注意

单击此处下载完整的示例代码

聊天机器人教程¶

在本教程中，我们将探讨循环序列到序列模型的一个有趣且实用的用例。我们将使用来自康奈尔电影对话语料库的电影剧本训练一个简单的聊天机器人。

对话模型是人工智能研究中的热门话题。聊天机器人可以在各种环境中找到，包括客户服务应用程序和在线帮助台。这些机器人通常由基于检索的模型驱动，这些模型会对特定形式的问题输出预定义的答案。在公司 IT 帮助台这样的高度受限领域，这些模型可能就足够了，但是，它们不够强大，无法满足更通用的用例。教会机器在多个领域与人类进行有意义的对话是一个尚未解决的研究问题。最近，深度学习的兴起使得 Google 的神经对话模型等强大的生成模型成为可能，这标志着朝着多领域生成对话模型迈出了一大步。在本教程中，我们将在 PyTorch 中实现这种类型的模型。

> hello?
Bot: hello .
> where am I?
Bot: you re in a hospital .
> who are you?
Bot: i m a lawyer .
> how are you doing?
Bot: i m fine .
> are you my friend?
Bot: no .
> you're under arrest
Bot: i m trying to help you !
> i'm just kidding
Bot: i m sorry .
> where are you from?
Bot: san francisco .
> it's time for me to leave
Bot: i know .
> goodbye
Bot: goodbye .

教程重点

处理康奈尔电影对话语料库数据集的加载和预处理
使用 Luong 注意力机制实现序列到序列模型
使用小批量联合训练编码器和解码器模型
实现贪婪搜索解码模块
与训练后的聊天机器人互动

鸣谢

本教程借鉴了以下资源的代码

吴元魁的 PyTorch 对话机器人实现：https://github.com/ywk991112/pytorch-chatbot
Sean Robertson 的实用 PyTorch seq2seq 翻译示例：https://github.com/spro/practical-pytorch/tree/master/seq2seq-translation
FloydHub 康奈尔电影语料库预处理代码：https://github.com/floydhub/textutil-preprocess-cornell-movie-corpus

准备工作¶

要开始使用，请下载电影对话语料库 zip 文件。

# and put in a ``data/`` directory under the current directory.
#
# After that, let’s import some necessities.
#

import torch
from torch.jit import script, trace
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import csv
import random
import re
import os
import unicodedata
import codecs
from io import open
import itertools
import math
import json


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

加载和预处理数据¶

下一步是重新格式化我们的数据文件，并将数据加载到我们可以处理的结构中。

的康奈尔电影对话语料库是电影角色对话的丰富数据集

10,292 对电影角色之间有 220,579 次对话交流
来自 617 部电影的 9,035 个角色
304,713 个总的语句

这个数据集很大很多样化，语言形式、时间段、情感等都有很大的差异。我们希望这种多样性能让我们的模型对各种形式的输入和查询具有鲁棒性。

首先，我们将查看数据文件中的几行，以查看原始格式。

corpus_name = "movie-corpus"
corpus = os.path.join("data", corpus_name)

def printLines(file, n=10):
    with open(file, 'rb') as datafile:
        lines = datafile.readlines()
    for line in lines[:n]:
        print(line)

printLines(os.path.join(corpus, "utterances.jsonl"))

b'{"id": "L1045", "conversation_id": "L1044", "text": "They do not!", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 1, "toks": [{"tok": "They", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "do", "tag": "VBP", "dep": "ROOT", "dn": [0, 2, 3]}, {"tok": "not", "tag": "RB", "dep": "neg", "up": 1, "dn": []}, {"tok": "!", "tag": ".", "dep": "punct", "up": 1, "dn": []}]}]}, "reply-to": "L1044", "timestamp": null, "vectors": []}\n'
b'{"id": "L1044", "conversation_id": "L1044", "text": "They do to!", "speaker": "u2", "meta": {"movie_id": "m0", "parsed": [{"rt": 1, "toks": [{"tok": "They", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "do", "tag": "VBP", "dep": "ROOT", "dn": [0, 2, 3]}, {"tok": "to", "tag": "TO", "dep": "dobj", "up": 1, "dn": []}, {"tok": "!", "tag": ".", "dep": "punct", "up": 1, "dn": []}]}]}, "reply-to": null, "timestamp": null, "vectors": []}\n'
b'{"id": "L985", "conversation_id": "L984", "text": "I hope so.", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 1, "toks": [{"tok": "I", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "hope", "tag": "VBP", "dep": "ROOT", "dn": [0, 2, 3]}, {"tok": "so", "tag": "RB", "dep": "advmod", "up": 1, "dn": []}, {"tok": ".", "tag": ".", "dep": "punct", "up": 1, "dn": []}]}]}, "reply-to": "L984", "timestamp": null, "vectors": []}\n'
b'{"id": "L984", "conversation_id": "L984", "text": "She okay?", "speaker": "u2", "meta": {"movie_id": "m0", "parsed": [{"rt": 1, "toks": [{"tok": "She", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "okay", "tag": "RB", "dep": "ROOT", "dn": [0, 2]}, {"tok": "?", "tag": ".", "dep": "punct", "up": 1, "dn": []}]}]}, "reply-to": null, "timestamp": null, "vectors": []}\n'
b'{"id": "L925", "conversation_id": "L924", "text": "Let\'s go.", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 0, "toks": [{"tok": "Let", "tag": "VB", "dep": "ROOT", "dn": [2, 3]}, {"tok": "\'s", "tag": "PRP", "dep": "nsubj", "up": 2, "dn": []}, {"tok": "go", "tag": "VB", "dep": "ccomp", "up": 0, "dn": [1]}, {"tok": ".", "tag": ".", "dep": "punct", "up": 0, "dn": []}]}]}, "reply-to": "L924", "timestamp": null, "vectors": []}\n'
b'{"id": "L924", "conversation_id": "L924", "text": "Wow", "speaker": "u2", "meta": {"movie_id": "m0", "parsed": [{"rt": 0, "toks": [{"tok": "Wow", "tag": "UH", "dep": "ROOT", "dn": []}]}]}, "reply-to": null, "timestamp": null, "vectors": []}\n'
b'{"id": "L872", "conversation_id": "L870", "text": "Okay -- you\'re gonna need to learn how to lie.", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 4, "toks": [{"tok": "Okay", "tag": "UH", "dep": "intj", "up": 4, "dn": []}, {"tok": "--", "tag": ":", "dep": "punct", "up": 4, "dn": []}, {"tok": "you", "tag": "PRP", "dep": "nsubj", "up": 4, "dn": []}, {"tok": "\'re", "tag": "VBP", "dep": "aux", "up": 4, "dn": []}, {"tok": "gon", "tag": "VBG", "dep": "ROOT", "dn": [0, 1, 2, 3, 6, 12]}, {"tok": "na", "tag": "TO", "dep": "aux", "up": 6, "dn": []}, {"tok": "need", "tag": "VB", "dep": "xcomp", "up": 4, "dn": [5, 8]}, {"tok": "to", "tag": "TO", "dep": "aux", "up": 8, "dn": []}, {"tok": "learn", "tag": "VB", "dep": "xcomp", "up": 6, "dn": [7, 11]}, {"tok": "how", "tag": "WRB", "dep": "advmod", "up": 11, "dn": []}, {"tok": "to", "tag": "TO", "dep": "aux", "up": 11, "dn": []}, {"tok": "lie", "tag": "VB", "dep": "xcomp", "up": 8, "dn": [9, 10]}, {"tok": ".", "tag": ".", "dep": "punct", "up": 4, "dn": []}]}]}, "reply-to": "L871", "timestamp": null, "vectors": []}\n'
b'{"id": "L871", "conversation_id": "L870", "text": "No", "speaker": "u2", "meta": {"movie_id": "m0", "parsed": [{"rt": 0, "toks": [{"tok": "No", "tag": "UH", "dep": "ROOT", "dn": []}]}]}, "reply-to": "L870", "timestamp": null, "vectors": []}\n'
b'{"id": "L870", "conversation_id": "L870", "text": "I\'m kidding.  You know how sometimes you just become this \\"persona\\"?  And you don\'t know how to quit?", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 2, "toks": [{"tok": "I", "tag": "PRP", "dep": "nsubj", "up": 2, "dn": []}, {"tok": "\'m", "tag": "VBP", "dep": "aux", "up": 2, "dn": []}, {"tok": "kidding", "tag": "VBG", "dep": "ROOT", "dn": [0, 1, 3]}, {"tok": ".", "tag": ".", "dep": "punct", "up": 2, "dn": [4]}, {"tok": " ", "tag": "_SP", "dep": "", "up": 3, "dn": []}]}, {"rt": 1, "toks": [{"tok": "You", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "know", "tag": "VBP", "dep": "ROOT", "dn": [0, 6, 11]}, {"tok": "how", "tag": "WRB", "dep": "advmod", "up": 3, "dn": []}, {"tok": "sometimes", "tag": "RB", "dep": "advmod", "up": 6, "dn": [2]}, {"tok": "you", "tag": "PRP", "dep": "nsubj", "up": 6, "dn": []}, {"tok": "just", "tag": "RB", "dep": "advmod", "up": 6, "dn": []}, {"tok": "become", "tag": "VBP", "dep": "ccomp", "up": 1, "dn": [3, 4, 5, 9]}, {"tok": "this", "tag": "DT", "dep": "det", "up": 9, "dn": []}, {"tok": "\\"", "tag": "``", "dep": "punct", "up": 9, "dn": []}, {"tok": "persona", "tag": "NN", "dep": "attr", "up": 6, "dn": [7, 8, 10]}, {"tok": "\\"", "tag": "\'\'", "dep": "punct", "up": 9, "dn": []}, {"tok": "?", "tag": ".", "dep": "punct", "up": 1, "dn": [12]}, {"tok": " ", "tag": "_SP", "dep": "", "up": 11, "dn": []}]}, {"rt": 4, "toks": [{"tok": "And", "tag": "CC", "dep": "cc", "up": 4, "dn": []}, {"tok": "you", "tag": "PRP", "dep": "nsubj", "up": 4, "dn": []}, {"tok": "do", "tag": "VBP", "dep": "aux", "up": 4, "dn": []}, {"tok": "n\'t", "tag": "RB", "dep": "neg", "up": 4, "dn": []}, {"tok": "know", "tag": "VB", "dep": "ROOT", "dn": [0, 1, 2, 3, 7, 8]}, {"tok": "how", "tag": "WRB", "dep": "advmod", "up": 7, "dn": []}, {"tok": "to", "tag": "TO", "dep": "aux", "up": 7, "dn": []}, {"tok": "quit", "tag": "VB", "dep": "xcomp", "up": 4, "dn": [5, 6]}, {"tok": "?", "tag": ".", "dep": "punct", "up": 4, "dn": []}]}]}, "reply-to": null, "timestamp": null, "vectors": []}\n'
b'{"id": "L869", "conversation_id": "L866", "text": "Like my fear of wearing pastels?", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 0, "toks": [{"tok": "Like", "tag": "IN", "dep": "ROOT", "dn": [2, 6]}, {"tok": "my", "tag": "PRP$", "dep": "poss", "up": 2, "dn": []}, {"tok": "fear", "tag": "NN", "dep": "pobj", "up": 0, "dn": [1, 3]}, {"tok": "of", "tag": "IN", "dep": "prep", "up": 2, "dn": [4]}, {"tok": "wearing", "tag": "VBG", "dep": "pcomp", "up": 3, "dn": [5]}, {"tok": "pastels", "tag": "NNS", "dep": "dobj", "up": 4, "dn": []}, {"tok": "?", "tag": ".", "dep": "punct", "up": 0, "dn": []}]}]}, "reply-to": "L868", "timestamp": null, "vectors": []}\n'

创建格式化的数据文件¶

为方便起见，我们将创建一个格式良好的数据文件，其中每行包含一个制表符分隔的查询句和响应句对。

以下函数有助于解析原始的 utterances.jsonl 数据文件。

loadLinesAndConversations 将文件的每一行拆分为包含以下字段的行字典：lineID、characterID 和文本，然后将它们分组为包含以下字段的对话：conversationID、movieID 和行。
extractSentencePairs 从对话中提取句子对

# Splits each line of the file to create lines and conversations
def loadLinesAndConversations(fileName):
    lines = {}
    conversations = {}
    with open(fileName, 'r', encoding='iso-8859-1') as f:
        for line in f:
            lineJson = json.loads(line)
            # Extract fields for line object
            lineObj = {}
            lineObj["lineID"] = lineJson["id"]
            lineObj["characterID"] = lineJson["speaker"]
            lineObj["text"] = lineJson["text"]
            lines[lineObj['lineID']] = lineObj

            # Extract fields for conversation object
            if lineJson["conversation_id"] not in conversations:
                convObj = {}
                convObj["conversationID"] = lineJson["conversation_id"]
                convObj["movieID"] = lineJson["meta"]["movie_id"]
                convObj["lines"] = [lineObj]
            else:
                convObj = conversations[lineJson["conversation_id"]]
                convObj["lines"].insert(0, lineObj)
            conversations[convObj["conversationID"]] = convObj

    return lines, conversations


# Extracts pairs of sentences from conversations
def extractSentencePairs(conversations):
    qa_pairs = []
    for conversation in conversations.values():
        # Iterate over all the lines of the conversation
        for i in range(len(conversation["lines"]) - 1):  # We ignore the last line (no answer for it)
            inputLine = conversation["lines"][i]["text"].strip()
            targetLine = conversation["lines"][i+1]["text"].strip()
            # Filter wrong samples (if one of the lists is empty)
            if inputLine and targetLine:
                qa_pairs.append([inputLine, targetLine])
    return qa_pairs

现在我们将调用这些函数并创建文件。我们将把它命名为 formatted_movie_lines.txt。

# Define path to new file
datafile = os.path.join(corpus, "formatted_movie_lines.txt")

delimiter = '\t'
# Unescape the delimiter
delimiter = str(codecs.decode(delimiter, "unicode_escape"))

# Initialize lines dict and conversations dict
lines = {}
conversations = {}
# Load lines and conversations
print("\nProcessing corpus into lines and conversations...")
lines, conversations = loadLinesAndConversations(os.path.join(corpus, "utterances.jsonl"))

# Write new csv file
print("\nWriting newly formatted file...")
with open(datafile, 'w', encoding='utf-8') as outputfile:
    writer = csv.writer(outputfile, delimiter=delimiter, lineterminator='\n')
    for pair in extractSentencePairs(conversations):
        writer.writerow(pair)

# Print a sample of lines
print("\nSample lines from file:")
printLines(datafile)

Processing corpus into lines and conversations...

Writing newly formatted file...

Sample lines from file:
b'They do to!\tThey do not!\n'
b'She okay?\tI hope so.\n'
b"Wow\tLet's go.\n"
b'"I\'m kidding.  You know how sometimes you just become this ""persona""?  And you don\'t know how to quit?"\tNo\n'
b"No\tOkay -- you're gonna need to learn how to lie.\n"
b"I figured you'd get to the good stuff eventually.\tWhat good stuff?\n"
b'What good stuff?\t"The ""real you""."\n'
b'"The ""real you""."\tLike my fear of wearing pastels?\n'
b'do you listen to this crap?\tWhat crap?\n'
b"What crap?\tMe.  This endless ...blonde babble. I'm like, boring myself.\n"

加载和裁剪数据¶

我们的下一个任务是创建一个词汇表并将查询/响应句子对加载到内存中。

请注意，我们正在处理单词序列，这些序列没有隐式映射到离散的数值空间。因此，我们必须通过将数据集中的每个唯一单词映射到一个索引值来创建一个映射。

为此，我们定义了一个 Voc 类，它保留从单词到索引的映射、从索引到单词的反向映射、每个单词的计数和总单词计数。该类提供了用于将单词添加到词汇表（addWord）、将句子中的所有单词添加到词汇表（addSentence）和修剪不常出现的单词（trim）的方法。稍后将详细介绍修剪。

# Default word tokens
PAD_token = 0  # Used for padding short sentences
SOS_token = 1  # Start-of-sentence token
EOS_token = 2  # End-of-sentence token

class Voc:
    def __init__(self, name):
        self.name = name
        self.trimmed = False
        self.word2index = {}
        self.word2count = {}
        self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
        self.num_words = 3  # Count SOS, EOS, PAD

    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.num_words
            self.word2count[word] = 1
            self.index2word[self.num_words] = word
            self.num_words += 1
        else:
            self.word2count[word] += 1

    # Remove words below a certain count threshold
    def trim(self, min_count):
        if self.trimmed:
            return
        self.trimmed = True

        keep_words = []

        for k, v in self.word2count.items():
            if v >= min_count:
                keep_words.append(k)

        print('keep_words {} / {} = {:.4f}'.format(
            len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index)
        ))

        # Reinitialize dictionaries
        self.word2index = {}
        self.word2count = {}
        self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
        self.num_words = 3 # Count default tokens

        for word in keep_words:
            self.addWord(word)

现在我们可以组装我们的词汇表和查询/响应句子对。在我们准备使用这些数据之前，我们必须进行一些预处理。

首先，我们必须使用 unicodeToAscii 将 Unicode 字符串转换为 ASCII 字符串。接下来，我们应该将所有字母转换为小写并修剪所有非字母字符，除了基本标点符号（normalizeString）。最后，为了帮助训练收敛，我们将过滤掉长度大于 MAX_LENGTH 阈值的句子（filterPairs）。

MAX_LENGTH = 10  # Maximum sentence length to consider

# 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)
    s = re.sub(r"\s+", r" ", s).strip()
    return s

# Read query/response pairs and return a voc object
def readVocs(datafile, corpus_name):
    print("Reading lines...")
    # Read the file and split into lines
    lines = open(datafile, 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]
    voc = Voc(corpus_name)
    return voc, pairs

# Returns True if both sentences in a pair 'p' are under the MAX_LENGTH threshold
def filterPair(p):
    # Input sequences need to preserve the last word for EOS token
    return len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH

# Filter pairs using the ``filterPair`` condition
def filterPairs(pairs):
    return [pair for pair in pairs if filterPair(pair)]

# Using the functions defined above, return a populated voc object and pairs list
def loadPrepareData(corpus, corpus_name, datafile, save_dir):
    print("Start preparing training data ...")
    voc, pairs = readVocs(datafile, corpus_name)
    print("Read {!s} sentence pairs".format(len(pairs)))
    pairs = filterPairs(pairs)
    print("Trimmed to {!s} sentence pairs".format(len(pairs)))
    print("Counting words...")
    for pair in pairs:
        voc.addSentence(pair[0])
        voc.addSentence(pair[1])
    print("Counted words:", voc.num_words)
    return voc, pairs


# Load/Assemble voc and pairs
save_dir = os.path.join("data", "save")
voc, pairs = loadPrepareData(corpus, corpus_name, datafile, save_dir)
# Print some pairs to validate
print("\npairs:")
for pair in pairs[:10]:
    print(pair)

Start preparing training data ...
Reading lines...
Read 221282 sentence pairs
Trimmed to 64313 sentence pairs
Counting words...
Counted words: 18082

pairs:
['they do to !', 'they do not !']
['she okay ?', 'i hope so .']
['wow', 'let s go .']
['what good stuff ?', 'the real you .']
['the real you .', 'like my fear of wearing pastels ?']
['do you listen to this crap ?', 'what crap ?']
['well no . . .', 'then that s all you had to say .']
['then that s all you had to say .', 'but']
['but', 'you always been this selfish ?']
['have fun tonight ?', 'tons']

另一个有助于在训练期间更快收敛的策略是从我们的词汇表中修剪很少使用的单词。减少特征空间也会降低模型必须学习近似的函数的难度。我们将通过一个两步过程来实现这一点

使用 voc.trim 函数修剪使用次数低于 MIN_COUNT 阈值的单词。
过滤掉包含已修剪单词的词对。

MIN_COUNT = 3    # Minimum word count threshold for trimming

def trimRareWords(voc, pairs, MIN_COUNT):
    # Trim words used under the MIN_COUNT from the voc
    voc.trim(MIN_COUNT)
    # Filter out pairs with trimmed words
    keep_pairs = []
    for pair in pairs:
        input_sentence = pair[0]
        output_sentence = pair[1]
        keep_input = True
        keep_output = True
        # Check input sentence
        for word in input_sentence.split(' '):
            if word not in voc.word2index:
                keep_input = False
                break
        # Check output sentence
        for word in output_sentence.split(' '):
            if word not in voc.word2index:
                keep_output = False
                break

        # Only keep pairs that do not contain trimmed word(s) in their input or output sentence
        if keep_input and keep_output:
            keep_pairs.append(pair)

    print("Trimmed from {} pairs to {}, {:.4f} of total".format(len(pairs), len(keep_pairs), len(keep_pairs) / len(pairs)))
    return keep_pairs


# Trim voc and pairs
pairs = trimRareWords(voc, pairs, MIN_COUNT)

keep_words 7833 / 18079 = 0.4333
Trimmed from 64313 pairs to 53131, 0.8261 of total

为模型准备数据¶

虽然我们已经付出了很多努力将我们的数据准备和处理成一个不错的词汇表对象和句子对列表，但我们的模型最终将期望数值火炬张量作为输入。在seq2seq 翻译教程中可以找到一种准备处理后的数据的方法。在该教程中，我们使用大小为 1 的批量，这意味着我们所要做的就是将句子对中的单词转换为词汇表中对应的索引，然后将它们提供给模型。

但是，如果您有兴趣加速训练或想要利用 GPU 并行化功能，则需要使用小批量进行训练。

使用小批量还意味着我们必须注意批量中句子长度的变化。为了容纳批量中不同大小的句子，我们将使我们的批量输入张量形状为(max_length, batch_size)，其中短于max_length的句子在EOS_token之后进行零填充。

如果我们只是通过将单词转换为它们的索引（indexesFromSentence）并将英语句子转换为张量并进行零填充，我们的张量将具有(batch_size, max_length) 的形状，并且索引第一维将返回所有时间步长的完整序列。但是，我们需要能够按时间索引我们的批量，并在批量中的所有序列中索引。因此，我们将输入批量的形状转置为(max_length, batch_size)，以便跨第一维索引返回跨批量中所有句子的时间步长。我们在 zeroPadding 函数中隐式地处理这种转置。

inputVar 函数处理将句子转换为张量，最终创建一个正确形状的零填充张量的过程。它还返回一个包含批量中每个序列的lengths 张量的张量，该张量将在稍后传递给我们的解码器。

outputVar 函数执行类似于 inputVar 的功能，但它不返回 lengths 张量，而是返回一个二进制掩码张量和最大目标句子长度。二进制掩码张量与输出目标张量具有相同的形状，但每个元素都是PAD_token 的元素为 0，其他元素为 1。

batch2TrainData 只需获取一堆词对并使用上述函数返回输入和目标张量即可。

def indexesFromSentence(voc, sentence):
    return [voc.word2index[word] for word in sentence.split(' ')] + [EOS_token]


def zeroPadding(l, fillvalue=PAD_token):
    return list(itertools.zip_longest(*l, fillvalue=fillvalue))

def binaryMatrix(l, value=PAD_token):
    m = []
    for i, seq in enumerate(l):
        m.append([])
        for token in seq:
            if token == PAD_token:
                m[i].append(0)
            else:
                m[i].append(1)
    return m

# Returns padded input sequence tensor and lengths
def inputVar(l, voc):
    indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]
    lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
    padList = zeroPadding(indexes_batch)
    padVar = torch.LongTensor(padList)
    return padVar, lengths

# Returns padded target sequence tensor, padding mask, and max target length
def outputVar(l, voc):
    indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]
    max_target_len = max([len(indexes) for indexes in indexes_batch])
    padList = zeroPadding(indexes_batch)
    mask = binaryMatrix(padList)
    mask = torch.BoolTensor(mask)
    padVar = torch.LongTensor(padList)
    return padVar, mask, max_target_len

# Returns all items for a given batch of pairs
def batch2TrainData(voc, pair_batch):
    pair_batch.sort(key=lambda x: len(x[0].split(" ")), reverse=True)
    input_batch, output_batch = [], []
    for pair in pair_batch:
        input_batch.append(pair[0])
        output_batch.append(pair[1])
    inp, lengths = inputVar(input_batch, voc)
    output, mask, max_target_len = outputVar(output_batch, voc)
    return inp, lengths, output, mask, max_target_len


# Example for validation
small_batch_size = 5
batches = batch2TrainData(voc, [random.choice(pairs) for _ in range(small_batch_size)])
input_variable, lengths, target_variable, mask, max_target_len = batches

print("input_variable:", input_variable)
print("lengths:", lengths)
print("target_variable:", target_variable)
print("mask:", mask)
print("max_target_len:", max_target_len)

input_variable: tensor([[  86,   24,  140,  829,   62],
        [   6,  355, 1362,  206,  566],
        [  36,  735,   14,   72, 1919],
        [  17,  140,  140, 2160,   85],
        [  62,   28,  158,   14,   14],
        [1012,  461,  140,    2,    2],
        [3223,   10,   14,    0,    0],
        [1012,    2,    2,    0,    0],
        [   6,    0,    0,    0,    0],
        [   2,    0,    0,    0,    0]])
lengths: tensor([10,  8,  8,  6,  6])
target_variable: tensor([[  18,   11,  101,   93,  277],
        [ 483,  113,   19,  311,   72],
        [   5,  241,   10,   72,   10],
        [  22,  706,    2,   19,    2],
        [2010,   14,    0,   24,    0],
        [1556,    2,    0,  136,    0],
        [  14,    0,    0,    5,    0],
        [   2,    0,    0,   48,    0],
        [   0,    0,    0,   14,    0],
        [   0,    0,    0,    2,    0]])
mask: tensor([[ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True, False,  True, False],
        [ True,  True, False,  True, False],
        [ True, False, False,  True, False],
        [ True, False, False,  True, False],
        [False, False, False,  True, False],
        [False, False, False,  True, False]])
max_target_len: 10

定义模型¶

Seq2Seq 模型¶

我们对话机器人的核心是一个序列到序列 (seq2seq) 模型。seq2seq 模型的目标是使用固定大小的模型，将可变长度序列作为输入，并返回可变长度序列作为输出。

Sutskever 等人发现通过使用两个独立的循环神经网络，我们可以完成这项任务。一个 RNN 充当编码器，将可变长度输入序列编码为固定长度的上下文向量。理论上，这个上下文向量（RNN 的最后一层隐藏层）将包含有关输入到机器人的查询句子的语义信息。第二个 RNN 是一个解码器，它接受一个输入词和上下文向量，并返回对序列中下一个词的猜测，以及在下次迭代中使用的隐藏状态。

图片来源：https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_intro/

编码器¶

编码器 RNN 在每次时间步长上依次遍历输入句子中的每个标记（例如单词），在每次时间步长上输出一个“输出”向量和一个“隐藏状态”向量。隐藏状态向量随后被传递到下一个时间步长，而输出向量被记录下来。编码器将它在序列中的每个点上看到的上下文转换为高维空间中的一组点，解码器将使用这些点为给定的任务生成有意义的输出。

我们编码器的核心是一个多层门控循环单元，由Cho 等人于 2014 年发明。我们将使用 GRU 的双向变体，这意味着实际上存在两个独立的 RNN：一个以正常顺序接收输入序列，另一个以相反顺序接收输入序列。每个网络的输出在每个时间步长上求和。使用双向 GRU 将使我们能够编码过去和未来的上下文。

双向 RNN

图片来源：https://colah.github.io/posts/2015-09-NN-Types-FP/

请注意，embedding 层用于在任意大小的特征空间中编码我们的词索引。对于我们的模型，该层将每个单词映射到大小为hidden_size 的特征空间。经过训练后，这些值应该编码具有相似含义的单词之间的语义相似性。

最后，如果将填充的序列批次传递给 RNN 模块，我们必须分别使用 nn.utils.rnn.pack_padded_sequence 和 nn.utils.rnn.pad_packed_sequence 在 RNN 传递周围打包和解包填充。

计算图

将词索引转换为嵌入。

为 RNN 模块打包填充的序列批次。

通过 GRU 进行正向传递。

解包填充。

对双向 GRU 输出求和。

返回输出和最终隐藏状态。

输入

input_seq：输入句子的批次；形状=(max_length, batch_size)
input_lengths：与批次中每个句子相对应的句子长度列表；形状=(batch_size)
hidden：隐藏状态；形状=(n_layers x num_directions, batch_size, hidden_size)

输出

outputs：来自 GRU 的最后一层隐藏层的输出特征（双向输出的总和）；形状=(max_length, batch_size, hidden_size)
hidden：来自 GRU 的更新后的隐藏状态；形状=(n_layers x num_directions, batch_size, hidden_size)

class EncoderRNN(nn.Module):
    def __init__(self, hidden_size, embedding, n_layers=1, dropout=0):
        super(EncoderRNN, self).__init__()
        self.n_layers = n_layers
        self.hidden_size = hidden_size
        self.embedding = embedding

        # Initialize GRU; the input_size and hidden_size parameters are both set to 'hidden_size'
        #   because our input size is a word embedding with number of features == hidden_size
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers,
                          dropout=(0 if n_layers == 1 else dropout), bidirectional=True)

    def forward(self, input_seq, input_lengths, hidden=None):
        # Convert word indexes to embeddings
        embedded = self.embedding(input_seq)
        # Pack padded batch of sequences for RNN module
        packed = nn.utils.rnn.pack_padded_sequence(embedded, input_lengths)
        # Forward pass through GRU
        outputs, hidden = self.gru(packed, hidden)
        # Unpack padding
        outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs)
        # Sum bidirectional GRU outputs
        outputs = outputs[:, :, :self.hidden_size] + outputs[:, : ,self.hidden_size:]
        # Return output and final hidden state
        return outputs, hidden

解码器¶

解码器 RNN 以逐个标记的方式生成响应句子。它使用编码器的上下文向量和内部隐藏状态来生成序列中的下一个词。它会继续生成词，直到输出一个EOS_token，表示句子的结束。一个香草 seq2seq 解码器的常见问题是，如果我们仅仅依靠上下文向量来编码整个输入序列的含义，我们很可能会丢失信息。这在处理长输入序列时尤其如此，极大地限制了我们解码器的能力。

为了解决这个问题，Bahdanau 等人创建了一个“注意力机制”，允许解码器关注输入序列的某些部分，而不是在每一步都使用整个固定上下文。

在高层次上，注意力是使用解码器的当前隐藏状态和编码器的输出计算的。输出的注意力权重与输入序列具有相同的形状，允许我们将其乘以编码器输出，得到一个加权和，它指示要关注的编码器输出的哪些部分。Sean Robertson 的图很好地描述了这一点

Luong 等人在 Bahdanau 等人的基础上进行了改进，创建了“全局注意力”。关键的区别在于，使用“全局注意力”，我们考虑了编码器所有隐藏状态，而 Bahdanau 等人的“局部注意力”只考虑了当前时间步的编码器隐藏状态。另一个区别是，使用“全局注意力”，我们只使用当前时间步的解码器隐藏状态来计算注意力权重或能量。Bahdanau 等人的注意力计算需要了解前一时间步的解码器状态。此外，Luong 等人还提供了各种方法来计算编码器输出和解码器输出之间的注意力能量，这些方法被称为“得分函数”

其中 \(h_t\) = 当前目标解码器状态，\(\bar{h}_s\) = 所有编码器状态。

总的来说，全局注意力机制可以用下图概括。注意，我们将“注意力层”实现为一个单独的 nn.Module，称为 Attn。此模块的输出是一个形状为(batch_size, 1, max_length) 的 softmax 归一化权重张量。

# Luong attention layer
class Attn(nn.Module):
    def __init__(self, method, hidden_size):
        super(Attn, self).__init__()
        self.method = method
        if self.method not in ['dot', 'general', 'concat']:
            raise ValueError(self.method, "is not an appropriate attention method.")
        self.hidden_size = hidden_size
        if self.method == 'general':
            self.attn = nn.Linear(self.hidden_size, hidden_size)
        elif self.method == 'concat':
            self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
            self.v = nn.Parameter(torch.FloatTensor(hidden_size))

    def dot_score(self, hidden, encoder_output):
        return torch.sum(hidden * encoder_output, dim=2)

    def general_score(self, hidden, encoder_output):
        energy = self.attn(encoder_output)
        return torch.sum(hidden * energy, dim=2)

    def concat_score(self, hidden, encoder_output):
        energy = self.attn(torch.cat((hidden.expand(encoder_output.size(0), -1, -1), encoder_output), 2)).tanh()
        return torch.sum(self.v * energy, dim=2)

    def forward(self, hidden, encoder_outputs):
        # Calculate the attention weights (energies) based on the given method
        if self.method == 'general':
            attn_energies = self.general_score(hidden, encoder_outputs)
        elif self.method == 'concat':
            attn_energies = self.concat_score(hidden, encoder_outputs)
        elif self.method == 'dot':
            attn_energies = self.dot_score(hidden, encoder_outputs)

        # Transpose max_length and batch_size dimensions
        attn_energies = attn_energies.t()

        # Return the softmax normalized probability scores (with added dimension)
        return F.softmax(attn_energies, dim=1).unsqueeze(1)

现在我们已经定义了注意力子模块，我们可以实现实际的解码器模型。对于解码器，我们将手动一次一步地馈送批处理。这意味着我们的嵌入词张量和 GRU 输出都将具有形状(1, batch_size, hidden_size)。

计算图

获取当前输入词的嵌入。

通过单向 GRU 进行前向传播。

从 (2) 中的当前 GRU 输出计算注意力权重。

将注意力权重乘以编码器输出以获得新的“加权和”上下文向量。

使用 Luong 等式 5 将加权上下文向量和 GRU 输出连接起来。

使用 Luong 等式 6 预测下一个词（不使用 softmax）。

返回输出和最终隐藏状态。

输入

input_step：输入序列批处理的一次时间步（一个词）；形状=(1, batch_size)
last_hidden：GRU 的最终隐藏层；形状=(n_layers x num_directions, batch_size, hidden_size)
encoder_outputs：编码器模型的输出；形状=(max_length, batch_size, hidden_size)

输出

output：softmax 归一化张量，给出每个词在解码序列中成为正确下一个词的概率；形状=(batch_size, voc.num_words)
hidden：GRU 的最终隐藏状态；形状=(n_layers x num_directions, batch_size, hidden_size)

class LuongAttnDecoderRNN(nn.Module):
    def __init__(self, attn_model, embedding, hidden_size, output_size, n_layers=1, dropout=0.1):
        super(LuongAttnDecoderRNN, self).__init__()

        # Keep for reference
        self.attn_model = attn_model
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.n_layers = n_layers
        self.dropout = dropout

        # Define layers
        self.embedding = embedding
        self.embedding_dropout = nn.Dropout(dropout)
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers, dropout=(0 if n_layers == 1 else dropout))
        self.concat = nn.Linear(hidden_size * 2, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)

        self.attn = Attn(attn_model, hidden_size)

    def forward(self, input_step, last_hidden, encoder_outputs):
        # Note: we run this one step (word) at a time
        # Get embedding of current input word
        embedded = self.embedding(input_step)
        embedded = self.embedding_dropout(embedded)
        # Forward through unidirectional GRU
        rnn_output, hidden = self.gru(embedded, last_hidden)
        # Calculate attention weights from the current GRU output
        attn_weights = self.attn(rnn_output, encoder_outputs)
        # Multiply attention weights to encoder outputs to get new "weighted sum" context vector
        context = attn_weights.bmm(encoder_outputs.transpose(0, 1))
        # Concatenate weighted context vector and GRU output using Luong eq. 5
        rnn_output = rnn_output.squeeze(0)
        context = context.squeeze(1)
        concat_input = torch.cat((rnn_output, context), 1)
        concat_output = torch.tanh(self.concat(concat_input))
        # Predict next word using Luong eq. 6
        output = self.out(concat_output)
        output = F.softmax(output, dim=1)
        # Return output and final hidden state
        return output, hidden

定义训练过程¶

掩蔽损失¶

由于我们正在处理填充序列的批次，因此我们不能在计算损失时简单地考虑张量的所有元素。我们定义 maskNLLLoss 来根据解码器的输出张量、目标张量和描述目标张量填充的二进制掩码张量来计算我们的损失。此损失函数计算与掩码张量中为1 的元素相对应的平均负对数似然。

def maskNLLLoss(inp, target, mask):
    nTotal = mask.sum()
    crossEntropy = -torch.log(torch.gather(inp, 1, target.view(-1, 1)).squeeze(1))
    loss = crossEntropy.masked_select(mask).mean()
    loss = loss.to(device)
    return loss, nTotal.item()

单个训练迭代¶

该 train 函数包含单个训练迭代（一批输入）的算法。

我们将使用一些巧妙的技巧来帮助收敛

第一个技巧是使用教师强迫。这意味着，以由 teacher_forcing_ratio 设置的某个概率，我们使用当前目标词作为解码器的下一个输入，而不是使用解码器的当前猜测。此技术充当解码器的训练轮，有助于更有效地训练。但是，教师强迫会导致推理期间模型不稳定，因为解码器可能没有足够的机会在训练期间真正构建自己的输出序列。因此，我们必须注意如何设置 teacher_forcing_ratio，不要被快速收敛所迷惑。
我们实现的第二个技巧是梯度裁剪。这是一种常用的技术，用于对抗“梯度爆炸”问题。本质上，通过将梯度裁剪或阈值设置为最大值，我们防止梯度呈指数增长，从而导致溢出 (NaN) 或超过成本函数中的陡峭悬崖。

图像来源：Goodfellow 等人。深度学习。2016 年。https://www.deeplearningbook.org/

操作序列

将整个输入批次前向传播到编码器。

将解码器输入初始化为 SOS_token，并将隐藏状态初始化为编码器的最终隐藏状态。

将输入批次序列一次一步地前向传播到解码器。

如果教师强迫：将下一个解码器输入设置为当前目标；否则：将下一个解码器输入设置为当前解码器输出。

计算并累积损失。

执行反向传播。

裁剪梯度。

更新编码器和解码器模型参数。

注意

PyTorch 的 RNN 模块 (RNN、LSTM、GRU) 可以像任何其他非循环层一样使用，只需将整个输入序列（或序列批次）传递给它们即可。我们在 encoder 中像这样使用 GRU 层。现实情况是在幕后，有一个迭代过程循环遍历每个时间步，计算隐藏状态。或者，您可以一次一步地运行这些模块。在这种情况下，我们必须像必须为 decoder 模型所做的那样，在训练过程中手动循环遍历序列。只要您保持对这些模块的正确概念模型，实现顺序模型就可以非常简单。

def train(input_variable, lengths, target_variable, mask, max_target_len, encoder, decoder, embedding,
          encoder_optimizer, decoder_optimizer, batch_size, clip, max_length=MAX_LENGTH):

    # Zero gradients
    encoder_optimizer.zero_grad()
    decoder_optimizer.zero_grad()

    # Set device options
    input_variable = input_variable.to(device)
    target_variable = target_variable.to(device)
    mask = mask.to(device)
    # Lengths for RNN packing should always be on the CPU
    lengths = lengths.to("cpu")

    # Initialize variables
    loss = 0
    print_losses = []
    n_totals = 0

    # Forward pass through encoder
    encoder_outputs, encoder_hidden = encoder(input_variable, lengths)

    # Create initial decoder input (start with SOS tokens for each sentence)
    decoder_input = torch.LongTensor([[SOS_token for _ in range(batch_size)]])
    decoder_input = decoder_input.to(device)

    # Set initial decoder hidden state to the encoder's final hidden state
    decoder_hidden = encoder_hidden[:decoder.n_layers]

    # Determine if we are using teacher forcing this iteration
    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False

    # Forward batch of sequences through decoder one time step at a time
    if use_teacher_forcing:
        for t in range(max_target_len):
            decoder_output, decoder_hidden = decoder(
                decoder_input, decoder_hidden, encoder_outputs
            )
            # Teacher forcing: next input is current target
            decoder_input = target_variable[t].view(1, -1)
            # Calculate and accumulate loss
            mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
            loss += mask_loss
            print_losses.append(mask_loss.item() * nTotal)
            n_totals += nTotal
    else:
        for t in range(max_target_len):
            decoder_output, decoder_hidden = decoder(
                decoder_input, decoder_hidden, encoder_outputs
            )
            # No teacher forcing: next input is decoder's own current output
            _, topi = decoder_output.topk(1)
            decoder_input = torch.LongTensor([[topi[i][0] for i in range(batch_size)]])
            decoder_input = decoder_input.to(device)
            # Calculate and accumulate loss
            mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
            loss += mask_loss
            print_losses.append(mask_loss.item() * nTotal)
            n_totals += nTotal

    # Perform backpropagation
    loss.backward()

    # Clip gradients: gradients are modified in place
    _ = nn.utils.clip_grad_norm_(encoder.parameters(), clip)
    _ = nn.utils.clip_grad_norm_(decoder.parameters(), clip)

    # Adjust model weights
    encoder_optimizer.step()
    decoder_optimizer.step()

    return sum(print_losses) / n_totals

训练迭代¶

现在是时候将完整的训练过程与数据联系起来。该 trainIters 函数负责运行 n_iterations 次训练，前提是传递了模型、优化器、数据等。此函数相当不言自明，因为我们在 train 函数中完成了繁重的工作。

需要注意的一点是，当我们保存模型时，我们会保存一个包含编码器和解码器 state_dicts（参数）、优化器 state_dicts、损失、迭代等信息的 tarball。以这种方式保存模型将为我们提供检查点的最大灵活性。加载检查点后，我们将能够使用模型参数来运行推理，或者我们可以从停止的地方继续训练。

def trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer, embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size, print_every, save_every, clip, corpus_name, loadFilename):

    # Load batches for each iteration
    training_batches = [batch2TrainData(voc, [random.choice(pairs) for _ in range(batch_size)])
                      for _ in range(n_iteration)]

    # Initializations
    print('Initializing ...')
    start_iteration = 1
    print_loss = 0
    if loadFilename:
        start_iteration = checkpoint['iteration'] + 1

    # Training loop
    print("Training...")
    for iteration in range(start_iteration, n_iteration + 1):
        training_batch = training_batches[iteration - 1]
        # Extract fields from batch
        input_variable, lengths, target_variable, mask, max_target_len = training_batch

        # Run a training iteration with batch
        loss = train(input_variable, lengths, target_variable, mask, max_target_len, encoder,
                     decoder, embedding, encoder_optimizer, decoder_optimizer, batch_size, clip)
        print_loss += loss

        # Print progress
        if iteration % print_every == 0:
            print_loss_avg = print_loss / print_every
            print("Iteration: {}; Percent complete: {:.1f}%; Average loss: {:.4f}".format(iteration, iteration / n_iteration * 100, print_loss_avg))
            print_loss = 0

        # Save checkpoint
        if (iteration % save_every == 0):
            directory = os.path.join(save_dir, model_name, corpus_name, '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size))
            if not os.path.exists(directory):
                os.makedirs(directory)
            torch.save({
                'iteration': iteration,
                'en': encoder.state_dict(),
                'de': decoder.state_dict(),
                'en_opt': encoder_optimizer.state_dict(),
                'de_opt': decoder_optimizer.state_dict(),
                'loss': loss,
                'voc_dict': voc.__dict__,
                'embedding': embedding.state_dict()
            }, os.path.join(directory, '{}_{}.tar'.format(iteration, 'checkpoint')))

定义评估¶

训练完模型后，我们希望能够自己与机器人交谈。首先，我们必须定义我们希望模型如何解码编码的输入。

贪婪解码¶

贪婪解码是我们训练期间使用的解码方法，当我们不使用教师强迫时。换句话说，在每个时间步，我们只选择具有最高 softmax 值的 decoder_output 中的词。这种解码方法在单一时间步级别上是最优的。

为了促进贪婪解码操作，我们定义了一个 GreedySearchDecoder 类。运行时，此类的对象会接收形状为(input_seq length, 1) 的输入序列 (input_seq)、标量输入长度 (input_length) 张量和一个 max_length 来限制响应句子长度。输入句子使用以下计算图进行评估

计算图

将输入前向传播到编码器模型。

准备编码器的最终隐藏层，作为解码器的第一个隐藏输入。

将解码器的第一个输入初始化为 SOS_token。

初始化张量以将解码后的词追加到其中。

迭代地一次解码一个词标记

通过解码器进行前向传播。

获取最可能的词标记及其 softmax 得分。

记录标记和得分。

准备当前标记作为下一个解码器输入。

返回词标记和得分的集合。

class GreedySearchDecoder(nn.Module):
    def __init__(self, encoder, decoder):
        super(GreedySearchDecoder, self).__init__()
        self.encoder = encoder
        self.decoder = decoder

    def forward(self, input_seq, input_length, max_length):
        # Forward input through encoder model
        encoder_outputs, encoder_hidden = self.encoder(input_seq, input_length)
        # Prepare encoder's final hidden layer to be first hidden input to the decoder
        decoder_hidden = encoder_hidden[:decoder.n_layers]
        # Initialize decoder input with SOS_token
        decoder_input = torch.ones(1, 1, device=device, dtype=torch.long) * SOS_token
        # Initialize tensors to append decoded words to
        all_tokens = torch.zeros([0], device=device, dtype=torch.long)
        all_scores = torch.zeros([0], device=device)
        # Iteratively decode one word token at a time
        for _ in range(max_length):
            # Forward pass through decoder
            decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs)
            # Obtain most likely word token and its softmax score
            decoder_scores, decoder_input = torch.max(decoder_output, dim=1)
            # Record token and score
            all_tokens = torch.cat((all_tokens, decoder_input), dim=0)
            all_scores = torch.cat((all_scores, decoder_scores), dim=0)
            # Prepare current token to be next decoder input (add a dimension)
            decoder_input = torch.unsqueeze(decoder_input, 0)
        # Return collections of word tokens and scores
        return all_tokens, all_scores

评估我的文本¶

现在我们已经定义了解码方法，我们可以编写函数来评估字符串输入句子。该 evaluate 函数管理处理输入句子的底层过程。我们首先将句子格式化为具有batch_size==1 的词索引的输入批次。我们通过将句子的词转换为其对应的索引，并将维度转置来为我们的模型准备张量来实现这一点。我们还创建一个 lengths 张量，其中包含输入句子的长度。在本例中，lengths 是标量，因为我们一次只评估一个句子 (batch_size==1)。接下来，我们使用 GreedySearchDecoder 对象 (searcher) 获取解码后的响应句子张量。最后，我们将响应的索引转换为词并返回解码后的词列表。

evaluateInput 充当我们聊天机器人的用户界面。调用时，将出现一个输入文本字段，我们可以在其中输入查询句子。在输入查询句子并按下Enter 键后，我们的文本将以与训练数据相同的方式进行规范化，并最终馈送到 evaluate 函数以获取解码后的输出句子。我们循环此过程，这样我们就可以一直与我们的机器人聊天，直到我们输入“q”或“quit”。

最后，如果输入的句子包含不在词汇表中的单词，我们会通过打印错误消息并提示用户输入另一个句子来优雅地处理这种情况。

def evaluate(encoder, decoder, searcher, voc, sentence, max_length=MAX_LENGTH):
    ### Format input sentence as a batch
    # words -> indexes
    indexes_batch = [indexesFromSentence(voc, sentence)]
    # Create lengths tensor
    lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
    # Transpose dimensions of batch to match models' expectations
    input_batch = torch.LongTensor(indexes_batch).transpose(0, 1)
    # Use appropriate device
    input_batch = input_batch.to(device)
    lengths = lengths.to("cpu")
    # Decode sentence with searcher
    tokens, scores = searcher(input_batch, lengths, max_length)
    # indexes -> words
    decoded_words = [voc.index2word[token.item()] for token in tokens]
    return decoded_words


def evaluateInput(encoder, decoder, searcher, voc):
    input_sentence = ''
    while(1):
        try:
            # Get input sentence
            input_sentence = input('> ')
            # Check if it is quit case
            if input_sentence == 'q' or input_sentence == 'quit': break
            # Normalize sentence
            input_sentence = normalizeString(input_sentence)
            # Evaluate sentence
            output_words = evaluate(encoder, decoder, searcher, voc, input_sentence)
            # Format and print response sentence
            output_words[:] = [x for x in output_words if not (x == 'EOS' or x == 'PAD')]
            print('Bot:', ' '.join(output_words))

        except KeyError:
            print("Error: Encountered unknown word.")

运行模型¶

最后，是时候运行我们的模型了！

无论我们是想训练还是测试聊天机器人模型，我们都必须初始化单个编码器和解码器模型。在下面的代码块中，我们设置了所需的配置，选择从头开始还是设置要加载的检查点，并构建和初始化模型。随意尝试不同的模型配置以优化性能。

# Configure models
model_name = 'cb_model'
attn_model = 'dot'
#``attn_model = 'general'``
#``attn_model = 'concat'``
hidden_size = 500
encoder_n_layers = 2
decoder_n_layers = 2
dropout = 0.1
batch_size = 64

# Set checkpoint to load from; set to None if starting from scratch
loadFilename = None
checkpoint_iter = 4000

从检查点加载的示例代码

loadFilename = os.path.join(save_dir, model_name, corpus_name,
                    '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
                    '{}_checkpoint.tar'.format(checkpoint_iter))

# Load model if a ``loadFilename`` is provided
if loadFilename:
    # If loading on same machine the model was trained on
    checkpoint = torch.load(loadFilename)
    # If loading a model trained on GPU to CPU
    #checkpoint = torch.load(loadFilename, map_location=torch.device('cpu'))
    encoder_sd = checkpoint['en']
    decoder_sd = checkpoint['de']
    encoder_optimizer_sd = checkpoint['en_opt']
    decoder_optimizer_sd = checkpoint['de_opt']
    embedding_sd = checkpoint['embedding']
    voc.__dict__ = checkpoint['voc_dict']


print('Building encoder and decoder ...')
# Initialize word embeddings
embedding = nn.Embedding(voc.num_words, hidden_size)
if loadFilename:
    embedding.load_state_dict(embedding_sd)
# Initialize encoder & decoder models
encoder = EncoderRNN(hidden_size, embedding, encoder_n_layers, dropout)
decoder = LuongAttnDecoderRNN(attn_model, embedding, hidden_size, voc.num_words, decoder_n_layers, dropout)
if loadFilename:
    encoder.load_state_dict(encoder_sd)
    decoder.load_state_dict(decoder_sd)
# Use appropriate device
encoder = encoder.to(device)
decoder = decoder.to(device)
print('Models built and ready to go!')

Building encoder and decoder ...
Models built and ready to go!

运行训练¶

如果你想训练模型，请运行以下代码块。

首先，我们设置训练参数，然后初始化优化器，最后调用trainIters 函数来运行我们的训练迭代。

# Configure training/optimization
clip = 50.0
teacher_forcing_ratio = 1.0
learning_rate = 0.0001
decoder_learning_ratio = 5.0
n_iteration = 4000
print_every = 1
save_every = 500

# Ensure dropout layers are in train mode
encoder.train()
decoder.train()

# Initialize optimizers
print('Building optimizers ...')
encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)
if loadFilename:
    encoder_optimizer.load_state_dict(encoder_optimizer_sd)
    decoder_optimizer.load_state_dict(decoder_optimizer_sd)

# If you have CUDA, configure CUDA to call
for state in encoder_optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.cuda()

for state in decoder_optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.cuda()

# Run training iterations
print("Starting Training!")
trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer,
           embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size,
           print_every, save_every, clip, corpus_name, loadFilename)

Building optimizers ...
Starting Training!
Initializing ...
Training...
Iteration: 1; Percent complete: 0.0%; Average loss: 8.9557
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运行评估¶

要与你的模型聊天，请运行以下代码块。

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

# Initialize search module
searcher = GreedySearchDecoder(encoder, decoder)

# Begin chatting (uncomment and run the following line to begin)
# evaluateInput(encoder, decoder, searcher, voc)

结论¶

这就是这次的全部内容，各位。恭喜你，你现在已经掌握了构建生成式聊天机器人模型的基础知识！如果你有兴趣，可以通过调整模型和训练参数以及自定义训练模型的数据来尝试调整聊天机器人的行为。

查看其他教程，了解更多 PyTorch 中酷炫的深度学习应用！

脚本的总运行时间：（5 分钟 49.643 秒）

由 Sphinx-Gallery 生成的画廊

聊天机器人教程¶

准备工作¶

加载和预处理数据¶

创建格式化的数据文件¶

加载和裁剪数据¶

为模型准备数据¶

定义模型¶

Seq2Seq 模型¶

编码器¶

解码器¶

定义训练过程¶

掩蔽损失¶

单个训练迭代¶

训练迭代¶

定义评估¶

贪婪解码¶

评估我的文本¶

运行模型¶

运行训练¶

运行评估¶

结论¶

文档

教程

资源