联合训练系统中模型训练并行度优化实践

在多模态大模型联合训练场景下，如何有效提升训练并行度是架构设计的关键挑战。本文通过构建图像-文本联合训练系统，实现训练过程的高效并行化。

数据处理流程

首先需要构建统一的数据管道：

import torch
from torch.utils.data import Dataset, DataLoader

class MultimodalDataset(Dataset):
    def __init__(self, image_paths, text_prompts):
        self.image_paths = image_paths
        self.text_prompts = text_prompts
        
    def __len__(self):
        return len(self.image_paths)
    
    def __getitem__(self, idx):
        # 图像处理
        image = self.load_and_transform_image(self.image_paths[idx])
        # 文本处理
        text = self.tokenize_text(self.text_prompts[idx])
        return {
            'image': image,
            'text': text,
            'index': idx
        }

模型融合方案

采用流水线并行策略，将图像分支和文本分支分别在不同设备上处理：

# 分布式训练配置
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist

# 图像分支
image_model = torchvision.models.resnet50(pretrained=True)
image_model = image_model.to(device)

# 文本分支
text_model = transformers.AutoModel.from_pretrained('bert-base-uncased')

# 联合训练模块
class MultimodalModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.image_branch = image_model
        self.text_branch = text_model
        
    def forward(self, image, text):
        img_features = self.image_branch(image)
        text_features = self.text_branch(**text)
        return img_features, text_features

# 并行化处理
model = MultimodalModel().to(device)
model = DDP(model, device_ids=[device])