import os import random import logging import torch from torch.cuda.amp import autocast as autocast import torch.nn as nn from .blip2 import Blip2Base, disabled_train from .modeling_llama import LlamaForCausalLM from transformers import LlamaTokenizer, LlamaConfig class VideoChat(Blip2Base): """ VideoChat model. """ def __init__(self, config): super().__init__() vit_model = config.get("vit_model", "eva_clip_g") vit_model_path = config.get("vit_model_path", None) q_former_model_path = config.get("q_former_model_path", None) llama_model_path = config.get("llama_model_path") videochat_model_path = config.get("videochat_model_path", "") img_size = config.get("img_size") drop_path_rate = config.get("drop_path_rate", 0) use_grad_checkpoint = config.get("use_grad_checkpoint", False) vit_precision = config.get("vit_precision", "fp16") freeze_vit = config.get("freeze_vit", True) freeze_qformer = config.get("freeze_qformer", True) low_resource = config.get("low_resource", False) # use 8 bit and put vit in cpu max_txt_len = config.get("max_txt_len", 32) # uniformerv2 freeze_mhra = config.get("freeze_mhra", False) temporal_downsample = config.get("temporal_downsample", True) no_lmhra = config.get("no_lmhra", False) double_lmhra = config.get("double_lmhra", False) lmhra_reduction = config.get("lmhra_reduction", 2.0) gmhra_layers = config.get("gmhra_layers", 8) gmhra_drop_path_rate = config.get("gmhra_drop_path_rate", 0.) gmhra_dropout = config.get("gmhra_dropout", 0.5) # qformer num_query_token = config.get("num_query_token") extra_num_query_token = config.get("extra_num_query_token", 64) self.tokenizer = self.init_tokenizer() self.low_resource = low_resource self.vit_precision = vit_precision print(f'Loading VIT. Use fp16: {vit_precision}') self.visual_encoder, self.ln_vision = self.init_vision_encoder( vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision, vit_model_path, temporal_downsample=temporal_downsample, no_lmhra=no_lmhra, double_lmhra=double_lmhra, lmhra_reduction=lmhra_reduction, gmhra_layers=gmhra_layers, gmhra_drop_path_rate=gmhra_drop_path_rate, gmhra_dropout=gmhra_dropout, ) if freeze_vit: print("freeze vision encoder") if not freeze_mhra: open_list = [] for name, param in self.visual_encoder.named_parameters(): if 'mhra' not in name: param.requires_grad = False else: open_list.append(name) print(f"open module: {open_list}") print("open ln_vision") else: for name, param in self.visual_encoder.named_parameters(): param.requires_grad = False self.visual_encoder = self.visual_encoder.eval() self.visual_encoder.train = disabled_train for name, param in self.ln_vision.named_parameters(): param.requires_grad = False self.ln_vision = self.ln_vision.eval() self.ln_vision.train = disabled_train print('Loading VIT Done') print('Loading Q-Former') self.Qformer, self.query_tokens = self.init_Qformer( num_query_token, self.visual_encoder.num_features, ) self.Qformer.cls = None self.Qformer.bert.embeddings.word_embeddings = None self.Qformer.bert.embeddings.position_embeddings = None for layer in self.Qformer.bert.encoder.layer: layer.output = None layer.intermediate = None self.load_from_pretrained(model_path=q_former_model_path) print(f"Add extra {extra_num_query_token} tokens in QFormer") self.extra_query_tokens = nn.Parameter( torch.zeros(1, extra_num_query_token, self.query_tokens.shape[-1]) ) if freeze_qformer: print("freeze Qformer") for name, param in self.Qformer.named_parameters(): param.requires_grad = False self.Qformer = self.Qformer.eval() self.Qformer.train = disabled_train self.query_tokens.requires_grad = False print('Loading Q-Former Done') print('Loading LLAMA') self.llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model_path, use_fast=False, use_auth_token=os.environ["HF_TOKEN"]) self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token import psutil import os print(u'当前进程的内存使用：%.4f GB' % (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024) ) info = psutil.virtual_memory() print( u'电脑总内存：%.4f GB' % (info.total / 1024 / 1024 / 1024) ) print(u'当前使用的总内存占比：',info.percent) print(u'cpu个数：',psutil.cpu_count()) if self.low_resource: self.llama_model = LlamaForCausalLM.from_pretrained( llama_model_path, torch_dtype=torch.float16, load_in_8bit=True, device_map="auto", use_auth_token=os.environ["HF_TOKEN"], ) else: self.llama_model = LlamaForCausalLM.from_pretrained( llama_model_path, torch_dtype=torch.float16, use_auth_token=os.environ["HF_TOKEN"], ) print("freeze LLAMA") for name, param in self.llama_model.named_parameters(): param.requires_grad = False print('Loading LLAMA Done') self.llama_proj = nn.Linear( self.Qformer.config.hidden_size, self.llama_model.config.hidden_size ) self.max_txt_len = max_txt_len # load weights of VideoChat if videochat_model_path: print(f"Load VideoChat from: {videochat_model_path}") ckpt = torch.load(videochat_model_path, map_location="cpu") msg = self.load_state_dict(ckpt['model'], strict=False) print(msg) def vit_to_cpu(self): self.ln_vision.to("cpu") self.ln_vision.float() self.visual_encoder.to("cpu") self.visual_encoder.float() def encode_img(self, image): device = image.device if self.low_resource: self.vit_to_cpu() image = image.to("cpu") with self.maybe_autocast(): T = image.shape[1] # use_image = True if T == 1 else False image = image.permute(0, 2, 1, 3, 4) # [B,T,C,H,W] -> [B,C,T,H,W] image_embeds = self.ln_vision(self.visual_encoder(image)).to(device) image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device) query_tokens = torch.cat([self.query_tokens, self.extra_query_tokens], dim=1) query_tokens = query_tokens.expand(image_embeds.shape[0], -1, -1) query_output = self.Qformer.bert( query_embeds=query_tokens, encoder_hidden_states=image_embeds, encoder_attention_mask=image_atts, return_dict=True, ) inputs_llama = self.llama_proj(query_output.last_hidden_state) atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device) return inputs_llama, atts_llama