[oneAPI] 使用序列到序列网络和注意力进行翻译

比赛：https://marketing.csdn.net/p/f3e44fbfe46c465f4d9d6c23e38e0517
Intel® DevCloud for oneAPI：https://devcloud.intel.com/oneapi/get_started/aiAnalyticsToolkitSamples/

oneAPI特殊写法

import intel_extension_for_pytorch as ipex

# Device configuration
device = torch.device('xpu' if torch.cuda.is_available() else 'cpu')

encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)

'''
Apply Intel Extension for PyTorch optimization against the model object and optimizer object.
'''
encoder, encoder_optimizer = ipex.optimize(encoder, optimizer=encoder_optimizer)
decoder, decoder_optimizer = ipex.optimize(decoder, optimizer=decoder_optimizer)

criterion = nn.NLLLoss()

使用序列到序列网络和注意力进行翻译

让神经网络将法语翻译成英语

[KEY: > input, = target, < output]

> il est en train de peindre un tableau .
= he is painting a picture .
< he is painting a picture .

> pourquoi ne pas essayer ce vin delicieux ?
= why not try that delicious wine ?
< why not try that delicious wine ?

> elle n est pas poete mais romanciere .
= she is not a poet but a novelist .
< she not not a poet but a novelist .

> vous etes trop maigre .
= you re too skinny .
< you re all alone .

我们的模型通过序列到序列网络的简单但强大的思想实现的，其中两个循环神经网络一起工作将一个序列转换为另一个序列。编码器网络将输入序列压缩为向量，解码器网络将该向量展开为新序列

Intel® Optimization for PyTorch

在本次实验中，我们利用PyTorch和Intel® Optimization for PyTorch的强大功能，对PyTorch进行了精心的优化和扩展。这些优化举措极大地增强了PyTorch在各种任务中的性能，尤其是在英特尔硬件上的表现更加突出。通过这些优化策略，我们的模型在训练和推断过程中变得更加敏捷和高效，显著地减少了计算时间，提高了整体效能。我们通过深度融合硬件和软件的精巧设计，成功地释放了硬件潜力，使得模型的训练和应用变得更加快速和高效。这一系列优化举措为人工智能应用开辟了新的前景，带来了全新的可能性。

导入包

# -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import re
import random

import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F

import numpy as np
from torch.utils.data import TensorDataset, DataLoader, RandomSampler

import intel_extension_for_pytorch as ipex

device = torch.device("xpu" if torch.cuda.is_available() else "cpu")

加载数据并对数据进行处理

该项目的数据是一组数千个英语到法语的翻译对。

数据地址 https://download.pytorch.org/tutorial/data.zip
data/eng-fra.txt 请先下载，该文件是制表符分隔的翻译对列表：

I am cold.    J'ai froid.

与字符级 RNN 教程中使用的字符编码类似，我们将语言中的每个单词表示为一个单热向量，或除单个 1（在单词索引处）之外的由 0 组成的巨大向量。与语言中可能存在的几十个字符相比，单词的数量要多得多，因此编码向量要大得多。然而，我们将数据修剪为每种语言仅使用几千个单词。

准备数据的完整流程是：

• 读取文本文件并分成行，将行分成对
• 标准化文本，按长度和内容过滤
• 从成对的句子中制作单词列表

---

我们需要每个单词有一个唯一的索引，以便稍后用作网络的输入和目标。为了跟踪所有这些，我们将使用一个名为的==帮助器类Lang==，它具有 word → index ( word2index) 和 index → word ( index2word) 字典，以及每个单词的计数 word2count，稍后将用于替换稀有单词。

######################################################################
# Loading data files
# ==================
SOS_token = 0
EOS_token = 1

class Lang:
    def __init__(self, name):
        self.name = name
        self.word2index = {}
        self.word2count = {}
        self.index2word = {0: "SOS", 1: "EOS"}
        self.n_words = 2  # Count SOS and EOS

    def addSentence(self, sentence):
        for word in sentence.split(' '):
            self.addWord(word)

    def addWord(self, word):
        if word not in self.word2index:
            self.word2index[word] = self.n_words
            self.word2count[word] = 1
            self.index2word[self.n_words] = word
            self.n_words += 1
        else:
            self.word2count[word] += 1

将 Unicode 字符转换为 ASCII，将所有内容变为小写，并修剪大部分标点符号

######################################################################
# The files are all in Unicode, to simplify we will turn Unicode
# characters to ASCII, make everything lowercase, and trim most
# punctuation.
#

# Turn a Unicode string to plain ASCII, thanks to
# https://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
    )

# Lowercase, trim, and remove non-letter characters
def normalizeString(s):
    s = unicodeToAscii(s.lower().strip())
    s = re.sub(r"([.!?])", r" \1", s)
    s = re.sub(r"[^a-zA-Z!?]+", r" ", s)
    return s.strip()

为了读取数据文件，我们将文件分成行，然后将行分成对。这些文件都是英语→其他语言，所以如果我们想从其他语言→英语翻译，我添加了标志reverse 来反转对。

######################################################################
# To read the data file we will split the file into lines, and then split
# lines into pairs. The files are all English → Other Language, so if we
# want to translate from Other Language → English I added the ``reverse``
# flag to reverse the pairs.
#

def readLangs(lang1, lang2, reverse=False):
    print("Reading lines...")

    # Read the file and split into lines
    lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\
        read().strip().split('\n')

    # Split every line into pairs and normalize
    pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]

    # Reverse pairs, make Lang instances
    if reverse:
        pairs = [list(reversed(p)) for p in pairs]
        input_lang = Lang(lang2)
        output_lang = Lang(lang1)
    else:
        input_lang = Lang(lang1)
        output_lang = Lang(lang2)

    return input_lang, output_lang, pairs

序列到序列网络和注意力模型与介绍

序列到序列网络，或 seq2seq 网络，或编码器解码器网络，是由两个称为编码器和解码器的 RNN 组成的模型。编码器读取输入序列并输出单个向量，解码器读取该向量以产生输出序列。

编码器

seq2seq 网络的编码器是一个 RNN，它为输入句子中的每个单词输出一些值。对于每个输入单词，编码器输出一个向量和一个隐藏状态，并将隐藏状态用于下一个输入单词。

######################################################################
# The Seq2Seq Model
# =================
class EncoderRNN(nn.Module):
    def __init__(self, input_size, hidden_size, dropout_p=0.1):
        super(EncoderRNN, self).__init__()
        self.hidden_size = hidden_size

        self.embedding = nn.Embedding(input_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
        self.dropout = nn.Dropout(dropout_p)

    def forward(self, input):
        embedded = self.dropout(self.embedding(input))
        output, hidden = self.gru(embedded)
        return output, hidden

解码器

简单解码器

在最简单的 seq2seq 解码器中，我们仅使用编码器的最后一个输出。最后一个输出有时称为上下文向量，因为它对整个序列的上下文进行编码。该上下文向量用作解码器的初始隐藏状态。

在解码的每一步，解码器都会获得一个输入令牌和隐藏状态。初始输入标记是字符串开始 标记，第一个隐藏状态是上下文向量（编码器的最后一个隐藏状态）。

######################################################################
# Simple Decoder
class DecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size):
        super(DecoderRNN, self).__init__()
        self.embedding = nn.Embedding(output_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
        self.out = nn.Linear(hidden_size, output_size)

    def forward(self, encoder_outputs, encoder_hidden, target_tensor=None):
        batch_size = encoder_outputs.size(0)
        decoder_input = torch.empty(batch_size, 1, dtype=torch.long, device=device).fill_(SOS_token)
        decoder_hidden = encoder_hidden
        decoder_outputs = []

        for i in range(MAX_LENGTH):
            decoder_output, decoder_hidden  = self.forward_step(decoder_input, decoder_hidden)
            decoder_outputs.append(decoder_output)

            if target_tensor is not None:
                # Teacher forcing: Feed the target as the next input
                decoder_input = target_tensor[:, i].unsqueeze(1) # Teacher forcing
            else:
                # Without teacher forcing: use its own predictions as the next input
                _, topi = decoder_output.topk(1)
                decoder_input = topi.squeeze(-1).detach()  # detach from history as input

        decoder_outputs = torch.cat(decoder_outputs, dim=1)
        decoder_outputs = F.log_softmax(decoder_outputs, dim=-1)
        return decoder_outputs, decoder_hidden, None # We return `None` for consistency in the training loop

    def forward_step(self, input, hidden):
        output = self.embedding(input)
        output = F.relu(output)
        output, hidden = self.gru(output, hidden)
        output = self.out(output)
        return output, hidden

注意力解码器

如果仅在编码器和解码器之间传递上下文向量，则该单个向量承担对整个句子进行编码的负担。

注意力机制允许解码器网络针对解码器自身输出的每一步“关注”编码器输出的不同部分。首先我们计算一组注意力权重。这些将乘以编码器输出向量以创建加权组合。结果（attn_applied在代码中调用）应包含有关输入序列特定部分的信息，从而帮助解码器选择正确的输出单词。

######################################################################
# Attention Decoder
class BahdanauAttention(nn.Module):
    def __init__(self, hidden_size):
        super(BahdanauAttention, self).__init__()
        self.Wa = nn.Linear(hidden_size, hidden_size)
        self.Ua = nn.Linear(hidden_size, hidden_size)
        self.Va = nn.Linear(hidden_size, 1)

    def forward(self, query, keys):
        scores = self.Va(torch.tanh(self.Wa(query) + self.Ua(keys)))
        scores = scores.squeeze(2).unsqueeze(1)

        weights = F.softmax(scores, dim=-1)
        context = torch.bmm(weights, keys)

        return context, weights

class AttnDecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size, dropout_p=0.1):
        super(AttnDecoderRNN, self).__init__()
        self.embedding = nn.Embedding(output_size, hidden_size)
        self.attention = BahdanauAttention(hidden_size)
        self.gru = nn.GRU(2 * hidden_size, hidden_size, batch_first=True)
        self.out = nn.Linear(hidden_size, output_size)
        self.dropout = nn.Dropout(dropout_p)

    def forward(self, encoder_outputs, encoder_hidden, target_tensor=None):
        batch_size = encoder_outputs.size(0)
        decoder_input = torch.empty(batch_size, 1, dtype=torch.long, device=device).fill_(SOS_token)
        decoder_hidden = encoder_hidden
        decoder_outputs = []
        attentions = []

        for i in range(MAX_LENGTH):
            decoder_output, decoder_hidden, attn_weights = self.forward_step(
                decoder_input, decoder_hidden, encoder_outputs
            )
            decoder_outputs.append(decoder_output)
            attentions.append(attn_weights)

            if target_tensor is not None:
                # Teacher forcing: Feed the target as the next input
                decoder_input = target_tensor[:, i].unsqueeze(1) # Teacher forcing
            else:
                # Without teacher forcing: use its own predictions as the next input
                _, topi = decoder_output.topk(1)
                decoder_input = topi.squeeze(-1).detach()  # detach from history as input

        decoder_outputs = torch.cat(decoder_outputs, dim=1)
        decoder_outputs = F.log_softmax(decoder_outputs, dim=-1)
        attentions = torch.cat(attentions, dim=1)

        return decoder_outputs, decoder_hidden, attentions


    def forward_step(self, input, hidden, encoder_outputs):
        embedded =  self.dropout(self.embedding(input))

        query = hidden.permute(1, 0, 2)
        context, attn_weights = self.attention(query, encoder_outputs)
        input_gru = torch.cat((embedded, context), dim=2)

        output, hidden = self.gru(input_gru, hidden)
        output = self.out(output)

        return output, hidden, attn_weights

训练过程

准备训练数据

为了训练，对于每一对，我们需要一个输入张量（输入句子中单词的索引）和目标张量（目标句子中单词的索引）。在创建这些向量时，我们会将 EOS 附加到两个序列中。

######################################################################
# Training
def indexesFromSentence(lang, sentence):
    return [lang.word2index[word] for word in sentence.split(' ')]

def tensorFromSentence(lang, sentence):
    indexes = indexesFromSentence(lang, sentence)
    indexes.append(EOS_token)
    return torch.tensor(indexes, dtype=torch.long, device=device).view(1, -1)

def tensorsFromPair(pair):
    input_tensor = tensorFromSentence(input_lang, pair[0])
    target_tensor = tensorFromSentence(output_lang, pair[1])
    return (input_tensor, target_tensor)

def get_dataloader(batch_size):
    input_lang, output_lang, pairs = prepareData('eng', 'fra', True)

    n = len(pairs)
    input_ids = np.zeros((n, MAX_LENGTH), dtype=np.int32)
    target_ids = np.zeros((n, MAX_LENGTH), dtype=np.int32)

    for idx, (inp, tgt) in enumerate(pairs):
        inp_ids = indexesFromSentence(input_lang, inp)
        tgt_ids = indexesFromSentence(output_lang, tgt)
        inp_ids.append(EOS_token)
        tgt_ids.append(EOS_token)
        input_ids[idx, :len(inp_ids)] = inp_ids
        target_ids[idx, :len(tgt_ids)] = tgt_ids

    train_data = TensorDataset(torch.LongTensor(input_ids).to(device),
                               torch.LongTensor(target_ids).to(device))

    train_sampler = RandomSampler(train_data)
    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
    return input_lang, output_lang, train_dataloader

训练模型

通过编码器运行输入句子，并跟踪每个输出和最新的隐藏状态。然后，解码器将令牌作为其第一个输入，并将编码器的最后一个隐藏状态作为其第一个隐藏状态。

######################################################################
# Training the Model
def train_epoch(dataloader, encoder, decoder, encoder_optimizer,
          decoder_optimizer, criterion):

    total_loss = 0
    for data in dataloader:
        input_tensor, target_tensor = data

        encoder_optimizer.zero_grad()
        decoder_optimizer.zero_grad()

        encoder_outputs, encoder_hidden = encoder(input_tensor)
        decoder_outputs, _, _ = decoder(encoder_outputs, encoder_hidden, target_tensor)

        loss = criterion(
            decoder_outputs.view(-1, decoder_outputs.size(-1)),
            target_tensor.view(-1)
        )
        loss.backward()

        encoder_optimizer.step()
        decoder_optimizer.step()

        total_loss += loss.item()

    return total_loss / len(dataloader)

打印已用时间和估计剩余时间

######################################################################
# This is a helper function to print time elapsed and estimated time
# remaining given the current time and progress %.
import time
import math

def asMinutes(s):
    m = math.floor(s / 60)
    s -= m * 60
    return '%dm %ds' % (m, s)

def timeSince(since, percent):
    now = time.time()
    s = now - since
    es = s / (percent)
    rs = es - s
    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))

train多次调用并偶尔打印进度（示例的百分比、到目前为止的时间、估计时间）和平均损失

######################################################################
# The whole training process looks like this:
#
# -  Start a timer
# -  Initialize optimizers and criterion
# -  Create set of training pairs
# -  Start empty losses array for plotting
def train(train_dataloader, encoder, decoder, n_epochs, learning_rate=0.001,
               print_every=100, plot_every=100):
    start = time.time()
    plot_losses = []
    print_loss_total = 0  # Reset every print_every
    plot_loss_total = 0  # Reset every plot_every

    encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
    decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)

    '''
    Apply Intel Extension for PyTorch optimization against the model object and optimizer object.
    '''
    encoder, encoder_optimizer = ipex.optimize(encoder, optimizer=encoder_optimizer)
    decoder, decoder_optimizer = ipex.optimize(decoder, optimizer=decoder_optimizer)

    criterion = nn.NLLLoss()

    for epoch in range(1, n_epochs + 1):
        loss = train_epoch(train_dataloader, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)
        print_loss_total += loss
        plot_loss_total += loss

        if epoch % print_every == 0:
            print_loss_avg = print_loss_total / print_every
            print_loss_total = 0
            print('%s (%d %d%%) %.4f' % (timeSince(start, epoch / n_epochs),
                                        epoch, epoch / n_epochs * 100, print_loss_avg))

        if epoch % plot_every == 0:
            plot_loss_avg = plot_loss_total / plot_every
            plot_losses.append(plot_loss_avg)
            plot_loss_total = 0

    showPlot(plot_losses)

绘图

######################################################################
# Plotting results
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import matplotlib.ticker as ticker
import numpy as np

def showPlot(points):
    plt.figure()
    fig, ax = plt.subplots()
    # this locator puts ticks at regular intervals
    loc = ticker.MultipleLocator(base=0.2)
    ax.yaxis.set_major_locator(loc)
    plt.plot(points)

评估

######################################################################
# Evaluation
def evaluate(encoder, decoder, sentence, input_lang, output_lang):
    with torch.no_grad():
        input_tensor = tensorFromSentence(input_lang, sentence)

        encoder_outputs, encoder_hidden = encoder(input_tensor)
        decoder_outputs, decoder_hidden, decoder_attn = decoder(encoder_outputs, encoder_hidden)

        _, topi = decoder_outputs.topk(1)
        decoded_ids = topi.squeeze()

        decoded_words = []
        for idx in decoded_ids:
            if idx.item() == EOS_token:
                decoded_words.append('')
                break
            decoded_words.append(output_lang.index2word[idx.item()])
    return decoded_words, decoder_attn

######################################################################
# We can evaluate random sentences from the training set and print out the
# input, target, and output to make some subjective quality judgements:
def evaluateRandomly(encoder, decoder, n=10):
    for i in range(n):
        pair = random.choice(pairs)
        print('>', pair[0])
        print('=', pair[1])
        output_words, _ = evaluate(encoder, decoder, pair[0], input_lang, output_lang)
        output_sentence = ' '.join(output_words)
        print('<', output_sentence)
        print('')

训练和评估

hidden_size = 128
batch_size = 32

input_lang, output_lang, train_dataloader = get_dataloader(batch_size)

encoder = EncoderRNN(input_lang.n_words, hidden_size).to(device)
decoder = AttnDecoderRNN(hidden_size, output_lang.n_words).to(device)

train(train_dataloader, encoder, decoder, 80, print_every=5, plot_every=5)

######################################################################
# Set dropout layers to ``eval`` mode
encoder.eval()
decoder.eval()
evaluateRandomly(encoder, decoder)

可视化注意力代码

def showAttention(input_sentence, output_words, attentions):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    cax = ax.matshow(attentions.cpu().numpy(), cmap='bone')
    fig.colorbar(cax)

    # Set up axes
    ax.set_xticklabels([''] + input_sentence.split(' ') +
                       [''], rotation=90)
    ax.set_yticklabels([''] + output_words)

    # Show label at every tick
    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

    plt.show()


def evaluateAndShowAttention(input_sentence):
    output_words, attentions = evaluate(encoder, decoder, input_sentence, input_lang, output_lang)
    print('input =', input_sentence)
    print('output =', ' '.join(output_words))
    showAttention(input_sentence, output_words, attentions[0, :len(output_words), :])


evaluateAndShowAttention('il n est pas aussi grand que son pere')

evaluateAndShowAttention('je suis trop fatigue pour conduire')

evaluateAndShowAttention('je suis desole si c est une question idiote')

evaluateAndShowAttention('je suis reellement fiere de vous')

结果

可以发现得到的翻译结果比较好

参考资料

https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html#