[VLM] Merged multi-modal processors for LLaVA-NeXT-Video and LLaVA-OneVision (#11717)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
@@ -3,47 +3,36 @@ 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 numpy as np
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import torch
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import torch.nn as nn
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from PIL import Image
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from transformers import (CLIPVisionConfig, LlavaOnevisionConfig,
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SiglipVisionConfig)
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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.attention import AttentionMetadata
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from vllm.config import VllmConfig
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from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
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InputContext, token_inputs)
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from vllm.model_executor.layers.activation import get_act_fn
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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 NestedTensors
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from vllm.multimodal.utils import (cached_get_tokenizer,
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repeat_and_pad_placeholder_tokens)
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from vllm.multimodal.inputs import MultiModalKwargs, NestedTensors
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from vllm.multimodal.parse import (MultiModalDataItems, VideoEmbeddingItems,
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VideoProcessorItems)
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from vllm.multimodal.processing import (MultiModalFieldConfig, ProcessorInputs,
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PromptReplacement)
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from vllm.sequence import IntermediateTensors
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from vllm.utils import is_list_of
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from .clip import (CLIPVisionModel, dummy_seq_data_for_clip,
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dummy_video_for_clip, get_clip_image_feature_size,
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get_clip_patch_grid_length, input_processor_for_clip)
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from .clip import CLIPVisionModel
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from .interfaces import SupportsMultiModal, SupportsPP
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from .llava import init_vision_tower_for_llava
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from .siglip import (SiglipVisionModel, dummy_seq_data_for_siglip,
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dummy_video_for_siglip, get_siglip_image_feature_size,
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get_siglip_patch_grid_length, input_processor_for_siglip)
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from .llava_next import LlavaNextMultiModalProcessor
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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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# Result in the max possible feature size (2x2 grid of 336x336px tiles)
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MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448
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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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@@ -92,286 +81,251 @@ LlavaOnevisionMultiInputs = Union[LlavaOnevisionImageInputs,
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LlavaOnevisionVideoPixelInputs]
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def _get_llava_onevision_image_unppaded_feature_size(height, width, patches,
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scale_height,
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scale_width):
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current_height = patches * scale_height
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current_width = patches * scale_width
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class LlavaOnevisionMultiModalProcessor(LlavaNextMultiModalProcessor):
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original_aspect_ratio = width / 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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new_height = int(height * (current_width / width))
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padding = (current_height - new_height) // 2
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current_height -= padding * 2
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else:
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new_width = int(width * (current_height / height))
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padding = (current_width - new_width) // 2
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current_width -= padding * 2
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def _get_hf_config(self) -> LlavaOnevisionConfig:
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return self.ctx.get_hf_config(LlavaOnevisionConfig)
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unpadded_features = current_height * current_width
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newline_features = current_height
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def _get_hf_processor(self) -> LlavaOnevisionProcessor:
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return self.ctx.get_hf_processor(LlavaOnevisionProcessor)
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ratio = math.sqrt(current_height * current_width / (9 * patches**2))
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if ratio > 1.1:
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unpadded_features = int(current_height // ratio) * int(
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current_width // ratio)
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newline_features = int(current_height // ratio)
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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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return (unpadded_features, newline_features)
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def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
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max_image_tokens = self._get_max_image_tokens()
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num_frames = self._get_dummy_num_frames(seq_len)
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max_video_tokens = self._get_max_video_tokens(num_frames)
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def get_llava_onevision_image_feature_size(
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hf_config: LlavaOnevisionConfig,
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*,
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input_height: int,
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input_width: int,
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) -> int:
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vision_config = hf_config.vision_config
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return {
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"image": max_image_tokens,
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"video": max_video_tokens,
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}
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if isinstance(vision_config, CLIPVisionConfig):
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num_patches = get_clip_patch_grid_length(
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image_size=vision_config.image_size,
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patch_size=vision_config.patch_size,
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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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base_feature_size = get_clip_image_feature_size(vision_config)
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elif isinstance(vision_config, SiglipVisionConfig):
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num_patches = get_siglip_patch_grid_length(
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image_size=vision_config.image_size,
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patch_size=vision_config.patch_size,
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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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new_height = int(original_height *
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(current_width / original_width))
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padding = (current_height - new_height) // 2
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current_height -= padding * 2
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else:
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new_width = int(original_width *
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(current_height / original_height))
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padding = (current_width - new_width) // 2
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current_width -= padding * 2
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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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unpadded_features = int(current_height // ratio) * int(
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current_width // ratio)
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newline_features = int(current_height // ratio)
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return (unpadded_features, newline_features)
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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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patch_grid_length = self._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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base_feature_size = get_siglip_image_feature_size(vision_config)
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else:
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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strategy = hf_config.vision_feature_select_strategy
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if strategy == "default":
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base_feature_size -= 1
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elif strategy == "full":
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pass
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else:
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raise ValueError(f"Unexpected select feature strategy: {strategy}")
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return num_frame_tokens * num_frames + 1 # Newline token
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num_patch_height, num_patch_width = get_anyres_image_grid_shape(
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image_size=(input_height, input_width),
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grid_pinpoints=hf_config.image_grid_pinpoints,
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patch_size=vision_config.image_size,
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)
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def _get_max_video_tokens(self, num_frames: int) -> int:
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return self._get_num_video_tokens(image_width=999999,
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image_height=999999,
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num_frames=num_frames)
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(
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unpadded_feature_size,
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newline_feature_size,
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) = _get_llava_onevision_image_unppaded_feature_size(
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input_height, input_width, num_patches, num_patch_height,
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num_patch_width)
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def _get_max_video_frames(self, max_tokens: int) -> int:
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num_frames = 0
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return unpadded_feature_size + newline_feature_size + base_feature_size
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while True:
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next_num_frames = num_frames + 1
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if self._get_max_video_tokens(next_num_frames) > max_tokens:
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break
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def get_max_llava_onevision_image_tokens(ctx: InputContext):
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return get_llava_onevision_image_feature_size(
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ctx.get_hf_config(LlavaOnevisionConfig),
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input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
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input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH,
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)
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num_frames = next_num_frames
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return num_frames
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def get_llava_onevision_video_frame_feature_size(
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hf_config: LlavaOnevisionConfig) -> int:
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# Support both CLIPVisionConfig and SiglipVisionConfig
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image_size = hf_config.vision_config.image_size
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patch_size = hf_config.vision_config.patch_size
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spatial_pool_stride = hf_config.spatial_pool_stride if hasattr(
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hf_config, "spatial_pool_stride") else 2
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def _get_dummy_num_frames(self, seq_len: int) -> int:
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mm_config = self.ctx.get_mm_config()
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max_images = mm_config.limit_per_prompt.get("image", 1)
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max_videos = mm_config.limit_per_prompt.get("video", 1)
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height = width = image_size // patch_size
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return math.ceil(height / spatial_pool_stride) * math.ceil(
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width / spatial_pool_stride)
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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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return max(max_total_frames // max(max_videos, 1), 1)
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def get_llava_onevision_video_tokens(ctx: InputContext,
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num_frames: int) -> int:
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hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
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def _get_video_token(self) -> str:
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return self._get_hf_processor().video_token
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# TODO: support configuring (not supported by HF right now)
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num_token_image_newline = 1
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tokens_per_frame = get_llava_onevision_video_frame_feature_size(hf_config)
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video_feature_size = num_frames * tokens_per_frame + num_token_image_newline
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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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return video_feature_size
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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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video_token = self._get_video_token()
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def get_max_llava_onevision_video_tokens(ctx: InputContext) -> int:
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return get_llava_onevision_video_tokens(ctx, _MAX_FRAMES_PER_VIDEO)
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def dummy_data_for_llava_onevision(ctx: InputContext, seq_len: int,
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mm_counts: Mapping[str, int]):
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hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
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vision_config = hf_config.vision_config
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num_videos = mm_counts["video"]
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# TODO: support configuring the number of frames
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num_frames = _MAX_FRAMES_PER_VIDEO
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video_feature_size = get_llava_onevision_video_tokens(ctx, num_frames)
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if isinstance(vision_config, CLIPVisionConfig):
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seq_data, ranges = dummy_seq_data_for_clip(
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vision_config,
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seq_len,
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num_videos,
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image_token_id=hf_config.video_token_index,
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image_feature_size_override=video_feature_size,
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mm_key="video")
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mm_data = dummy_video_for_clip(vision_config,
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num_frames=num_frames,
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num_videos=num_videos)
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return DummyData(seq_data, mm_data, ranges)
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elif isinstance(vision_config, SiglipVisionConfig):
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seq_data, ranges = dummy_seq_data_for_siglip(
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vision_config,
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seq_len,
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num_videos,
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image_token_id=hf_config.video_token_index,
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image_feature_size_override=video_feature_size,
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mm_key="video")
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mm_data = dummy_video_for_siglip(vision_config,
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num_frames=num_frames,
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num_videos=num_videos)
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return DummyData(seq_data, mm_data, ranges)
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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def input_processor_when_multimodal_input_image(ctx: InputContext,
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inputs: DecoderOnlyInputs):
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multi_modal_data = inputs.get("multi_modal_data")
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if multi_modal_data is None or "image" not in multi_modal_data:
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return inputs
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model_config = ctx.model_config
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hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
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vision_config = hf_config.vision_config
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image_data = multi_modal_data["image"]
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if isinstance(image_data, Image.Image):
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width, height = image_data.size
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image_feature_size = get_llava_onevision_image_feature_size(
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hf_config,
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input_height=height,
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input_width=width,
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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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text_image_outputs = 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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elif is_list_of(image_data, Image.Image):
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image_feature_size = [
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get_llava_onevision_image_feature_size(hf_config,
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input_height=img.height,
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input_width=img.width)
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for img in image_data
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pixel_values_videos = []
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for video in videos:
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item_processor_data = dict(prompt=video_token, videos=video)
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item_outputs = super()._call_hf_processor(
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prompt=prompt,
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mm_data=item_processor_data,
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mm_kwargs=mm_kwargs,
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)
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pixel_values_videos.append(
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item_outputs.pop("pixel_values_videos")[0])
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combined_outputs = dict(
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**text_image_outputs,
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pixel_values_videos=pixel_values_videos,
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)
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return BatchFeature(combined_outputs)
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def _get_prompt_replacements(
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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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) -> list[PromptReplacement]:
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image_repls = super()._get_prompt_replacements(
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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._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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|
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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._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 image_repls + [
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PromptReplacement(
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modality="video",
|
||||
target=[video_token_id],
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replacement=get_video_replacement,
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||||
),
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||||
]
|
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elif isinstance(image_data, torch.Tensor):
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num_images, image_feature_size, hidden_size = image_data.shape
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elif is_list_of(image_data, torch.Tensor):
|
||||
image_feature_size = [item.shape[1] for item in image_data]
|
||||
else:
|
||||
raise TypeError(f"Invalid image type: {type(image_data)}")
|
||||
|
||||
vision_config = hf_config.vision_config
|
||||
def _get_dummy_processor_inputs(
|
||||
self,
|
||||
seq_len: int,
|
||||
mm_counts: Mapping[str, int],
|
||||
) -> ProcessorInputs:
|
||||
num_images = mm_counts.get("image", 0)
|
||||
num_videos = mm_counts.get("video", 0)
|
||||
|
||||
if isinstance(vision_config, CLIPVisionConfig):
|
||||
return input_processor_for_clip(
|
||||
model_config,
|
||||
vision_config,
|
||||
inputs,
|
||||
image_token_id=hf_config.image_token_index,
|
||||
image_feature_size_override=image_feature_size,
|
||||
image_token = self._get_image_token()
|
||||
video_token = self._get_video_token()
|
||||
target_width, target_height = self._get_dummy_image_size()
|
||||
|
||||
mm_data = {
|
||||
"image":
|
||||
self._get_dummy_images(width=target_width,
|
||||
height=target_height,
|
||||
num_images=num_images),
|
||||
"video":
|
||||
self._get_dummy_videos(
|
||||
width=target_width,
|
||||
height=target_height,
|
||||
num_frames=self._get_dummy_num_frames(seq_len),
|
||||
num_videos=num_videos,
|
||||
)
|
||||
}
|
||||
|
||||
return ProcessorInputs(
|
||||
prompt_text=image_token * num_images + video_token * num_videos,
|
||||
mm_data=mm_data,
|
||||
)
|
||||
elif isinstance(vision_config, SiglipVisionConfig):
|
||||
return input_processor_for_siglip(
|
||||
model_config,
|
||||
vision_config,
|
||||
inputs,
|
||||
image_token_id=hf_config.image_token_index,
|
||||
image_feature_size_override=image_feature_size,
|
||||
)
|
||||
|
||||
msg = f"Unsupported vision config: {type(vision_config)}"
|
||||
raise NotImplementedError(msg)
|
||||
|
||||
|
||||
def input_processor_when_multimodal_input_video(ctx: InputContext,
|
||||
inputs: DecoderOnlyInputs):
|
||||
multi_modal_data = inputs.get("multi_modal_data")
|
||||
if multi_modal_data is None or "video" not in multi_modal_data:
|
||||
return inputs
|
||||
video_data = multi_modal_data["video"]
|
||||
|
||||
model_config = ctx.model_config
|
||||
hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
|
||||
|
||||
if isinstance(video_data, np.ndarray):
|
||||
# Supports both CLIP and Siglip
|
||||
num_frames = video_data.shape[0]
|
||||
video_feature_size = get_llava_onevision_video_tokens(ctx, num_frames)
|
||||
tokenizer = cached_get_tokenizer(model_config.tokenizer)
|
||||
|
||||
new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
|
||||
tokenizer,
|
||||
inputs.get("prompt"),
|
||||
inputs["prompt_token_ids"],
|
||||
placeholder_token_id=hf_config.video_token_index,
|
||||
repeat_count=video_feature_size,
|
||||
)
|
||||
|
||||
return token_inputs(prompt_token_ids=new_token_ids,
|
||||
prompt=new_prompt,
|
||||
multi_modal_data=multi_modal_data,
|
||||
multi_modal_placeholders={"video": ranges})
|
||||
|
||||
elif is_list_of(video_data, np.ndarray):
|
||||
video_feature_size = []
|
||||
for video in video_data:
|
||||
num_frames = video.shape[0]
|
||||
video_feature_size.append(
|
||||
get_llava_onevision_video_tokens(ctx, num_frames))
|
||||
|
||||
tokenizer = cached_get_tokenizer(model_config.tokenizer)
|
||||
new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
|
||||
tokenizer,
|
||||
inputs.get("prompt"),
|
||||
inputs["prompt_token_ids"],
|
||||
placeholder_token_id=hf_config.video_token_index,
|
||||
repeat_count=video_feature_size,
|
||||
)
|
||||
return token_inputs(prompt_token_ids=new_token_ids,
|
||||
prompt=new_prompt,
|
||||
multi_modal_data=multi_modal_data,
|
||||
multi_modal_placeholders={"video": ranges})
|
||||
else:
|
||||
raise TypeError(f"Invalid video type: {type(video_data)}")
|
||||
|
||||
msg = f"Unsupported video type: {type(video_data)}"
|
||||
raise NotImplementedError(msg)
|
||||
|
||||
|
||||
def input_processor_for_llava_onevision(ctx: InputContext,
|
||||
inputs: DecoderOnlyInputs):
|
||||
multi_modal_data = inputs.get("multi_modal_data")
|
||||
if multi_modal_data is None or ("video" not in multi_modal_data
|
||||
and "image" not in multi_modal_data):
|
||||
return inputs
|
||||
if "image" in multi_modal_data:
|
||||
return input_processor_when_multimodal_input_image(ctx, inputs)
|
||||
if "video" in multi_modal_data:
|
||||
return input_processor_when_multimodal_input_video(ctx, inputs)
|
||||
|
||||
msg = "Unsupported multi data type"
|
||||
raise NotImplementedError(msg)
|
||||
|
||||
|
||||
class LlavaOnevisionMultiModalProjector(nn.Module):
|
||||
@@ -394,14 +348,7 @@ class LlavaOnevisionMultiModalProjector(nn.Module):
|
||||
return hidden_states
|
||||
|
||||
|
||||
@MULTIMODAL_REGISTRY.register_image_input_mapper()
|
||||
@MULTIMODAL_REGISTRY.register_input_mapper("video")
|
||||
@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
|
||||
"image", get_max_llava_onevision_image_tokens)
|
||||
@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
|
||||
"video", get_max_llava_onevision_video_tokens)
|
||||
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_onevision)
|
||||
@INPUT_REGISTRY.register_input_processor(input_processor_for_llava_onevision)
|
||||
@MULTIMODAL_REGISTRY.register_processor(LlavaOnevisionMultiModalProcessor)
|
||||
class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
|
||||
SupportsPP):
|
||||
|
||||
|
||||
Reference in New Issue
Block a user