import os
import psutil
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.llama_model = LlamaForCausalLM.from_pretrained(
llama_model_path,
torch_dtype=torch.float16,
use_auth_token=os.environ["HF_TOKEN"],
load_in_8bit=True,
device_map="auto"
)
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
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"],
load_in_8bit=True,
device_map="auto"
)
'''
print("freeze LLAMA")
for name, param in self.llama_model.named_parameters():
param.requires_grad = False
print('Loading LLAMA Done')
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())
self.llama_proj = nn.Linear(
self.Qformer.config.hidden_size, self.llama_model.config.hidden_size
)
self.max_txt_len = max_txt_len
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())
# load weights of VideoChat
if videochat_model_path:
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())
print(f"Load VideoChat from: {videochat_model_path}")
ckpt = torch.load(videochat_model_path, map_location="cpu")
print(u'ckpt load success.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())
msg = self.load_state_dict(ckpt['model'], strict=False)
print(msg)
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())
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