ovis2_5.py 24.4 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""PyTorch Ovis model."""

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from collections.abc import Iterable, Mapping
from functools import partial
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from typing import Annotated, Literal
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import torch
import torch.nn as nn
from transformers import BaseImageProcessor, BatchFeature, PretrainedConfig

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from vllm.config import MultiModalConfig, VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.model_executor.layers.linear import ReplicatedLinear
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.models.ovis import VisualEmbedding
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from vllm.model_executor.models.siglip2navit import Siglip2NavitModel
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from vllm.model_executor.models.utils import (
    AutoWeightsLoader,
    flatten_bn,
    init_vllm_registered_model,
    maybe_prefix,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    MultiModalDataDict,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
)
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from vllm.multimodal.parse import ImageSize, MultiModalDataItems
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from vllm.multimodal.processing import (
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    BaseDummyInputsBuilder,
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    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
)
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from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.processors.ovis2_5 import Ovis2_5Processor
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
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IMAGE_TOKEN = "<image>"
VIDEO_TOKEN = "<video>"
INDICATOR_IDS = [-301, -302, -303, -304]

IMAGE_PAD_TOKEN_MAP = {
    "gemma2": "<unused0>",
    "llama": "<|reserved_special_token_0|>",
    "qwen2": "<|image_pad|>",
    "qwen3": "<|image_pad|>",
}
IMAGE_PAD_TOKEN_ID_MAP = {
    "gemma2": 7,
    "llama": 128002,
    "qwen2": 151655,
    "qwen3": 151655,
}


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class Ovis2_5ImagePatchInputs(TensorSchema):
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    """
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    Dimensions:
        - bnp: Batch size * number of images * number of patches
        - patch_size: patch_size_x * patch_size_y * num_channels
        - patch_indicators: Batch size * (number of patches + 1)
        - bn: Batch size * number of images
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    """

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    type: Literal["image_patches"]
    flat_data: Annotated[torch.Tensor, TensorShape("bnp", "patch_size")]
    indicator_tokens: Annotated[torch.Tensor, TensorShape("patch_indicators")]
    patches_per_item: Annotated[list[int], TensorShape("bn")]
    grids: Annotated[torch.Tensor, TensorShape("bn", 3)]
    # This is used to restore the first two dimensions of `flat_data`.

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class Ovis2_5VideoPatchInputs(TensorSchema):
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    """
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    Dimensions:
        - bnp: Batch size * number of videos * number of patches
        - patch_size: patch_size_x * patch_size_y * num_channels
        - patch_indicators: Batch size * (number of patches + 1)
        - bn: Batch size * number of videos
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    """

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    type: Literal["video_patches"]
    flat_data: Annotated[torch.Tensor, TensorShape("bnp", "patch_size")]
    indicator_tokens: Annotated[torch.Tensor, TensorShape("patch_indicators")]
    patches_per_item: Annotated[list[int], TensorShape("bn")]
    grids: Annotated[torch.Tensor, TensorShape("bn", 3)]
    # This is used to restore the first two dimensions of `flat_data`.
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class VisualTokenizer(torch.nn.Module):
    """
    VIT
    """

    def __init__(
        self,
        config: PretrainedConfig,
        visual_vocab_size: int,
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        quant_config: QuantizationConfig | None = None,
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        multimodal_config: MultiModalConfig | None = None,
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        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.vit = self._init_backbone(
            config=config,
            quant_config=quant_config,
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            multimodal_config=multimodal_config,
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            prefix=f"{prefix}.vit",
        )
        # reserved tokens for INDICATOR_IDS
        head_dim = visual_vocab_size - len(INDICATOR_IDS)
        self.head = torch.nn.Sequential(
            ReplicatedLinear(
                self.config.hidden_size * self.config.hidden_stride**2,
                head_dim,
                bias=False,
                return_bias=False,
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            ),
            torch.nn.LayerNorm(head_dim),
        )
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    def _init_backbone(
        self,
        config: PretrainedConfig,
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        quant_config: QuantizationConfig | None = None,
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        multimodal_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ):
        model_type = config.model_type
        if model_type == "siglip2_navit":
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            return Siglip2NavitModel(
                config=config,
                quant_config=quant_config,
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                multimodal_config=multimodal_config,
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                prefix=prefix,
            )
        raise ValueError(f"Unsupported visual tokenizer model_type: {model_type}")
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    @property
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    def dtype(self) -> torch.dtype:
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        return next(self.head.parameters()).dtype

    @property
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    def device(self) -> torch.device:
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        return next(self.head.parameters()).device

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    def tokenize(self, logits: torch.Tensor) -> torch.Tensor:
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        tokens = torch.softmax(logits, dim=-1, dtype=torch.float32).to(logits.dtype)
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        return tokens

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    def encode(
        self, pixel_values: torch.Tensor, grid_thws: torch.Tensor
    ) -> torch.Tensor:
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        features = self.vit(pixel_values, grid_thws)
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        # refer to qwen2.5-vl patchmerger
        seq_len, _ = features.shape
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        features = features.reshape(seq_len // (self.config.hidden_stride**2), -1)
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        return features

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    def forward(
        self, pixel_values: torch.Tensor, grid_thws: torch.Tensor
    ) -> torch.Tensor:
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        features = self.encode(pixel_values, grid_thws)
        logits = self.head(features)
        tokens = self.tokenize(logits)
        # tokens' shape is [#Token, VocabSize-4],
        # so padding with [#Token, 4], after which,
        # tokens' shape should become [#Token, VocabSize];
        tokens = torch.nn.functional.pad(
            tokens,
            (0, len(INDICATOR_IDS)),
            mode="constant",
            value=0,
        )
        return tokens


class Ovis2_5ProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
        return self.ctx.get_hf_config()

    def get_hf_processor(self, **kwargs):
        vit_config = self.get_hf_config().vit_config
        return self.ctx.get_hf_processor(
            Ovis2_5Processor,
            image_pad_token=self.get_image_pad_token(),
            patch_size=vit_config.patch_size,
            hidden_stride=vit_config.hidden_stride,
            temporal_patch_size=vit_config.temporal_patch_size,
        )

    def get_image_pad_token(self) -> str:
        hf_text_config = self.get_hf_config().get_text_config()
        text_model_type = hf_text_config.model_type
        return IMAGE_PAD_TOKEN_MAP.get(text_model_type)

    def get_image_processor(self) -> BaseImageProcessor:
        return self.get_hf_processor().image_processor  # type: ignore

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    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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        return {"image": None, "video": 1}

    def get_image_size_with_most_features(self) -> ImageSize:
        # NOTE(myselvess): max_pixels 1792 * 1792 hardcoded in original code
        # TODO(myselvess): Be adjusted based on the max_pixels
        return ImageSize(width=1792, height=1792)

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 1,
    ) -> tuple[ImageSize, int]:
        hf_config = self.get_hf_config()
        vit_config = hf_config.vit_config
        patch_size = vit_config.patch_size
        temporal_patch_size = vit_config.temporal_patch_size
        # NOTE: Frames are padded to be divisible by `temporal_patch_size`
        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L294
        padded_num_frames = num_frames + (-num_frames % temporal_patch_size)
        grid_t = max(padded_num_frames // temporal_patch_size, 1)
        grid_h = image_height // patch_size
        grid_w = image_width // patch_size
        num_patches = grid_t * grid_h * grid_w
        num_vision_tokens = num_patches
        return num_vision_tokens

    def get_max_image_tokens(self) -> int:
        target_width, target_height = self.get_image_size_with_most_features()
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        return self.get_num_image_tokens(
            image_width=target_width, image_height=target_height
        )
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    def _get_max_video_frames(self, max_tokens: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()
        num_frames = 0
        while True:
            next_num_frames = num_frames + 1
            next_max_tokens = self.get_num_video_tokens(
                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
                image_processor=None,
            )
            if next_max_tokens > max_tokens:
                break
            num_frames = next_num_frames
        return num_frames

    def get_num_frames_with_most_features(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        max_images = mm_counts.get("image", 0)
        max_videos = mm_counts.get("video", 0)
        max_image_tokens = self.get_max_image_tokens() * max_images
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        max_total_frames = self._get_max_video_frames(seq_len - max_image_tokens)
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        max_frames_per_video = max_total_frames // max(max_videos, 1)
        return max(max_frames_per_video, 1)

    def get_num_video_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
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        image_processor: BaseImageProcessor | None,
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    ) -> int:
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        num_video_tokens = self.get_num_image_tokens(
            image_width=image_width, image_height=image_height, num_frames=num_frames
        )
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        return num_video_tokens

    def get_max_video_tokens(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        target_width, target_height = self.get_image_size_with_most_features()
        return self.get_num_video_tokens(
            image_width=target_width,
            image_height=target_height,
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            num_frames=self.get_num_frames_with_most_features(seq_len, mm_counts),
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            image_processor=None,
        )


class Ovis2_5DummyInputsBuilder(BaseDummyInputsBuilder[Ovis2_5ProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)
        return IMAGE_TOKEN * num_images + VIDEO_TOKEN * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
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        mm_options: Mapping[str, BaseDummyOptions] | None = None,
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    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

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        target_width, target_height = self.info.get_image_size_with_most_features()
        target_num_frames = self.info.get_num_frames_with_most_features(
            seq_len, mm_counts
        )
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        image_overrides = mm_options.get("image") if mm_options else None
        video_overrides = mm_options.get("video") if mm_options else None

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        mm_data = {
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            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            ),
            "video": self._get_dummy_videos(
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                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
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                overrides=video_overrides,
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            ),
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        }
        return mm_data


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class Ovis2_5MultiModalProcessor(BaseMultiModalProcessor[Ovis2_5ProcessingInfo]):
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    def visual_indicators_to_visual_tokens(
        self,
        visual_indicators: list[int],
    ) -> list[int]:
        """
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        Filter image indicators placeholders and convert them to corresponding
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        tokens in visual tokenizer.
        """
        hf_config = self.info.get_hf_config()
        vte_vocab_size = hf_config.visual_vocab_size
        return [
            vte_vocab_size - len(INDICATOR_IDS) + abs(x + 300) - 1
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            for x in visual_indicators
            if x < -300
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        ]

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        if not mm_data:
            # Avoid warning from HF logger for text-only input
            tokenizer = self.info.get_tokenizer()
            prompt_ids = tokenizer.encode(prompt, add_special_tokens=False)
            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )
        hf_processor = self.info.get_hf_processor()

        if "videos" in mm_data:
            visual_indicators = [
                hf_processor.construct_visual_indicators((1, 1, 1), True)
                for grid in processed_outputs["video_grids"]
            ]
            indicator_tokens = [
                self.visual_indicators_to_visual_tokens(indicator)
                for indicator in visual_indicators
            ]
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            processed_outputs["video_indicator_tokens"] = torch.tensor(indicator_tokens)
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        if "images" in mm_data:
            visual_indicators = [
                hf_processor.construct_visual_indicators((1, 1, 1), False)
                for grid in processed_outputs["grids"]
            ]
            indicator_tokens = [
                self.visual_indicators_to_visual_tokens(indicator)
                for indicator in visual_indicators
            ]

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            processed_outputs["indicator_tokens"] = torch.tensor(indicator_tokens)
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        return processed_outputs

    def _apply_hf_processor_tokens_only(
        self,
        prompt_tokens: list[int],
    ) -> list[int]:
        return prompt_tokens

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
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        return dict(
            pixel_values=MultiModalFieldConfig.batched("image"),
            grids=MultiModalFieldConfig.batched("image"),
            indicator_tokens=MultiModalFieldConfig.batched("image"),
            video_pixel_values=MultiModalFieldConfig.batched("video"),
            video_indicator_tokens=MultiModalFieldConfig.batched("video"),
            video_grids=MultiModalFieldConfig.batched("video"),
        )
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    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> list[PromptReplacement]:
        def get_replacement_ovis(item_idx, modality: str):
            if modality == "image":
                out_item = out_mm_kwargs["image"][item_idx]
                grid = out_item["grids"].data
            elif modality == "video":
                out_item = out_mm_kwargs["video"][item_idx]
                grid = out_item["video_grids"].data
            hf_processor = self.info.get_hf_processor()
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            return hf_processor.construct_visual_placeholders(
                grid[0],
            )
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        return [
            PromptReplacement(
                modality=modality,
                target=IMAGE_TOKEN if modality == "image" else VIDEO_TOKEN,
                replacement=partial(get_replacement_ovis, modality=modality),
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            )
            for modality in ("image", "video")
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        ]


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@MULTIMODAL_REGISTRY.register_processor(
    Ovis2_5MultiModalProcessor,
    info=Ovis2_5ProcessingInfo,
    dummy_inputs=Ovis2_5DummyInputsBuilder,
)
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class Ovis2_5(nn.Module, SupportsMultiModal, SupportsPP):
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    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return IMAGE_TOKEN
        if modality.startswith("video"):
            return VIDEO_TOKEN

        raise ValueError("Only image or video modality is supported")

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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
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        multimodal_config = vllm_config.model_config.multimodal_config
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        self.config: PretrainedConfig = config

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        with self._mark_language_model(vllm_config):
            self.llm = init_vllm_registered_model(
                vllm_config=vllm_config.with_hf_config(config.text_config),
                prefix=maybe_prefix(prefix, "llm"),
            )
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        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.visual_tokenizer = VisualTokenizer(
                config=config.vit_config,
                visual_vocab_size=config.visual_vocab_size,
                multimodal_config=multimodal_config,
                quant_config=quant_config,
                prefix=f"{prefix}.visual_tokenizer",
            )
            self.vte = VisualEmbedding(config.visual_vocab_size, config.hidden_size)
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        text_model_type = self.config.get_text_config().model_type
        self.image_pad_token_id = IMAGE_PAD_TOKEN_ID_MAP[text_model_type]

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        self.make_empty_intermediate_tensors = (
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            self.get_language_model().make_empty_intermediate_tensors
        )
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    def _parse_and_validate_image_input(
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        self, **kwargs: object
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    ) -> Ovis2_5ImagePatchInputs | None:
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        pixel_values = kwargs.pop("pixel_values", None)
        indicator_tokens = kwargs.pop("indicator_tokens", None)
        grids = kwargs.pop("grids", None)
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        if pixel_values is None and indicator_tokens is None:
            return None

        if pixel_values is not None and indicator_tokens is not None:
            if not isinstance(pixel_values, (torch.Tensor, list)):
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                raise ValueError(
                    f"Incorrect type of pixel values. Got type: {type(pixel_values)}"
                )
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            if not isinstance(indicator_tokens, (torch.Tensor, list)):
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                raise ValueError(
                    "Incorrect type of indicator_tokens. "
                    f"Got type: {type(indicator_tokens)}"
                )
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            return Ovis2_5ImagePatchInputs(
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                type="image_patches",
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                flat_data=flatten_bn(pixel_values, concat=True),
                patches_per_item=[
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                    x.shape[0] // (self.config.vit_config.hidden_stride**2)
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                    for x in pixel_values
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                ],
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                indicator_tokens=flatten_bn(indicator_tokens, concat=True),
                grids=flatten_bn(grids, concat=True),
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            )

        raise AssertionError("This line should be unreachable.")

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    def _parse_and_validate_video_input(
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        self, **kwargs: object
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    ) -> Ovis2_5VideoPatchInputs | None:
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        pixel_values = kwargs.pop("video_pixel_values", None)
        indicator_tokens = kwargs.pop("video_indicator_tokens", None)
        grids = kwargs.pop("video_grids", None)
        if pixel_values is None and indicator_tokens is None:
            return None

        if pixel_values is not None and indicator_tokens is not None:
            if not isinstance(pixel_values, (torch.Tensor, list)):
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                raise ValueError(
                    f"Incorrect type of pixel values. Got type: {type(pixel_values)}"
                )
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            if not isinstance(indicator_tokens, (torch.Tensor, list)):
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                raise ValueError(
                    "Incorrect type of indicator_tokens. "
                    f"Got type: {type(indicator_tokens)}"
                )
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            return Ovis2_5VideoPatchInputs(
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                type="video_patches",
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                flat_data=flatten_bn(pixel_values, concat=True),
                patches_per_item=[
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                    x.shape[0] // (self.config.vit_config.hidden_stride**2)
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                    for x in pixel_values
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                ],
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                indicator_tokens=flatten_bn(indicator_tokens, concat=True),
                grids=flatten_bn(grids, concat=True),
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            )

        raise AssertionError("This line should be unreachable.")

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    def _process_visual_input(
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        self, visual_input: Ovis2_5ImagePatchInputs | Ovis2_5VideoPatchInputs
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    ) -> MultiModalEmbeddings:
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        image_patches_flat = visual_input["flat_data"]
        patches_per_image = visual_input["patches_per_item"]
        indicator_tokens = visual_input["indicator_tokens"]
        grid_thws = visual_input["grids"]
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        indicator_per_image = list(
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            map(lambda x: 2 if x > 1 else x + 2, patches_per_image)
        )
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        target_dtype = self.visual_tokenizer.dtype
        visual_tokens = self.visual_tokenizer(
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            image_patches_flat.to(target_dtype), grid_thws
        )
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        visual_embeds = self.vte(visual_tokens)  # 1:1 numeric eq.
        indicator_embeds = self.vte(indicator_tokens)

        visual_embeds_per_image = visual_embeds.split(patches_per_image, dim=0)
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        indicator_embeds_per_image = indicator_embeds.split(indicator_per_image)
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        vision_embeddings = []
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        for indicator, visual in zip(
            indicator_embeds_per_image, visual_embeds_per_image
        ):
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            vision_embeddings_per_image = []
            visual = visual.unsqueeze(0)
            for i in range(visual.shape[0]):
                vision_embeddings_per_image.append(
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                    torch.cat([indicator[i : i + 1], visual[i]], dim=0)
                )
            vision_embeddings_per_image.append(indicator[i + 1 :])
            vision_embeddings.append(torch.cat(vision_embeddings_per_image, dim=0))
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        return tuple(vision_embeddings)

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    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        modalities = {}

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
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            if (
                input_key in ("pixel_values", "indicator_tokens", "grids")
                and "images" not in modalities
            ):
                modalities["images"] = self._parse_and_validate_image_input(**kwargs)
            if (
                input_key
                in ("video_pixel_values", "video_indicator_tokens", "video_grids")
                and "videos" not in modalities
            ):
                modalities["videos"] = self._parse_and_validate_video_input(**kwargs)
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        return modalities

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    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
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        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
            return []

        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
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                image_embeddings = self._process_visual_input(image_input)
                multimodal_embeddings += tuple(image_embeddings)
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            if modality == "videos":
                video_input = modalities["videos"]
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                video_embeddings = self._process_visual_input(video_input)
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                multimodal_embeddings += tuple(video_embeddings)
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        return multimodal_embeddings
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    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
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        **kwargs: object,
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    ) -> torch.Tensor | IntermediateTensors:
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        if intermediate_tensors is not None:
            inputs_embeds = None

        # up until here we have a inputs_embeds 100% numerical identity
        # between the OG HF Transformers implementation and ours
        hidden_states = self.llm(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
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    ) -> torch.Tensor | None:
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        return self.llm.compute_logits(hidden_states)
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    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)