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'))