[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
@@ -3,38 +3,32 @@ 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 transformers import (CLIPVisionConfig, LlavaNextVideoConfig,
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SiglipVisionConfig)
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from transformers import (BatchFeature, LlavaNextVideoConfig,
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LlavaNextVideoProcessor)
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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.models.clip import CLIPVisionModel
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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 (ImageSize, MultiModalDataItems,
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VideoEmbeddingItems, 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 dummy_image_for_clip, dummy_seq_data_for_clip
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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_image_for_siglip,
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dummy_seq_data_for_siglip)
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from .siglip import SiglipVisionModel
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from .utils import (AutoWeightsLoader, 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 = 32
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_MAX_NUM_VIDEOS = 1
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from .vision import BaseVisionLanguageMultiModalProcessor
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class LlavaNextVideoPixelInputs(TypedDict):
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@@ -50,144 +44,149 @@ class LlavaNextVideoPixelInputs(TypedDict):
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"""
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def get_llava_next_video_frame_feature_size(
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hf_config: LlavaNextVideoConfig) -> 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
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class LlavaNextVideoMultiModalProcessor(BaseVisionLanguageMultiModalProcessor):
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return int((image_size / patch_size / spatial_pool_stride)**2)
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def _get_hf_config(self) -> LlavaNextVideoConfig:
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return self.ctx.get_hf_config(LlavaNextVideoConfig)
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def _get_hf_processor(self) -> LlavaNextVideoProcessor:
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return self.ctx.get_hf_processor(LlavaNextVideoProcessor)
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def _get_max_llm_tokens(ctx: InputContext) -> int:
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"""
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Calculated from the maximum video frames under the context length
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constraints of the language model.
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"""
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hf_text_config = ctx.model_config.hf_text_config
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model_config = ctx.model_config
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max_tokens = model_config.max_model_len
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rope_scaling = model_config.rope_scaling
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"video": 1}
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if rope_scaling:
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rope_scaling_factor = hf_text_config.rope_scaling["factor"]
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else:
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rope_scaling_factor = 1
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def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
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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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max_tokens *= rope_scaling_factor
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return {"video": max_video_tokens}
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return max_tokens
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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(pixel_values_videos=MultiModalFieldConfig.batched("video"))
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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 = hf_config.spatial_pool_stride
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def get_max_llava_next_video_tokens(ctx: InputContext) -> int:
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# Currently set to 32 frames
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# TODO: max_tokens = _get_max_llm_tokens(ctx)
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hf_config = ctx.get_hf_config(LlavaNextVideoConfig)
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tokens_per_frame = get_llava_next_video_frame_feature_size(hf_config)
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return _MAX_FRAMES_PER_VIDEO * tokens_per_frame
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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 dummy_data_for_llava_next_video(ctx: InputContext, seq_len: int,
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mm_counts: Mapping[str, int]):
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hf_config = ctx.get_hf_config(LlavaNextVideoConfig)
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vision_config = hf_config.vision_config
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# TODO: support multiple videos
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num_videos = mm_counts["video"]
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if num_videos != _MAX_NUM_VIDEOS:
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raise NotImplementedError(
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f"Only {_MAX_NUM_VIDEOS} videos are supported")
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# TODO: support configuring the number of frames
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frames_per_video = _MAX_FRAMES_PER_VIDEO
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# num_images = num_videos * frames_per_video
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# fills the sequence with as longer video data as possible
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tokens_per_frame = get_llava_next_video_frame_feature_size(hf_config)
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video_feature_size = frames_per_video * tokens_per_frame
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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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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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pil_frame = dummy_image_for_clip(vision_config, num_images=1)
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np_frame = np.array(pil_frame["image"])
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mm_data_per_video = np.repeat([np_frame], frames_per_video, axis=0)
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mm_data = {"video": mm_data_per_video}
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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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return num_frame_tokens * num_frames
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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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def _get_max_video_frames(self, max_tokens: int) -> int:
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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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if self._get_max_video_tokens(next_num_frames) > 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_dummy_num_frames(self, seq_len: int) -> int:
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mm_config = self.ctx.get_mm_config()
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max_videos = mm_config.limit_per_prompt.get("video", 1)
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max_total_frames = self._get_max_video_frames(seq_len)
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return max(max_total_frames // max(max_videos, 1), 1)
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def _get_dummy_image_size(self) -> ImageSize:
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image_size = self._vision_encoder_info.get_image_size()
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return ImageSize(image_size, image_size)
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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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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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hf_config = self._get_hf_config()
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video_token_id = hf_config.video_token_index
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def get_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._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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PromptReplacement(
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modality="video",
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target=[video_token_id],
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replacement=get_replacement,
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),
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]
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def _get_dummy_processor_inputs(
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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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) -> ProcessorInputs:
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num_videos = mm_counts.get("video", 0)
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video_token = self._get_video_token()
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target_width, target_height = self._get_dummy_image_size()
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mm_data = {
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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=self._get_dummy_num_frames(seq_len),
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num_videos=num_videos,
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)
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}
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return ProcessorInputs(
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prompt_text=video_token * num_videos,
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mm_data=mm_data,
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)
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pil_frame = dummy_image_for_siglip(vision_config, num_images=1)
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np_frame = np.array(pil_frame["image"])
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mm_data_per_video = np.repeat([np_frame], frames_per_video, axis=0)
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mm_data = {"video": mm_data_per_video}
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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_for_llava_next_video(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 "video" not in multi_modal_data:
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return inputs
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if "multi_modal_placeholders" in inputs and "video" in inputs[
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"multi_modal_placeholders"]:
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# The inputs already have placeholders.
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return inputs
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video_data = multi_modal_data["video"]
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model_config = ctx.model_config
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hf_config = ctx.get_hf_config(LlavaNextVideoConfig)
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vision_config = hf_config.vision_config
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if isinstance(video_data, np.ndarray):
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# Supports both CLIP and Siglip
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num_frames = video_data.shape[0]
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frame_feature_size = \
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get_llava_next_video_frame_feature_size(hf_config)
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video_feature_size = num_frames * frame_feature_size
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tokenizer = cached_get_tokenizer(model_config.tokenizer)
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new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
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tokenizer,
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inputs.get("prompt"),
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inputs["prompt_token_ids"],
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placeholder_token_id=hf_config.video_token_index,
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repeat_count=video_feature_size,
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)
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return token_inputs(prompt_token_ids=new_token_ids,
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prompt=new_prompt,
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multi_modal_data=multi_modal_data,
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multi_modal_placeholders={"video": ranges})
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elif is_list_of(video_data, np.ndarray):
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raise NotImplementedError(
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"Processing multiple videos is not supported")
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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# adopted from transformers modeling_llava_next_video.py
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class LlavaNextVideoPooler(nn.Module):
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@@ -246,11 +245,7 @@ class LlavaNextMultiModalProjector(nn.Module):
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return hidden_states
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@MULTIMODAL_REGISTRY.register_input_mapper("video")
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@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
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"video", get_max_llava_next_video_tokens)
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@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_next_video)
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@INPUT_REGISTRY.register_input_processor(input_processor_for_llava_next_video)
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@MULTIMODAL_REGISTRY.register_processor(LlavaNextVideoMultiModalProcessor)
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class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
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SupportsPP):
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