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# Copyright 2023 Haotian Liu & Qinghao Ye (Modified from LLaVA)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
from abc import ABC, abstractmethod
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from PIL import Image
dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, dir_path)
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
LlamaForCausalLM,
LlamaModel,
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.models.clip.image_processing_clip import CLIPImageProcessor
from .configuration_mplug_owl2 import (
MPLUGOwl2Config,
MplugOwlVisionConfig,
MplugOwlVisualAbstractorConfig
)
from .modeling_attn_mask_utils import _prepare_4d_causal_attention_mask # Force copy
from .modeling_llama2 import replace_llama_modality_adaptive
from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN = "<|image|>"
def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split(DEFAULT_IMAGE_TOKEN)]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == 'pt':
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f'Unsupported tensor type: {return_tensors}')
return input_ids
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
class MPLUGOwl2MetaModel:
def __init__(self, config):
super(MPLUGOwl2MetaModel, self).__init__(config)
self.vision_model = MplugOwlVisionModel(
MplugOwlVisionConfig(**config.visual_config["visual_model"])
)
self.visual_abstractor = MplugOwlVisualAbstractorModel(
MplugOwlVisualAbstractorConfig(**config.visual_config["visual_abstractor"]),
config.hidden_size,
)
def get_vision_tower(self):
vision_model = getattr(self, "vision_model", None)
if type(vision_model) is list:
vision_model = vision_model[0]
return vision_model
def get_visual_abstractor(self):
visual_abstractor = getattr(self, "visual_abstractor", None)
if type(visual_abstractor) is list:
visual_abstractor = visual_abstractor[0]
return visual_abstractor
class MPLUGOwl2MetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def encode_images(self, images):
image_features = self.get_model().vision_model(images).last_hidden_state
image_features = (
self.get_model()
.visual_abstractor(encoder_hidden_states=image_features)
.last_hidden_state
)
return image_features
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, images
):
if images is None or input_ids.shape[1] == 1:
if (
past_key_values is not None
and images is not None
and input_ids.shape[1] == 1
):
attention_mask = torch.ones(
(attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1),
dtype=attention_mask.dtype,
device=attention_mask.device,
)
multiway_indices = torch.zeros_like(input_ids).long().to(self.device)
return (
input_ids,
multiway_indices,
attention_mask,
past_key_values,
None,
labels,
)
if type(images) is list or images.ndim == 5:
concat_images = torch.cat([image for image in images], dim=0)
image_features = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
image_features = [x.flatten(0, 1) for x in image_features]
else:
image_features = self.encode_images(images)
new_input_embeds = []
new_modality_indicators = []
new_labels = [] if labels is not None else None
cur_image_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
half_len = cur_input_ids.shape[0] // 2
cur_image_features = image_features[cur_image_idx]
cur_input_embeds_1 = self.get_model().embed_tokens(
cur_input_ids[:half_len]
)
cur_input_embeds_2 = self.get_model().embed_tokens(
cur_input_ids[half_len:]
)
cur_input_embeds = torch.cat(
[cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2],
dim=0,
)
new_input_embeds.append(cur_input_embeds)
cur_modality_indicators = (
torch.zeros(len(cur_input_embeds)).long().to(self.device)
)
new_modality_indicators.append(cur_modality_indicators)
if labels is not None:
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
cur_new_input_embeds = []
cur_modality_indicators = []
if labels is not None:
cur_labels = labels[batch_idx]
cur_new_labels = []
assert cur_labels.shape == cur_input_ids.shape
while image_token_indices.numel() > 0:
cur_image_features = image_features[cur_image_idx]
image_token_start = image_token_indices[0]
cur_new_input_embeds.append(
self.get_model().embed_tokens(cur_input_ids[:image_token_start])
)
cur_new_input_embeds.append(cur_image_features)
cur_modality_indicators.append(
torch.zeros(len(cur_input_ids[:image_token_start])).long()
)
cur_modality_indicators.append(
torch.ones(len(cur_image_features)).long()
)
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(
torch.full(
(cur_image_features.shape[0],),
IGNORE_INDEX,
device=labels.device,
dtype=labels.dtype,
)
)
cur_labels = cur_labels[image_token_start + 1 :]
cur_image_idx += 1
cur_input_ids = cur_input_ids[image_token_start + 1 :]
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
if cur_input_ids.numel() > 0:
cur_new_input_embeds.append(
self.get_model().embed_tokens(cur_input_ids)
)
cur_modality_indicators.append(torch.zeros(len(cur_input_ids)).long())
if labels is not None:
cur_new_labels.append(cur_labels)
cur_new_input_embeds = [
x.to(device=self.device) for x in cur_new_input_embeds
]
cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
new_input_embeds.append(cur_new_input_embeds)
cur_modality_indicators = [
x.to(device=self.device) for x in cur_modality_indicators
]
cur_modality_indicators = torch.cat(cur_modality_indicators, dim=0)
new_modality_indicators.append(cur_modality_indicators)
if labels is not None:
cur_new_labels = torch.cat(cur_new_labels, dim=0)
new_labels.append(cur_new_labels)
if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
max_len = max(x.shape[0] for x in new_input_embeds)
new_input_embeds_align = []
for cur_new_embed in new_input_embeds:
cur_new_embed = torch.cat(
(
cur_new_embed,
torch.zeros(
(max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]),
dtype=cur_new_embed.dtype,
device=cur_new_embed.device,
),
),
dim=0,
)
new_input_embeds_align.append(cur_new_embed)
new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
new_modality_indicators_align = []
for cur_modality_indicator in new_modality_indicators:
cur_new_embed = torch.cat(
(
cur_modality_indicator,
torch.zeros(
max_len - cur_modality_indicator.shape[0],
dtype=cur_modality_indicator.dtype,
device=cur_modality_indicator.device,
),
),
dim=0,
)
new_modality_indicators_align.append(cur_new_embed)
new_modality_indicators = torch.stack(new_modality_indicators_align, dim=0)
if labels is not None:
new_labels_align = []
_new_labels = new_labels
for cur_new_label in new_labels:
cur_new_label = torch.cat(
(
cur_new_label,
torch.full(
(max_len - cur_new_label.shape[0],),
IGNORE_INDEX,
dtype=cur_new_label.dtype,
device=cur_new_label.device,
),
),
dim=0,
)
new_labels_align.append(cur_new_label)
new_labels = torch.stack(new_labels_align, dim=0)
if attention_mask is not None:
new_attention_mask = []
for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(
attention_mask, _new_labels, new_labels
):
new_attn_mask_pad_left = torch.full(
(cur_new_labels.shape[0] - labels.shape[1],),
True,
dtype=attention_mask.dtype,
device=attention_mask.device,
)
new_attn_mask_pad_right = torch.full(
(cur_new_labels_align.shape[0] - cur_new_labels.shape[0],),
False,
dtype=attention_mask.dtype,
device=attention_mask.device,
)
cur_new_attention_mask = torch.cat(
(
new_attn_mask_pad_left,
cur_attention_mask,
new_attn_mask_pad_right,
),
dim=0,
)
new_attention_mask.append(cur_new_attention_mask)
attention_mask = torch.stack(new_attention_mask, dim=0)
assert attention_mask.shape == new_labels.shape
else:
new_input_embeds = torch.stack(new_input_embeds, dim=0)
new_modality_indicators = torch.stack(new_modality_indicators, dim=0)
if labels is not None:
new_labels = torch.stack(new_labels, dim=0)
if attention_mask is not None:
new_attn_mask_pad_left = torch.full(
(
attention_mask.shape[0],
new_input_embeds.shape[1] - input_ids.shape[1],
),
True,
dtype=attention_mask.dtype,
device=attention_mask.device,
)
attention_mask = torch.cat(
(new_attn_mask_pad_left, attention_mask), dim=1
)
assert attention_mask.shape == new_input_embeds.shape[:2]
return (
None,
new_modality_indicators,
attention_mask,
past_key_values,
new_input_embeds,
new_labels,
)
class MPLUGOwl2LlamaModel(MPLUGOwl2MetaModel, LlamaModel):
config_class = MPLUGOwl2Config
def __init__(self, config: MPLUGOwl2Config):
super(MPLUGOwl2LlamaModel, self).__init__(config)
class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausalLM):
config_class = MPLUGOwl2Config
def __init__(self, config):
super(LlamaForCausalLM, self).__init__(config)
self.model = MPLUGOwl2LlamaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Tokenizer and image processor will be initialized lazily in score()
self._tokenizer = None
self._image_processor = None
self._preferential_ids = None
self.post_init()
def _init_processors(self):
"""Lazily initialize tokenizer and image processor from the model's directory."""
if self._tokenizer is None:
# Use the model's name_or_path from config, fallback to HF repo name
model_path = getattr(self.config, '_name_or_path', None)
if model_path is None or model_path == './' or not model_path.startswith(('/', 'http', 'mapo80')):
model_path = "mapo80/DeQA-Doc-Sharpness"
self._tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
self._image_processor = CLIPImageProcessor.from_pretrained(model_path)
self._preferential_ids = [id_[1] for id_ in self._tokenizer(
["excellent", "good", "fair", "poor", "bad"]
)["input_ids"]]
@property
def tokenizer(self):
self._init_processors()
return self._tokenizer
@property
def image_processor(self):
self._init_processors()
return self._image_processor
@property
def preferential_ids_(self):
self._init_processors()
return self._preferential_ids
def get_model(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
(
input_ids,
modality_indicators,
attention_mask,
past_key_values,
inputs_embeds,
labels,
) = self.prepare_inputs_labels_for_multimodal(
input_ids, attention_mask, past_key_values, labels, images
)
outputs = self.model(
input_ids=input_ids,
modality_indicators=modality_indicators,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def score(
self,
images: List[Image.Image],
task_: str = "quality",
input_: str = "image",
) -> torch.Tensor:
"""
Score images based on quality assessment.
Args:
images: List of PIL Images to score
task_: Type of assessment (default: "quality")
input_: Input type - "image" or "video" (default: "image")
Returns:
torch.Tensor: Quality scores (1-5 scale)
"""
if not hasattr(self, "weight_tensor"):
self.weight_tensor = torch.Tensor([5., 4., 3., 2., 1.]).half().to(self.device)
prompt = "USER: How would you rate the {} of this {}?\n<|image|>\nASSISTANT: The {} of the {} is".format(
task_, input_, task_, input_
)
if input_ == "image":
# Process single images
images = [expand2square(img, tuple(int(x*255) for x in self.image_processor.image_mean)) for img in images]
input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
with torch.inference_mode():
image_tensor = self.image_processor.preprocess(images, return_tensors="pt")["pixel_values"].half().to(self.device)
output_logits = self(
input_ids=input_ids.repeat(image_tensor.shape[0], 1),
images=image_tensor
)["logits"][:, -1, self.preferential_ids_]
return torch.softmax(output_logits, -1) @ self.weight_tensor
else:
# Process videos (list of frame sequences)
video = [[expand2square(frame, tuple(int(x*255) for x in self.image_processor.image_mean)) for frame in vid] for vid in images]
input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
with torch.inference_mode():
video_tensors = [
self.image_processor.preprocess(vid, return_tensors="pt")["pixel_values"].half().to(self.device)
for vid in video
]
output_logits = self(
input_ids=input_ids.repeat(len(video_tensors), 1),
images=video_tensors
)["logits"][:, -1, self.preferential_ids_]
return torch.softmax(output_logits, -1) @ self.weight_tensor
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
images=None,
**kwargs,
):
if past_key_values:
input_ids = input_ids[:, -1:]
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"images": images,
}
)
return model_inputs
AutoConfig.register("mplug_owl2", MPLUGOwl2Config)
AutoModelForCausalLM.register(MPLUGOwl2Config, MPLUGOwl2LlamaForCausalLM)
replace_llama_modality_adaptive()
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