372 lines
12 KiB
Python
372 lines
12 KiB
Python
import os
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import sys
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
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# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2"
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import torch, gc
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gc.collect()
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torch.cuda.empty_cache()
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import deepspeed
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DS_CONFIG = "ds_zero2_no_offload.json"
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from datasets import Dataset
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from modelscope import snapshot_download, AutoTokenizer
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from swanlab.integration.transformers import SwanLabCallback
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from qwen_vl_utils import process_vision_info
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from peft import LoraConfig, TaskType, get_peft_model, PeftModel,get_peft_model_state_dict
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from transformers import (
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TrainingArguments, # type: ignore
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Trainer, # type: ignore
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Qwen2_5_VLForConditionalGeneration,
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AutoProcessor,
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)
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from transformers.data.data_collator import DataCollatorForSeq2Seq
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import swanlab
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import json
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# device = torch.device("cuda:2" if torch.cuda.is_available() else "cpu") # 多GPU时可指定起始位置/编号
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# 检查 CUDA 是否可用
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if torch.cuda.is_available():
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# 从环境变量中获取 local_rank。DeepSpeed/PyTorch DDP 会自动设置这个变量。
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# 如果环境变量不存在（例如在非分布式模式下运行），默认为 0。
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local_rank = int(os.environ.get("LOCAL_RANK", "0"))
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# 根据 local_rank 动态创建设备对象
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device = torch.device(f"cuda:{local_rank}")
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torch.cuda.set_device(device)
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print(f"Process with local_rank {local_rank} is using device: {device}")
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else:
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device = torch.device("cpu")
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def load_and_convert_data(file_path):
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"""加载并转换数据"""
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loaded_data = []
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with open(file_path, 'r', encoding='utf-8') as file:
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for line in file:
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loaded_data.append(json.loads(line))
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# 将 loaded_data 转换为适合 Dataset 的格式
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dataset_dicts = []
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for item in loaded_data:
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user_content = item[0]['content']
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assistant_content = item[1]['content']
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# 提取图像和文本信息
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image_info = next((x for x in user_content if x['type'] == 'image'), None)
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text_info = next((x for x in user_content if x['type'] == 'text'), None)
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# 构造新的字典
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dataset_entry = {
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'role': 'user',
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'image_path': image_info['image'] if image_info else None,
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'question': text_info['text'] if text_info else None,
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'assistant_answer': assistant_content
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}
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dataset_dicts.append(dataset_entry)
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return dataset_dicts
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def process_func_batch(examples):
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MAX_LENGTH = 2048
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input_ids, attention_mask, labels, pixel_values, image_grid_thw = [], [], [], [], []
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for example in zip(examples["question"], examples["assistant_answer"], examples["image_path"]):
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input_content, output_content, file_path = example
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": f"{file_path}",
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"resized_height": 280,
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"resized_width": 280,
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},
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{"type": "text", "text": input_content},
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],
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}
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]
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=False, # 先不填充
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return_tensors="pt",
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)
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inputs_dict = {key: value.tolist() for key, value in inputs.items()}
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instruction_input_ids = inputs_dict['input_ids'][0]
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instruction_attention_mask = inputs_dict['attention_mask'][0]
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response = tokenizer(f"{output_content}", add_special_tokens=False)
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response_input_ids = response['input_ids']
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response_attention_mask = response['attention_mask']
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# 计算剩余可用长度给response
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remaining_length = MAX_LENGTH - len(instruction_input_ids) - 1 # 减去一个PAD token的空间
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if remaining_length < 0:
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# 如果指令部分已经超过最大长度，则需要截断指令部分
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truncation_length = len(instruction_input_ids) + remaining_length
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instruction_input_ids = instruction_input_ids[:truncation_length]
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instruction_attention_mask = instruction_attention_mask[:truncation_length]
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remaining_length = 0
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# 截断response部分以适应剩余空间
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current_input_ids = (
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instruction_input_ids + response_input_ids[:remaining_length] + [tokenizer.pad_token_id]
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)
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current_attention_mask = (
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instruction_attention_mask + response_attention_mask[:remaining_length] + [1]
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)
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current_labels = (
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[-100] * len(instruction_input_ids) +
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response_input_ids[:remaining_length] +
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[tokenizer.pad_token_id]
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)
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# 填充到MAX_LENGTH
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if len(current_input_ids) < MAX_LENGTH:
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current_input_ids += [tokenizer.pad_token_id] * (MAX_LENGTH - len(current_input_ids))
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current_attention_mask += [0] * (MAX_LENGTH - len(current_attention_mask))
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current_labels += [-100] * (MAX_LENGTH - len(current_labels))
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input_ids.append(current_input_ids)
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attention_mask.append(current_attention_mask)
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labels.append(current_labels)
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pixel_values.append(inputs_dict['pixel_values'])
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image_grid_thw.append(torch.tensor(inputs_dict['image_grid_thw']).squeeze(0))
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return {
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"input_ids": torch.tensor(input_ids),
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"attention_mask": torch.tensor(attention_mask),
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"labels": torch.tensor(labels),
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"pixel_values": torch.tensor(pixel_values),
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"image_grid_thw": torch.stack(image_grid_thw)
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}
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def predict(messages, model):
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# 准备推理
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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device = next(model.parameters()).device
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# 将所有张量移动到指定的设备上
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for key, value in inputs.items():
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inputs[key] = value.to(device)
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# 生成输出
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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del inputs
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return output_text[0]
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# 在modelscope上下载Qwen2-VL模型到本地目录下
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# model_dir = snapshot_download("Qwen/Qwen2-VL-2B-Instruct", cache_dir="./", revision="master")
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# 使用Transformers加载模型权重
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tokenizer = AutoTokenizer.from_pretrained("/home/gyk/models/Qwen2.5-VL-7B-Instruct/", use_fast=True)
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min_pixels = 256*28*28
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max_pixels = 1280*28*28
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processor = AutoProcessor.from_pretrained("/home/gyk/models/Qwen2.5-VL-7B-Instruct/", min_pixels=min_pixels, max_pixels=max_pixels,use_fast=True)
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device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained("/home/gyk/models/Qwen2.5-VL-7B-Instruct/", device_map=device_map, torch_dtype=torch.bfloat16)
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model.enable_input_require_grads() # 开启梯度检查点时，要执行该方法/
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# 处理数据集：读取json文件
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# 分别加载 test 和 val 数据集
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test_data_path = 'data_test.jsonl'
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val_data_path = 'data_val.jsonl'
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test_dataset_dicts = load_and_convert_data(test_data_path)
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val_dataset_dicts = load_and_convert_data(val_data_path)
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# 创建 Dataset 对象
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test_tmp_dataset = Dataset.from_list(test_dataset_dicts)
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val_tmp_dataset = Dataset.from_list(val_dataset_dicts)
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indices = list(range(1000))
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test_tmp_dataset = test_tmp_dataset.select(indices)
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indices = list(range(50))
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val_tmp_dataset = val_tmp_dataset.select(indices)
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test_dataset = test_tmp_dataset.map(process_func_batch, batched=True,batch_size=4)
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val_dataset = val_tmp_dataset.map(process_func_batch, batched=True, batch_size=4)
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print("Test and Val Datasets have been created.")
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# 配置LoRA
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config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
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inference_mode=False, # 训练模式
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r=64, # Lora 秩
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lora_alpha=16, # Lora alaph，具体作用参见 Lora 原理
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lora_dropout=0.05, # Dropout 比例
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bias="none",
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)
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# 获取LoRA模型
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# 转换模型
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peft_model = get_peft_model(model, config)
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peft_model.config.use_cache = False # type: ignore
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# 配置训练参数
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args = TrainingArguments(
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output_dir="./output2/Qwen2.5-VL-7B",
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per_device_train_batch_size=2,
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gradient_accumulation_steps=8,
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logging_steps=10,
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logging_first_step=True,
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num_train_epochs=4,
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save_steps=50,
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learning_rate=1e-4,
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save_on_each_node=True,
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gradient_checkpointing=True,
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report_to="none",
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# bf16=True,
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fp16=True,
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max_grad_norm=1.0,
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deepspeed=DS_CONFIG
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)
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# 设置SwanLab回调
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swanlab_callback = SwanLabCallback(
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project="Qwen2.5-VL-finetune",
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experiment_name="qwen2.5-vl-refcocog",
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config={
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"model": "https://modelscope.cn/models/Qwen/Qwen2.5-VL-7B-Instruct",
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"dataset": "https://huggingface.co/datasets/Kangheng/refcocog",
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"github": "https://github.com/datawhalechina/self-llm",
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"prompt": "Please provide the bounding box for the following descriptio: ",
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"train_data_number": len(test_dataset),
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"lora_rank": 64,
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"lora_alpha": 16,
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"lora_dropout": 0.1,
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},
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)
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# 配置Trainer
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trainer = Trainer(
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model=peft_model,
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args=args,
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train_dataset=test_dataset,
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eval_dataset=val_dataset,
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data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
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callbacks=[swanlab_callback],
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)
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# 开启模型训练
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trainer.train()
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trainer.save_model('./output2/Qwen2.5-VL-7B')
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trainer.save_state()
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# ====================测试模式===================
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# 配置测试参数
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val_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
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inference_mode=True, # 训练模式
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r=64, # Lora 秩
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lora_alpha=16, # Lora alaph，具体作用参见 Lora 原理
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lora_dropout=0.05, # Dropout 比例
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bias="none",
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)
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# 获取测试模型
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val_peft_model = PeftModel.from_pretrained(model, model_id="./output2/Qwen2.5-VL-7B/", config=val_config)
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# 创建一个列表来保存所有需要的信息
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results_to_save = []
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# 同时创建test_image_list用于swanlab日志记录
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test_image_list = []
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for item in val_dataset:
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if not isinstance (item, dict):
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print("item解析错误")
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sys.exit()
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# 准备输入消息
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messages = [{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": item['image_path']
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},
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{
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"type": "text",
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"text": item['question']
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}
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]}]
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# 获取模型预测
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response = predict(messages, val_peft_model)
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messages.append({"role": "assistant", "content": f"{response}"})
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# 打印或记录预测信息
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print(messages[-1])
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# 添加预测结果、原始答案和图片路径到结果列表中
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results_to_save.append({
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'image_path': item['image_path'],
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'question':item['question'],
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'original_answer': item['assistant_answer'],
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'predicted_answer': response,
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})
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# 同时添加到test_image_list用于SwanLab日志记录
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test_image_list.append(swanlab.Image(item['image_path'], caption=response))
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# 定义保存文件的路径
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output_file_path = './predictions_results.json'
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# 将结果写入JSON文件
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with open(output_file_path, 'w', encoding='utf-8') as file:
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json.dump(results_to_save, file, ensure_ascii=False, indent=4)
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print(f"Results have been saved to {output_file_path}")
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swanlab.init()
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# 使用SwanLab记录预测结果
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swanlab.log({"Prediction": test_image_list})
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# 在Jupyter Notebook中运行时要停止SwanLab记录，需要调用swanlab.finish()
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swanlab.finish()
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