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import os
import logging
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.distributed as dist
from torch import nn, Tensor
from transformers import AutoModel, AutoTokenizer, AutoConfig
from transformers.file_utils import ModelOutput
from visual_bge.eva_clip import create_eva_vision_and_transforms
from PIL import Image
logger = logging.getLogger(__name__)
@dataclass
class EncoderOutput(ModelOutput):
q_reps: Optional[Tensor] = None
c_reps: Optional[Tensor] = None
loss: Optional[Tensor] = None
scores: Optional[Tensor] = None
class Visualized_BGE(nn.Module):
def __init__(self,
model_name_bge: str = None,
model_weight = None, # "/path/to/your/weight/file/"
normlized: bool = True,
sentence_pooling_method: str = 'cls',
negatives_cross_device: bool = False,
temperature: float = 0.02, # 1.0
from_pretrained=None, # local config file and model
):
super().__init__()
assert 'bge' in model_name_bge
assert model_weight is not None
self.model_name_bge = model_name_bge
if 'bge-base-en-v1.5' in model_name_bge:
model_name_eva = "EVA02-CLIP-B-16"
self.hidden_dim = 768
self.depth = 12
elif 'bge-m3' in model_name_bge:
model_name_eva = "EVA02-CLIP-L-14"
self.hidden_dim = 1024
self.depth = 24
else:
raise Exception(f'Unavailable model_name {model_name_bge}')
if not from_pretrained:
bge_config = AutoConfig.from_pretrained(model_name_bge)
bge = AutoModel.from_config(bge_config)
else:
print("Loading from local path.")
bge_config = AutoConfig.from_pretrained(from_pretrained, local_files_only=True)
bge = AutoModel.from_config(bge_config)
self.bge_encoder = bge.encoder
self.bge_embeddings = bge.embeddings
self.bge_pooler = bge.pooler
self.model_visual, self.preprocess_train, self.preprocess_val= create_eva_vision_and_transforms(
model_name_eva,
force_custom_clip=True)
self.visual_proj = nn.Linear(self.hidden_dim, self.hidden_dim)
self.cross_entropy = nn.CrossEntropyLoss(reduction='mean')
self.normlized = normlized
self.sentence_pooling_method = sentence_pooling_method
self.temperature = temperature
if not normlized:
self.temperature = 1.0
logger.info("reset temperature = 1.0 due to using inner product to compute similarity")
self.negatives_cross_device = negatives_cross_device
if self.negatives_cross_device:
if not dist.is_initialized():
raise ValueError('Distributed training has not been initialized for representation all gather.')
self.process_rank = dist.get_rank()
self.world_size = dist.get_world_size()
self.load_model(model_weight)
if not from_pretrained:
self.tokenizer = AutoTokenizer.from_pretrained(model_name_bge, use_fast=False)
else:
self.tokenizer = AutoTokenizer.from_pretrained(from_pretrained, use_fast=False)
if torch.cuda.is_available():
self.device = torch.device('cuda')
self.to(self.device)
else:
self.device = torch.device('cpu')
self.dtype = next(bge.parameters()).dtype
def load_model(self, model_weight):
self.load_state_dict(torch.load(model_weight, map_location='cpu'))
def gradient_checkpointing_enable(self, **kwargs):
# self.bge_encoder.gradient_checkpointing_enable()
self.model_visual.set_grad_checkpointing(True)
def encode(self, image=None, text=None):
# used for simple inference
if image is not None:
image = self.preprocess_val(image).unsqueeze(0)
if text is not None:
text = self.tokenizer(text, return_tensors="pt", padding=True)
return self.encode_mm(image.to(self.device), text.to(self.device))
else:
return self.encode_image(image.to(self.device))
else:
if text is not None:
text = self.tokenizer(text, return_tensors="pt", padding=True)
return self.encode_text(text.to(self.device))
else:
return None
def get_extended_attention_mask(
self, attention_mask: Tensor, input_shape: Tuple[int], device: torch.device = None, dtype: torch.float = torch.float16
) -> Tensor:
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
attention_mask (`torch.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (`Tuple[int]`):
The shape of the input to the model.
Returns:
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
"""
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
# Provided a padding mask of dimensions [batch_size, seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(
f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})"
)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = extended_attention_mask.to(dtype=dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(dtype).min
return extended_attention_mask
def sentence_embedding(self, hidden_state, mask):
if self.sentence_pooling_method == 'mean':
s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)
d = mask.sum(axis=1, keepdim=True).float()
return s / d
elif self.sentence_pooling_method == 'cls':
return hidden_state[:, 0]
def encode_text(self, texts):
'''
encode text only
'''
input_ids = texts['input_ids']
attention_mask = texts['attention_mask']
input_shape = input_ids.size()
device = input_ids.device
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
head_mask = [None] * self.depth
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape).to(self.dtype)
embedding_output = self.bge_embeddings(
input_ids=input_ids,
position_ids=None,
token_type_ids=token_type_ids,
inputs_embeds=None,
past_key_values_length=0,
)
encoder_outputs = self.bge_encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=False,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
sequence_output = encoder_outputs[0]
# pooled_output = self.bge_pooler(sequence_output) if self.bge_pooler is not None else None
t_reps = self.sentence_embedding(sequence_output, texts['attention_mask']) # tensor: reps with pooling
if self.normlized:
t_reps = torch.nn.functional.normalize(t_reps, dim=-1)
return t_reps.contiguous()
def encode_mm(self, images:torch.Tensor, texts):
img_token_emb = self.img_token_embedding(images) #[B, Patch_num, C]
img_token_emb = img_token_emb[:,1:] # img_cls is not used here
img_token_emb = self.visual_proj(img_token_emb)
device = img_token_emb.device
img_token_len = img_token_emb.size()[1]
# image position embedding, default position: bge_cls + img tokens + texts
img_token_position_ids = torch.arange(1, 1 + img_token_len).to(device=device)
img_position_embeddings = self.bge_embeddings.position_embeddings(img_token_position_ids)
img_token_emb = img_token_emb + img_position_embeddings
img_token_emb = self.bge_embeddings.LayerNorm(img_token_emb)
### deal with prompt/text
prompt_input_ids = texts['input_ids']
prompt_attention_mask = texts['attention_mask']
prom_input_shape = prompt_input_ids.size()
# bert
batch_size = prom_input_shape[0]
prompt_len = prom_input_shape[1]
prompt_start = 1 + img_token_len
cls_id = torch.tensor([0]).to(device=device)
prompt_position_ids = torch.arange(prompt_start, prompt_start + prompt_len - 1).to(device=device)
prompt_position_ids = torch.cat([cls_id, prompt_position_ids]).to(device=device)
prompt_token_type_ids = torch.zeros(prom_input_shape, dtype=torch.long, device=device)
prompt_embedding_output = self.bge_embeddings(
input_ids=prompt_input_ids,
position_ids=prompt_position_ids,
token_type_ids=prompt_token_type_ids,
inputs_embeds=None,
past_key_values_length=0,
) # [B, T, C]
cls_token = prompt_embedding_output[:, 0:1, :] # bge_cls token
prompt_embedding_output = prompt_embedding_output[:, 1:]
prompt_img_embedding = torch.cat([cls_token, img_token_emb, prompt_embedding_output], dim=1)
img_attention_mask = torch.ones(batch_size, img_token_len, device=device)
prom_img_attention_mask = torch.cat([img_attention_mask, prompt_attention_mask], dim=1)
prom_img_input_shape = prompt_img_embedding.size()
head_mask = [None] * self.depth
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(prom_img_attention_mask, prom_img_input_shape).to(self.dtype)
encoder_outputs = self.bge_encoder(
prompt_img_embedding,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=False,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
sequence_output = encoder_outputs[0]
prompt_img_reps = self.sentence_embedding(sequence_output, prom_img_attention_mask) # tensor: reps with pooling
if self.normlized:
prompt_img_reps = torch.nn.functional.normalize(prompt_img_reps, dim=-1)
return prompt_img_reps
def compute_similarity(self, q_reps, p_reps):
if len(p_reps.size()) == 2:
return torch.matmul(q_reps, p_reps.transpose(0, 1))
return torch.matmul(q_reps, p_reps.transpose(-2, -1))
def img_token_embedding(self, images):
if images is None:
return None
img_token_emb = self.model_visual.encode_image(images, normalize=False) # return_all_features=True, [B, Patch_num, C]
return img_token_emb.contiguous()
def encode_image(self, images):
if images is None:
return None
batch_size = images.shape[0]
prompts = [""] * batch_size
prompts = self.tokenizer(prompts, return_tensors="pt", padding=True)
prompts = prompts.to(images.device)
img_reps = self.encode_mm(images, prompts)
return img_reps
def forward(self, mm_it_query=None, image_candidate=None, text_candidate=None, text_query=None, mm_it_candidate=None, task_type=None):
### for stage-2 training
if task_type == "edit_image":
mm_query_reps = self.encode_mm(mm_it_query[0], mm_it_query[1])
image_candi_reps = self.encode_image(image_candidate)
query_reps = mm_query_reps
candi_reps = image_candi_reps
elif task_type == "t2it":
text_query_reps = self.encode_text(text_query)
mmit_candi_reps = self.encode_mm(mm_it_candidate[0], mm_it_candidate[1])
query_reps = text_query_reps
candi_reps = mmit_candi_reps
if self.training:
if self.negatives_cross_device:
query_reps = self._dist_gather_tensor(query_reps)
candi_reps = self._dist_gather_tensor(candi_reps)
scores = self.compute_similarity(query_reps, candi_reps)
scores = scores / self.temperature
scores = scores.view(query_reps.size(0), -1)
target = torch.arange(scores.size(0), device=scores.device, dtype=torch.long)
target = target * (candi_reps.size(0) // query_reps.size(0))
loss_edit = self.compute_loss(scores, target)
loss = loss_edit
logging.info("task types: %s; loss: %s" %(task_type, str(loss_edit)))
else:
scores = self.compute_similarity(query_reps, candi_reps)
loss=None
return EncoderOutput(
loss=loss,
scores=scores,
q_reps=query_reps,
c_reps=candi_reps,
)
def compute_loss(self, scores, target):
return self.cross_entropy(scores, target)
def _dist_gather_tensor(self, t: Optional[torch.Tensor]):
if t is None:
return None
t = t.contiguous()
all_tensors = [torch.empty_like(t) for _ in range(self.world_size)]
dist.all_gather(all_tensors, t)
all_tensors[self.process_rank] = t
all_tensors = torch.cat(all_tensors, dim=0)
return all_tensors
def save(self, output_dir: str):
torch.save(self.state_dict(), os.path.join(output_dir, 'Visualized_BGE.pth'))
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