957 lines
36 KiB
Python
957 lines
36 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Final, Literal, Optional, Protocol, 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, LlavaOnevisionConfig,
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LlavaOnevisionProcessor)
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from transformers.models.llava_onevision.modeling_llava_onevision 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.config import VllmConfig
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from vllm.model_executor.layers.activation import get_act_fn
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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 (MultiModalDataDict, MultiModalFieldConfig,
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MultiModalKwargs)
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from vllm.multimodal.parse import (ImageSize, MultiModalDataItems,
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VideoEmbeddingItems, VideoProcessorItems)
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from vllm.multimodal.processing import PromptReplacement, PromptUpdate
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from vllm.sequence import IntermediateTensors
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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 LlavaDummyInputsBuilder, init_vision_tower_for_llava
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from .llava_next import (BaseLlavaNextMultiModalProcessor, LlavaNextLikeConfig,
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LlavaNextProcessingInfo)
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from .siglip import SiglipVisionModel
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from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
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maybe_prefix, merge_multimodal_embeddings)
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# For profile run
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_MAX_FRAMES_PER_VIDEO = 16
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class LlavaOnevisionVideoPixelInputs(TypedDict):
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type: Literal["pixel_values_videos"]
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pixel_values_videos: Union[torch.Tensor, list[torch.Tensor]]
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"""
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Shape: `(batch_size * num_videos, num_frames, num_channels, height, width)`
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Note that `num_videos` may be different for each batch, and 'num_frames'
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may be different for each video, in which case the data is passed as a
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list instead of a batched tensor.
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"""
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class LlavaOnevisionImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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pixel_values: 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 LlavaOnevisionImageEmbeddingInputs(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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LlavaOnevisionImageInputs = Union[LlavaOnevisionImagePixelInputs,
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LlavaOnevisionImageEmbeddingInputs]
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LlavaOnevisionMultiInputs = Union[LlavaOnevisionImageInputs,
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LlavaOnevisionVideoPixelInputs]
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class LlavaOnevisionLikeConfig(LlavaNextLikeConfig, Protocol):
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video_token_index: Final[int]
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class LlavaOnevisionProcessingInfo(LlavaNextProcessingInfo):
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def get_hf_config(self) -> LlavaOnevisionLikeConfig:
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return self.ctx.get_hf_config(LlavaOnevisionConfig)
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def get_hf_processor(self, **kwargs: object):
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return self.ctx.get_hf_processor(LlavaOnevisionProcessor, **kwargs)
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"image": None, "video": None}
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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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# with additional logic afterwards taken from LlavaOnevisionProcessor
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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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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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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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ratio = math.sqrt(current_height * current_width / (9 * npatches**2))
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if ratio > 1.1:
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height_factor = int(current_height // ratio)
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width_factor = int(current_width // ratio)
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unpadded_features = height_factor * width_factor
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newline_features = height_factor
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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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# NOTE: This hardcoded value is found via processor tests
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return ImageSize(width=1153, height=944)
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def _get_num_frame_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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spatial_pool_stride = getattr(hf_config, "spatial_pool_stride", 2)
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vision_encoder_info = self.get_vision_encoder_info()
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patch_grid_length = vision_encoder_info.get_patch_grid_length()
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pooled_grid_length = math.ceil(patch_grid_length / spatial_pool_stride)
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return pooled_grid_length * pooled_grid_length
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def get_num_video_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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num_frames: int,
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) -> int:
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num_frame_tokens = self._get_num_frame_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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return num_frame_tokens * num_frames + 1 # Newline token
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def _get_max_video_frames(self, max_tokens: int) -> int:
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target_width, target_height = self.get_image_size_with_most_features()
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num_frames = 0
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while True:
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next_num_frames = num_frames + 1
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next_max_tokens = self.get_num_video_tokens(
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image_width=target_width,
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image_height=target_height,
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num_frames=next_num_frames,
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)
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if next_max_tokens > max_tokens:
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break
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num_frames = next_num_frames
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return num_frames
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def get_num_frames_with_most_features(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> int:
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max_images = mm_counts.get("image", 0)
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max_videos = mm_counts.get("video", 0)
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max_image_tokens = self.get_max_image_tokens() * max_images
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max_total_frames = self._get_max_video_frames(seq_len -
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max_image_tokens)
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max_frames_per_video = min(max_total_frames // max(max_videos, 1),
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_MAX_FRAMES_PER_VIDEO)
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return max(max_frames_per_video, 1)
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def get_max_video_tokens(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> int:
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target_width, target_height = self.get_image_size_with_most_features()
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return self.get_num_video_tokens(
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image_width=target_width,
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image_height=target_height,
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num_frames=self.get_num_frames_with_most_features(
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seq_len, mm_counts),
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)
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class LlavaOnevisionDummyInputsBuilder(
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LlavaDummyInputsBuilder[LlavaOnevisionProcessingInfo]):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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num_images = mm_counts.get("image", 0)
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num_videos = mm_counts.get("video", 0)
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processor = self.info.get_hf_processor()
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image_token = processor.image_token
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video_token = processor.video_token
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return image_token * num_images + video_token * num_videos
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def get_dummy_mm_data(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> MultiModalDataDict:
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num_images = mm_counts.get("image", 0)
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num_videos = mm_counts.get("video", 0)
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target_width, target_height = \
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self.info.get_image_size_with_most_features()
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target_num_frames = \
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self.info.get_num_frames_with_most_features(seq_len,
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mm_counts)
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return {
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"image":
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self._get_dummy_images(width=target_width,
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height=target_height,
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num_images=num_images),
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"video":
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self._get_dummy_videos(
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width=target_width,
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height=target_height,
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num_frames=target_num_frames,
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num_videos=num_videos,
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)
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}
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class LlavaOnevisionMultiModalProcessor(
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BaseLlavaNextMultiModalProcessor[LlavaOnevisionProcessingInfo]):
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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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pixel_values_videos=MultiModalFieldConfig.batched("video"),
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)
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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) -> BatchFeature:
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mm_data = dict(mm_data)
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videos = mm_data.pop("videos", [])
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assert isinstance(videos, list)
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if not videos:
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return super()._call_hf_processor(
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prompt=prompt,
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mm_data=mm_data,
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mm_kwargs=mm_kwargs,
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)
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# LLaVA-OneVision processor doesn't support multiple videos
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# with different sizes when converting back to tensors
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# So, we process each component separately
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# NOTE: No prompt replacement is applied in this case
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processor = self.info.get_hf_processor()
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image_token = processor.image_token
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video_token = processor.video_token
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text_outputs = super()._call_hf_processor(
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prompt=prompt,
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mm_data={},
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mm_kwargs=mm_kwargs,
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)
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images = mm_data.pop("images", [])
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assert isinstance(images, list)
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if images:
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processor_outputs = super()._call_hf_processor(
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prompt=image_token * len(images),
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mm_data={"images": images},
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mm_kwargs=mm_kwargs,
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)
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image_outputs = {
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k: v
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for k, v in processor_outputs.items()
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if k in ("pixel_values", "image_sizes")
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}
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else:
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image_outputs = {}
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pixel_values_videos = []
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for video in videos:
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item_outputs = super()._call_hf_processor(
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prompt=video_token,
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mm_data={"videos": video},
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mm_kwargs=mm_kwargs,
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)
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pixel_values_videos.append(item_outputs["pixel_values_videos"][0])
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video_outputs = {"pixel_values_videos": pixel_values_videos}
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combined_outputs = dict(
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text_outputs,
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**image_outputs,
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**video_outputs,
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)
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return BatchFeature(combined_outputs)
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def _hf_processor_applies_updates(
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self,
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prompt_text: str,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> bool:
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base_result = super()._hf_processor_applies_updates(
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prompt_text=prompt_text,
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mm_items=mm_items,
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hf_processor_mm_kwargs=hf_processor_mm_kwargs,
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)
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return base_result and mm_items.get_count("video", strict=False) == 0
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def _get_prompt_updates(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargs,
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) -> Sequence[PromptUpdate]:
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image_repls = super()._get_prompt_updates(
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mm_items=mm_items,
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hf_processor_mm_kwargs=hf_processor_mm_kwargs,
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out_mm_kwargs=out_mm_kwargs,
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)
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hf_config = self.info.get_hf_config()
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video_token_id = hf_config.video_token_index
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def get_video_replacement(item_idx: int):
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videos = mm_items.get_items(
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"video", (VideoEmbeddingItems, VideoProcessorItems))
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if isinstance(videos, VideoEmbeddingItems):
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num_video_tokens = videos.get_feature_size(item_idx)
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else:
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image_size = videos.get_frame_size(item_idx)
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num_video_tokens = self.info.get_num_video_tokens(
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image_width=image_size.width,
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image_height=image_size.height,
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num_frames=videos.get_num_frames(item_idx),
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)
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return [video_token_id] * num_video_tokens
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return [
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*image_repls,
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PromptReplacement(
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modality="video",
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target=[video_token_id],
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replacement=get_video_replacement,
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),
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]
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class LlavaOnevisionMultiModalProjector(nn.Module):
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def __init__(self, config: LlavaOnevisionConfig):
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super().__init__()
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self.linear_1 = nn.Linear(config.vision_config.hidden_size,
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config.text_config.hidden_size,
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bias=config.multimodal_projector_bias)
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self.act = get_act_fn(config.projector_hidden_act)
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self.linear_2 = nn.Linear(config.text_config.hidden_size,
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config.text_config.hidden_size,
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bias=config.multimodal_projector_bias)
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def forward(self, image_features: torch.Tensor) -> torch.Tensor:
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hidden_states = self.linear_1(image_features)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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@MULTIMODAL_REGISTRY.register_processor(
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LlavaOnevisionMultiModalProcessor,
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info=LlavaOnevisionProcessingInfo,
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dummy_inputs=LlavaOnevisionDummyInputsBuilder)
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class LlavaOnevisionForConditionalGeneration(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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self.config = config
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self.multimodal_config = multimodal_config
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# Initialize the vision tower only up to the required feature layer
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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.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
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self.language_model = init_vllm_registered_model(
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vllm_config=vllm_config,
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hf_config=config.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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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.make_empty_intermediate_tensors = (
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self.language_model.model.make_empty_intermediate_tensors)
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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_image_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[LlavaOnevisionImageInputs]:
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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:
|
|
if not isinstance(pixel_values, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of pixel values. "
|
|
f"Got type: {type(pixel_values)}")
|
|
|
|
if not isinstance(image_sizes, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of image sizes. "
|
|
f"Got type: {type(image_sizes)}")
|
|
|
|
return LlavaOnevisionImagePixelInputs(
|
|
type="pixel_values",
|
|
pixel_values=self._validate_image_pixel_values(
|
|
flatten_bn(pixel_values)),
|
|
image_sizes=self._validate_image_sizes(
|
|
flatten_bn(image_sizes, concat=True)),
|
|
)
|
|
|
|
if image_embeds is not None:
|
|
if not isinstance(image_embeds, torch.Tensor):
|
|
raise ValueError("Incorrect type of image embeds. "
|
|
f"Got type: {type(image_embeds)}")
|
|
|
|
return LlavaOnevisionImageEmbeddingInputs(
|
|
type="image_embeds",
|
|
data=flatten_bn(image_embeds),
|
|
)
|
|
|
|
raise AssertionError("This line should be unreachable.")
|
|
|
|
def _validate_video_pixel_values(
|
|
self, data: Union[torch.Tensor, list[torch.Tensor]]
|
|
) -> Union[torch.Tensor, list[torch.Tensor]]:
|
|
|
|
h = w = self.config.vision_config.image_size
|
|
expected_dims = (3, h, w)
|
|
|
|
def _validate_shape(d: torch.Tensor):
|
|
actual_dims = tuple(d.shape[2:])
|
|
|
|
if actual_dims != expected_dims:
|
|
expected_expr = ("num_frames", *map(str, expected_dims))
|
|
raise ValueError(
|
|
"The expected shape of pixel values in each video frame "
|
|
f"is {expected_expr}. You supplied {tuple(d.shape)}.")
|
|
|
|
for d in data:
|
|
_validate_shape(d)
|
|
|
|
return data
|
|
|
|
def _parse_and_validate_video_input(
|
|
self,
|
|
**kwargs: object) -> Optional[LlavaOnevisionVideoPixelInputs]:
|
|
"""
|
|
A legal video input should have the following dimensions:
|
|
{
|
|
"pixel_values_videos" :
|
|
list[b, Tensor(nb_frames, nb_channels, height, width)]
|
|
}
|
|
"""
|
|
pixel_values_videos = kwargs.pop("pixel_values_videos", None)
|
|
if pixel_values_videos is None:
|
|
return None
|
|
|
|
if not isinstance(pixel_values_videos, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of pixel_values_videos. "
|
|
f"Got type: {type(pixel_values_videos)}")
|
|
|
|
return LlavaOnevisionVideoPixelInputs(
|
|
type="pixel_values_videos",
|
|
pixel_values_videos=flatten_bn(pixel_values_videos),
|
|
)
|
|
|
|
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
|
|
mm_input_by_modality = {}
|
|
|
|
# Preserve the order of modalities if there are multiple of them
|
|
# from the order of kwargs.
|
|
for input_key in kwargs:
|
|
if input_key in ("pixel_values", "image_embeds"
|
|
) and "image" not in mm_input_by_modality:
|
|
mm_input_by_modality[
|
|
"image"] = self._parse_and_validate_image_input(**kwargs)
|
|
if input_key in ("pixel_values_videos", "video_embeds"
|
|
) and "video" not in mm_input_by_modality:
|
|
mm_input_by_modality[
|
|
"video"] = self._parse_and_validate_video_input(**kwargs)
|
|
|
|
return mm_input_by_modality
|
|
|
|
def _select_image_features(self, image_features: torch.Tensor, *,
|
|
strategy: str) -> torch.Tensor:
|
|
if strategy == "default":
|
|
return image_features[:, 1:]
|
|
elif strategy == "full":
|
|
return image_features
|
|
|
|
raise ValueError(f"Unexpected select feature strategy: {strategy}")
|
|
|
|
def _image_pixels_to_features(
|
|
self,
|
|
vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
|
|
pixel_values: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
|
|
# NOTE: we skip the step to select the vision feature layer since
|
|
# this is already done inside the vision tower
|
|
image_features = vision_tower(pixel_values)
|
|
return self._select_image_features(
|
|
image_features,
|
|
strategy=self.config.vision_feature_select_strategy,
|
|
)
|
|
|
|
# Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
|
|
def _merge_image_patch_embeddings(self,
|
|
image_size: torch.Tensor,
|
|
patch_embeddings: torch.Tensor,
|
|
*,
|
|
image_newline=None,
|
|
vision_aspect_ratio="anyres_max_9",
|
|
strategy: str) -> torch.Tensor:
|
|
if strategy == "flat":
|
|
return patch_embeddings.flatten(0, 1)
|
|
|
|
if strategy.startswith("spatial"):
|
|
height = width = self.config.vision_config.image_size \
|
|
// self.config.vision_config.patch_size
|
|
|
|
base_patch_embeds = patch_embeddings[0]
|
|
if height * width != base_patch_embeds.shape[0]:
|
|
raise ValueError(
|
|
"The number of patches is not consistent with the "
|
|
"image size.")
|
|
|
|
if patch_embeddings.shape[0] > 1:
|
|
other_patch_embeds = patch_embeddings[1:]
|
|
|
|
# Move to CPU to avoid floating-point errors
|
|
orig_height, orig_width = image_size.tolist()
|
|
|
|
# image_aspect_ratio == "anyres"
|
|
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
|
|
(orig_height, orig_width),
|
|
self.config.image_grid_pinpoints,
|
|
self.config.vision_config.image_size,
|
|
)
|
|
num_patches = num_patch_height * num_patch_width
|
|
|
|
# Image patches might be padded for batch processing
|
|
other_patch_embeds = other_patch_embeds[:num_patches] \
|
|
.view(num_patch_height, num_patch_width, height, width, -1)
|
|
|
|
if "unpad" in strategy:
|
|
other_patch_embeds = other_patch_embeds \
|
|
.permute(4, 0, 2, 1, 3).contiguous() \
|
|
.flatten(1, 2).flatten(2, 3)
|
|
other_patch_embeds = unpad_image(other_patch_embeds,
|
|
(orig_height, orig_width))
|
|
max_num_patches = int(
|
|
vision_aspect_ratio.removeprefix("anyres_max_"))
|
|
channels, curr_height, curr_width = other_patch_embeds.shape
|
|
ratio = math.sqrt(curr_height * curr_width /
|
|
(max_num_patches * height**2))
|
|
if ratio > 1.1:
|
|
other_patch_embeds = other_patch_embeds[None]
|
|
other_patch_embeds = nn.functional.interpolate(
|
|
other_patch_embeds, [
|
|
int(curr_height // ratio),
|
|
int(curr_width // ratio)
|
|
],
|
|
mode="bilinear")[0]
|
|
if image_newline is not None:
|
|
other_patch_embeds = torch.cat(
|
|
(
|
|
other_patch_embeds,
|
|
image_newline[:, None, None] \
|
|
.expand(*other_patch_embeds.shape[:-1], 1) \
|
|
.to(other_patch_embeds.device),
|
|
),
|
|
dim=-1)
|
|
other_patch_embeds = other_patch_embeds \
|
|
.flatten(1, 2).transpose(0, 1)
|
|
else:
|
|
other_patch_embeds = other_patch_embeds \
|
|
.permute(0, 2, 1, 3, 4).contiguous() \
|
|
.flatten(0, 3)
|
|
|
|
merged_patch_embeddings = torch.cat(
|
|
(base_patch_embeds, other_patch_embeds), dim=0)
|
|
else:
|
|
if "unpad" in strategy:
|
|
merged_patch_embeddings = torch.cat(
|
|
(base_patch_embeds,
|
|
self.image_newline[None] \
|
|
.to(base_patch_embeds.device)
|
|
), dim=0)
|
|
else:
|
|
merged_patch_embeddings = base_patch_embeds
|
|
|
|
return merged_patch_embeddings
|
|
|
|
raise ValueError(f"Unexpected patch merge strategy: {strategy}")
|
|
|
|
def _process_image_pixels(
|
|
self,
|
|
inputs: LlavaOnevisionImagePixelInputs,
|
|
) -> Union[torch.Tensor, list[torch.Tensor]]:
|
|
assert self.vision_tower is not None
|
|
|
|
pixel_values = inputs["pixel_values"]
|
|
|
|
if isinstance(pixel_values, torch.Tensor):
|
|
b, num_patches, c, h, w = pixel_values.shape
|
|
stacked_pixel_values = pixel_values.view(b * num_patches, c, h, w)
|
|
stacked_image_features = self._image_pixels_to_features(
|
|
self.vision_tower, stacked_pixel_values)
|
|
stacked_patch_embeddings = self.multi_modal_projector(
|
|
stacked_image_features)
|
|
|
|
return stacked_patch_embeddings.view(
|
|
b, num_patches, *stacked_patch_embeddings.shape[1:])
|
|
|
|
num_patches_per_batch = [v.shape[0] for v in pixel_values]
|
|
stacked_pixel_values = torch.cat(pixel_values)
|
|
stacked_image_features = self._image_pixels_to_features(
|
|
self.vision_tower, stacked_pixel_values)
|
|
|
|
return [
|
|
self.multi_modal_projector(image_features) for image_features in
|
|
torch.split(stacked_image_features, num_patches_per_batch)
|
|
]
|
|
|
|
def _process_image_input(
|
|
self,
|
|
image_input: LlavaOnevisionImageInputs,
|
|
) -> Union[torch.Tensor, list[torch.Tensor]]:
|
|
if image_input["type"] == "image_embeds":
|
|
return [image_input["data"]]
|
|
|
|
patch_embeddings = self._process_image_pixels(image_input)
|
|
|
|
image_sizes = image_input.get("image_sizes")
|
|
if image_sizes is None:
|
|
batch_size = len(image_input["pixel_values"])
|
|
vision_config = self.config.vision_config
|
|
default_height = default_width = vision_config.image_size
|
|
image_sizes = torch.as_tensor([[default_height, default_width]
|
|
for _ in range(batch_size)])
|
|
|
|
return [
|
|
self._merge_image_patch_embeddings(
|
|
image_sizes[i],
|
|
patch_features_batch,
|
|
image_newline=self.image_newline,
|
|
strategy="spatial_unpad")
|
|
for i, patch_features_batch in enumerate(patch_embeddings)
|
|
]
|
|
|
|
def _video_pixels_to_features(
|
|
self,
|
|
vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
|
|
pixel_values: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
|
|
# NOTE: we skip the step to select the vision feature layer since
|
|
# this is already done inside the vision tower
|
|
video_features = vision_tower(pixel_values)
|
|
video_features = self._select_image_features(
|
|
video_features,
|
|
strategy=self.config.vision_feature_select_strategy,
|
|
)
|
|
video_features = self.multi_modal_projector(video_features)
|
|
video_features = self.apply_pooling(video_features)
|
|
return video_features
|
|
|
|
def _process_video_pixels(self, inputs: LlavaOnevisionVideoPixelInputs):
|
|
assert self.vision_tower is not None
|
|
|
|
video_pixels = inputs["pixel_values_videos"]
|
|
|
|
if isinstance(video_pixels, torch.Tensor):
|
|
total_videos, frames, c, h, w = video_pixels.shape
|
|
video_pixels_flat = video_pixels.view(total_videos * frames, c, h,
|
|
w)
|
|
|
|
embeddings_flat = self._video_pixels_to_features(
|
|
self.vision_tower, video_pixels_flat)
|
|
|
|
embeddings_flat = embeddings_flat.reshape(
|
|
total_videos, frames * embeddings_flat.shape[1], -1)
|
|
|
|
image_newline = self.image_newline[None, None, :].expand(
|
|
total_videos, -1, -1)
|
|
return torch.cat((embeddings_flat, image_newline), dim=1)
|
|
|
|
frames_per_video = [len(video) for video in video_pixels]
|
|
video_pixels_flat = torch.cat(video_pixels)
|
|
|
|
embeddings_flat = self._video_pixels_to_features(
|
|
self.vision_tower, video_pixels_flat)
|
|
|
|
image_newline = self.image_newline[None, None, :]
|
|
|
|
return [
|
|
torch.cat(
|
|
(
|
|
embeds.reshape(1, num_frame * embeddings_flat.shape[1],
|
|
-1),
|
|
image_newline,
|
|
),
|
|
dim=1,
|
|
) for num_frame, embeds in zip(
|
|
frames_per_video,
|
|
torch.split(embeddings_flat, frames_per_video),
|
|
)
|
|
]
|
|
|
|
def apply_pooling(self, image_features: torch.Tensor, stride: int = 2):
|
|
vision_config = self.config.vision_config
|
|
height = width = vision_config.image_size // vision_config.patch_size
|
|
batch_frames, _, dim = image_features.shape
|
|
image_features = image_features.view(batch_frames, height, width, -1)
|
|
image_features = image_features.permute(0, 3, 1, 2)
|
|
|
|
# TODO support other pooling types config
|
|
height, width = image_features.shape[2:]
|
|
scaled_shape = [math.ceil(height / stride), math.ceil(width / stride)]
|
|
image_feature = nn.functional.interpolate(image_features,
|
|
size=scaled_shape,
|
|
mode='bilinear')
|
|
image_feature = image_feature.permute(0, 2, 3, 1)
|
|
image_feature = image_feature.view(batch_frames, -1, dim)
|
|
return image_feature
|
|
|
|
def get_language_model(self) -> torch.nn.Module:
|
|
return self.language_model
|
|
|
|
def get_multimodal_embeddings(
|
|
self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
|
|
mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
|
|
**kwargs)
|
|
if not mm_input_by_modality:
|
|
return None
|
|
|
|
# The result multimodal_embeddings is tuple of tensors, with each
|
|
# tensor correspoending to a multimodal data item (image or video).
|
|
multimodal_embeddings: tuple[torch.Tensor, ...] = ()
|
|
|
|
# NOTE: It is important to iterate over the keys in this dictionary
|
|
# to preserve the order of the modalities.
|
|
for modality in mm_input_by_modality:
|
|
multimodal_input = mm_input_by_modality[modality]
|
|
if modality == "image":
|
|
vision_embeddings = self._process_image_input(multimodal_input)
|
|
multimodal_embeddings += tuple(vision_embeddings)
|
|
if modality == "video":
|
|
video_embeddings = self._process_video_pixels(multimodal_input)
|
|
multimodal_embeddings += tuple(video_embeddings)
|
|
|
|
return multimodal_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
|
if multimodal_embeddings is not None:
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, multimodal_embeddings,
|
|
[self.config.image_token_index, self.config.video_token_index])
|
|
return inputs_embeds
|
|
|
|
def get_input_embeddings_v0(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
image_input: Optional[LlavaOnevisionImagePixelInputs] = None,
|
|
video_input: Optional[LlavaOnevisionVideoPixelInputs] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.get_input_embeddings(input_ids)
|
|
if image_input is not None:
|
|
image_embeds = self._process_image_input(image_input)
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids,
|
|
inputs_embeds,
|
|
image_embeds,
|
|
placeholder_token_id=self.config.image_token_index,
|
|
)
|
|
|
|
if video_input is not None:
|
|
video_embeds = self._process_video_pixels(video_input)
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids,
|
|
inputs_embeds,
|
|
video_embeds,
|
|
placeholder_token_id=self.config.video_token_index,
|
|
)
|
|
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
"""Run forward pass for LlaVA-Onevision.
|
|
Args:
|
|
input_ids: Flattened (concatenated) input_ids corresponding to a
|
|
batch.
|
|
pixel_values_videos: Pixels in each frames for each input videos.
|
|
"""
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
# NOTE: In v1, inputs_embeds is always generated at model runner from
|
|
# `get_multimodal_embeddings` and `get_input_embeddings`, this
|
|
# condition is only for v0 compatibility.
|
|
elif inputs_embeds is None:
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
video_input = self._parse_and_validate_video_input(**kwargs)
|
|
|
|
if image_input is None and video_input is None:
|
|
inputs_embeds = None
|
|
else:
|
|
inputs_embeds = self.get_input_embeddings_v0(
|
|
input_ids,
|
|
image_input=image_input,
|
|
video_input=video_input)
|
|
input_ids = 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,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|