代码实现

PyTorch 代码实现

#!/usr/bin/env python3

"""
Implementation of Transformer: Attention Is All You Need

References:
    - [Paper arXiv: Attention Is All You Need](https://arxiv.org/abs/1706.03762)
    - [Blog Post: The Illustrated Transformer](http://jalammar.github.io/illustrated-transformer/)
    - [YouTube Video: The Illustrated Transformer](https://youtu.be/ISNdQcPhsts?si=VyFfVKoITGOV78OA)
"""

import argparse
import math
import os
import sys
import torch
import torch.nn as nn
import torch.optim as optim
from torchinfo import summary


class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_seq_length):
        super(PositionalEncoding, self).__init__()
        pe = torch.zeros(max_seq_length, d_model)
        position = torch.arange(0, max_seq_length, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)
        )
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        self.register_buffer("pe", pe.unsqueeze(0))

    def forward(self, x):
        return x + self.pe[:, : x.size(1)]


class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        # Ensure that the model dimension (d_model) is divisible by the number of heads
        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
        # Initialize dimensions
        self.d_model = d_model  # Model's dimension
        self.num_heads = num_heads  # Number of attention heads
        self.d_k = (
            d_model // num_heads
        )  # Dimension of each head's key, query, and value
        # Linear layers for transforming inputs
        self.W_q = nn.Linear(d_model, d_model)  # Query transformation
        self.W_k = nn.Linear(d_model, d_model)  # Key transformation
        self.W_v = nn.Linear(d_model, d_model)  # Value transformation
        self.W_o = nn.Linear(d_model, d_model)  # Output transformation

    def scaled_dot_product_attention(self, Q, K, V, mask=None):
        # Calculate attention scores
        attn_scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
        # Apply mask if provided (useful for preventing attention to certain parts like padding)
        if mask is not None:
            attn_scores = attn_scores.masked_fill(mask == 0, -1e9)
        # Softmax is applied to obtain attention probabilities
        attn_probs = torch.softmax(attn_scores, dim=-1)
        # Multiply by values to obtain the final output
        output = torch.matmul(attn_probs, V)
        return output

    def split_heads(self, x):
        # Reshape the input to have num_heads for multi-head attention
        batch_size, seq_length, d_model = x.size()
        return x.view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)

    def combine_heads(self, x):
        # Combine the multiple heads back to original shape
        batch_size, _, seq_length, d_k = x.size()
        return x.transpose(1, 2).contiguous().view(batch_size, seq_length, self.d_model)

    def forward(self, Q, K, V, mask=None):
        # Apply linear transformations and split heads
        Q = self.split_heads(self.W_q(Q))
        K = self.split_heads(self.W_k(K))
        V = self.split_heads(self.W_v(V))
        # Perform scaled dot-product attention
        attn_output = self.scaled_dot_product_attention(Q, K, V, mask)
        # Combine heads and apply output transformation
        output = self.W_o(self.combine_heads(attn_output))
        return output


class EncoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, dropout):
        super(EncoderLayer, self).__init__()
        self.self_attn = MultiHeadAttention(d_model, num_heads)
        self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask):
        attn_output = self.self_attn(x, x, x, mask)
        x = self.norm1(x + self.dropout(attn_output))
        ff_output = self.feed_forward(x)
        x = self.norm2(x + self.dropout(ff_output))
        return x


class DecoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, dropout):
        super(DecoderLayer, self).__init__()
        self.self_attn = MultiHeadAttention(d_model, num_heads)
        self.cross_attn = MultiHeadAttention(d_model, num_heads)
        self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, enc_output, src_mask, tgt_mask):
        attn_output = self.self_attn(x, x, x, tgt_mask)
        x = self.norm1(x + self.dropout(attn_output))
        attn_output = self.cross_attn(x, enc_output, enc_output, src_mask)
        x = self.norm2(x + self.dropout(attn_output))
        ff_output = self.feed_forward(x)
        x = self.norm3(x + self.dropout(ff_output))
        return x


class PositionWiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff):
        super(PositionWiseFeedForward, self).__init__()
        self.fc1 = nn.Linear(d_model, d_ff)
        self.fc2 = nn.Linear(d_ff, d_model)
        self.relu = nn.ReLU()

    def forward(self, x):
        return self.fc2(self.relu(self.fc1(x)))


class Transformer(nn.Module):
    def __init__(
        self,
        src_vocab_size,
        tgt_vocab_size,
        d_model,
        num_heads,
        num_layers,
        d_ff,
        max_seq_length,
        dropout,
    ):
        super(Transformer, self).__init__()
        self.encoder_embedding = nn.Embedding(src_vocab_size, d_model)
        self.decoder_embedding = nn.Embedding(tgt_vocab_size, d_model)
        self.positional_encoding = PositionalEncoding(d_model, max_seq_length)
        self.encoder_layers = nn.ModuleList(
            [EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)]
        )
        self.decoder_layers = nn.ModuleList(
            [DecoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)]
        )
        self.fc = nn.Linear(d_model, tgt_vocab_size)
        self.dropout = nn.Dropout(dropout)

    def generate_mask(self, src, tgt):
        src_mask = (src != 0).unsqueeze(1).unsqueeze(2)
        tgt_mask = (tgt != 0).unsqueeze(1).unsqueeze(3)
        seq_length = tgt.size(1)
        nopeak_mask = (
            1 - torch.triu(torch.ones(1, seq_length, seq_length), diagonal=1)
        ).bool()
        tgt_mask = tgt_mask & nopeak_mask
        return src_mask, tgt_mask

    def forward(self, src, tgt):
        src_mask, tgt_mask = self.generate_mask(src, tgt)
        src_embedded = self.dropout(
            self.positional_encoding(self.encoder_embedding(src))
        )
        tgt_embedded = self.dropout(
            self.positional_encoding(self.decoder_embedding(tgt))
        )
        enc_output = src_embedded
        for enc_layer in self.encoder_layers:
            enc_output = enc_layer(enc_output, src_mask)
        dec_output = tgt_embedded
        for dec_layer in self.decoder_layers:
            dec_output = dec_layer(dec_output, enc_output, src_mask, tgt_mask)

        output = self.fc(dec_output)
        return output


def main(args):
    transformer = Transformer(
        args.src_vocab_size,
        args.tgt_vocab_size,
        args.d_model,
        args.num_heads,
        args.num_layers,
        args.d_ff,
        args.max_seq_length,
        args.dropout,
    )

    # Generate random sample data
    src_data = torch.randint(
        1, args.src_vocab_size, (64, args.max_seq_length)
    )  # (batch_size, seq_length)
    tgt_data = torch.randint(
        1, args.tgt_vocab_size, (64, args.max_seq_length)
    )  # (batch_size, seq_length)

    print(summary(transformer, input_data=[src_data, tgt_data], device="cpu"))

    criterion = nn.CrossEntropyLoss(ignore_index=0)
    optimizer = optim.Adam(
        transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9
    )

    transformer.train()

    for epoch in range(10):
        optimizer.zero_grad()
        output = transformer(src_data, tgt_data[:, :-1])
        loss = criterion(
            output.contiguous().view(-1, args.tgt_vocab_size),
            tgt_data[:, 1:].contiguous().view(-1),
        )
        loss.backward()
        optimizer.step()
        print(f"Epoch: {epoch+1}, Loss: {loss.item()}")

    transformer.eval()

    # Generate random sample validation data
    val_src_data = torch.randint(
        1, args.src_vocab_size, (64, args.max_seq_length)
    )  # (batch_size, seq_length)
    val_tgt_data = torch.randint(
        1, args.tgt_vocab_size, (64, args.max_seq_length)
    )  # (batch_size, seq_length)

    with torch.no_grad():
        val_output = transformer(val_src_data, val_tgt_data[:, :-1])
        val_loss = criterion(
            val_output.contiguous().view(-1, args.tgt_vocab_size),
            val_tgt_data[:, 1:].contiguous().view(-1),
        )
        print(f"Validation Loss: {val_loss.item()}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument(
        "--src_vocab_size", type=int, default=5000, help="source vocabulary size"
    )
    parser.add_argument(
        "--tgt_vocab_size", type=int, default=5000, help="target vocabulary size"
    )
    parser.add_argument(
        "--d_model",
        type=int,
        default=512,
        help="dimensionality of model's input and output",
    )
    parser.add_argument(
        "--num_heads",
        type=int,
        default=8,
        help="number of attention heads in multi-head attention",
    )
    parser.add_argument(
        "--num_layers",
        type=int,
        default=6,
        help="number of layers for both the encoder and the decoder",
    )
    parser.add_argument(
        "--d_ff",
        type=int,
        default=2048,
        help="dimensionality of the inner layer in the feed-forward network",
    )
    parser.add_argument(
        "--max_seq_length",
        type=int,
        default=100,
        help="the maximum length of the sequence",
    )
    parser.add_argument(
        "--dropout",
        type=float,
        default=0.1,
        help="dropout rate for regularization",
    )

    try:
        args = parser.parse_args()
        main(args)
    except KeyboardInterrupt:
        print("Interrupted")
        try:
            sys.exit(0)
        except SystemExit:
            os._exit(0)