850 lines
33 KiB
Python
850 lines
33 KiB
Python
import math
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from functools import cached_property
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from typing import (Iterable, List, Literal, Mapping, Optional, 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.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 CacheConfig, MultiModalConfig
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from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
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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.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
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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.utils import (cached_get_tokenizer,
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repeat_and_pad_placeholder_tokens)
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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 .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 .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
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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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_MAX_NUM_VIDEOS = 1
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class LlavaOnevisionVideoPixelInputs(TypedDict):
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type: Literal["pixel_values_videos"]
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data: Union[torch.Tensor, List[torch.Tensor]]
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"""
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Shape: `(batch_size, num_frames, num_channels, height, width)`
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Note that `num_frames` may be different for each batch, in which case
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the data is passed as a list instead of a batched tensor.
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Note that it only supports one video input for one batch.
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"""
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class LlavaOnevisionImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: Union[torch.Tensor, List[torch.Tensor]]
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"""
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Shape:
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`(batch_size * num_images, 1 + num_patches, num_channels, height, width)`
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Note that `num_patches` may be different per batch and image,
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in which case the data is passed as a list instead of a batched tensor.
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"""
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image_sizes: NotRequired[torch.Tensor]
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"""
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Shape: `(batch_size * num_images, 2)`
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This should be in `(height, width)` format.
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"""
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class 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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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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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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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 * 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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return (unpadded_features, newline_features)
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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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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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)
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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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)
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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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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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(
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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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return unpadded_feature_size + newline_feature_size + base_feature_size
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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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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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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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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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# 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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return video_feature_size
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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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# 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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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 = 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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)
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mm_data = dummy_video_for_clip(vision_config, num_frames=num_frames)
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return seq_data, mm_data
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elif isinstance(vision_config, SiglipVisionConfig):
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seq_data = 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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)
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mm_data = dummy_video_for_siglip(vision_config, num_frames=num_frames)
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return seq_data, mm_data
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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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)
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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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]
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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):
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image_feature_size = [item.shape[1] for item in image_data]
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else:
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raise TypeError(f"Invalid image type: {type(image_data)}")
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vision_config = hf_config.vision_config
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if isinstance(vision_config, CLIPVisionConfig):
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return input_processor_for_clip(
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model_config,
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vision_config,
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inputs,
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image_token_id=hf_config.image_token_index,
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image_feature_size_override=image_feature_size,
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)
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elif isinstance(vision_config, SiglipVisionConfig):
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return input_processor_for_siglip(
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model_config,
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vision_config,
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inputs,
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image_token_id=hf_config.image_token_index,
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image_feature_size_override=image_feature_size,
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)
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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_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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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(LlavaOnevisionConfig)
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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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video_feature_size = get_llava_onevision_video_tokens(ctx, num_frames)
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tokenizer = cached_get_tokenizer(model_config.tokenizer)
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new_prompt, new_token_ids = 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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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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def input_processor_for_llava_onevision(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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and "image" not in multi_modal_data):
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return inputs
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if "image" in multi_modal_data:
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return input_processor_when_multimodal_input_image(ctx, inputs)
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if "video" in multi_modal_data:
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return input_processor_when_multimodal_input_video(ctx, inputs)
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msg = "Unsupported multi data type"
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raise NotImplementedError(msg)
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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=True)
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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=True)
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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_image_input_mapper()
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@MULTIMODAL_REGISTRY.register_input_mapper("video")
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@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
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"image", get_max_llava_onevision_image_tokens)
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@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
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"video", get_max_llava_onevision_video_tokens)
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@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_onevision)
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@INPUT_REGISTRY.register_input_processor(input_processor_for_llava_onevision)
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class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
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SupportsPP):
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def __init__(self,
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config: LlavaOnevisionConfig,
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multimodal_config: MultiModalConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None) -> None:
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super().__init__()
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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, quant_config, require_post_norm=False)
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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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config.text_config, cache_config, quant_config)
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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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@cached_property
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def sampler(self):
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if hasattr(self.language_model, "sampler"):
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return self.language_model.sampler
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return Sampler()
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def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
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expected_dims = (2, )
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def _validate_shape(d: torch.Tensor):
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actual_dims = tuple(d.shape)
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if actual_dims != expected_dims:
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expected_expr = str(expected_dims)
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raise ValueError(
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f"The expected shape of image sizes per image per batch "
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f"is {expected_expr}. You supplied {tuple(d.shape)}.")
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for d in data:
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_validate_shape(d)
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return data
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def _validate_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:
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if not isinstance(pixel_values, (torch.Tensor, list)):
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raise ValueError("Incorrect type of pixel values. "
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f"Got type: {type(pixel_values)}")
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if not isinstance(image_sizes, (torch.Tensor, list)):
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raise ValueError("Incorrect type of image sizes. "
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f"Got type: {type(image_sizes)}")
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return LlavaOnevisionImagePixelInputs(
|
|
type="pixel_values",
|
|
data=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 = kwargs.pop("pixel_values_videos", None)
|
|
|
|
if pixel_values is None:
|
|
return None
|
|
|
|
if not (is_list_of(pixel_values,
|
|
(torch.Tensor)) # different shape videos
|
|
or isinstance(pixel_values,
|
|
torch.Tensor)): # same shape videos
|
|
raise ValueError("Incorrect type of pixel values. "
|
|
f"Got type: {type(pixel_values)}")
|
|
|
|
return LlavaOnevisionVideoPixelInputs(
|
|
type="pixel_values_videos",
|
|
data=pixel_values,
|
|
)
|
|
|
|
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
|
|
modalities = {}
|
|
|
|
if "pixel_values" in kwargs:
|
|
modalities["images"] = self._parse_and_validate_image_input(
|
|
**kwargs)
|
|
|
|
if "pixel_values_videos" in kwargs:
|
|
modalities["videos"] = self._parse_and_validate_video_input(
|
|
**kwargs)
|
|
|
|
return modalities
|
|
|
|
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["data"]
|
|
|
|
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["data"])
|
|
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
|
|
b, num_videos, frames, c, h, w = pixel_values.shape
|
|
assert (num_videos == _MAX_NUM_VIDEOS)
|
|
pixel_values = pixel_values.reshape(b * num_videos * frames, c, h, w)
|
|
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)
|
|
video_features = video_features.reshape(
|
|
b, frames * video_features.shape[1], -1)
|
|
image_newline = self.image_newline[None, None, :].repeat(b, 1, 1).to(
|
|
video_features.device)
|
|
video_features = torch.cat((video_features, image_newline), dim=1)
|
|
video_features = video_features.flatten(0, 1)
|
|
|
|
return video_features
|
|
|
|
def _process_video_pixels(self, inputs: LlavaOnevisionVideoPixelInputs):
|
|
assert self.vision_tower is not None
|
|
|
|
video_pixels = inputs["data"]
|
|
|
|
# TODO: support multiple videos per input
|
|
if isinstance(video_pixels, torch.Tensor):
|
|
stacked_embeddings = self._video_pixels_to_features(
|
|
self.vision_tower, video_pixels)
|
|
return stacked_embeddings
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported type of video input {type(video_pixels)}")
|
|
|
|
def apply_pooling(self, image_features, stride=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 forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = 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:
|
|
input_ids = None
|
|
inputs_embeds = None
|
|
else:
|
|
modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
|
|
if modalities:
|
|
inputs_embeds = self.language_model.model.get_input_embeddings(
|
|
input_ids)
|
|
if "images" in modalities:
|
|
image_input = modalities["images"]
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, vision_embeddings,
|
|
self.config.image_token_index)
|
|
if "videos" in modalities:
|
|
video_input = modalities["videos"]
|
|
video_embeddings = self._process_video_pixels(video_input)
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, video_embeddings,
|
|
self.config.video_token_index)
|
|
input_ids = None
|
|
else:
|
|
inputs_embeds = None
|
|
|
|
hidden_states = self.language_model.model(input_ids,
|
|
positions,
|
|
kv_caches,
|
|
attn_metadata,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
return self.language_model.sample(logits, sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
loader = AutoWeightsLoader(self)
|
|
loader.load_weights(weights)
|