mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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584 lines
22 KiB
Python
584 lines
22 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from abc import abstractmethod
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from collections.abc import Iterable, Mapping
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from typing import Annotated, Final, Literal, Protocol, TypeAlias, TypeVar
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import torch
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import torch.nn as nn
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from transformers import BatchFeature, LlavaNextConfig, LlavaNextProcessor
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from transformers.models.llava_next.modeling_llava_next import (
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get_anyres_image_grid_shape,
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unpad_image,
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)
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from vllm.config import VllmConfig
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import MultiModalFieldConfig
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from vllm.multimodal.parse import ImageSize
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from vllm.sequence import IntermediateTensors
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .clip import CLIPVisionModel
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from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
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from .llava import (
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BaseLlavaMultiModalProcessor,
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BaseLlavaProcessingInfo,
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LlavaDummyInputsBuilder,
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LlavaLikeConfig,
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LlavaMultiModalProjector,
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init_vision_tower_for_llava,
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)
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from .siglip import SiglipVisionModel
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from .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 .vision import get_num_selected_vision_tokens
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class LlavaNextImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- np: Number of patches + 1
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- c: Number of channels (3)
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- h: Height
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- w: Width
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Note that `num_patches` may be different per batch and image,
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in which case the data is passed as a list instead of a batched tensor.
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"""
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type: Literal["pixel_values"] = "pixel_values"
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pixel_values: Annotated[
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torch.Tensor | list[torch.Tensor],
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TensorShape("bn", "np", 3, "h", "w", dynamic_dims={"np"}),
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]
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image_sizes: Annotated[torch.Tensor | None, TensorShape("bn", 2)]
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# This should be in `(height, width)` format.
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class LlavaNextImageEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- ifs: Image feature size
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- hs: Hidden size (must match language model backbone)
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"""
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type: Literal["image_embeds"] = "image_embeds"
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data: Annotated[torch.Tensor, TensorShape("bn", "ifs", "hs")]
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LlavaNextImageInputs: TypeAlias = (
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LlavaNextImagePixelInputs | LlavaNextImageEmbeddingInputs
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)
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class LlavaNextLikeConfig(LlavaLikeConfig, Protocol):
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image_grid_pinpoints: Final[list[list[int]]]
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class LlavaNextProcessingInfo(BaseLlavaProcessingInfo):
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def get_hf_config(self) -> LlavaNextLikeConfig:
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return self.ctx.get_hf_config(LlavaNextConfig)
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def get_hf_processor(self, **kwargs: object):
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hf_processor = self.ctx.get_hf_processor(LlavaNextProcessor, **kwargs)
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# In case patch_size is omitted from `processor_config.json`
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# e.g. for E5-V: https://huggingface.co/royokong/e5-v
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if hf_processor.patch_size is None:
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patch_size = self.get_vision_encoder_info().get_patch_size()
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hf_processor.patch_size = patch_size
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return hf_processor
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# Based on: https://github.com/huggingface/text-generation-inference/blob/v3.0.1/server/text_generation_server/models/vlm_causal_lm.py#L113
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def get_num_image_tokens(
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self,
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*,
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image_width: int,
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image_height: int,
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) -> int:
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hf_config = self.get_hf_config()
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vision_encoder_info = self.get_vision_encoder_info()
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base_feature_size = get_num_selected_vision_tokens(
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vision_encoder_info.get_num_image_tokens(
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image_width=image_width,
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image_height=image_height,
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),
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hf_config.vision_feature_select_strategy,
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)
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num_patch_height, num_patch_width = get_anyres_image_grid_shape(
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image_size=(image_height, image_width),
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grid_pinpoints=hf_config.image_grid_pinpoints,
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patch_size=vision_encoder_info.get_image_size(),
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)
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(
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unpadded_feature_size,
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newline_feature_size,
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) = self._get_num_unpadded_features(
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original_height=image_height,
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original_width=image_width,
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npatches=vision_encoder_info.get_patch_grid_length(),
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num_patch_height=num_patch_height,
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num_patch_width=num_patch_width,
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)
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return unpadded_feature_size + newline_feature_size + base_feature_size
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# Based on: https://github.com/huggingface/text-generation-inference/blob/v3.0.1/server/text_generation_server/models/vlm_causal_lm.py#L86
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def _get_num_unpadded_features(
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self,
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*,
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original_height: int,
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original_width: int,
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npatches: int,
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num_patch_height: int,
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num_patch_width: int,
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) -> tuple[int, int]:
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current_height = npatches * num_patch_height
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current_width = npatches * num_patch_width
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aspect_ratio = original_width / original_height
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current_aspect_ratio = current_width / current_height
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if aspect_ratio > current_aspect_ratio:
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new_height = int(
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round(original_height * (current_width / original_width), 7)
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)
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padding = (current_height - new_height) // 2
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current_height = current_height - (2 * padding)
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else:
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new_width = int(
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round(original_width * (current_height / original_height), 7)
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)
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padding = (current_width - new_width) // 2
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current_width = current_width - (2 * padding)
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unpadded_features = current_height * current_width
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newline_features = current_height
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return (unpadded_features, newline_features)
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def get_image_size_with_most_features(self) -> ImageSize:
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hf_config = self.get_hf_config()
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largest_feature_size, largest_feature_pinpoint = 0, None
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for height, width in hf_config.image_grid_pinpoints:
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feat_size = self.get_num_image_tokens(
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image_width=width, image_height=height
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)
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if feat_size > largest_feature_size:
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largest_feature_size = feat_size
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largest_feature_pinpoint = ImageSize(width=width, height=height)
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if largest_feature_size == 0 or largest_feature_pinpoint is None:
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raise ValueError("Cannot have a largest feature size of 0!")
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return largest_feature_pinpoint
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_I = TypeVar("_I", bound=LlavaNextProcessingInfo)
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class BaseLlavaNextMultiModalProcessor(BaseLlavaMultiModalProcessor[_I]):
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# Copied from BaseMultiModalProcessor
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@abstractmethod
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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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raise NotImplementedError
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class LlavaNextMultiModalProcessor(
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BaseLlavaNextMultiModalProcessor[LlavaNextProcessingInfo]
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):
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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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return dict(
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pixel_values=MultiModalFieldConfig.batched("image"),
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image_sizes=MultiModalFieldConfig.batched("image"),
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image_embeds=MultiModalFieldConfig.batched("image"),
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)
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@MULTIMODAL_REGISTRY.register_processor(
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LlavaNextMultiModalProcessor,
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info=LlavaNextProcessingInfo,
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dummy_inputs=LlavaDummyInputsBuilder,
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)
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class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
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merge_by_field_config = True
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_prefix={
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# mapping for new names in checkpoint saved after transformers v4.52
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"model.language_model.": "language_model.model.",
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"model.vision_tower.": "vision_tower.",
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"model.multi_modal_projector.": "multi_modal_projector.",
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"model.image_newline": "image_newline",
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"lm_head.": "language_model.lm_head.",
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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 = "") -> 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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vision_feature_layer = config.vision_feature_layer
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# Determine the layer up to which we will initialize the vision tower
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if isinstance(vision_feature_layer, int):
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vision_hidden_size = config.vision_config.hidden_size
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self.select_layers = None
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# Used for multimodal granite models to control encoder outputs
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elif isinstance(vision_feature_layer, (list, tuple)):
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vision_hidden_size = config.vision_config.hidden_size * len(
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vision_feature_layer
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)
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self.select_layers = vision_feature_layer
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else:
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raise TypeError(
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f"vision_layer_feature type: {type(vision_feature_layer)}"
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" is not supported"
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)
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self.config = config
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self.multimodal_config = multimodal_config
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# TODO: Optionally initializes this for supporting embeddings.
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self.vision_tower = init_vision_tower_for_llava(
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config,
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quant_config,
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require_post_norm=False,
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prefix=maybe_prefix(prefix, "vision_tower"),
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)
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self.image_newline = nn.Parameter(torch.empty(config.text_config.hidden_size))
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self.multi_modal_projector = LlavaMultiModalProjector(
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vision_hidden_size=vision_hidden_size,
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text_hidden_size=config.text_config.hidden_size,
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projector_hidden_act=config.projector_hidden_act,
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multimodal_projector_bias=config.multimodal_projector_bias,
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)
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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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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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) -> LlavaNextImageInputs | None:
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pixel_values = kwargs.pop("pixel_values", None)
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image_sizes = kwargs.pop("image_sizes", None)
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image_embeds = kwargs.pop("image_embeds", None)
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if pixel_values is None and image_embeds is None:
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return None
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if pixel_values is not None:
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expected_h = expected_w = self.config.vision_config.image_size
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return LlavaNextImagePixelInputs(
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type="pixel_values",
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pixel_values=pixel_values,
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image_sizes=image_sizes,
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resolve_bindings={
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"h": expected_h,
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"w": expected_w,
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},
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)
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if image_embeds is not None:
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return LlavaNextImageEmbeddingInputs(
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type="image_embeds",
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data=image_embeds,
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)
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raise AssertionError("This line should be unreachable.")
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def _image_pixels_to_features(
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self,
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vision_tower: CLIPVisionModel | SiglipVisionModel,
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pixel_values: torch.Tensor,
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) -> torch.Tensor:
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# NOTE: we skip the step to select the vision feature layer since
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# this is already done inside the vision tower
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return vision_tower(
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pixel_values,
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select_layers=self.select_layers,
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feature_select_strategy=self.config.vision_feature_select_strategy,
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)
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# Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
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def _merge_image_patch_embeddings(
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self, image_size: torch.Tensor, patch_embeddings: torch.Tensor, *, strategy: str
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) -> torch.Tensor:
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if strategy == "flat":
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return patch_embeddings.flatten(0, 1)
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if strategy.startswith("spatial"):
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height = width = (
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self.config.vision_config.image_size
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// self.config.vision_config.patch_size
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)
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base_patch_embeds = patch_embeddings[0]
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if height * width != base_patch_embeds.shape[0]:
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raise ValueError(
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"The number of patches is not consistent with the image size."
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)
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if patch_embeddings.shape[0] > 1:
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other_patch_embeds = patch_embeddings[1:]
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# Move to CPU to avoid floating-point errors
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orig_height, orig_width = image_size.tolist()
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# image_aspect_ratio == "anyres"
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num_patch_height, num_patch_width = get_anyres_image_grid_shape(
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(orig_height, orig_width),
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self.config.image_grid_pinpoints,
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self.config.vision_config.image_size,
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)
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num_patches = num_patch_height * num_patch_width
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# Image patches might be padded for batch processing
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other_patch_embeds = other_patch_embeds[:num_patches].view(
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num_patch_height, num_patch_width, height, width, -1
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)
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if "unpad" in strategy:
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other_patch_embeds = (
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other_patch_embeds.permute(4, 0, 2, 1, 3)
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.contiguous()
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.flatten(1, 2)
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.flatten(2, 3)
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)
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other_patch_embeds = unpad_image(
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other_patch_embeds, (orig_height, orig_width)
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)
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other_patch_embeds = torch.cat(
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(
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other_patch_embeds,
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self.image_newline[:, None, None]
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.expand(*other_patch_embeds.shape[:-1], 1)
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.to(other_patch_embeds.device),
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),
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dim=-1,
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)
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other_patch_embeds = other_patch_embeds.flatten(1, 2).transpose(
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0, 1
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)
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else:
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other_patch_embeds = (
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other_patch_embeds.permute(0, 2, 1, 3, 4)
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.contiguous()
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.flatten(0, 3)
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)
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merged_patch_embeddings = torch.cat(
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(base_patch_embeds, other_patch_embeds), dim=0
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)
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else:
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if "unpad" in strategy:
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merged_patch_embeddings = torch.cat(
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(
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base_patch_embeds,
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self.image_newline[None].to(base_patch_embeds.device),
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),
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dim=0,
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)
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else:
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merged_patch_embeddings = base_patch_embeds
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return merged_patch_embeddings
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raise ValueError(f"Unexpected patch merge strategy: {strategy}")
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def _process_image_pixels(
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self,
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inputs: LlavaNextImagePixelInputs,
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) -> torch.Tensor | tuple[torch.Tensor, ...]:
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assert self.vision_tower is not None
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pixel_values = inputs["pixel_values"]
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if isinstance(pixel_values, torch.Tensor):
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b, num_patches, c, h, w = pixel_values.shape
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stacked_pixel_values = pixel_values.view(b * num_patches, c, h, w)
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stacked_image_features = self._image_pixels_to_features(
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self.vision_tower, stacked_pixel_values
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)
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stacked_patch_embeddings = self.multi_modal_projector(
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stacked_image_features
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)
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return stacked_patch_embeddings.view(
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b, num_patches, *stacked_patch_embeddings.shape[1:]
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)
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num_patches_per_batch = [v.shape[0] for v in pixel_values]
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stacked_pixel_values = torch.cat(pixel_values)
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stacked_image_features = self._image_pixels_to_features(
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self.vision_tower, stacked_pixel_values
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)
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return torch.split(
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self.multi_modal_projector(stacked_image_features), num_patches_per_batch
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)
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def _process_image_input(
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self,
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image_input: LlavaNextImageInputs,
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) -> torch.Tensor | list[torch.Tensor]:
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if image_input["type"] == "image_embeds":
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return [image_input["data"]]
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patch_embeddings = self._process_image_pixels(image_input)
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image_sizes = image_input.get("image_sizes")
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if image_sizes is None:
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batch_size = len(image_input["data"])
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vision_config = self.config.vision_config
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default_height = default_width = vision_config.image_size
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image_sizes = torch.as_tensor(
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[[default_height, default_width] for _ in range(batch_size)]
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)
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return [
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self._merge_image_patch_embeddings(
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image_sizes[i], patch_features_batch, strategy="spatial_unpad"
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)
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for i, patch_features_batch in enumerate(patch_embeddings)
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]
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def get_language_model(self) -> torch.nn.Module:
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return self.language_model
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def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
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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 []
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vision_embeddings = self._process_image_input(image_input)
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return vision_embeddings
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def embed_input_ids(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings: MultiModalEmbeddings | None = None,
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*,
|
|
is_multimodal: torch.Tensor | None = None,
|
|
# Multi-modal token ID may exceed vocab size
|
|
handle_oov_mm_token: bool = True,
|
|
) -> torch.Tensor:
|
|
# This is to satisfy the type checker for each overload
|
|
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,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
"""Run forward pass for LlaVA-NeXT.
|
|
|
|
One key thing to understand is the `input_ids` already accounts for the
|
|
positions of the to-be-inserted image embeddings.
|
|
|
|
Concretely, consider a text prompt:
|
|
`"A chat between a curious human and an artificial intelligence
|
|
assistant. The assistant gives helpful, detailed, and polite answers to
|
|
the human's questions.
|
|
USER: <image>\\nWhat is shown in this image? ASSISTANT:"`.
|
|
|
|
Tokenizer outputs:
|
|
`[1, 319, 13563, 1546, 263, 12758, 5199, 322, 385, 23116, 21082, 20255,
|
|
29889, 450, 20255, 4076, 8444, 29892, 13173, 29892, 322, 1248, 568,
|
|
6089, 304, 278, 5199, 29915, 29879, 5155, 29889, 3148, 1001, 29901,
|
|
29871, 32000, 13, 5618, 338, 4318, 297, 445, 1967, 29973, 319, 1799,
|
|
9047, 13566, 29901]`.
|
|
|
|
To reserve space in KV cache, we have to insert placeholder tokens
|
|
before they are inputted to the model, so the input processor prepends
|
|
additional image tokens (denoted as `32000`), resulting in:
|
|
`[1, 319, 13563, 1546, 263, 12758, 5199, 322, 385, 23116, 21082, 20255,
|
|
29889, 450, 20255, 4076, 8444, 29892, 13173, 29892, 322, 1248, 568,
|
|
6089, 304, 278, 5199, 29915, 29879, 5155, 29889, 3148, 1001, 29901,
|
|
29871, 32000, ..., 32000, 13, 5618, 338, 4318, 297, 445, 1967, 29973,
|
|
319, 1799, 9047, 13566, 29901]`.
|
|
|
|
Unlike in LLaVA-1.5, the number of image tokens inputted to the language
|
|
model depends on the original size of the input image. Including the
|
|
original image token in the input, the required number of image tokens
|
|
is given by [`LlavaNextProcessingInfo.get_num_image_tokens`][vllm.\
|
|
model_executor.models.llava_next.LlavaNextProcessingInfo.get_num_image_tokens].
|
|
|
|
This way, the `positions` and `attn_metadata` are consistent
|
|
with the `input_ids`.
|
|
|
|
Args:
|
|
input_ids: Flattened (concatenated) input_ids corresponding to a
|
|
batch.
|
|
positions: Position indices for the input tokens.
|
|
intermediate_tensors: Intermediate tensors from prior forward pass.
|
|
inputs_embeds: Optional tensor of input embeddings.
|
|
|
|
Info:
|
|
[`LlavaNextImageInputs`][vllm.model_executor.models.llava_next.LlavaNextImageInputs]
|
|
"""
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
hidden_states = self.language_model.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
return self.language_model.compute_logits(hidden_states)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|