Source code for pyiqa.archs.q_align.cmp_modelling_mplug_owl2
# 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.
from abc import ABC, abstractmethod
from typing import List, Optional, Tuple, Union
from datasets import load_dataset
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
import numpy as np
from PIL import Image
from transformers import AutoConfig, AutoModelForCausalLM, CLIPImageProcessor
from transformers.models.llama import LlamaTokenizer
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig
from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel
from .modeling_llama2 import LlamaModel, LlamaForCausalLM, replace_llama_modality_adaptive
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def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
prompt_chunks = [tokenizer(chunk).input_ids if len(chunk) > 0 else [] 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
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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
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def optimize_score_map_pytorch_cuda(c, seed=0, original_seed=20020, num_iterations=100):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.manual_seed(seed)
np.random.seed(seed)
c = torch.tensor(c, dtype=torch.float32, device=device, requires_grad=False)
initial_scores = torch.rand(c.shape[0], device=device, requires_grad=True)
optimizer = torch.optim.Adam([initial_scores], lr=0.1)
for _ in range(num_iterations):
optimizer.zero_grad()
sum_log_diff = torch.sum(c * torch.log(torch.maximum(norm_cdf(initial_scores[:, None] - initial_scores), torch.tensor(1e-6, device=device))))
sum_squares = torch.sum(initial_scores ** 2) / 2
loss = -(sum_log_diff - sum_squares)
loss.backward()
optimizer.step()
optimized_scores = initial_scores.detach().cpu().numpy()
min_score, max_score = np.min(optimized_scores), np.max(optimized_scores)
# Scale scores to 0-100
scaled_scores = 100 * (optimized_scores - min_score) / (max_score - min_score)
# Reset the seed
np.random.seed(original_seed)
return torch.tensor(scaled_scores[-1], device=device)
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def softmax(logits):
# exp_logits = np.exp(logits - np.max(logits))
probs = np.exp(logits) / np.sum(np.exp(logits))
return probs
# return exp_logits / exp_logits.sum()
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def update_matrix(anchor_matrix, scores, indices):
n = anchor_matrix.shape[0]
new_row = np.zeros((1, n))
new_col = np.zeros((n + 1, 1))
new_row[0, indices] = scores
new_col[indices, 0] = 1-scores # Assuming symmetric preference for simplicity
anchor_matrix = np.vstack([anchor_matrix, new_row])
anchor_matrix = np.hstack([anchor_matrix, new_col])
anchor_matrix[n, n] = 0.5
return anchor_matrix
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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
)
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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
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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
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class MPLUGOwl2MetaForCausalLM(ABC):
@abstractmethod
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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
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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:
# multimodal LLM, but the current sample is not multimodal
# FIXME: this is a hacky fix, for deepspeed zero3 to work
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)
# Add modality indicator
assert image_token_start == len(cur_input_ids[:image_token_start])
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)
# Modality
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)
# Embedding
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)
# Modality
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)
# Label
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)
# Attention Mask
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
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class MPLUGOwl2LlamaModel(MPLUGOwl2MetaModel, LlamaModel):
config_class = MPLUGOwl2Config
def __init__(self, config: MPLUGOwl2Config):
super(MPLUGOwl2LlamaModel, self).__init__(config)
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class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausalLM):
config_class = MPLUGOwl2Config
def __init__(self, config):
super(LlamaForCausalLM, self).__init__(config)
self.model = MPLUGOwl2LlamaModel(config)
self.tokenizer = LlamaTokenizer.from_pretrained("VQA-CityU/Compare2Score_1", trust_remote_code=True)
self.image_processor = CLIPImageProcessor.from_pretrained("VQA-CityU/Compare2Score_1", trust_remote_code=True)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.preferential_ids_ = [id_[1] for id_ in self.tokenizer(["inferior", "worse", "similar", "better", "superior"])["input_ids"]]
self.anchor_images = load_dataset("VQA-CityU/Anchor_images")
self.weight_tensor = np.array([0., 0.25, 0.5, 0.75, 1.], dtype=np.float16)
self.anchor_matrix = np.array(
[[5.0000000e-01, 2.5912809e-01, 3.3130276e-04, 1.6087297e-06, 1.1803027e-09],
[7.4087191e-01, 5.0000000e-01, 2.4985345e-01, 9.9954158e-02, 1.8675303e-08],
[9.9966872e-01, 7.5014657e-01, 5.0000000e-01, 4.9968880e-01, 2.4852838e-01],
[9.9999839e-01, 9.0004587e-01, 5.0031120e-01, 5.0000000e-01, 2.5400183e-01],
[1.0000000e+00, 1.0000000e+00, 7.5147164e-01, 7.4599814e-01, 5.0000000e-01]],
dtype=np.float32)
anchor_intervals = 5#16
num_anchor_image_per_interval = 1
num_anchor_image = anchor_intervals * num_anchor_image_per_interval
self.anchor_indices = np.arange(0,num_anchor_image)
# Initialize weights and apply final processing
self.post_init()
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def score(self, image):
prompt = "USER: <|image|> <|image|> Compared with the first image, what is your quality rating for second image? \nASSISTANT: The quality of the second image is"
input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
anchor_images = [item['image'] for item in self.anchor_images['train']]
probabilities = []
for index in self.anchor_indices:
anchor_image = anchor_images[index]
images = [anchor_image, image]
images = [expand2square(img, tuple(int(x*255) for x in self.image_processor.image_mean)) for img in images]
image_tensor = self.image_processor.preprocess(images, return_tensors='pt')['pixel_values'].half().to(self.device)
with torch.inference_mode():
output_logits = self(input_ids, images=image_tensor)["logits"][:, -1, self.preferential_ids_]
output_logits = output_logits.cpu().detach().numpy() / 100
probabilities.append(np.dot(softmax(output_logits), self.weight_tensor))
updated_matrix = update_matrix(self.anchor_matrix, np.squeeze(np.array(probabilities)), self.anchor_indices)
score = optimize_score_map_pytorch_cuda(updated_matrix, seed=0, original_seed=20020, num_iterations=100)
return score
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def forward(
self,
input_ids: torch.LongTensor = None,
# modality_indicators: 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)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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 so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model/pipeline parallelism
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,
)
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def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values:
input_ids = input_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
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": kwargs.get("images", None),
}
)
return model_inputs
AutoConfig.register("mplug_owl2", MPLUGOwl2Config)
AutoModelForCausalLM.register(MPLUGOwl2Config, MPLUGOwl2LlamaForCausalLM)
replace_llama_modality_adaptive()