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MindOfMatter
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'''
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* Copyright (c) 2022, salesforce.com, inc.
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* All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
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* By Junnan Li
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'''
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from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
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from transformers import BertTokenizer
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import transformers
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transformers.logging.set_verbosity_error()
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import torch
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from torch import nn
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import torch.nn.functional as F
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from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
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class BLIP_Pretrain(nn.Module):
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def __init__(self,
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med_config = 'configs/bert_config.json',
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image_size = 224,
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vit = 'base',
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vit_grad_ckpt = False,
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vit_ckpt_layer = 0,
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embed_dim = 256,
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queue_size = 57600,
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momentum = 0.995,
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):
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"""
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Args:
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med_config (str): path for the mixture of encoder-decoder model's configuration file
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image_size (int): input image size
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vit (str): model size of vision transformer
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"""
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super().__init__()
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self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0)
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if vit=='base':
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
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map_location="cpu", check_hash=True)
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state_dict = checkpoint["model"]
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msg = self.visual_encoder.load_state_dict(state_dict,strict=False)
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elif vit=='large':
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from timm.models.helpers import load_custom_pretrained
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from timm.models.vision_transformer import default_cfgs
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load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k'])
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self.tokenizer = init_tokenizer()
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encoder_config = BertConfig.from_json_file(med_config)
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encoder_config.encoder_width = vision_width
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self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False)
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self.text_encoder.resize_token_embeddings(len(self.tokenizer))
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text_width = self.text_encoder.config.hidden_size
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self.vision_proj = nn.Linear(vision_width, embed_dim)
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self.text_proj = nn.Linear(text_width, embed_dim)
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self.itm_head = nn.Linear(text_width, 2)
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# create momentum encoders
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self.visual_encoder_m, vision_width = create_vit(vit,image_size)
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self.vision_proj_m = nn.Linear(vision_width, embed_dim)
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self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False)
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self.text_proj_m = nn.Linear(text_width, embed_dim)
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self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],
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[self.vision_proj,self.vision_proj_m],
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[self.text_encoder,self.text_encoder_m],
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[self.text_proj,self.text_proj_m],
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]
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self.copy_params()
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# create the queue
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self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
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self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
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self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))
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self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
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self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
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self.queue_size = queue_size
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self.momentum = momentum
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self.temp = nn.Parameter(0.07*torch.ones([]))
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# create the decoder
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decoder_config = BertConfig.from_json_file(med_config)
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decoder_config.encoder_width = vision_width
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self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config)
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self.text_decoder.resize_token_embeddings(len(self.tokenizer))
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tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention')
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def forward(self, image, caption, alpha):
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with torch.no_grad():
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self.temp.clamp_(0.001,0.5)
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image_embeds = self.visual_encoder(image)
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image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
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image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)
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text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30,
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return_tensors="pt").to(image.device)
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text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
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return_dict = True, mode = 'text')
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text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)
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# get momentum features
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with torch.no_grad():
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self._momentum_update()
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image_embeds_m = self.visual_encoder_m(image)
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image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)
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image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)
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text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,
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return_dict = True, mode = 'text')
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text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1)
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text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)
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sim_i2t_m = image_feat_m @ text_feat_all / self.temp
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sim_t2i_m = text_feat_m @ image_feat_all / self.temp
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sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)
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sim_targets.fill_diagonal_(1)
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sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
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sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets
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sim_i2t = image_feat @ text_feat_all / self.temp
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sim_t2i = text_feat @ image_feat_all / self.temp
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loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()
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loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean()
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loss_ita = (loss_i2t+loss_t2i)/2
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self._dequeue_and_enqueue(image_feat_m, text_feat_m)
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###============== Image-text Matching ===================###
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encoder_input_ids = text.input_ids.clone()
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encoder_input_ids[:,0] = self.tokenizer.enc_token_id
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# forward the positve image-text pair
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bs = image.size(0)
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output_pos = self.text_encoder(encoder_input_ids,
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attention_mask = text.attention_mask,
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encoder_hidden_states = image_embeds,
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encoder_attention_mask = image_atts,
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return_dict = True,
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)
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with torch.no_grad():
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weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4
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weights_t2i.fill_diagonal_(0)
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weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4
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weights_i2t.fill_diagonal_(0)
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# select a negative image for each text
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image_embeds_neg = []
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for b in range(bs):
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neg_idx = torch.multinomial(weights_t2i[b], 1).item()
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image_embeds_neg.append(image_embeds[neg_idx])
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image_embeds_neg = torch.stack(image_embeds_neg,dim=0)
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# select a negative text for each image
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text_ids_neg = []
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text_atts_neg = []
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for b in range(bs):
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neg_idx = torch.multinomial(weights_i2t[b], 1).item()
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text_ids_neg.append(encoder_input_ids[neg_idx])
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text_atts_neg.append(text.attention_mask[neg_idx])
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text_ids_neg = torch.stack(text_ids_neg,dim=0)
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text_atts_neg = torch.stack(text_atts_neg,dim=0)
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text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)
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text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)
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image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)
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image_atts_all = torch.cat([image_atts,image_atts],dim=0)
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output_neg = self.text_encoder(text_ids_all,
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attention_mask = text_atts_all,
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encoder_hidden_states = image_embeds_all,
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encoder_attention_mask = image_atts_all,
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return_dict = True,
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)
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vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)
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vl_output = self.itm_head(vl_embeddings)
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itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],
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dim=0).to(image.device)
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loss_itm = F.cross_entropy(vl_output, itm_labels)
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##================= LM ========================##
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decoder_input_ids = text.input_ids.clone()
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decoder_input_ids[:,0] = self.tokenizer.bos_token_id
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decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100)
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decoder_output = self.text_decoder(decoder_input_ids,
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attention_mask = text.attention_mask,
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encoder_hidden_states = image_embeds,
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encoder_attention_mask = image_atts,
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labels = decoder_targets,
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return_dict = True,
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)
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loss_lm = decoder_output.loss
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return loss_ita, loss_itm, loss_lm
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@torch.no_grad()
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def copy_params(self):
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for model_pair in self.model_pairs:
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for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
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param_m.data.copy_(param.data) # initialize
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param_m.requires_grad = False # not update by gradient
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@torch.no_grad()
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def _momentum_update(self):
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for model_pair in self.model_pairs:
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for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
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param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)
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@torch.no_grad()
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def _dequeue_and_enqueue(self, image_feat, text_feat):
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# gather keys before updating queue
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image_feats = concat_all_gather(image_feat)
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text_feats = concat_all_gather(text_feat)
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batch_size = image_feats.shape[0]
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ptr = int(self.queue_ptr)
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assert self.queue_size % batch_size == 0 # for simplicity
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# replace the keys at ptr (dequeue and enqueue)
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self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
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self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
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ptr = (ptr + batch_size) % self.queue_size # move pointer
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self.queue_ptr[0] = ptr
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def blip_pretrain(**kwargs):
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model = BLIP_Pretrain(**kwargs)
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return model
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@torch.no_grad()
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def concat_all_gather(tensor):
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"""
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Performs all_gather operation on the provided tensors.
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*** Warning ***: torch.distributed.all_gather has no gradient.
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"""
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tensors_gather = [torch.ones_like(tensor)
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for _ in range(torch.distributed.get_world_size())]
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torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
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output = torch.cat(tensors_gather, dim=0)
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return output
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from typing import List
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def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):
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uninitialized_encoder_weights: List[str] = []
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if decoder.__class__ != encoder.__class__:
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print(
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f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
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)
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def tie_encoder_to_decoder_recursively(
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decoder_pointer: nn.Module,
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encoder_pointer: nn.Module,
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module_name: str,
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uninitialized_encoder_weights: List[str],
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skip_key: str,
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depth=0,
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):
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assert isinstance(decoder_pointer, nn.Module) and isinstance(
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encoder_pointer, nn.Module
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), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
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if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
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assert hasattr(encoder_pointer, "weight")
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encoder_pointer.weight = decoder_pointer.weight
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if hasattr(decoder_pointer, "bias"):
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assert hasattr(encoder_pointer, "bias")
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encoder_pointer.bias = decoder_pointer.bias
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print(module_name+' is tied')
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return
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encoder_modules = encoder_pointer._modules
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decoder_modules = decoder_pointer._modules
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if len(decoder_modules) > 0:
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assert (
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len(encoder_modules) > 0
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), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
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all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()])
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encoder_layer_pos = 0
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for name, module in decoder_modules.items():
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if name.isdigit():
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encoder_name = str(int(name) + encoder_layer_pos)
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decoder_name = name
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if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len(
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encoder_modules
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) != len(decoder_modules):
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# this can happen if the name corresponds to the position in a list module list of layers
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# in this case the decoder has added a cross-attention that the encoder does not have
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# thus skip this step and subtract one layer pos from encoder
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encoder_layer_pos -= 1
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continue
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elif name not in encoder_modules:
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continue
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elif depth > 500:
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raise ValueError(
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"Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
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)
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else:
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decoder_name = encoder_name = name
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tie_encoder_to_decoder_recursively(
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decoder_modules[decoder_name],
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encoder_modules[encoder_name],
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module_name + "/" + name,
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uninitialized_encoder_weights,
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skip_key,
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depth=depth + 1,
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)
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all_encoder_weights.remove(module_name + "/" + encoder_name)
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uninitialized_encoder_weights += list(all_encoder_weights)
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# tie weights recursively
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tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key)
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