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452
vllm/model_executor/models/deepseek_ocr2.py
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452
vllm/model_executor/models/deepseek_ocr2.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only Deepseek-OCR model compatible with HuggingFace weights."""
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from functools import partial
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import torch
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import torch.nn as nn
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from transformers import BatchFeature
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.model_executor.models.interfaces import (
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MultiModalEmbeddings,
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SupportsLoRA,
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SupportsMultiModal,
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SupportsPP,
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)
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader,
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WeightsMapper,
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init_vllm_registered_model,
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maybe_prefix,
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)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
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MultiModalDataDict,
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MultiModalFieldConfig,
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MultiModalKwargsItems,
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NestedTensors,
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)
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from vllm.multimodal.parse import (
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ImageEmbeddingItems,
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ImageProcessorItems,
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ImageSize,
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MultiModalDataItems,
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)
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from vllm.multimodal.processing import (
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BaseDummyInputsBuilder,
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BaseMultiModalProcessor,
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BaseProcessingInfo,
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PromptReplacement,
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PromptUpdate,
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)
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from vllm.sequence import IntermediateTensors
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from vllm.tokenizers import cached_tokenizer_from_config
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from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekVLV2Config
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from vllm.transformers_utils.processors.deepseek_ocr import (
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BASE_SIZE,
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CROP_MODE,
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DeepseekOCRProcessor,
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)
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from ...transformers_utils.processors.deepseek_ocr import count_tiles
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from .deepencoder import ImageEncoderViT
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from .deepencoder2 import build_qwen2_decoder_as_encoder
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from .deepseek_ocr import DeepseekOCRImagePixelInputs
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from .deepseek_vl2 import MlpProjector
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# The image token id may be various
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IMAGE_SIZE = 768 # different from deepseek-ocr
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_IMAGE_TOKEN = "<image>"
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class DeepseekOCR2ProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(DeepseekVLV2Config)
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def get_hf_processor(self, **kwargs: object):
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v2_processor_config = dict(
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image_size=IMAGE_SIZE,
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base_size=BASE_SIZE,
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crop_mode=CROP_MODE,
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strategy="v2",
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)
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return self.ctx.get_hf_processor(
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DeepseekOCRProcessor, **{**kwargs, **v2_processor_config}
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)
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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return {"image": None}
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def get_num_image_tokens(
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self, *, image_width: int, image_height: int, cropping: bool = True
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) -> int:
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image_size = IMAGE_SIZE
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base_size = BASE_SIZE
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patch_size = 16
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downsample_ratio = 4
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if CROP_MODE:
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if image_width <= 768 and image_height <= 768:
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crop_ratio = [1, 1]
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else:
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# find the closest aspect ratio to the target
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crop_ratio = count_tiles(
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image_width, image_height, image_size=IMAGE_SIZE
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)
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num_width_tiles, num_height_tiles = crop_ratio
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else:
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num_width_tiles = num_height_tiles = 1
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h = w = math.ceil((base_size // patch_size) / downsample_ratio)
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h2 = w2 = math.ceil((image_size // patch_size) / downsample_ratio)
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global_views_tokens = h * w
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if num_width_tiles > 1 or num_height_tiles > 1:
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local_views_tokens = (num_height_tiles * h2) * (num_width_tiles * w2)
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else:
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local_views_tokens = 0
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return global_views_tokens + local_views_tokens + 1
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def get_image_size_with_most_features(self) -> ImageSize:
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if IMAGE_SIZE == 1024 and BASE_SIZE == 1280:
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return ImageSize(width=1024 * 2, height=1024 * 2)
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return ImageSize(width=768 * 2, height=768 * 2)
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class DeepseekOCR2DummyInputsBuilder(
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BaseDummyInputsBuilder[DeepseekOCR2ProcessingInfo]
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):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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num_images = mm_counts.get("image", 0)
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processor = self.info.get_hf_processor()
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image_token = processor.image_token
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return image_token * num_images
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def get_dummy_mm_data(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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mm_options: Mapping[str, BaseDummyOptions],
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) -> MultiModalDataDict:
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num_images = mm_counts.get("image", 0)
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max_image_size = self.info.get_image_size_with_most_features()
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return {
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"image": self._get_dummy_images(
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width=max_image_size.width,
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height=max_image_size.height,
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num_images=num_images,
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)
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}
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class DeepseekOCR2MultiModalProcessor(
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BaseMultiModalProcessor[DeepseekOCR2ProcessingInfo]
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):
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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tok_kwargs: Mapping[str, object],
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) -> BatchFeature:
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if mm_data:
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processed_outputs = self.info.ctx.call_hf_processor(
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self.info.get_hf_processor(**mm_kwargs),
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dict(prompt=prompt, **mm_data),
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mm_kwargs,
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)
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else:
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tokenizer = self.info.get_tokenizer()
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processed_outputs = tokenizer(
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prompt, add_special_tokens=True, return_tensors="pt"
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)
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return processed_outputs
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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images_spatial_crop = hf_inputs.get("images_spatial_crop", torch.empty((0, 2)))
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is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
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patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
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return dict(
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pixel_values=MultiModalFieldConfig.batched("image"),
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images_spatial_crop=MultiModalFieldConfig.batched("image"),
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images_crop=MultiModalFieldConfig.flat_from_sizes(
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"image", patches_per_image
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),
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)
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def _get_prompt_updates(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargsItems,
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) -> Sequence[PromptUpdate]:
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hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
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image_token_id = hf_processor.image_token_id
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assert isinstance(image_token_id, int)
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def get_replacement_deepseek_vl2(item_idx: int):
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images = mm_items.get_items(
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"image", (ImageEmbeddingItems, ImageProcessorItems)
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)
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if isinstance(images, ImageEmbeddingItems):
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num_image_tokens = images.get_feature_size(item_idx)
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else:
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size = images.get_image_size(item_idx)
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num_image_tokens = self.info.get_num_image_tokens(
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image_width=size.width,
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image_height=size.height,
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cropping=CROP_MODE,
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)
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return [image_token_id] * num_image_tokens
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return [
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PromptReplacement(
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modality="image",
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target=[image_token_id],
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replacement=get_replacement_deepseek_vl2,
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)
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]
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@MULTIMODAL_REGISTRY.register_processor(
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DeepseekOCR2MultiModalProcessor,
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info=DeepseekOCR2ProcessingInfo,
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dummy_inputs=DeepseekOCR2DummyInputsBuilder,
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)
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class DeepseekOCR2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_prefix={
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# map prefix for language backbone
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"model.embed_tokens.": "language_model.model.embed_tokens.",
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"model.layers.": "language_model.model.layers.",
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"model.norm.": "language_model.model.norm.",
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"lm_head.": "language_model.lm_head.",
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# remove "model." prefix for other components
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"model.": "",
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}
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)
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@classmethod
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def get_placeholder_str(cls, modality: str, i: int) -> str | None:
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if modality.startswith("image"):
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return "<image>"
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raise ValueError("Only image modality is supported")
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config: DeepseekVLV2Config = vllm_config.model_config.hf_config
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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.multimodal_config = multimodal_config
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self.vision_config = config.vision_config
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self.projector_config = config.projector_config
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self.text_config = config.text_config
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model_config = vllm_config.model_config
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tokenizer = cached_tokenizer_from_config(model_config)
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self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN]
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with self._mark_tower_model(vllm_config, "image"):
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self.sam_model = ImageEncoderViT(
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depth=12,
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embed_dim=768,
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img_size=1024,
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mlp_ratio=4,
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norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
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num_heads=12,
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patch_size=16,
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qkv_bias=True,
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use_rel_pos=True,
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global_attn_indexes=[2, 5, 8, 11],
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window_size=14,
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out_chans=256,
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last_conv_output=896,
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)
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self.qwen2_model = build_qwen2_decoder_as_encoder()
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self.projector = MlpProjector(self.projector_config)
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self.tile_tag = config.tile_tag
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self.global_view_pos = config.global_view_pos
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# special token for image token sequence format
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n_embed = self.projector_config.n_embed
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embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
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if self.tile_tag == "2D":
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# This is a typo in original implementation
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self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
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else:
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raise ValueError(
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f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
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)
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with self._mark_language_model(vllm_config):
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self.language_model = init_vllm_registered_model(
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vllm_config=vllm_config,
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hf_config=self.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors
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)
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def _parse_and_validate_image_input(
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self, **kwargs: object
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) -> DeepseekOCRImagePixelInputs | None:
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pixel_values = kwargs.pop("pixel_values", None)
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images_spatial_crop = kwargs.pop("images_spatial_crop", None)
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images_crop = kwargs.pop("images_crop", None)
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if pixel_values is None or torch.sum(pixel_values).item() == 0:
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return None
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base_size = self.vision_config.image_size
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return DeepseekOCRImagePixelInputs(
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type="pixel_values",
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data=pixel_values,
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images_crop=images_crop,
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images_spatial_crop=images_spatial_crop,
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resolve_bindings={
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"base_size": base_size,
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},
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)
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def _encode_global_features(self, image_tensor: torch.Tensor) -> torch.Tensor:
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global_features_1 = self.sam_model(image_tensor)
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global_features_2 = self.qwen2_model(global_features_1)
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features = self.projector(global_features_2)
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_, hw, dim = features.shape
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return features.view(-1, dim)
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def _encode_local_features(self, patches: torch.Tensor) -> torch.Tensor | None:
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if torch.sum(patches).item() == 0:
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return None
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local_features = self.sam_model(patches)
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local_features = self.qwen2_model(local_features)
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features = self.projector(local_features)
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_, _, dim = features.shape
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return features.view(-1, dim)
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def _pixel_values_to_embedding(
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self,
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pixel_values: torch.Tensor,
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images_crop: torch.Tensor,
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images_spatial_crop: torch.Tensor,
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) -> NestedTensors:
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images_in_this_batch = []
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is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
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patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
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images_crop = images_crop.split(patches_per_image.tolist())
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for jdx in range(images_spatial_crop.size(0)):
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patches = images_crop[jdx]
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image_ori = pixel_values[[jdx]]
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global_features = self._encode_global_features(image_ori)
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local_features = self._encode_local_features(patches)
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if local_features is not None:
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combined = torch.cat(
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[local_features, global_features, self.view_seperator[None, :]],
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dim=0,
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)
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else:
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combined = torch.cat(
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[global_features, self.view_seperator[None, :]], dim=0
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)
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images_in_this_batch.append(combined)
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return images_in_this_batch
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def _process_image_input(
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self, image_input: DeepseekOCRImagePixelInputs
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) -> torch.Tensor:
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pixel_values = image_input.data
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images_crop = image_input.images_crop
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images_spatial_crop = image_input.images_spatial_crop.to(dtype=torch.long)
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vision_features = self._pixel_values_to_embedding(
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pixel_values=pixel_values,
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images_crop=images_crop,
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images_spatial_crop=images_spatial_crop,
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)
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return vision_features
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def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
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image_input = self._parse_and_validate_image_input(**kwargs)
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if image_input is None:
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return None
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vision_embeddings = self._process_image_input(image_input)
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return vision_embeddings
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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**kwargs: object,
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):
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if intermediate_tensors is not None:
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inputs_embeds = None
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hidden_states = self.language_model(
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input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
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)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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return self.language_model.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)
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autoloaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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return autoloaded_weights
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def get_mm_mapping(self) -> MultiModelKeys:
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"""
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Get the module prefix in multimodal models
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"""
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return MultiModelKeys.from_string_field(
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language_model="language_model",
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connector="projector",
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tower_model=["sam_model", "qwen2_model"],
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)
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