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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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# Adapted from NVIDIA Eagle2.5-VL model
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# https://huggingface.co/nvidia/Eagle2.5-8B
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from collections.abc import Iterable
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from typing import Annotated, Literal, TypeAlias
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.models.siglip import SiglipVisionModel
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.processing import PromptUpdateDetails
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from vllm.sequence import IntermediateTensors
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from vllm.tokenizers import TokenizerLike
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .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 .internvl import (
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IMG_CONTEXT,
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IMG_END,
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IMG_START,
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BaseInternVLDummyInputsBuilder,
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BaseInternVLMultiModalProcessor,
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BaseInternVLProcessingInfo,
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BaseInternVLProcessor,
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)
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from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
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class Eagle2_5_VLImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- bnp: Batch size * number of images * (1 + num_patches)
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- c: Number of channels (3)
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- h: Height of each image patch
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- w: Width of each image patch
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"""
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type: Literal["pixel_values"]
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pixel_values_flat: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
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num_patches: Annotated[torch.Tensor, TensorShape("bn")]
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class Eagle2_5_VLImageEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- n: Number of images
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- f: Total image feature size
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- h: Hidden size (must match the hidden size of language model backbone)
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"""
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type: Literal["image_embeds"]
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data: Annotated[torch.Tensor | list[torch.Tensor], TensorShape("n", "f", "h")]
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Eagle2_5_VLImageInputs: TypeAlias = (
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Eagle2_5_VLImagePixelInputs | Eagle2_5_VLImageEmbeddingInputs
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)
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class Eagle2_5_VLProcessor(BaseInternVLProcessor):
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"""
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Custom processor for Eagle2.5-VL model.
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Extends BaseInternVLProcessor with Eagle-specific token handling.
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"""
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def __init__(
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self,
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config: PretrainedConfig,
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tokenizer: TokenizerLike,
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*,
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min_dynamic_patch: int | None = None,
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max_dynamic_patch: int | None = None,
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dynamic_image_size: bool | None = None,
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) -> None:
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# Skip super().__init__() to avoid config manipulation
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# Directly initialize all required attributes
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self.config = config
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self.tokenizer = tokenizer
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# Image size with force_image_size override
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image_size: int = config.vision_config.image_size
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if hasattr(config, "force_image_size") and config.force_image_size:
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image_size = config.force_image_size
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patch_size: int = config.vision_config.patch_size
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downsample_ratio: float = getattr(config, "downsample_ratio", 0.5)
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# Compute num_image_token
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self.num_image_token = int(
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(image_size // patch_size) ** 2 * (downsample_ratio**2)
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)
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self.image_size = image_size
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# Dynamic patch settings with defaults
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self.min_dynamic_patch = (
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min_dynamic_patch
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if min_dynamic_patch is not None
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else getattr(config, "min_dynamic_patch", 1)
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)
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self.max_dynamic_patch = (
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max_dynamic_patch
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if max_dynamic_patch is not None
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else getattr(config, "max_dynamic_patch", 12)
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)
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self.dynamic_image_size = (
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dynamic_image_size
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if dynamic_image_size is not None
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else getattr(config, "dynamic_image_size", True)
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)
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self.use_thumbnail: bool = getattr(config, "use_thumbnail", True)
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@property
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def image_token_id(self) -> int:
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"""Get the image token ID from config or tokenizer."""
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if hasattr(self.config, "image_token_index"):
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return self.config.image_token_index
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# Fallback to tokenizer vocab - use <IMG_CONTEXT> (ID: 151667)
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vocab = self.tokenizer.get_vocab()
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if IMG_CONTEXT in vocab:
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return vocab[IMG_CONTEXT]
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raise ValueError(f"Cannot find image token '{IMG_CONTEXT}' in vocabulary")
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def get_image_repl(
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self,
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feature_size: int,
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num_patches: int | None,
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) -> PromptUpdateDetails[str]:
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"""Get image replacement string for prompt."""
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repl_features = IMG_CONTEXT * feature_size
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repl_full = IMG_START + repl_features + IMG_END
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return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
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class Eagle2_5_VLProcessingInfo(BaseInternVLProcessingInfo):
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"""Processing info for Eagle2.5-VL model."""
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def get_hf_processor(self, **kwargs) -> Eagle2_5_VLProcessor:
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return self.ctx.init_processor(
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Eagle2_5_VLProcessor,
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config=self.ctx.get_hf_config(),
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tokenizer=self.get_tokenizer(),
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**kwargs,
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)
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class Eagle2_5_VLDummyInputsBuilder(
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BaseInternVLDummyInputsBuilder[Eagle2_5_VLProcessingInfo]
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):
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"""Dummy inputs builder for Eagle2.5-VL model."""
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pass
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class Eagle2_5_VLMultiModalProcessor(
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BaseInternVLMultiModalProcessor[Eagle2_5_VLProcessingInfo]
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):
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"""Multi-modal processor for Eagle2.5-VL model."""
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pass
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@MULTIMODAL_REGISTRY.register_processor(
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Eagle2_5_VLMultiModalProcessor,
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info=Eagle2_5_VLProcessingInfo,
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dummy_inputs=Eagle2_5_VLDummyInputsBuilder,
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)
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class Eagle2_5_VLForConditionalGeneration(
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nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA
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):
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"""
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Eagle2.5-VL model for conditional generation.
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Architecture:
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- Vision Encoder: SigLIP
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- Language Model: Qwen2
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- Projection: MLP with pixel shuffle downsampling
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"""
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supports_encoder_tp_data = True
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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 = "") -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_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.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
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# Image configuration
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image_size = (
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getattr(config, "force_image_size", None) or config.vision_config.image_size
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)
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patch_size = config.vision_config.patch_size
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self.patch_size = patch_size
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self.downsample_ratio = getattr(config, "downsample_ratio", 0.5)
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self.num_image_token = int(
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(image_size // patch_size) ** 2 * (self.downsample_ratio**2)
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)
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self.select_layer = getattr(config, "select_layer", -1)
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# Vision encoder (SigLIP)
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self.vision_model = self._init_vision_model(
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config,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "vision_model"),
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)
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# Language model (Qwen2)
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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=config.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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# MLP projection
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self.mlp1 = self._init_mlp1(config)
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self.img_context_token_id = None
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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 _init_vision_model(
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self,
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config: PretrainedConfig,
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quant_config: QuantizationConfig | None,
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prefix: str,
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):
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"""Initialize SigLIP vision model."""
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vision_config = config.vision_config
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# Determine number of hidden layers based on select_layer
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vision_feature_layer = self.select_layer
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if vision_feature_layer < 0:
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num_hidden_layers = (
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vision_config.num_hidden_layers + vision_feature_layer + 1
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)
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else:
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num_hidden_layers = vision_feature_layer + 1
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# Disable the pooling head - Eagle2.5 needs all patch tokens,
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# not a single pooled output
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vision_config.vision_use_head = False
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return SiglipVisionModel(
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vision_config,
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quant_config=quant_config,
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multimodal_config=self.multimodal_config,
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num_hidden_layers_override=num_hidden_layers,
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prefix=prefix,
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)
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def _init_mlp1(self, config: PretrainedConfig) -> nn.Module:
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"""Initialize MLP projection layer."""
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vit_hidden_size = config.vision_config.hidden_size
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llm_hidden_size = config.text_config.hidden_size
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return nn.Sequential(
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nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
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nn.Linear(
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vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size
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),
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nn.GELU(),
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nn.Linear(llm_hidden_size, llm_hidden_size),
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)
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def pixel_shuffle(self, x: torch.Tensor, scale_factor: float = 0.5) -> torch.Tensor:
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"""
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Pixel shuffle operation for downsampling vision features.
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Args:
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x: Input tensor of shape (n, w, h, c)
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scale_factor: Downsampling factor
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Returns:
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Downsampled tensor
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"""
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n, w, h, c = x.size()
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# N, W, H, C --> N, W, H * scale, C // scale
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x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
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# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
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x = x.permute(0, 2, 1, 3).contiguous()
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# N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
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x = x.view(
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n,
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int(h * scale_factor),
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int(w * scale_factor),
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int(c / (scale_factor * scale_factor)),
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)
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x = x.permute(0, 2, 1, 3).contiguous()
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return x
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def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
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"""
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Extract visual features from pixel values.
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Args:
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pixel_values: Input pixel values of shape (batch, channels, height, width)
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Returns:
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Visual embeddings
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"""
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vit_embeds = self.vision_model(pixel_values=pixel_values)
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h = w = int(vit_embeds.shape[1] ** 0.5)
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vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
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vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
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vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
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vit_embeds = self.mlp1(vit_embeds)
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return vit_embeds
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def _parse_and_validate_image_input(
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self, **kwargs: object
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) -> Eagle2_5_VLImageInputs | None:
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"""Parse and validate image inputs."""
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pixel_values_flat = kwargs.pop("pixel_values_flat", None)
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image_num_patches = kwargs.pop("image_num_patches", None)
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image_embeds = kwargs.pop("image_embeds", None)
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if pixel_values_flat is None and image_embeds is None:
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return None
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if image_embeds is not None:
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return Eagle2_5_VLImageEmbeddingInputs(
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type="image_embeds",
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data=image_embeds,
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)
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image_token_id = kwargs.get("image_token_id")
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if image_token_id is not None:
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if isinstance(image_token_id, torch.Tensor):
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image_token_id = image_token_id.flatten().unique().item()
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assert isinstance(image_token_id, int)
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self.img_context_token_id = image_token_id
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if pixel_values_flat is not None:
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image_size = getattr(self.config, "force_image_size", None)
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if image_size is None:
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image_size = self.config.vision_config.image_size
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expected_h = expected_w = image_size
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resolve_bindings = {"h": expected_h, "w": expected_w}
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return Eagle2_5_VLImagePixelInputs(
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type="pixel_values",
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pixel_values_flat=pixel_values_flat,
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num_patches=image_num_patches,
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resolve_bindings=resolve_bindings,
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)
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raise AssertionError("This line should be unreachable.")
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def _process_image_input(
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|
self,
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|
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|
image_input: Eagle2_5_VLImageInputs,
|
|
|
|
|
) -> tuple[torch.Tensor, ...]:
|
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|
|
|
"""Process image input to get embeddings."""
|
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|
if image_input["type"] == "image_embeds":
|
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|
return image_input["data"]
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|
assert self.vision_model is not None
|
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|
image_embeds = self.extract_feature(image_input["pixel_values_flat"])
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|
num_patches = image_input["num_patches"]
|
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|
|
|
|
|
|
|
# Only one image in the current batch
|
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|
|
|
if len(num_patches) == 1:
|
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|
|
|
return (image_embeds.view(-1, self.config.text_config.hidden_size),)
|
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|
|
|
|
|
|
|
|
# Split embeddings by image
|
|
|
|
|
feature_size = image_embeds.shape[1]
|
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|
|
|
image_embeds = image_embeds.view(-1, self.config.text_config.hidden_size)
|
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|
|
|
image_feature_sizes = [
|
|
|
|
|
num_patches * feature_size for num_patches in num_patches
|
|
|
|
|
]
|
|
|
|
|
return image_embeds.split(image_feature_sizes)
|
|
|
|
|
|
|
|
|
|
def get_language_model(self) -> torch.nn.Module:
|
|
|
|
|
return self.language_model
|
|
|
|
|
|
|
|
|
|
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
|
|
|
|
|
"""Embed multimodal inputs."""
|
|
|
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
|
|
|
if image_input is None:
|
|
|
|
|
return []
|
|
|
|
|
|
|
|
|
|
image_embeddings = self._process_image_input(image_input)
|
|
|
|
|
return tuple(image_embeddings)
|
|
|
|
|
|
|
|
|
|
def embed_input_ids(
|
|
|
|
|
self,
|
|
|
|
|
input_ids: torch.Tensor,
|
|
|
|
|
multimodal_embeddings: MultiModalEmbeddings | None = None,
|
|
|
|
|
*,
|
|
|
|
|
is_multimodal: torch.Tensor | None = None,
|
|
|
|
|
handle_oov_mm_token: bool = False,
|
|
|
|
|
) -> torch.Tensor:
|
|
|
|
|
"""Embed input IDs with optional multimodal embeddings."""
|
|
|
|
|
if multimodal_embeddings is None or is_multimodal is None:
|
|
|
|
|
return super().embed_input_ids(input_ids)
|
|
|
|
|
|
|
|
|
|
return super().embed_input_ids(
|
|
|
|
|
input_ids,
|
|
|
|
|
multimodal_embeddings=multimodal_embeddings,
|
|
|
|
|
is_multimodal=is_multimodal,
|
|
|
|
|
handle_oov_mm_token=handle_oov_mm_token,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
def forward(
|
|
|
|
|
self,
|
|
|
|
|
input_ids: torch.Tensor,
|
|
|
|
|
positions: torch.Tensor,
|
|
|
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
|
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
|
|
|
**kwargs: object,
|
|
|
|
|
) -> IntermediateTensors:
|
|
|
|
|
"""Forward pass through the model."""
|
|
|
|
|
if intermediate_tensors is not None:
|
|
|
|
|
input_ids = None
|
|
|
|
|
inputs_embeds = None
|
|
|
|
|
|
|
|
|
|
forward_kwargs = {
|
|
|
|
|
"input_ids": input_ids,
|
|
|
|
|
"positions": positions,
|
|
|
|
|
"intermediate_tensors": intermediate_tensors,
|
|
|
|
|
"inputs_embeds": inputs_embeds,
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
hidden_states = self.language_model.model(**forward_kwargs)
|
|
|
|
|
return hidden_states
|
|
|
|
|
|
|
|
|
|
def compute_logits(
|
|
|
|
|
self,
|
|
|
|
|
hidden_states: torch.Tensor,
|
|
|
|
|
) -> torch.Tensor | None:
|
|
|
|
|
"""Compute logits from hidden states."""
|
|
|
|
|
return self.language_model.compute_logits(hidden_states)
|
|
|
|
|
|
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
|
|
|
"""Load model weights."""
|
|
|
|
|
loader = AutoWeightsLoader(self)
|
|
|
|
|
return loader.load_weights(weights)
|
|
|
|
|
|
|
|
|
|
def get_mm_mapping(self) -> MultiModelKeys:
|
|
|
|
|
"""Get the module prefix mapping for multimodal models."""
|
|
|
|
|
return MultiModelKeys.from_string_field(
|
|
|
|
|
language_model="language_model",
|
|
|
|
|
connector="mlp1",
|
|
|
|
|
tower_model="vision_model",
|
|
|
|
|
)
|
Kim Hee Su