Parallel Transformer代码

以下是使用PyTorch实现的Parallel Transformer代码，其中包括了模型定义、数据预处理、训练和测试等部分：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler
from torch.nn.utils.rnn import pad_sequence
from torch.optim import Adam

import argparse
import os
import time
import math

from tqdm import tqdm

class ParallelTransformerModel(nn.Module):
    def __init__(self, vocab_size, embed_dim, num_heads, num_layers, hidden_dim, dropout):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.pos_encoding = PositionalEncoding(embed_dim, dropout)
        self.transformer_layers = nn.ModuleList([
            TransformerEncoderLayer(embed_dim, num_heads, hidden_dim, dropout)
            for _ in range(num_layers)
        ])
        self.fc = nn.Linear(embed_dim, vocab_size)

    def forward(self, x):
        x = self.embedding(x)
        x = self.pos_encoding(x)
        for layer in self.transformer_layers:
            x = layer(x)
        x = self.fc(x)
        return x

class PositionalEncoding(nn.Module):
    def __init__(self, embed_dim, dropout=0.1, max_len=5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)
        pe = torch.zeros(max_len, embed_dim)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, embed_dim, 2).float() * (-math.log(10000.0) / embed_dim))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

class TransformerEncoderLayer(nn.Module):
    def __init__(self, embed_dim, num_heads, hidden_dim, dropout):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)
        self.fc1 = nn.Linear(embed_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, embed_dim)
        self.norm1 = nn.LayerNorm(embed_dim)
        self.norm2 = nn.LayerNorm(embed_dim)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

    def forward(self, x):
        x_norm = self.norm1(x)
        attn_out, _ = self.self_attn(x_norm, x_norm, x_norm)
        x = x + self.dropout1(attn_out)
        x_norm = self.norm2(x)
        fc_out = self.fc2(F.relu(self.fc1(x_norm)))
        x = x + self.dropout2(fc_out)
        return x

class TextDataset(Dataset):
    def __init__(self, data_file, vocab_file):
        self.data = []
        self.vocab = {}
        self.max_len = 0
        with open(vocab_file, 'r', encoding='utf-8') as f:
            for idx, line in enumerate(f):
                self.vocab[line.strip()] = idx
        with open(data_file, 'r', encoding='utf-8') as f:
            for line in f:
                tokens = line.strip().split()
                if self.max_len < len(tokens):
                    self.max_len = len(tokens)
                self.data.append([self.vocab[token] for token in tokens])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx]

    def collate_fn(self, batch):
        batch = pad_sequence([torch.tensor(data) for data in batch], batch_first=True)
        return batch

def train(args, model, dataloader, criterion, optimizer, epoch):
    model.train()
    epoch_loss = 0
    for batch in tqdm(dataloader, desc=f'Train epoch {epoch}'):
        optimizer.zero_grad()
        inputs, targets = batch[:, :-1], batch[:, 1:]
        inputs, targets = inputs.to(args.device), targets.to(args.device)
        outputs = model(inputs)
        loss = criterion(outputs.view(-1, outputs.shape[-1]), targets.view(-1))
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
    return epoch_loss / len(dataloader)

def evaluate(args, model, dataloader, criterion, epoch):
    model.eval()
    epoch_loss = 0
    with torch.no_grad():
        for batch in tqdm(dataloader, desc=f'Eval epoch {epoch}'):
            inputs, targets = batch[:, :-1], batch[:, 1:]
            inputs, targets = inputs.to(args.device), targets.to(args.device)
            outputs = model(inputs)
            loss = criterion(outputs.view(-1, outputs.shape[-1]), targets.view(-1))
            epoch_loss += loss.item()
    return epoch_loss / len(dataloader)

def main(args):
    torch.manual_seed(args.seed)

    # Initialize distributed training
    torch.cuda.set_device(args.local_rank)
    torch.distributed.init_process_group(backend='nccl', init_method='env://')

    # Load and preprocess data
    train_dataset = TextDataset(args.train_file, args.vocab_file)
    eval_dataset = TextDataset(args.eval_file, args.vocab_file)
    train_sampler = DistributedSampler(train_dataset)
    eval_sampler = DistributedSampler(eval_dataset)
    train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, collate_fn=train_dataset.collate_fn, num_workers=args.num_workers, sampler=train_sampler)
    eval_dataloader = DataLoader(eval_dataset, batch_size=args.batch_size, collate_fn=eval_dataset.collate_fn, num_workers=args.num_workers, sampler=eval_sampler)

    # Initialize model and optimizer
    model = ParallelTransformerModel(len(train_dataset.vocab), args.embed_dim, args.num_heads, args.num_layers, args.hidden_dim, args.dropout)
    model = DDP(model, device_ids=[args.local_rank], output_device=args.local_rank)
    criterion = nn.CrossEntropyLoss(ignore_index=0)
    optimizer = Adam(model.parameters(), lr=args.learning_rate)

    # Train and evaluate
    for epoch in range(1, args.num_epochs + 1):
        train_loss = train(args, model, train_dataloader, criterion, optimizer, epoch)
        eval_loss = evaluate(args, model, eval_dataloader, criterion, epoch)

        # Average loss across all processes
        train_loss = torch.tensor(train_loss).to(args.device)
        eval_loss = torch.tensor(eval_loss).to(args.device)
        torch.distributed.reduce(train_loss, dst=0)
        torch.distributed.reduce(eval_loss, dst=0)
        if args.local_rank == 0:
            train_loss /= torch.distributed.get_world_size()
            eval_loss /= torch.distributed.get_world_size()

            print(f'Train epoch {epoch}, loss: {train_loss.item():.4f}')
            print(f'Eval epoch {epoch}, loss: {eval_loss.item():.4f}')

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--train_file', type=str, default='train.txt')
    parser.add_argument('--eval_file', type=str, default='eval.txt')
    parser.add_argument('--vocab_file', type=str, default='vocab.txt')
    parser.add_argument('--batch_size', type=int, default=128)
    parser.add_argument('--num_epochs', type=int, default=10)
    parser.add_argument('--embed_dim', type=int, default=256)
    parser.add_argument('--num_heads', type=int, default=8)
    parser.add_argument('--num_layers', type=int, default=6)
    parser.add_argument('--hidden_dim', type=int, default=1024)
    parser.add_argument('--dropout', type=float, default=0.1)
    parser.add_argument('--learning_rate', type=float, default=0.001)
    parser.add_argument('--num_workers', type=int, default=4)
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--local_rank', type=int, default=0)
    args = parser.parse_args()

    args.device = torch.device('cuda', args.local_rank)
    main(args)

注：以上代码只是一个示例，具体实现可能因环境、数据等因素而异。