vllm.model_executor.models.molmo ¶

class BlockCollection(nn.Module):
    """Collection of residual attention blocks used in Vision Transformer."""

    def __init__(
        self,
        config: VisionBackboneConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.resblocks = nn.ModuleList(
            [
                ResidualAttentionBlock(
                    config, quant_config, prefix=f"{prefix}.resblocks.{i}"
                )
                for i in range(config.image_num_layers)
            ]
        )

    def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
        hidden_states = []
        for r in self.resblocks:
            x = r(x)
            hidden_states.append(x)
        return hidden_states

class ImageProjectorMLP(nn.Module):
    """Molmo's image_projector mlp."""

    def __init__(
        self,
        config: PretrainedConfig,
        input_dim: int | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size // 2

        self.merged_linear = MergedColumnParallelLinear(
            input_dim or self.hidden_size,
            [self.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.merged_linear",
        )
        # Activation function.
        self.act_fn = SiluAndMul()

        # Feed-forward output projection.
        self.down_proj = RowParallelLinear(
            self.intermediate_size,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
        )

    def forward(
        self,
        x: torch.Tensor,
    ) -> torch.Tensor:
        gate_up, _ = self.merged_linear(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x

class LanguageModelMLP(nn.Module):
    """Molmo's LLM mlp."""

    def __init__(
        self,
        config: PretrainedConfig,
        input_dim: int | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size // 2

        self.gate_up_proj = MergedColumnParallelLinear(
            input_dim or self.hidden_size,
            [self.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        # Activation function.
        self.act_fn = MulAndSilu()
        # Feed-forward output projection.
        self.down_proj = RowParallelLinear(
            self.intermediate_size,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
        )

    def forward(
        self,
        x: torch.Tensor,
    ) -> torch.Tensor:
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x

class MolmoAttention(nn.Module):
    """Molmo's LLM attention."""

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads

        assert self.hidden_size % self.total_num_heads == 0
        assert self.total_num_heads % self.tp_size == 0

        self.num_heads = self.total_num_heads // self.tp_size
        self.total_num_kv_heads = config.num_key_value_heads or self.total_num_heads
        if self.total_num_kv_heads >= self.tp_size:
            assert self.total_num_kv_heads % self.tp_size == 0
        else:
            assert self.tp_size % self.total_num_kv_heads == 0

        self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
        self.head_dim = self.hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.max_position_embeddings = config.max_position_embeddings

        # Attention input projection. Projects x -> (q, k, v)
        self.qkv_proj = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=config.qkv_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.tp_rank: int | None = None
        self.k_norm: nn.Module | None = None
        self.q_norm: nn.Module | None = None
        if config.attention_layer_norm:
            self.tp_rank = get_tensor_model_parallel_rank()
            self.k_norm = RMSNorm(
                self.total_num_kv_heads * self.head_dim, eps=config.layer_norm_eps
            )
            self.q_norm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)

        # Rotary embeddings.
        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=self.max_position_embeddings,
            rope_parameters=config.rope_parameters,
        )
        self.scaling = self.head_dim**-0.5
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
        )

        # Attention output projection.
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

    def _apply_qk_norm(
        self, q: torch.Tensor, k: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if self.tp_size > 1:
            q = tensor_model_parallel_all_gather(q.contiguous())
            k = tensor_model_parallel_all_gather(k.contiguous())
        q = self.q_norm(q)
        k = self.k_norm(k)
        if self.tp_size > 1:
            splitter = partial(split_tensor_along_last_dim, num_partitions=self.tp_size)
            q = splitter(q)[self.tp_rank]
            k = splitter(k)[self.tp_rank]
        return q, k

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        if self.q_norm is not None and self.k_norm is not None:
            q, k = self._apply_qk_norm(q, k)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output

@MULTIMODAL_REGISTRY.register_processor(
    MolmoMultiModalProcessor,
    info=MolmoProcessingInfo,
    dummy_inputs=MolmoDummyInputsBuilder,
)
class MolmoForCausalLM(
    nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA, SupportsQuant
):
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_substr={
            # vision backbone mapping
            "image_projector.w1.": "image_projector.gate_proj.",
            "image_projector.w3.": "image_projector.up_proj.",
            "image_projector.w2.": "image_projector.down_proj.",
            # language backbone mapping
            "att_proj": "self_attn.qkv_proj",
            "attn_out": "self_attn.o_proj",
            "q_norm": "self_attn.q_norm",
            "k_norm": "self_attn.k_norm",
            "ff_proj": "mlp.gate_up_proj",
            "ff_out": "mlp.down_proj",
            "attn_norm": "input_layernorm",
            "ff_norm": "post_attention_layernorm",
        },
        orig_to_new_prefix={
            # vision backbone mapping
            "model.vision_backbone.": "vision_backbone.",
            # language backbone mapping
            "model.transformer.blocks.": "model.layers.",
            "model.transformer.ln_f.": "model.norm.",
            # lm_head is renamed to model.transformer.mlp.down_proj firstly,
            # we need to run a second renaming for it
            "model.transformer.mlp.down_proj.": "lm_head.",
        },
    )

    packed_modules_mapping = {
        "qkv_proj": ["qkv_proj"],
        "gate_up_proj": ["gate_up_proj"],  # language model
        "merged_linear": ["gate_proj", "up_proj"],  # image_projector
    }

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

    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.multimodal_config = multimodal_config

        vision_config = VisionBackboneConfig()

        with self._mark_tower_model(vllm_config, "image"):
            self.vision_backbone = MolmoVisionBackbone(
                config,
                vision_config,
                quant_config,
                prefix=maybe_prefix(prefix, "vision_backbone"),
            )

        with self._mark_language_model(vllm_config):
            self.model = MolmoModel(
                vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
            )

        self.img_patch_id = None

        if self.config.weight_tying:
            self.lm_head = self.model.transformer.wte
        else:
            self.lm_head = ParallelLMHead(
                config.embedding_size or config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "lm_head"),
            )

        self.logits_processor = LogitsProcessor(
            config.embedding_size or config.vocab_size
        )

        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

    def _parse_and_validate_image_input(
        self,
        **kwargs: object,
    ) -> MolmoImageInputs | None:
        images = kwargs.pop("images", None)
        image_masks = kwargs.pop("image_masks", None)
        image_input_idx = kwargs.pop("image_input_idx", None)
        num_crops = kwargs.pop("num_crops", None)

        if images is None:
            return None

        img_patch_id = kwargs.pop("img_patch_id", None)
        if isinstance(img_patch_id, torch.Tensor):
            img_patch_id = img_patch_id.item()

        assert isinstance(img_patch_id, int)
        self.img_patch_id = img_patch_id

        return MolmoImageInputs(
            images=images,
            image_masks=image_masks,
            image_input_idx=image_input_idx,
            num_crops=num_crops,
        )

    def _process_image_input(
        self,
        image_input: MolmoImageInputs,
    ) -> list[torch.Tensor]:
        images = image_input["images"]
        image_masks = image_input["image_masks"]
        image_input_idx = image_input["image_input_idx"]
        num_crops = image_input["num_crops"]

        # Call the vision backbone on the whole batch at once
        image_features = self.vision_backbone(
            images=images.unsqueeze(0),
            image_masks=None if image_masks is None else image_masks.unsqueeze(0),
        ).squeeze(0)

        # Only the features corresponding to patch tokens are relevant
        # Re-order the features using the image_input_idx tensor
        results = []
        num_crops_list = num_crops.tolist()
        for feats, img_idx in zip(
            image_features.split(num_crops_list),
            image_input_idx.split(num_crops_list),
        ):
            is_valid = img_idx >= 0
            valid_img_idx = img_idx[is_valid]
            order = torch.argsort(valid_img_idx)
            results.append(feats[is_valid][order])
        return results

    def embed_multimodal(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 forward(
        self,
        input_ids: torch.LongTensor,
        positions: torch.LongTensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> torch.Tensor:
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
        )

        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
        loader = AutoWeightsLoader(self)
        weights = _get_weights_with_merged_embedding(weights)
        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="model",
            connector="vision_backbone.image_projector",
            tower_model="vision_backbone",
        )

class MolmoImageInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - bnc: Batch size * number of images * number of crops (dynamic)
        - np: Number of patches
        - tp: Token sequence positions
        - pd: Patch dimension
    """

    images: Annotated[torch.Tensor, TensorShape("bnc", "np", "pd")]

    image_masks: Annotated[torch.Tensor | None, TensorShape("bnc", "np")]

    image_input_idx: Annotated[torch.Tensor, TensorShape("bnc", "tp")]
    """An index tensor that maps image features to their corresponding patch tokens."""

    num_crops: Annotated[torch.Tensor, TensorShape("bn")]

class MolmoProcessorWrapper:
    """
    Wraps `MolmoProcessor` so that it can be called directly.

    The original definition can be found here:
    https://huggingface.co/allenai/Molmo-7B-D-0924/blob/main/preprocessing_molmo.py
    """

    def __init__(self, processor: ProcessorMixin):
        super().__init__()

        self.processor = processor

    @cached_property
    def vocab(self) -> dict[str, int]:
        return self.processor.tokenizer.vocab  # type: ignore

    @cached_property
    def max_crops(self) -> int:
        image_processor = self.processor.image_processor  # type: ignore

        max_crops = image_processor.max_crops
        assert isinstance(max_crops, int)

        return max_crops

    @cached_property
    def base_image_input_size(self) -> tuple[int, int]:
        image_processor = self.processor.image_processor  # type: ignore

        base_image_input_size = image_processor.base_image_input_size
        if isinstance(base_image_input_size, int):
            return base_image_input_size, base_image_input_size

        return tuple(base_image_input_size)

    @cached_property
    def image_patch_size(self) -> int:
        image_processor = self.processor.image_processor  # type: ignore

        image_patch_size = image_processor.image_patch_size
        assert isinstance(image_patch_size, int)

        return image_patch_size

    @cached_property
    def overlap_margins(self) -> tuple[int, int]:
        image_processor = self.processor.image_processor  # type: ignore

        left_margin, right_margin = image_processor.overlap_margins
        assert isinstance(left_margin, int)
        assert isinstance(right_margin, int)

        return left_margin, right_margin

    @cached_property
    def image_token_length_w(self) -> int:
        image_processor = self.processor.image_processor  # type: ignore

        image_token_length_w = image_processor.image_token_length_w
        assert isinstance(image_token_length_w, int)

        return image_token_length_w

    @cached_property
    def image_token_length_h(self) -> int:
        image_processor = self.processor.image_processor  # type: ignore

        image_token_length_h = image_processor.image_token_length_h
        assert isinstance(image_token_length_h, int)

        return image_token_length_h

    @property
    def message_format(self) -> str | None:
        return "role"

    @property
    def always_start_with_space(self) -> bool:
        return True

    @cached_property
    def image_patch_id(self) -> int:
        return self.vocab[IMAGE_PATCH_TOKEN]

    @cached_property
    def im_col_id(self) -> int:
        return self.vocab[IM_COL_TOKEN]

    @cached_property
    def im_start_id(self) -> int:
        return self.vocab[IM_START_TOKEN]

    @cached_property
    def im_end_id(self) -> int:
        return self.vocab[IM_END_TOKEN]

    @property
    def pooling_size(self) -> int:
        return POOLING_SIZE

    def select_tiling(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> tuple[int, int]:
        max_crops = self.max_crops
        left_margin, right_margin = self.overlap_margins
        base_image_input_size = self.base_image_input_size
        base_image_input_d = self.image_patch_size

        total_margin_pixels = base_image_input_d * (right_margin + left_margin)
        crop_patches = base_image_input_size[0] // base_image_input_d
        crop_window_patches = crop_patches - (right_margin + left_margin)
        crop_window_size = crop_window_patches * base_image_input_d
        tiling_h, tiling_w = select_tiling(
            height=image_height - total_margin_pixels,
            width=image_width - total_margin_pixels,
            patch_size=crop_window_size,
            max_num_patches=max_crops,
        )

        return tiling_w, tiling_h

    def get_patches_grid_size(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> tuple[int, int]:
        left_margin, right_margin = self.overlap_margins
        base_image_input_size = self.base_image_input_size
        base_image_input_d = self.image_patch_size
        pooling_size = self.pooling_size

        crop_patches = base_image_input_size[0] // base_image_input_d
        tiling_w, tiling_h = self.select_tiling(
            image_height=image_height,
            image_width=image_width,
        )

        nrows, ncols = get_patches_grid_size(
            tiling_h=tiling_h,
            tiling_w=tiling_w,
            crop_patches=crop_patches,
            left_margin=left_margin,
            right_margin=right_margin,
            pooling_size=pooling_size,
        )

        return ncols, nrows

    def __call__(
        self,
        text: TextInput | list[TextInput] | None = None,
        images: ImageInput | list[ImageInput] | None = None,
        return_tensors: str | TensorType | None = None,
        **kwargs,
    ) -> BatchFeature:
        outputs = self.processor.process(  # type: ignore
            text, images, **kwargs
        )

        if images is None:
            images = []
        if not isinstance(images, list):
            images = [images]

        input_ids: torch.Tensor = outputs.pop("input_ids")
        outputs["input_ids"] = input_ids.unsqueeze(0)

        image_input_idx = outputs.pop("image_input_idx", None)
        if image_input_idx is not None:
            feat_is_patch = image_input_idx >= 0

            tilings = [
                self.select_tiling(
                    image_width=image.size[0],
                    image_height=image.size[1],
                )
                for image in images
            ]
            # For each image: tiling_h * tiling_w + extra
            num_crops = torch.tensor(tilings).prod(-1) + 1
            assert num_crops.sum() == len(feat_is_patch)

            outputs["image_input_idx"] = image_input_idx
            outputs["num_crops"] = num_crops
            outputs["img_patch_id"] = self.image_patch_id

        return BatchFeature(outputs)

class MolmoVisionBackbone(nn.Module, SupportsQuant):
    packed_modules_mapping = {"merged_linear": ["gate_proj", "up_proj"]}

    def __init__(
        self,
        config: PretrainedConfig,
        vision_config: VisionBackboneConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.vit_layers = VIT_LAYERS
        self.image_num_patch = vision_config.image_num_patch
        self.llm_patches_per_crop = (
            (self.image_num_patch[0] + 1) // POOLING_SIZE,
            (self.image_num_patch[1] + 1) // POOLING_SIZE,
        )
        self.image_vit = VisionTransformer(
            vision_config, quant_config=quant_config, prefix=f"{prefix}.image_vit"
        )
        self.num_prefix_tokens = self.image_vit.num_prefix_tokens
        assert self.num_prefix_tokens in {0, 1}, (
            "Only 0 or 1 prefix tokens are supported"
        )
        self.image_pooling_2d = MultiHeadDotProductAttention(
            vision_config,
            nlayers=len(self.vit_layers),
            quant_config=quant_config,
            prefix=f"{prefix}.image_pooling_2d",
        )
        self.image_projector = ImageProjectorMLP(
            config,
            input_dim=vision_config.image_emb_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.image_projector",
        )

        image_dim = vision_config.image_emb_dim * len(self.vit_layers)
        self.pad_embed = nn.Parameter(torch.zeros((2, image_dim)))

    @property
    def dtype(self) -> torch.dtype:
        return self.image_vit.patch_embedding.weight.dtype

    @property
    def device(self) -> torch.device:
        return self.image_vit.patch_embedding.weight.device

    def encode_image(self, images: torch.Tensor) -> torch.Tensor:
        """
        : param images: (batch_size, num_crops, num_patch, n_pixels)
        """
        B, T, N, D = images.shape

        mask = ~torch.all(images.view(B * T, N, D) == -1, dim=(1, 2), keepdim=True)

        images = images.view(B * T, N, D)
        image_features = self.image_vit(images)

        if self.vit_layers is not None:
            features = []
            for layer in self.vit_layers:
                features.append(image_features[layer])
            image_features = torch.cat(features, dim=-1)
        else:
            image_features = image_features[-1]

        if self.num_prefix_tokens > 0:
            image_features = image_features[:, 1:]

        image_features = image_features * mask
        image_features = image_features.view(B, T, N, -1)

        return image_features

    def forward(
        self,
        images: torch.Tensor,
        image_masks: torch.Tensor,
    ) -> torch.Tensor:
        # image_features: (batch_size, num_crops(=num_image), num_patch, nximage_emb_dim) # noqa: E501
        batch_size, num_image = images.shape[:2]
        images = images.to(device=self.device, dtype=self.dtype)
        image_features = self.encode_image(images)

        og_dtype = image_features.dtype
        assert image_masks is not None
        pad_embed = self.pad_embed[:, None, None, None, :]
        all_pad = image_masks == 0
        partial_pad = torch.logical_and(image_masks < 1, torch.logical_not(all_pad)).to(
            dtype=torch.float32
        )
        all_pad = all_pad.to(dtype=torch.float32)
        image_features = image_features + pad_embed[0] * torch.unsqueeze(all_pad, -1)
        image_features = image_features + pad_embed[1] * torch.unsqueeze(
            partial_pad, -1
        )

        image_features = image_features.to(og_dtype)

        image_features = image_features.reshape(
            (batch_size, num_image) + self.image_num_patch + (-1,),
        )

        if missing_w := self.image_num_patch[0] % POOLING_SIZE:
            # Padding for image pooling (see below)
            image_features = F.pad(
                image_features,
                (0, 0, 0, missing_w, 0, missing_w, 0, 0, 0, 0),
            )

        # image pooling
        image_features = rearrange(
            image_features,
            "b n (h dh) (w dw) c -> (b n h w) (dh dw) c",
            dh=POOLING_SIZE,
            dw=POOLING_SIZE,
        )

        query = image_features.mean(-2, keepdim=True)
        image_features = self.image_pooling_2d(query, image_features)

        h, w = self.llm_patches_per_crop
        image_features = image_features.view(batch_size, num_image, h * w, -1)

        image_features = self.image_projector(image_features)

        # image_features: (batch_size, num_image, num_patch, d_model)
        return image_features

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("merged_linear", "gate_proj", 0),
            ("merged_linear", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()

        for name, loaded_weight in weights:
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params

class MultiHeadDotProductAttention(nn.Module):
    """Multi-head attention used in Vision Transformer."""

    def __init__(
        self,
        config: VisionBackboneConfig,
        use_bias: bool = True,
        nlayers: int = 1,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()

        self.hidden_size = config.image_emb_dim
        self.total_num_heads = config.image_num_heads
        tp_size = get_tensor_model_parallel_world_size()

        assert self.hidden_size % self.total_num_heads == 0
        assert self.total_num_heads % tp_size == 0

        self.num_heads = self.total_num_heads // tp_size
        self.head_dim = self.hidden_size // self.total_num_heads

        self.total_num_kv_heads = config.image_num_key_value_heads
        if self.total_num_kv_heads >= tp_size:
            assert self.total_num_kv_heads % tp_size == 0
        else:
            assert tp_size % self.total_num_kv_heads == 0

        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)

        self.wq = ColumnParallelLinear(
            nlayers * self.hidden_size,
            self.total_num_heads * self.head_dim,
            bias=use_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.wq",
        )
        self.wk = ColumnParallelLinear(
            nlayers * self.hidden_size,
            self.total_num_kv_heads * self.head_dim,
            bias=use_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.wk",
        )
        self.wv = ColumnParallelLinear(
            nlayers * self.hidden_size,
            self.total_num_kv_heads * self.head_dim,
            bias=use_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.wv",
        )
        self.wo = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=use_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.wo",
        )

        self.scale = self.head_dim**-0.5
        self.attn = MMEncoderAttention(
            self.num_heads,
            self.head_dim,
            self.scale,
            num_kv_heads=self.num_kv_heads,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self, inputs_q: torch.Tensor, inputs_kv: torch.Tensor | None = None
    ) -> torch.Tensor:
        if inputs_kv is not None:
            inputs_k = inputs_kv
            inputs_v = inputs_kv
        else:
            inputs_k = inputs_q
            inputs_v = inputs_q

        xq, _ = self.wq(inputs_q)
        xk, _ = self.wk(inputs_k)
        xv, _ = self.wv(inputs_v)

        output = self.attn(xq, xk, xv)
        output, _ = self.wo(output)

        return output