mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-15 05:55:01 +08:00
577 lines
23 KiB
Python
577 lines
23 KiB
Python
from functools import cached_property
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from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
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TypedDict, Union)
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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, unpad_image)
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from typing_extensions import NotRequired
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from vllm.attention import AttentionMetadata
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import MultiModalFieldConfig, NestedTensors
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from vllm.multimodal.parse import ImageSize
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from vllm.sequence import IntermediateTensors
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from .clip import CLIPVisionModel
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from .interfaces import SupportsMultiModal, SupportsPP
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from .llava import (LlavaMultiModalProcessor, LlavaMultiModalProjector,
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init_vision_tower_for_llava)
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from .siglip import SiglipVisionModel
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from .utils import (AutoWeightsLoader, embed_multimodal, flatten_bn,
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init_vllm_registered_model, maybe_prefix)
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class LlavaNextImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: Union[torch.Tensor, List[torch.Tensor]]
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"""
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Shape:
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`(batch_size * num_images, 1 + num_patches, num_channels, height, 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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image_sizes: NotRequired[torch.Tensor]
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"""
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Shape: `(batch_size * num_images, 2)`
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This should be in `(height, width)` format.
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"""
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class LlavaNextImageEmbeddingInputs(TypedDict):
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type: Literal["image_embeds"]
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data: torch.Tensor
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"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
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`hidden_size` must match the hidden size of language model backbone.
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"""
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LlavaNextImageInputs = Union[LlavaNextImagePixelInputs,
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LlavaNextImageEmbeddingInputs]
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class LlavaNextMultiModalProcessor(LlavaMultiModalProcessor):
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def _get_hf_config(self) -> LlavaNextConfig:
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return self.ctx.get_hf_config(LlavaNextConfig)
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def _get_hf_processor(self) -> LlavaNextProcessor:
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return self.ctx.get_hf_processor(LlavaNextProcessor)
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def _get_image_token(self) -> str:
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return self._get_hf_processor().image_token
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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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def _get_max_image_tokens(self) -> int:
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largest_feature_size, _ = self._get_pinpoint_with_most_features()
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return largest_feature_size
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def _get_dummy_image_size(self) -> ImageSize:
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_, pinpoint = self._get_pinpoint_with_most_features()
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return pinpoint
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# Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L106
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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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base_feature_size = self._apply_feature_select_strategy(
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hf_config.vision_feature_select_strategy,
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self._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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)
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num_patches = self._vision_encoder_info.get_num_patches()
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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=self._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=num_patches,
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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/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L79
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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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original_aspect_ratio = original_width / original_height
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current_aspect_ratio = current_width / current_height
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if original_aspect_ratio > current_aspect_ratio:
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scale_factor = current_width / original_width
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new_height = int(original_height * scale_factor)
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padding = (current_height - new_height) // 2
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current_height -= 2 * padding
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else:
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scale_factor = current_height / original_height
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new_width = int(original_width * scale_factor)
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padding = (current_width - new_width) // 2
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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_pinpoint_with_most_features(self) -> tuple[int, ImageSize]:
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"""
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Get the grid pinpoint with the most features and
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the corresponding feature size.
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"""
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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(image_width=width,
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image_height=height)
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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,
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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_size, largest_feature_pinpoint
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@MULTIMODAL_REGISTRY.register_processor(LlavaNextMultiModalProcessor)
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class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
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SupportsPP):
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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.feature_sample_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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self.feature_sample_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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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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self.image_newline = nn.Parameter(
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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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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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@cached_property
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def sampler(self):
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if hasattr(self.language_model, "sampler"):
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return self.language_model.sampler
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return get_sampler()
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def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
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expected_dims = (2, )
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def _validate_shape(d: torch.Tensor):
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actual_dims = tuple(d.shape)
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if actual_dims != expected_dims:
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expected_expr = str(expected_dims)
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raise ValueError(
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f"The expected shape of image sizes per image per batch "
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f"is {expected_expr}. You supplied {tuple(d.shape)}.")
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for d in data:
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_validate_shape(d)
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return data
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def _validate_pixel_values(
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self, data: Union[torch.Tensor, List[torch.Tensor]]
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) -> Union[torch.Tensor, List[torch.Tensor]]:
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h = w = self.config.vision_config.image_size
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expected_dims = (3, h, w)
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def _validate_shape(d: torch.Tensor):
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actual_dims = tuple(d.shape[1:])
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if actual_dims != expected_dims:
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expected_expr = ("num_patches", *map(str, expected_dims))
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raise ValueError(
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"The expected shape of pixel values per image per batch "
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f"is {expected_expr}. You supplied {tuple(d.shape)}.")
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for d in data:
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_validate_shape(d)
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return data
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def _parse_and_validate_image_input(
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self, **kwargs: object) -> Optional[LlavaNextImageInputs]:
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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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if not isinstance(pixel_values, (torch.Tensor, list)):
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raise ValueError("Incorrect type of pixel values. "
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f"Got type: {type(pixel_values)}")
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if not isinstance(image_sizes, (torch.Tensor, list)):
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raise ValueError("Incorrect type of image sizes. "
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f"Got type: {type(image_sizes)}")
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return LlavaNextImagePixelInputs(
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type="pixel_values",
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data=self._validate_pixel_values(flatten_bn(pixel_values)),
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image_sizes=self._validate_image_sizes(
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flatten_bn(image_sizes, concat=True)),
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)
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if image_embeds is not None:
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if not isinstance(image_embeds, torch.Tensor):
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raise ValueError("Incorrect type of image embeds. "
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f"Got type: {type(image_embeds)}")
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return LlavaNextImageEmbeddingInputs(
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type="image_embeds",
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data=flatten_bn(image_embeds),
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)
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raise AssertionError("This line should be unreachable.")
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def _select_image_features(self, image_features: torch.Tensor, *,
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strategy: str) -> torch.Tensor:
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# Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421 # noqa
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if strategy == "default":
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return image_features[:, 1:]
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elif strategy == "full":
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return image_features
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raise ValueError(f"Unexpected select feature strategy: {strategy}")
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def _image_pixels_to_features(
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self,
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vision_tower: Union[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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image_features = vision_tower(
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pixel_values, feature_sample_layers=self.feature_sample_layers)
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return self._select_image_features(
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image_features,
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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(self, image_size: torch.Tensor,
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patch_embeddings: torch.Tensor, *,
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strategy: str) -> 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 = self.config.vision_config.image_size \
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// self.config.vision_config.patch_size
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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 "
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"image size.")
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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] \
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.view(num_patch_height, num_patch_width, height, width, -1)
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if "unpad" in strategy:
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other_patch_embeds = other_patch_embeds \
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.permute(4, 0, 2, 1, 3).contiguous() \
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.flatten(1, 2).flatten(2, 3)
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other_patch_embeds = unpad_image(other_patch_embeds,
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(orig_height, orig_width))
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other_patch_embeds = torch.cat((
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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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), dim=-1)
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other_patch_embeds = other_patch_embeds \
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.flatten(1, 2).transpose(0, 1)
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else:
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other_patch_embeds = other_patch_embeds \
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.permute(0, 2, 1, 3, 4).contiguous() \
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.flatten(0, 3)
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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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else:
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if "unpad" in strategy:
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merged_patch_embeddings = torch.cat(
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(base_patch_embeds,
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self.image_newline[None] \
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.to(base_patch_embeds.device)
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), dim=0)
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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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) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
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assert self.vision_tower is not None
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pixel_values = inputs["data"]
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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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stacked_patch_embeddings = self.multi_modal_projector(
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stacked_image_features)
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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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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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return torch.split(self.multi_modal_projector(stacked_image_features),
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num_patches_per_batch)
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def _process_image_input(
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self,
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image_input: LlavaNextImageInputs,
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) -> Union[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([[default_height, default_width]
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for _ in range(batch_size)])
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return [
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self._merge_image_patch_embeddings(image_sizes[i],
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patch_features_batch,
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strategy="spatial_unpad")
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for i, patch_features_batch in enumerate(patch_embeddings)
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]
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def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
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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 get_input_embeddings(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings: Optional[NestedTensors] = None,
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) -> torch.Tensor:
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if multimodal_embeddings is None:
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return self.language_model.get_input_embeddings(input_ids)
|
|
|
|
inputs_embeds = embed_multimodal(
|
|
input_ids,
|
|
self.config.image_token_index,
|
|
self.language_model.model.get_input_embeddings,
|
|
multimodal_embeddings,
|
|
)
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> Union[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 :func:`get_llava_next_image_feature_size`.
|
|
|
|
This way, the `positions` and `attn_metadata` are consistent
|
|
with the `input_ids`.
|
|
|
|
Args:
|
|
input_ids: Flattened (concatenated) input_ids corresponding to a
|
|
batch.
|
|
pixel_values: The pixels in each grid patch for each input image.
|
|
image_sizes: The original `(height, width)` for each input image.
|
|
|
|
See also:
|
|
:class:`LlavaNextImageInputs`
|
|
"""
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
# NOTE: In v1, inputs_embeds is always generated at model runner, this
|
|
# condition is for v0 compatibility.
|
|
elif inputs_embeds is None:
|
|
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
|
|
inputs_embeds = self.get_input_embeddings(input_ids,
|
|
vision_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model.model(input_ids,
|
|
positions,
|
|
kv_caches,
|
|
attn_metadata,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
return self.language_model.sample(logits, sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|