663 lines
26 KiB
Python
663 lines
26 KiB
Python
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 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, LlavaNextConfig, SiglipVisionConfig
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from transformers.models.llava_next.modeling_llava_next import (
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get_anyres_image_grid_shape, unpad_image)
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from typing_extensions import NotRequired
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, MultiModalConfig, PoolerConfig
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from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
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InputContext)
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from vllm.model_executor.layers.pooler import Pooler, PoolingType
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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.pooling_metadata import PoolingMetadata
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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.sequence import IntermediateTensors, PoolerOutput
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from vllm.utils import is_list_of
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from .clip import (CLIPVisionModel, dummy_image_for_clip,
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dummy_seq_data_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 LlavaMultiModalProjector, 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, 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, embed_multimodal, flatten_bn,
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init_vllm_registered_model)
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class LlavaNextImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: Union[torch.Tensor, List[torch.Tensor]]
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"""
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Shape:
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`(batch_size * num_images, 1 + num_patches, num_channels, height, width)`
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Note that `num_patches` may be different per batch and image,
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in which case the data is passed as a list instead of a batched tensor.
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"""
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image_sizes: NotRequired[torch.Tensor]
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"""
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Shape: `(batch_size * num_images, 2)`
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This should be in `(height, width)` format.
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"""
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class LlavaNextImageEmbeddingInputs(TypedDict):
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type: Literal["image_embeds"]
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data: torch.Tensor
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"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
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`hidden_size` must match the hidden size of language model backbone.
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"""
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LlavaNextImageInputs = Union[LlavaNextImagePixelInputs,
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LlavaNextImageEmbeddingInputs]
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# Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L79
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def _get_llava_next_num_unpadded_features(
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original_height: int,
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original_width: int,
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npatches: int,
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num_patch_height: int,
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num_patch_width: int,
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) -> Tuple[int, int]:
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current_height = npatches * num_patch_height
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current_width = npatches * num_patch_width
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original_aspect_ratio = original_width / original_height
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current_aspect_ratio = current_width / current_height
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if original_aspect_ratio > current_aspect_ratio:
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scale_factor = current_width / original_width
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new_height = int(original_height * scale_factor)
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padding = (current_height - new_height) // 2
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current_height -= 2 * padding
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else:
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scale_factor = current_height / original_height
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new_width = int(original_width * scale_factor)
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padding = (current_width - new_width) // 2
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current_width -= 2 * padding
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unpadded_features = current_height * current_width
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newline_features = current_height
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return (unpadded_features, newline_features)
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# Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L106
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def get_llava_next_image_feature_size(
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hf_config: LlavaNextConfig,
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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_next_num_unpadded_features(input_height, input_width,
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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_next_image_tokens(ctx: InputContext):
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"""Compute the max feature size for all possible image grid pinpoints."""
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return _get_pinpoint_with_largest_features(ctx)[0]
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def _get_pinpoint_with_largest_features(
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ctx: InputContext) -> Tuple[int, Tuple[int, int]]:
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"""Get the grid pinpoint with the largest features & its feature size."""
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hf_config = ctx.get_hf_config(LlavaNextConfig)
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largest_feature_size = 0
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largest_feature_pinpoint = None
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for (height, width) in hf_config.image_grid_pinpoints:
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feat_size = get_llava_next_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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if feat_size > largest_feature_size:
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largest_feature_size = feat_size
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largest_feature_pinpoint = (height, width)
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if not largest_feature_size or largest_feature_pinpoint is None:
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raise ValueError("Cannot have a largest feature size of 0!")
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return largest_feature_size, largest_feature_pinpoint
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def dummy_data_for_llava_next(ctx: InputContext, seq_len: int,
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mm_counts: Mapping[str, int]):
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hf_config = ctx.get_hf_config(LlavaNextConfig)
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vision_config = hf_config.vision_config
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num_images = mm_counts["image"]
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image_feature_size, pinpoint = _get_pinpoint_with_largest_features(ctx)
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max_feat_height, max_feat_width = pinpoint
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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_images,
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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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mm_data = dummy_image_for_clip(
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vision_config,
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num_images,
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image_width_override=max_feat_width,
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image_height_override=max_feat_height,
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)
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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_images,
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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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mm_data = dummy_image_for_siglip(
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vision_config,
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num_images,
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image_width_override=max_feat_width,
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image_height_override=max_feat_height,
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)
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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(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(LlavaNextConfig)
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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_next_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_next_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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@MULTIMODAL_REGISTRY.register_image_input_mapper()
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@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_next_image_tokens)
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@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_next)
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@INPUT_REGISTRY.register_input_processor(input_processor_for_llava_next)
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class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
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SupportsPP):
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def __init__(self,
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config: LlavaNextConfig,
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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,
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pooler_config: Optional[PoolerConfig] = 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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# TODO: Optionally initializes this for supporting embeddings.
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self.vision_tower = init_vision_tower_for_llava(
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config,
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quant_config,
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require_post_norm=False,
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prefix="vision_tower")
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self.image_newline = nn.Parameter(
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torch.empty(config.text_config.hidden_size))
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self.multi_modal_projector = LlavaMultiModalProjector(
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vision_hidden_size=config.vision_config.hidden_size,
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text_hidden_size=config.text_config.hidden_size,
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projector_hidden_act=config.projector_hidden_act)
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self.language_model = init_vllm_registered_model(
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config.text_config,
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cache_config,
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quant_config,
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prefix="language_model")
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# The same model class supports both language generation and embedding
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# because the architecture name is the same
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self._pooler = Pooler.from_config_with_defaults(
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pooler_config,
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pooling_type=PoolingType.LAST,
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normalize=True,
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softmax=False)
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors)
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@cached_property
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def sampler(self):
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if hasattr(self.language_model, "sampler"):
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return self.language_model.sampler
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return Sampler()
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def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
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expected_dims = (2, )
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def _validate_shape(d: torch.Tensor):
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actual_dims = tuple(d.shape)
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if actual_dims != expected_dims:
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expected_expr = str(expected_dims)
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raise ValueError(
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f"The expected shape of image sizes per image per batch "
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f"is {expected_expr}. You supplied {tuple(d.shape)}.")
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for d in data:
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_validate_shape(d)
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return data
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def _validate_pixel_values(
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self, data: Union[torch.Tensor, List[torch.Tensor]]
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) -> Union[torch.Tensor, List[torch.Tensor]]:
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h = w = self.config.vision_config.image_size
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expected_dims = (3, h, w)
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def _validate_shape(d: torch.Tensor):
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actual_dims = tuple(d.shape[1:])
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if actual_dims != expected_dims:
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expected_expr = ("num_patches", *map(str, expected_dims))
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raise ValueError(
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"The expected shape of pixel values per image per batch "
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f"is {expected_expr}. You supplied {tuple(d.shape)}.")
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for d in data:
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_validate_shape(d)
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return data
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def _parse_and_validate_image_input(
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self, **kwargs: object) -> Optional[LlavaNextImageInputs]:
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pixel_values = kwargs.pop("pixel_values", None)
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image_sizes = kwargs.pop("image_sizes", None)
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image_embeds = kwargs.pop("image_embeds", None)
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if pixel_values is None and image_embeds is None:
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return None
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if pixel_values is not None:
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if not isinstance(pixel_values, (torch.Tensor, list)):
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raise ValueError("Incorrect type of pixel values. "
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f"Got type: {type(pixel_values)}")
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if not isinstance(image_sizes, (torch.Tensor, list)):
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raise ValueError("Incorrect type of image sizes. "
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f"Got type: {type(image_sizes)}")
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return LlavaNextImagePixelInputs(
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type="pixel_values",
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data=self._validate_pixel_values(flatten_bn(pixel_values)),
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image_sizes=self._validate_image_sizes(
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flatten_bn(image_sizes, concat=True)),
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)
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if image_embeds is not None:
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if not isinstance(image_embeds, torch.Tensor):
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raise ValueError("Incorrect type of image embeds. "
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f"Got type: {type(image_embeds)}")
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return LlavaNextImageEmbeddingInputs(
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type="image_embeds",
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data=flatten_bn(image_embeds),
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)
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raise AssertionError("This line should be unreachable.")
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def _select_image_features(self, image_features: torch.Tensor, *,
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strategy: str) -> torch.Tensor:
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# Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421 # noqa
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if strategy == "default":
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return image_features[:, 1:]
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elif strategy == "full":
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return image_features
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raise ValueError(f"Unexpected select feature strategy: {strategy}")
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def _image_pixels_to_features(
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self,
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vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
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pixel_values: torch.Tensor,
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) -> torch.Tensor:
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# NOTE: we skip the step to select the vision feature layer since
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# this is already done inside the vision tower
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image_features = vision_tower(pixel_values)
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return self._select_image_features(
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image_features,
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strategy=self.config.vision_feature_select_strategy,
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)
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# Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
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def _merge_image_patch_embeddings(self, image_size: torch.Tensor,
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patch_embeddings: torch.Tensor, *,
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strategy: str) -> torch.Tensor:
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if strategy == "flat":
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return patch_embeddings.flatten(0, 1)
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if strategy.startswith("spatial"):
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height = width = self.config.vision_config.image_size \
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// self.config.vision_config.patch_size
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base_patch_embeds = patch_embeddings[0]
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if height * width != base_patch_embeds.shape[0]:
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raise ValueError(
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"The number of patches is not consistent with the "
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"image size.")
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if patch_embeddings.shape[0] > 1:
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other_patch_embeds = patch_embeddings[1:]
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# Move to CPU to avoid floating-point errors
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orig_height, orig_width = image_size.tolist()
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# image_aspect_ratio == "anyres"
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num_patch_height, num_patch_width = get_anyres_image_grid_shape(
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(orig_height, orig_width),
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self.config.image_grid_pinpoints,
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self.config.vision_config.image_size,
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)
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num_patches = num_patch_height * num_patch_width
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# Image patches might be padded for batch processing
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other_patch_embeds = other_patch_embeds[:num_patches] \
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.view(num_patch_height, num_patch_width, height, width, -1)
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if "unpad" in strategy:
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other_patch_embeds = other_patch_embeds \
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.permute(4, 0, 2, 1, 3).contiguous() \
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.flatten(1, 2).flatten(2, 3)
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other_patch_embeds = unpad_image(other_patch_embeds,
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(orig_height, orig_width))
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other_patch_embeds = torch.cat((
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other_patch_embeds,
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self.image_newline[:, None, None] \
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|
.expand(*other_patch_embeds.shape[:-1], 1) \
|
|
.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: LlavaNextImagePixelInputs,
|
|
) -> 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: LlavaNextImageInputs,
|
|
) -> 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,
|
|
strategy="spatial_unpad")
|
|
for i, patch_features_batch in enumerate(patch_embeddings)
|
|
]
|
|
|
|
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-NeXT.
|
|
|
|
One key thing to understand is the `input_ids` already accounts for the
|
|
positions of the to-be-inserted image embeddings.
|
|
|
|
Concretely, consider a text prompt:
|
|
`"A chat between a curious human and an artificial intelligence
|
|
assistant. The assistant gives helpful, detailed, and polite answers to
|
|
the human's questions.
|
|
USER: <image>\\nWhat is shown in this image? ASSISTANT:"`.
|
|
|
|
Tokenizer outputs:
|
|
`[1, 319, 13563, 1546, 263, 12758, 5199, 322, 385, 23116, 21082, 20255,
|
|
29889, 450, 20255, 4076, 8444, 29892, 13173, 29892, 322, 1248, 568,
|
|
6089, 304, 278, 5199, 29915, 29879, 5155, 29889, 3148, 1001, 29901,
|
|
29871, 32000, 13, 5618, 338, 4318, 297, 445, 1967, 29973, 319, 1799,
|
|
9047, 13566, 29901]`.
|
|
|
|
To reserve space in KV cache, we have to insert placeholder tokens
|
|
before they are inputted to the model, so the input processor prepends
|
|
additional image tokens (denoted as `32000`), resulting in:
|
|
`[1, 319, 13563, 1546, 263, 12758, 5199, 322, 385, 23116, 21082, 20255,
|
|
29889, 450, 20255, 4076, 8444, 29892, 13173, 29892, 322, 1248, 568,
|
|
6089, 304, 278, 5199, 29915, 29879, 5155, 29889, 3148, 1001, 29901,
|
|
29871, 32000, ..., 32000, 13, 5618, 338, 4318, 297, 445, 1967, 29973,
|
|
319, 1799, 9047, 13566, 29901]`.
|
|
|
|
Unlike in LLaVA-1.5, the number of image tokens inputted to the language
|
|
model depends on the original size of the input image. Including the
|
|
original image token in the input, the required number of image tokens
|
|
is given by :func:`get_llava_next_image_feature_size`.
|
|
|
|
This way, the `positions` and `attn_metadata` are consistent
|
|
with the `input_ids`.
|
|
|
|
Args:
|
|
input_ids: Flattened (concatenated) input_ids corresponding to a
|
|
batch.
|
|
pixel_values: The pixels in each grid patch for each input image.
|
|
image_sizes: The original `(height, width)` for each input image.
|
|
|
|
See also:
|
|
:class:`LlavaNextImageInputs`
|
|
"""
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
else:
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
|
|
if image_input is not None:
|
|
inputs_embeds = embed_multimodal(
|
|
input_ids,
|
|
self.config.image_token_index,
|
|
self.language_model.model.get_input_embeddings,
|
|
lambda _: self._process_image_input(image_input),
|
|
)
|
|
else:
|
|
inputs_embeds = self.language_model.model.get_input_embeddings(
|
|
input_ids)
|
|
|
|
# always pass the input via `inputs_embeds`
|
|
# to make sure the computation graph is consistent
|
|
# for `torch.compile` integration
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model.model(input_ids,
|
|
positions,
|
|
kv_caches,
|
|
attn_metadata,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
return self.language_model.sample(logits, sampling_metadata)
|
|
|
|
def pooler(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> Optional[PoolerOutput]:
|
|
return self._pooler(hidden_states, pooling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
loader = AutoWeightsLoader(self)
|
|
loader.load_weights(weights)
|