import os import deepspeed #deepspeed.initialize(config="./deepspeed_config.json",log_level='DEBUG') from tqdm import tqdm import shutil os.environ['HF_ENDPOINT']="https://hf-mirror.com" from qwenva import tokenizer from qwenva import processor from qwenva import qwenva images_file_path='./data/download/llava-v1.5-instruct' import torch from torch.utils.data import Dataset, DataLoader import os import json from PIL import Image import json with open('/root/autodl-tmp/LLaVA-Instruct-150K/qwenva_mix665k.json', 'r', encoding='utf-8') as f: chat_data = json.load(f) import torch image_token=tokenizer.encode('')[0] pad_token=tokenizer.pad_token_id image_token=tokenizer.encode('')[0] pad_token=tokenizer.pad_token_id def process_data(sample,max_len=8012): conversations=sample['conversations'] labels=[] input_ids=[] flag=0 messages=[] input_ids=[] try: for index,item in enumerate(conversations): if item['from']=='human': old_input_ids=input_ids messages.append({'role':'user','content':item['value']}) input_ids=tokenizer.apply_chat_template( messages, add_generation_prompt=True ) #input_ids+=input_token[] labels+=[-100]*(len(input_ids)-len(old_input_ids)) if index==flag: if image_token in input_ids: image_index=input_ids.index(image_token) labels[image_index]=image_token else: image_index=-100 elif item['from']=='gpt': old_input_ids=input_ids messages.append({'role':'assistant','content':item['value']}) input_ids=tokenizer.apply_chat_template( messages ) labels+=input_ids[len(old_input_ids):] except: print("error in process_data_1") exit() #填充或者截断，使得长度相同 try: if len(input_ids)>max_len: input_ids=input_ids[:max_len] labels=labels[:max_len] attention_mask=[1]*len(input_ids) else: attention_mask=[1]*len(input_ids)+[0]*(max_len-len(input_ids)) input_ids+=[pad_token]*(max_len-len(input_ids)) labels+=[-100]*(max_len-len(labels)) except: print("error in process_data_2") exit() #转化为张量 try: input_ids=torch.tensor(input_ids) attention_mask=torch.tensor(attention_mask) labels=torch.tensor(labels) image_index=torch.tensor(image_index) except: print("error in tensor") exit() return { 'input_ids':input_ids, 'attention_mask':attention_mask, 'labels':labels, 'image_idx':image_index } import os import torch from torch.utils.data import Dataset from PIL import Image class MyDataset(Dataset): def __init__(self, images_file_path,data,max_len=1024): self.max_len=max_len self.images_file_path = images_file_path self.data = data self.max_len=max_len def __len__(self): return len(self.data) def __getitem__(self, index): output_=process_data(self.data[index],max_len=self.max_len) if output_['image_idx']!=-100: img_path=os.path.join(self.images_file_path,self.data[index]['image']) img=Image.open(img_path) input_pixel= processor(images=img, return_tensors="pt") output_['input_pixel']=input_pixel['pixel_values'].squeeze() else: output_['input_pixel']=torch.zeros(3,224,224).to(device=output_['input_ids'].device,dtype=output_['input_ids'].dtype) return output_ dataset=MyDataset(images_file_path,chat_data,max_len=2048) train_loader=DataLoader(dataset,batch_size=16,shuffle=True) import argparse model_engine,optimizer,_,_=deepspeed.initialize( model=qwenva, args=argparse.Namespace(), model_parameters=qwenva.parameters(), config_params="./deepspeed_config.json" ) #checkpoint_path = "./best_model_2" #model_engine.load_checkpoint(checkpoint_path) #保存编译模型权重 #torch.save(model_engine.module.state_dict(), "./compiled_model.pth") for name, param in model_engine.module._orig_mod.text_embedding.named_parameters(): param.requires_grad = True #print("embedding权重梯度打开:",name) #for name, param in model_engine.module._orig_mod.align_layer.named_parameters(): #param.requires_grad = True #print("align_layer权重梯度打开",name) for name,param in model_engine.module._orig_mod.lm_head.named_parameters(): param.requires_grad = True #print("lm_head权重梯度打开",name) for name,param in model_engine.module._orig_mod.transformer.named_parameters(): param.requires_grad = True #print("transformer权重梯度打开",name) for name,param in model_engine.module._orig_mod.named_parameters(): if param.requires_grad: print(f"Layer: {name}, Requires Grad: {param.requires_grad}") #optimizer = optim.Adam(model.parameters(), lr=0.001) import torch.nn as nn loss_fn = nn.CrossEntropyLoss() #eps = 1e-8 accumulation_steps = 1 # 训练函数 def train(model_engine, train_dataloader, loss_fn, device, epochs): model_engine.train() #model_engine.to(device) for epoch in range(epochs): # 使用 tqdm 显示进度条 with tqdm(total=len(train_dataloader), desc=f'Epoch {epoch + 1}/{epochs}', unit='batch') as pbar: #optimizer.zero_grad() try: for batch_idx, batch in enumerate(train_dataloader): # 将数据拷贝到 GPU 上 input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) input_pixel = batch['input_pixel'].to(device) labels = batch['labels'].to(device) image_idx=batch['image_idx'].to(device) logits = model_engine(input_ids, attention_mask, input_pixel,image_idx) # 计算损失 #max_logits= logits.max(dim=-1, keepdim=True)[0] # 计算最大值 #stable_logits= logits - max_logits # 减去最大值得到数值稳定的值 loss= loss_fn(logits[:, :-1, :].reshape(-1, logits.shape[-1]), labels[:, 1:].reshape(-1).clone()) model_engine.backward(loss) if (batch_idx+1)%accumulation_steps==0: model_engine.step() pbar.update(1) pbar.set_postfix(loss=loss.item()) # 显示当前损失 if (batch_idx+1)%4100==0: # 如果文件夹存在，则删除并重新创建 if os.path.exists("./best_model_2"): shutil.rmtree("./best_model_2") # 删除文件夹及其内容 os.makedirs("./best_model_2") # 重新创建文件夹 model_engine.save_checkpoint("./best_model_2") torch.save(model_engine.module.state_dict(), "./compiled_model_3.pth") print(f" model saved at batch {batch_idx+1}") except Exception as e: print(f"error in train {e}") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") train(model_engine, train_loader, loss_fn, device, epochs=2)