transformer_postag.py

# Defined in Section 4.7.3

import math
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence
from collections import defaultdict
from vocab import Vocab
from utils import load_treebank, length_to_mask

#tqdm是一个Pyth模块，能以进度条的方式显式迭代的进度
from tqdm.auto import tqdm

class TransformerDataset(Dataset):
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return len(self.data)
    def __getitem__(self, i):
        return self.data[i]

def collate_fn(examples):
    lengths = torch.tensor([len(ex[0]) for ex in examples])
    inputs = [torch.tensor(ex[0]) for ex in examples]
    targets = [torch.tensor(ex[1]) for ex in examples]
    # 对batch内的样本进行padding，使其具有相同长度
    inputs = pad_sequence(inputs, batch_first=True, padding_value=vocab["<pad>"])
    targets = pad_sequence(targets, batch_first=True, padding_value=vocab["<pad>"])
    return inputs, lengths, targets, inputs != vocab["<pad>"]

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=512):
        super(PositionalEncoding, self).__init__()

        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term2 = torch.pow(torch.tensor(10000.0), torch.arange(0, d_model, 2).float() / d_model)
        div_term1 = torch.pow(torch.tensor(10000.0), torch.arange(1, d_model, 2).float() / d_model)
        # 高级切片方式，即从0开始，两个步长取一个。即奇数和偶数位置赋值不一样。直观来看就是每一句话的
        pe[:, 0::2] = torch.sin(position * div_term2)
        pe[:, 1::2] = torch.cos(position * div_term1)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

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

class Transformer(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class,
                 dim_feedforward=512, num_head=2, num_layers=2, dropout=0.1, max_len=512, activation: str = "relu"):
        super(Transformer, self).__init__()
        # 词嵌入层
        self.embedding_dim = embedding_dim
        self.embeddings = nn.Embedding(vocab_size, embedding_dim)
        self.position_embedding = PositionalEncoding(embedding_dim, dropout, max_len)
        # 编码层：使用Transformer
        encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_head, dim_feedforward, dropout, activation)
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        # 输出层
        self.output = nn.Linear(hidden_dim, num_class)

    def forward(self, inputs, lengths):
        inputs = torch.transpose(inputs, 0, 1)
        hidden_states = self.embeddings(inputs)
        hidden_states = self.position_embedding(hidden_states)
        attention_mask = length_to_mask(lengths) == False
        hidden_states = self.transformer(hidden_states, src_key_padding_mask=attention_mask).transpose(0, 1)
        logits = self.output(hidden_states)
        log_probs = F.log_softmax(logits, dim=-1)
        return log_probs

embedding_dim = 128
hidden_dim = 128
batch_size = 32
num_epoch = 5

#加载数据
train_data, test_data, vocab, pos_vocab = load_treebank()
train_dataset = TransformerDataset(train_data)
test_dataset = TransformerDataset(test_data)
train_data_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)
test_data_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, shuffle=False)

num_class = len(pos_vocab)

#加载模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Transformer(len(vocab), embedding_dim, hidden_dim, num_class)
model.to(device) #将模型加载到GPU中（如果已经正确安装）

#训练过程
nll_loss = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001) #使用Adam优化器

model.train()
for epoch in range(num_epoch):
    total_loss = 0
    for batch in tqdm(train_data_loader, desc=f"Training Epoch {epoch}"):
        inputs, lengths, targets, mask = [x.to(device) for x in batch]
        log_probs = model(inputs, lengths)
        loss = nll_loss(log_probs[mask], targets[mask])
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print(f"Loss: {total_loss:.2f}")

#测试过程
acc = 0
total = 0
for batch in tqdm(test_data_loader, desc=f"Testing"):
    inputs, lengths, targets, mask = [x.to(device) for x in batch]
    with torch.no_grad():
        output = model(inputs, lengths)
        acc += (output.argmax(dim=-1) == targets)[mask].sum().item()
        total += mask.sum().item()

#输出在测试集上的准确率
print(f"Acc: {acc / total:.2f}")