):
+    input_features: torch.Tensor
+    """Shape: `(batch_size * num_audio, seq_length, num_features)`"""
+    input_features_mask: torch.Tensor
+    """Shape: `(batch_size * num_audio, seq_length)`"""
+
+
+Gemma3nImageInputs = Gemma3nImagePixelInputs
+
+
+class Gemma3ProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Gemma3nConfig)
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(Gemma3nProcessor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def _resolve_image_kwargs(
+        self,
+        processor: Gemma3Processor,
+        keys: set[str],
+    ) -> dict[str, Any]:
+        image_processor = processor.image_processor
+        kwargs = processor._merge_kwargs(
+            Gemma3ProcessorKwargs,
+            tokenizer_init_kwargs=processor.tokenizer.init_kwargs,
+        )
+
+        images_kwargs = kwargs["images_kwargs"]
+
+        def _resolve_kw(key: str):
+            val = getattr(image_processor, key)
+            if val is None:
+                val = images_kwargs[key]
+
+            return val
+
+        return {k: _resolve_kw(k) for k in keys}
+
+    def get_num_crops(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Gemma3Processor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        images_kwargs = self._resolve_image_kwargs(
+            processor, {
+                "do_pan_and_scan", "pan_and_scan_min_crop_size",
+                "pan_and_scan_max_num_crops",
+                "pan_and_scan_min_ratio_to_activate"
+            })
+
+        do_pan_and_scan = images_kwargs["do_pan_and_scan"]
+        pan_and_scan_min_crop_size = images_kwargs[
+            "pan_and_scan_min_crop_size"]
+        pan_and_scan_max_num_crops = images_kwargs[
+            "pan_and_scan_max_num_crops"]
+        pan_and_scan_min_ratio_to_activate = images_kwargs[
+            "pan_and_scan_min_ratio_to_activate"]
+
+        if not do_pan_and_scan:
+            return 0
+
+        if envs.VLLM_USE_V1:
+            logger.warning_once(
+                "`do_pan_and_scan=True` has suboptimal results on V1 "
+                "because of the simplified attention pattern being used.")
+
+        # Based on Gemma3ImageProcessor.pan_and_scan
+        if image_width >= image_height:
+            if image_width / image_height < pan_and_scan_min_ratio_to_activate:
+                return 0
+
+            num_crops_w = min(
+                int(math.floor(image_width / pan_and_scan_min_crop_size)),
+                int(math.floor(image_width / image_height + 0.5)),
+            )
+
+            num_crops_w = max(2, num_crops_w)
+            num_crops_w = min(pan_and_scan_max_num_crops, num_crops_w)
+            num_crops_h = 1
+        else:
+            if image_height / image_width < pan_and_scan_min_ratio_to_activate:
+                return 0
+
+            num_crops_h = min(
+                int(math.floor(image_height / pan_and_scan_min_crop_size)),
+                int(math.floor(image_height / image_width + 0.5)),
+            )
+
+            num_crops_h = max(2, num_crops_h)
+            num_crops_h = min(pan_and_scan_max_num_crops, num_crops_h)
+            num_crops_w = 1
+
+        crop_size_w = int(math.ceil(image_width / num_crops_w))
+        crop_size_h = int(math.ceil(image_height / num_crops_h))
+
+        if min(crop_size_w, crop_size_h) < pan_and_scan_min_crop_size:
+            return 0
+
+        return num_crops_w * num_crops_h
+
+    def get_image_repl(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Gemma3Processor],
+    ) -> PromptUpdateDetails[str]:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        boi_token = processor.boi_token
+
+        num_crops = self.get_num_crops(
+            image_width=image_width,
+            image_height=image_height,
+            processor=processor,
+        )
+
+        if num_crops == 0:
+            image_text = boi_token
+        else:
+            crops_image_tokens = " ".join(boi_token for _ in range(num_crops))
+            image_text = (
+                f"Here is the original image {boi_token} and here are some "
+                f"crops to help you see better {crops_image_tokens}")
+
+        repl_full = image_text.replace(boi_token,
+                                       processor.full_image_sequence)
+
+        tokenizer = processor.tokenizer
+        vocab = tokenizer.get_vocab()
+        image_token_id = vocab[tokenizer.image_token]
+
+        return PromptUpdateDetails.select_token_id(repl_full, image_token_id)
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Gemma3Processor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        num_crops = self.get_num_crops(
+            image_width=image_width,
+            image_height=image_height,
+            processor=processor,
+        )
+        image_seq_len = processor.image_seq_length
+
+        return (num_crops + 1) * image_seq_len
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        processor = self.get_hf_processor()
+
+        images_kwargs = self._resolve_image_kwargs(
+            processor, {"pan_and_scan_max_num_crops"})
+        max_num_crops = images_kwargs["pan_and_scan_max_num_crops"]
+
+        # Result in the max possible feature size (h:w = max_num_crops:1)
+        return ImageSize(height=50 * max_num_crops, width=50)
+
+
+class Gemma3DummyInputsBuilder(BaseDummyInputsBuilder[Gemma3ProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.boi_token
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processed_outputs = super()._call_hf_processor(
+            prompt,
+            mm_data,
+            mm_kwargs,
+        )
+
+        # HF processor pops the `num_crops` kwarg, which is needed by vLLM
+        if (images := mm_data.get("images")) is not None:
+            parsed_images = (self._get_data_parser().parse_mm_data({
+                "image":
+                images
+            }).get_items("image", ImageProcessorItems))
+            image_sizes = [
+                parsed_images.get_image_size(i)
+                for i in range(len(parsed_images))
+            ]
+            hf_processor = self.info.get_hf_processor(**mm_kwargs)
+
+            num_crops = [
+                self.info.get_num_crops(image_width=size.width,
+                                        image_height=size.height,
+                                        processor=hf_processor)
+                for size in image_sizes
+            ]
+            processed_outputs["num_crops"] = torch.tensor(num_crops)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        num_crops = hf_inputs.get("num_crops", torch.empty(0))
+
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_crops + 1),
+            num_crops=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_token = hf_processor.boi_token
+
+        def get_replacement_gemma3(item_idx: int):
+            images = mm_items.get_items("image", ImageProcessorItems)
+
+            image_size = images.get_image_size(item_idx)
+            return self.info.get_image_repl(
+                image_width=image_size.width,
+                image_height=image_size.height,
+                processor=hf_processor,
+            )
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=get_replacement_gemma3,
+            )
+        ]
+
+    def _apply_token_matches(
+        self,
+        prompt: list[int],
+        mm_matches: Mapping[str, Sequence[PromptTargetMatch]],
+        mm_item_counts: Mapping[str, int],
+    ) -> list[int]:
+        token_ids = super()._apply_token_matches(
+            prompt,
+            mm_matches,
+            mm_item_counts,
+        )
+
+        # "\n\n\n" and "\n\n\n\n" are single tokens
+        # Since our replacement can insert "\n\n" next to "\n"
+        # tokens, we have to combine them to be consistent with
+        # the output of the tokenizer
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+        newline_1 = vocab["\n"]
+        newline_2 = vocab["\n\n"]
+        newline_3 = vocab["\n\n\n"]
+        newline_4 = vocab["\n\n\n\n"]
+
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_1, newline_2],
+            [newline_3],
+        )
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_2, newline_1],
+            [newline_3],
+        )
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_2, newline_2],
+            [newline_4],
+        )
+
+        return token_ids
+
+    def _find_mm_placeholders(
+        self,
+        mm_prompt_updates: Mapping[str, Sequence[BoundPromptUpdate]],
+        new_token_ids: list[int],
+        mm_item_counts: Mapping[str, int],
+    ) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
+        # We need to detect "\n\n" inside "\n\n\n" and "\n\n\n\n"
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+        newline_1 = vocab["\n"]
+        newline_2 = vocab["\n\n"]
+        newline_3 = vocab["\n\n\n"]
+        newline_4 = vocab["\n\n\n\n"]
+
+        def get_repl_toks(tok: int) -> list[int]:
+            if tok == newline_3:
+                return [newline_1, newline_2]
+            if tok == newline_4:
+                return [newline_2, newline_2]
+
+            return [tok]
+
+        repl_token_ids = list[int]()
+        repl_orig_idxs = list[int]()
+        for orig_idx, orig_tok in enumerate(new_token_ids):
+            repl_toks = get_repl_toks(orig_tok)
+            repl_token_ids.extend(repl_toks)
+            repl_orig_idxs.extend(orig_idx for _ in range(len(repl_toks)))
+
+        repls = find_mm_placeholders(mm_prompt_updates, repl_token_ids,
+                                     mm_item_counts)
+
+        return {
+            modality: [
+                PlaceholderFeaturesInfo(
+                    modality=p.modality,
+                    item_idx=p.item_idx,
+                    start_idx=repl_orig_idxs[p.start_idx],
+                    tokens=p.tokens,
+                    is_embed=p.is_embed,
+                ) for p in placeholders
+            ]
+            for modality, placeholders in repls.items()
+        }
+
+
+class Gemma3nMultimodalEmbedder(nn.Module):
+    """Embeds token ids or soft tokens for multimodal content into language model space."""
+
+    def __init__(
+        self,
+        multimodal_config: Union[Gemma3nAudioConfig, Gemma3nVisionConfig],
+        text_config: Gemma3nTextConfig,
+    ):
+        super().__init__()
+
+        self.multimodal_hidden_size = multimodal_config.hidden_size
+        self.eps = multimodal_config.rms_norm_eps
+        self.vocab_offset = multimodal_config.vocab_offset
+        self.vocab_size = multimodal_config.vocab_size
+        self.text_hidden_size = text_config.hidden_size
+
+
+        self.embedding = VocabParallelEmbedding(
+            self.vocab_size,
+            self.multimodal_hidden_size,
+        )
+
+        self.hard_embedding_norm = RMSNorm(
+            self.multimodal_hidden_size,
+            eps=self.eps,
+        )
+
+        self.soft_embedding_norm = RMSNorm(
+            self.multimodal_hidden_size,
+            eps=self.eps,
+        )
+
+        self.embedding_projection = RowParallelLinear(
+            self.multimodal_hidden_size,
+            self.text_hidden_size,
+            bias=False,
+        )
+
+        self.embedding_post_projection_norm = RMSNorm(
+            self.text_hidden_size,
+            eps=self.eps,
+            has_weight=False,
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Embeds token ids or soft tokens for multimodal content into language model space.
+
+        Args:
+            input_ids: A torch.LongTensor containing the token ids to embed. Values should be in the range
+                `[vocab_offset, vocab_offset + vocab_size)`.
+            inputs_embeds: A torch.Tensor containing the soft tokens to embed.
+
+        Returns:
+            A torch.Tensor of embeddings with  shape `[batch_size, seq_len, self.config.text_config.hidden_size]`.
+        """
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            )
+
+        if inputs_embeds is not None:
+            emb_norm = self.soft_embedding_norm(inputs_embeds)
+        else:
+            hard_emb = self.embedding(input_ids - self.vocab_offset)
+            emb_norm = self.hard_embedding_norm(hard_emb)
+
+        emb_norm_proj, _ = self.embedding_projection(emb_norm)
+        return self.embedding_post_projection_norm(emb_norm_proj)
+
+
+@MULTIMODAL_REGISTRY.register_processor(Gemma3MultiModalProcessor,
+                                        info=Gemma3ProcessingInfo,
+                                        dummy_inputs=Gemma3DummyInputsBuilder)
+class Gemma3nForConditionalGeneration(nn.Module, SupportsMultiModal):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # mapping for new names in checkpoint saved after transformers v4.52
+            "model.language_model.": "language_model.model.",
+            "model.vision_tower.": "vision_tower.",
+            "model.multi_modal_projector.": "multi_modal_projector.",
+            "lm_head.": "language_model.lm_head.",
+        })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.quant_config = quant_config
+        self.multimodal_config = multimodal_config
+        self.sliding_window = getattr(config.text_config,
+                                      "interleaved_sliding_window", None)
+
+        self.vision_tower = AutoModel.from_config(config=config.vision_config)
+        self.audio_tower = AutoModel.from_config(config=config.audio_config)
+        self.embed_vision = Gemma3nMultimodalEmbedder(config.vision_config, config.text_config)
+        self.embed_audio = Gemma3nMultimodalEmbedder(config.audio_config, config.text_config)
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Gemma3nForCausalLM"],
+        )
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def _process_image_input(
+        self,
+        image_input: Gemma3nImageInputs,
+    ) -> list[torch.Tensor]:
+        assert self.vision_tower is not None
+
+        pixel_values = image_input["pixel_values"]
+        vision_outputs = self.vision_tower(
+            pixel_values=pixel_values, do_pooling=False, return_dict=True
+        ).last_hidden_state
+        vision_outputs = vision_outputs.reshape(
+            vision_outputs.shape[0],
+            self.config.vision_config.hidden_size,
+            self.config.vision_soft_tokens_per_image,
+        ).permute(0, 2, 1)
+        # Normalize and embed the soft tokens into language model space.
+        vision_outputs *= self.config.vision_config.hidden_size**0.5
+        return self.embed_vision(inputs_embeds=vision_outputs)
+
+    def _process_audio_input(
+        self, audio_input: Gemma3nAudioInputs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert self.audio_tower is not None
+        input_features = audio_input["input_features"]
+        input_features_mask = audio_input["input_features_mask"]
+        audio_outputs, audio_mask = self.audio_tower(input_features, input_features_mask)
+        return self.embed_audio(inputs_embeds=audio_outputs), audio_mask
+    
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                self.config.image_token_index,
+            )
+        return inputs_embeds
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs: object) -> IntermediateTensors:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            if vision_embeddings is not None:
+                kwargs = self.prepare_attn_masks(
+                    input_ids,
+                    positions,
+                    mask_dtype=self.dtype,
+                    **kwargs,
+                )
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds,
+                                                  **kwargs)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="multi_modal_projector",
+            tower_model="vision_tower")