Transformer_translation
import torch
import warnings
warnings.filterwarnings("ignore")
import torch
import torch.nn as nn
import math
from transformers import MarianTokenizer
from datasets import load_dataset
from typing import List
from torch import Tensor
from torch.nn import Transformer
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
from timeit import default_timer as timer
import urllib.request
import os
from torch.cuda.amp import GradScaler, autocast
import logging
tokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-de-en')
# 定义特殊token的索引
PAD_IDX = tokenizer.pad_token_id
BOS_IDX = tokenizer.bos_token_id if tokenizer.bos_token_id is not None else 1
EOS_IDX = tokenizer.eos_token_id
UNK_IDX = tokenizer.unk_token_id
class PositionalEncoding(nn.Module):
def __init__(self, emb_size: int, dropout: float, maxlen: int = 5000):
super(PositionalEncoding, self).__init__()
den = torch.exp(-torch.arange(0, emb_size, 2) * math.log(10000) / emb_size)
pos = torch.arange(0, maxlen).reshape(maxlen, 1)
pos_embedding = torch.zeros((maxlen, emb_size))
pos_embedding[:, 0::2] = torch.sin(pos * den)
pos_embedding[:, 1::2] = torch.cos(pos * den)
pos_embedding = pos_embedding.unsqueeze(-2)
self.dropout = nn.Dropout(dropout)
self.register_buffer('pos_embedding', pos_embedding)
def forward(self, token_embedding: Tensor):
return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])
class TokenEmbedding(nn.Module):
def __init__(self, vocab_size: int, emb_size):
super(TokenEmbedding, self).__init__()
self.embedding = nn.Embedding(vocab_size, emb_size)
self.emb_size = emb_size
def forward(self, tokens: Tensor):
return self.embedding(tokens.long()) * math.sqrt(self.emb_size)
class Seq2SeqTransformer(nn.Module):
def __init__(self, num_encoder_layers: int, num_decoder_layers: int,
emb_size: int, nhead: int, src_vocab_size: int,
tgt_vocab_size: int, dim_feedforward: int = 512, dropout: float = 0.1):
super(Seq2SeqTransformer, self).__init__()
self.transformer = Transformer(d_model=emb_size,
nhead=nhead,
num_encoder_layers=num_encoder_layers,
num_decoder_layers=num_decoder_layers,
dim_feedforward=dim_feedforward,
dropout=dropout)
self.generator = nn.Linear(emb_size, tgt_vocab_size)
self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)
self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)
self.positional_encoding = PositionalEncoding(emb_size, dropout=dropout)
def forward(self, src: Tensor, trg: Tensor, src_mask: Tensor,
tgt_mask: Tensor, src_padding_mask: Tensor,
tgt_padding_mask: Tensor, memory_key_padding_mask: Tensor):
src_emb = self.positional_encoding(self.src_tok_emb(src))
tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))
outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,
src_padding_mask, tgt_padding_mask, memory_key_padding_mask)
return self.generator(outs)
def encode(self, src: Tensor, src_mask: Tensor):
return self.transformer.encoder(self.positional_encoding(self.src_tok_emb(src)), src_mask)
def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):
return self.transformer.decoder(self.positional_encoding(self.tgt_tok_emb(tgt)), memory, tgt_mask)
def generate_square_subsequent_mask(sz):
mask = (torch.triu(torch.ones((sz, sz), device=DEVICE)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def create_mask(src, tgt):
src_seq_len = src.shape[0]
tgt_seq_len = tgt.shape[0]
tgt_mask = generate_square_subsequent_mask(tgt_seq_len)
src_mask = torch.zeros((src_seq_len, src_seq_len), device=DEVICE).type(torch.bool)
src_padding_mask = (src == PAD_IDX).transpose(0, 1)
tgt_padding_mask = (tgt == PAD_IDX).transpose(0, 1)
return src_mask, tgt_mask, src_padding_mask, tgt_padding_mask
def greedy_decode(model, src, src_mask, max_len, start_symbol):
src = src.to(DEVICE)
src_mask = src_mask.to(DEVICE)
memory = model.encode(src, src_mask)
ys = torch.ones(1, 1).fill_(start_symbol).type(torch.long).to(DEVICE)
for i in range(max_len-1):
memory = memory.to(DEVICE)
tgt_mask = (generate_square_subsequent_mask(ys.size(0))
.type(torch.bool)).to(DEVICE)
out = model.decode(ys, memory, tgt_mask)
out = out.transpose(0, 1)
prob = model.generator(out[:, -1])
_, next_word = torch.max(prob, dim=1)
next_word = next_word.item()
ys = torch.cat([ys, torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=0)
if next_word == EOS_IDX:
break
return ys
def translate(model: torch.nn.Module, src_sentence: str):
model.eval()
tokens = tokenizer(src_sentence, return_tensors='pt', padding=True)
src = tokens['input_ids'].transpose(0, 1).to(DEVICE)
src_mask = (torch.zeros(src.shape[0], src.shape[0])).type(torch.bool).to(DEVICE)
tgt_tokens = greedy_decode(model, src, src_mask, max_len=src.shape[0] + 5, start_symbol=BOS_IDX).flatten()
return tokenizer.decode(tgt_tokens.tolist(), skip_special_tokens=True)
BATCH_SIZE = 32 # 减小批次大小，原来是64
EMB_SIZE = 512 # 保持不变
NHEAD = 8 # 保持不变
FFN_HID_DIM = 512 # 改回512，原来改成了1024
NUM_ENCODER_LAYERS = 3 # 改回3，原来改成了4
NUM_DECODER_LAYERS = 3 # 改回3，原来改成了4
NUM_EPOCHS = 18 # 保持不变
SRC_VOCAB_SIZE = tokenizer.vocab_size
TGT_VOCAB_SIZE = tokenizer.vocab_size
DEVICE = 'cuda'
transformer = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,
NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)
transformer.load_state_dict(torch.load('./transformer_translation.pth'))
transformer = transformer.to(DEVICE)
print('开始翻译...')
print(translate(transformer, "Eine Gruppe von Freunden spielt Billiade."))
print('翻译完成！！')