Chatbot Tutorial

Trong tutorial này, chúng tôi khám phá một trường hợp thú vị, sử dụng các mô hình tuần tự (sequence-to-sequence). Chúng ta sẽ train một chatbot đơn giản bằng cách sử dụng các kịch bản phim từ Cornell Movie-Dialogs Corpus.

Mô hình hội thoại là hot topic trong nghiên cứu về trí tuệ nhân tạo (AI). Chatbots có thể tìm thấy trong nhiều thiết lập khác nhau, bao gồm các ứng dụng dịch vụ khách hàng, bộ phận hỗ trợ trực tuyến. Các chatbot này thường được hỗ trợ bởi các mô hình dựa trên truy xuất, đưa ra câu trả lời trước cho các câu hỏi ở một số dạng nhất định. Trong miền bị hạn chế cao như bộ phận IT helpdesk của công ty, những mô hình này có thể đủ, tuy nhiên chúng không đủ mạnh cho các trường hợp khái quát, mở rộng hơn. Học máy thực hiện cuộc trò truyện có ý nghĩa với con người trong nhiều lĩnh vực là vấn đề đang được nghiên cứu và giải quyết. Gần đây, sự bùng nổ học sâu (deep learning) đã cho phép tạo ra các mô hình tổng quát mạnh mẽ như Google’s Neural Conversational Model, đánh dấu một bước tiến lớn hướng tới các mô hình đàm thoại tổng hợp đa miền. Trong tutorial này, chúng tôi sẽ triển khai loại model này trong PyTorch.

> 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 .

Điểm nổi bật của Tutorial

• Xử lý việc tải và tiền xử lý tập dữ liệu Cornell Movie-Dialogs Corpus
• Triển khai mô hình sequence-to-sequence với Luong attention mechanism(s)
• Cùng train các mô hình mã hóa và giải mã sử dụng mini-batches
• Triển khai mô-đun giải mã greedy-search
• Tương tác với chatbot đã được train

Lời cảm ơn

Tutorial này tham khảo code từ nguồn sau:

1. Yuan-Kuei Wu’s pytorch-chatbot implementation: https://github.com/ywk991112/pytorch-chatbot
2. Sean Robertson’s practical-pytorch seq2seq-translation example: https://github.com/spro/practical-pytorch/tree/master/seq2seq-translation
3. FloydHub Cornell Movie Corpus preprocessing code: https://github.com/floydhub/textutil-preprocess-cornell-movie-corpus

Chuẩn bị

Để bắt đầu, download Movie-Dialogs Corpus zip file.

# 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")

Tải và tiền xử lý dữ liệu

Bước tiếp theo là định dạng lại tệp dữ liệu của chúng tôi và tải dữ liệu vào các cấu trúc mà chúng tôi có thể làm việc.

Cornell Movie-Dialogs Corpus là một tập dữ liệu phong phú về hội thoại nhân vật trong phim:

• 220,579 trao đổi hội thoại, giữa 10,292 cặp nhân vật trong phim
• 9,035 nhân vật 617 bộ phim
• 304,713 phát biểu

Tập dữ liệu này rất lớn và đa dạng, đồng thời có sự khác biệt lớn về hình thức ngôn ngữ, khoảng thời gian. Chúng tôi hy vọng rằng, sự đa dạng này làm cho mô hình của chúng tôi trở nên mạnh mẽ đối với nhiều dạng đầu vào, truy vấn.

Trước tiên, chúng ta sẽ xem xét một số dòng trong tệp dữ liệu của mình để xem định dạng ban đầu.

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"))

Out:

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'

Tạo định dạng cho file dữ liệu

Để thuận tiện, chúng tôi sẽ tạo một tệp dữ liệu theo định dạng json, trong đó mỗi dòng chưa một câu truy vấn được phân tách bằng tab và một cặp câu phản hồi.

Các hàm sau đây giúp phân tích dữ liệu thô trong file dữ liệu utterances.jsonl.

• loadLinesAndConversations chuyển đổi từng dòng trong file sang dạng dictionary với các trường: lineID, characterID và text, sau đó nhóm chúng thành các cuộc trò chuyện bằng các trường: conversationID, movieID, và dòng.
• extractSentencePairs trích xuất cặp câu từ các đoạn hội thoại

# 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

Bây giờ, chúng ta sẽ gọi hàm và tạo file. Chúng tôi gọi là 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)

Out:

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"

Tải và cắt dữ liệu

Công việc tiếp theo của chúng ta là tạo các từ vựng và tải các cặp câu truy vấn/trả lời vào bộ nhớ.

Lưu ý rằng, chúng ta đang xử lý tuần tự theo từ (word), không có ánh xạ rõ ràng tới một không gian số rời rạc. Vì vậy, chúng ta phải tạo một từ bằng cách ánh xạ từng từ duy nhất mà chúng ta gặp trong tập dữ liệu của mình tới một giá trị chỉ mục.

Để làm điều này, chúng tôi định nghĩa một lớp Voc, lớp này giữ ánh xạ các từ (word) sang chỉ mục (index), ánh xạ ngược các chỉ mục thành các từ, số lượng mỗi từ và tổng số từ. Lớp này cung cấp các phương thức để thêm một từ vào danh sách từ vựng (addWord), thêm tất cả các từ trong một câu (addSentence) và cắt bớt những từ ít thấy (trim), thêm phần cắt về sau.

# 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)

Bây giờ, chúng ta có thể tập hợp các cặp từ vựng và câu truy vấn/phản hồi. Trước khi sẵn sàng sử dụng dữ liệu này, chúng ta phải thực hiện một số quá trình tiền xử lý.

Đầu tiên, chúng ta phải chuyển đổi chuỗi Unicode sang ASCII bằng unicodeToAscii. Tiếp theo, chúng ta nên chuyển đổi tất cả các chữ cái thành chữ thường và cắt bớt tất cả các ký tự không phải chữ cái ngoại trừ dấu câu cơ bản (normalizeString). Cuối cùng, để hỗ trợ việc huấn luyện hội tụ, chúng ta sẽ lọc ra các câu có độ dài lớn hơn ngưỡng MAX_LENGTH (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)

Out:

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']

Một phương pháp khác có lợi để đạt được sự hội tụ nhanh hơn trong quá trình đào tạo là cắt bớt những từ hiếm khi được xử dụng ra khỏi vốn từ vựng của chúng ta. Việc giảm không gian đặc trưng cũng sẽ làm giảm độ khó của hàm mà mô hình phải học để gần đúng. Chúng tôi sẽ thực hiện việc này như một quy trình gồm 2 bước:

1. Cắt bớt các từ sử dụng dưới ngưỡng MIN_COUNT dùng hàm voc.trim.
2. Lọc các cặp có từ được cắt bớt.

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)

Out:

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

Chuẩn bị dữ liệu cho Model

Mặc dù, chúng tôi đã rất cố gắng trong việc chuẩn bị và tổng hợp dữ liệu của mình thành một đối tượng từ vựng và danh sách các cặp câu. Nhưng cuối cùng, các model của chúng tôi sẽ mong muốn numerical torch tensors làm đầu vào. Một cách chuẩn bị dữ liệu đã xử ly cho models, bạn có thể tìm trong seq2seq translation tutorial. Trong tutorial này, chúng tôi sử dụng batch size of 1, tất cả những gì chúng ta phải làm là chuyển đổi các từ trong cập câu thành chỉ mục tương ứng của chúng trong từ vựng và đưa dữ liệu này vào model.

Tuy nhiên, nếu bạn quan tâm đến tốc độ train và/hoặc muốn tận dụng khả năng chạy song song GPU, bạn sẽ cần train với mini-batches.

Dùng mini-batches cũng có nghĩa là chúng ta phải lưu ý đến sự thay đổi độ dài câu trong các đợt của mình. Đê chứa các câu có kích thước khác nhau trong cùng một đợt, chúng tôi tạo tensor theo đợt, có hình dạng (batch_size, max_length). Trong đó, những câu ngắn hơn max_length được đệm bằng 0 sau EOS_token.

Nếu chúng ta chỉ đơn giản chuyển đổi các câu tiếng Anh của mình thành tensor bằng cách chuyển đổi các từ thành chỉ mục của chúng (indexesFromSentence) và zero-pad, tensor của chúng ta sẽ có hình dạng (batch_size, max_length) và việc lập chỉ mục cho chiều thứ nhất sẽ trả về một chuỗi đầy đủ trong tất cả time-steps. Tuy nhiên, chúng tôi cần có khả năng lập chỉ mục batch của mình theo thời gian và trên tất cả các chuỗi trong batch. Do đó, chúng tôi chuyển đổi hình dạng batch đầu vào của mình thành (max_length, batch_size), việc lập chỉ mục theo chiều thứ nhất trả về time step trên tất cả các câu trong batch. Chúng tôi xử lý việc chuyển đổi này một cách ngầm định trong hàm zeroPadding.

Hàm inputVar xử lý quá trình chuyển đổi các câu thành tensor, cuối cùng tạo ra một tensor zero-padded có hình dạng chính xác. Nó cũng trả về một tensor lengths cho mỗi chuỗi trong batch và sẽ được chuyển đến bộ giải mã của chúng tôi sau này.

Hàm outputVar thực hiện chức năng tương tự như inputVar, nhưng thay vì trả về một tensor lengths, nó trả về một tensor đánh dấu nhị phân và độ dài câu mục tiêu lớn nhất. Tensor mặt nạ nhị phân có hình dạng giống như tensor mục tiêu đầu ra, nhưng mọi phần tử là PAD_token đều bằng 0 và tất cả các phần tử khác là 1.

batch2TrainData đơn giản lấy một loạt các cặp, trả về đầu vào và tensor đích bằng cách sử dụng các hàm nói trên.

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)

Out:

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

Định nghĩa Models

Seq2Seq Model

Bộ não chatbot của chúng tôi là mô hình tuần tự (seq2seq). Mục tiêu của mô hình seq2seq là lấy chuỗi có độ dài thay đổi làm đầu vào và trả về chuỗi có độ dài thay đổi làm đầu ra bằng cách sử dụng mô hình có kích thước cố định.

Sutskever và cộng sự. đã phát hiện ra rằng bằng cách sử dụng hai mạng lưới thần kinh tái phát riêng biệt cùng nhau, chúng ta có thể hoàn thành nhiệm vụ này. Một RNN hoạt động như một bộ mã hóa, mã hóa chuỗi đầu vào có độ dài thay đổi thành vectơ ngữ cảnh có độ dài cố định. Về lý thuyết, vectơ ngữ cảnh này (lớp ẩn cuối cùng của RNN) sẽ chứa thông tin ngữ nghĩa về câu truy vấn được đưa vào bot. RNN thứ hai là một bộ giải mã, lấy một từ đầu vào và vectơ ngữ cảnh rồi trả về dự đoán cho từ tiếp theo trong chuỗi và trạng thái ẩn để sử dụng trong lần lặp tiếp theo.

Image source: https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_intro/

Encoder

Bộ mã hóa RNN lặp lại câu đầu vào một mã thông báo (ví dụ: word), tại mỗi time step, xuất ra một vectơ “output” và một vectơ “hidden state”. Sau đó, vectơ hidden state được chuyển sang time step tiếp theo, trong khi vectơ đầu ra được ghi lại. Bộ mã hóa chuyển đổi bối cảnh mà nó nhìn thấy tại mỗi điểm trong chuỗi thành một tập hợp các điểm trong không gian đa chiều mà bộ giải mã sẽ sử dụng để tạo đầu ra có ý nghĩa cho nhiệm vụ nhất định.

Trọng tâm của bộ mã hóa của chúng tôi là một bộ mã hóa nhiều lớp Gated Recurrent Unit, được phát minh bởi Cho et al. năm 2014. Chúng tôi sẽ sử dụng biến thể 2 chiều của GRU, có nghĩa là về cơ bản có hai RNN độc lập: một cái được cung cấp chuỗi đầu vào theo thứ tự tuần tự thông thường, và một cái được cung cấp chuỗi đầu vào theo thứ tự ngược lại. Đầu ra của mỗi mạng được tổng hợp ở mỗi time step. Việc sử dụng GRU hai chiều sẽ mang lại cho chúng ta lợi thế trong việc mã hóa cả bối cảnh trong quá khứ và tương lai.

RNN hai chiều:

Image source: https://colah.github.io/posts/2015-09-NN-Types-FP/

Lưu ý rằng lớp embedding được sử dụng để mã hóa các chỉ mục từ của chúng ta trong không gian đặc trưng có kích thước tùy ý. Đối với các mô hình của chúng tôi, lớp này sẽ ánh xạ từng từ tới một không gian đặc trưng có kích thước Hidden_size. Khi được huấn luyện, các giá trị này sẽ mã hóa sự giống nhau về ngữ nghĩa giữa các từ có nghĩa tương tự nhau.

Cuối cùng, nếu chuyển một loạt các chuỗi được đệm tới mô-đun RNN, chúng ta phải đóng gói và giải nén phần đệm xung quanh RNN bằng cách sử dụng nn.utils.rnn.pack_padded_sequence and nn.utils.rnn.pad_packed_sequence tương ứng.

Đồ thị tính toán:

1. Chuyển đổi chỉ mục từ sang nhúng.
2. Đóng gói chuỗi trình tự được đệm cho mô-đun RNN.
3. Chuyển tiếp qua GRU.
4. Giải nén phần đệm.
5. Tổng hợp đầu ra GRU hai chiều.
6. Trả về đầu ra và trạng thái ẩn cuối cùng.

Inputs:

• input_seq: tập dữ liệu đầu vào là các câu; shape=(max_length, batch_size)
• input_lengths: danh sách độ dài từng câu tương ứng với từng câu trong tập dữ liệu batch; shape=(batch_size)
• hidden: trạng thái ẩn; shape=(n_layers x num_directions, batch_size, hidden_size)

Outputs:

• outputs: các tính năng đầu ra từ lớp ẩn cuối cùng của GRU (tổng đầu ra hai chiều); shape=(max_length, batch_size, hidden_size)
• hidden: trạng thái ẩn được cập nhật từ GRU; shape=(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

Decoder

Bộ giải mã RNN tạo câu phản hồi theo kiểu token-by-token. Nó sử dụng các vectơ ngữ cảnh của bộ mã hóa và các trạng thái ẩn bên trong để tạo từ tiếp theo trong chuỗi. Nó tiếp tục tạo ra các từ cho đến khi xuất ra EOS_token, đại diện cho phần cuối của câu. Một vấn đề thường gặp với bộ giải mã vanilla seq2seq là nếu chúng ta chỉ dựa vào vectơ ngữ cảnh để mã hóa toàn bộ ý nghĩa của chuỗi đầu vào thì rất có thể chúng ta sẽ bị mất thông tin. Điều này đặc biệt xảy ra khi xử lý các chuỗi đầu vào dài, hạn chế rất nhiều khả năng của bộ giải mã của chúng tôi.

Để giải quyết vấn đề này, Bahdanau et al. đã tạo ra “attention mechanism” cho phép bộ giải mã quan tâm đến một số phần nhất định của chuỗi đầu vào, thay vì sử dụng toàn bộ bối cảnh ở mỗi bước.

Ở mức độ cao, sự attention được tính toán bằng các sử dụng trạng thái ẩn hiện tại của bộ giải mã và đầu ra của bộ mã hóa. Các trọng số attention đầu ra có hình dạng như chuỗi đầu vào, cho phép chúng tôi nhân chúng với đầu ra của bộ mã hóa, cung cấp cho chúng tôi tổng có trọng số, chỉ ra các phần của đầu ra bộ mã hóa cần quan tâm. Sean Robertson’s đã mô tả rất rõ điều này:

Luong và cộng sự. đã cải tiến dự trên nền tảng Bahdanau và cộng sự bằng cách tạo ra “Global attention”. Sự khác biệt chính là “Global attention”, chúng tôi xem xét tất cả trạng thái ẩn của bộ mã hóa, trái ngược với “Local attention” của Bahdanau và cộng sự, chỉ xem xét trạng thái ẩn của bộ mã hóa từ bước thời gian hiện tại. Một điểm khác biệt nữa là với “Global attention”, chúng tôi tính toán trọng số attention, hoặc năng lượng, sử dụng trạng thái ẩn của bộ giải mã từ bước thời gian hiện tại. Bahdanau và cộng sự tính toán attention yêu cầu kiến thức về trạng thái của bộ giải mã từ bước thời gian trước đó. Ngoài ra, Luong và cộng sự. cung cấp các phương pháp khác nhau để tính toán mức năng lượng attention giữa đầu ra bộ mã hóa và đầu ra bộ giải mã được gọi là “score functions”:

trong đó ht​ = trạng thái bộ giải mã mục tiêu hiện tại và hˉs​ = tất cả các trạng thái bộ mã hóa.

Nhìn chung, cơ chế Global attention có thể tóm tắt bằng hình sau. Lưu ý rằng, chúng tôi sẽ triển khai lớp “Attention Layer” dưới dạng một nn.Module riêng biệt có tên Attn. Đầu ra của mô-đun này là một tensor trọng số chuẩn hóa có shape (batch_size, 1, max_length).

# 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)

Bây giờ, chúng ta đã xác định được mô-đun con attention của mình, chúng ta có thể triển khai mô hình bộ giải mã thực tế. Đối với bộ giải mã, chúng tôi sẽ cung cấp dữ liệu theo cách thủ công từng bước một. Điều này có nghĩa là tensor “từ” được nhúng và đầu ra GRU sẽ có shape (1, batch_size, hidden_size).

Đồ thị tính toán:

1. Nhận nhúng của của “từ” đầu vào hiện tại.
2. Chuyển tiếp thông qua GRU đơn hướng.
3. Tính toán trọng số attention từ đầu ra GRU hiện tại (2).
4. Nhân các trọng số attention với đầu ra của bộ mã hóa, để có được vector ngữ cảnh “weighted sum” mới.
5. Ghép nối vector ngữ cảnh có trọng số và đầu ra GRU bằng các sử dụng Luong eq. 5.
6. Dự đoán từ tiếp theo bằng Luong eq. 6 (không có softmax).
7. Trả về đầu ra và trạng thái ẩn cuối cùng.

Inputs:

• input_step: một time step (một từ) của chuỗi đầu vào; shape=(1, batch_size)
• last_hidden: lớp ẩn cuối cùng của GRU; shape=(n_layers x num_directions, batch_size, hidden_size)
• encoder_outputs: đầu ra của mô hình bộ mã hóa; shape=(max_length, batch_size, hidden_size)

Outputs:

• output: tensor chuẩn hóa softmax cho xác suất mỗi từ là từ tiếp theo chính xác trong chuỗi được giải mã; shape=(batch_size, voc.num_words)
• hidden: trạng thái ẩn cuối cùng của GRU; shape=(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

Xác định quy trình huấn luyện

Mất mát che giấu

Vì chúng ta đang xử lý các dãy và được đệm liên tục, nên chúng ta không thể đơn giản xem xét tất cả các phần tử của tensor khi tính toán mất mát. Chúng tôi xác định maskNLLLoss để tính toán mất mát dựa trên tensor đầu ra của bộ giải mã, tensor đích và tensor mặt nạ nhị phân mô tả phần đệm của tensor đích. Hàm mất mát này tính toán khả năng ghi nhật ký âm trung bình của các phần tử, tương ứng với 1 trong tensor mặt nạ.

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()

Huấn luyện đơn

Hàm train chứa thuật toán cho một lần lặp huấn luyện duy nhất (một lô đầu vào).

Chúng tôi sẽ sử dụng một vài thủ thuật thông minh để hỗ trợ hội tụ:

• Đầu tiên là sử dụng teacher forcing. Điều này có nghĩa là ở một xác suất nào đó, được đặt bởi teacher_forcing_ratio, chúng tôi sử dụng từ mục tiêu hiện tại làm đầu vào tiếp theo của bộ giải mã thay vì sử dụng dự đoán hiện tại của bộ giải mã. Kỹ thuật này hoạt động như bánh xe huấn luyện cho bộ giải mã, hỗ trợ việc đào tạo hiệu quả hơn. Tuy nhiên, việc ép buộc có thể dẫn đến sự mất ổn định của mô hình trong quá trình suy luận, vì bộ giải mã có thể không có đủ cơ hội để thực sự tạo ra các chuỗi đầu ra của chính nó trong quá trình đào tạo. Vì vậy, chúng ta cần lưu ý đến cách thiết lập teacher_forcing_ratio, và không bị đánh lừa bởi sự hội tụ nhanh.
• Thủ thuật thứ hai mà chúng tôi thực hiện là gradient clipping. Kỹ thuật được dùng để giải quyết vấn đề “exploding gradient”. Về bản chất, bằng cách cắt hoặc đặt ngưỡng cho độ dốc đến giá trị tối đa, chúng tôi ngăn độ dốc tăng theo cấp số nhân và overflow (NaN), hoặc vượt qua trong hàm chi phí.

Image source: Goodfellow et al. Deep Learning. 2016. https://www.deeplearningbook.org/

Trình tự các thao tác:

1. Chuyển tiếp toàn bộ batch đầu vào thông qua bộ mã hóa.
2. Khởi tạo đầu vào bộ giải mã dưới dạng SOS_token, và trạng thái ẩn làm trạng thái ẩn cuối cùng của bộ mã hóa.
3. Chuyển tiếp chuỗi đầu vào qua bộ giải mã từng bước một.
4. Nếu huấn luyện ép buộc: đặt đầu vào bộ giải mã tiếp theo làm mục tiêu hiện tại; ngược lại: đặt đầu vào bộ giải mã tiếp theo làm đầu ra bộ giải mã hiện tại.
5. Tính toán và tích lũy mất mát.
6. Thực hiện lan truyền ngược.
7. Clip gradients.
8. Cập nhật các tham số mô hình bộ mã hóa và bộ giải mã.

 Note

Các mô-đun RNN của PyTorch (RNN, LSTM, GRU) có thể được sử dụng giống như bất kỳ lớp non-recurrent nào khác, bằng cách chỉ cần chuyển cho chúng toàn bộ chuỗi đầu vào (hoặc một loạt batch). Chúng tôi sử dụng lớp GRU như thế này trong encoder. Thực tế là có một quá trình lặp đi lặp lại qua từng bước thời gian để tính toán các trạng thái ẩn. Ngoài ra, bạn có thể chạy các mô-đun này từng bước một. Trong trường hợp này, chúng tôi lặp lại các trình tự theo cách thủ công trong quá trình huấn luyện giống như chúng tôi phải làm đối với mô hình decoder. Miễn là bạn duy trì mô hình khái niệm chính xác của các mô-đun này thì việc triển khai các mô hình tuần tự có thể rất đơn giản.

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

Huấn luyện lặp lại

Cuối cùng đã đến lúc gắn kết toàn bộ quy trình đào tạo với dữ liệu. Hàm trainIters chịu trách nhiệm chạy n_iterations huấn luyện dựa trên các mô hình, trình tối ưu hóa, dữ liệu, v.v. đã được thông qua. Hàm này khá dễ hiểu vì chúng ta đã thực hiện công việc nặng nhọc với hàm train.

Một điều cần chú ý là khi lưu mô hình, chúng ta lưu một tarball chứa bộ mã hóa và giải mã state_dicts (parameters), state_dicts của trình tối ưu hóa, loss, iteration, v.v. Việc lưu mô hình theo cách này sẽ mang lại cho chúng ta kết quả cuối cùng linh hoạt với checkpoint. Sau khi tải checkpoint, chúng ta sẽ có thể sử dụng các tham số mô hình để chạy suy luận hoặc có thể tiếp tục huấn luyện ngay từ nơi chúng ta đã dừng lại.

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')))

Đánh giá kết quả

Sau khi đào tạo một mô hình, chúng tôi muốn có thể tự nói chuyện với bot. Đầu tiên, chúng ta phải xác định cách chúng ta muốn mô hình giải mã đầu vào được mã hóa.

Greedy decoding

Greedy decoding là phương pháp giải mã mà chúng tôi sử dụng trong quá trình đào tạo khi KHÔNG sử dụng sự ép buộc. Nói cách khác, với mỗi bước thời gian, chúng ta chỉ cần chọn từ trong decoder_output có giá trị softmax cao nhất. Phương pháp giải mã này là tối ưu ở mức độ bước thời gian duy nhất.

Để tạo điều kiện thuận lợi cho hoạt greedy decoding, chúng tôi định nghĩa lớp GreedySearchDecoder. Khi chạy, một đối tượng của lớp này nhận một chuỗi đầu vào (input_seq) có hình dạng (input_seq length, 1), một tensor độ dài đầu vào vô hướng (input_length) và một max_length để giới hạn độ dài câu phản hồi. Câu đầu vào được đánh giá bằng biểu đồ tính toán sau:

Đồ thị tính toán:

1. Chuyển tiếp đầu vào thông qua mô hình bộ mã hóa.
2. Chuẩn bị lớp ẩn cuối cùng của bộ mã hóa làm đầu vào ẩn đầu tiên cho bộ giải mã.
3. Khởi tạo đầu vào đầu tiên của bộ giải mã là SOS_token.
4. Khởi tạo tensor để nối các “từ” được giải mã vào.
5. Lặp lại decode từng token của mỗi từ:
   1. Chuyển tiếp qua bộ giải mã.
   2. Nhận mã token “từ” có khả năng nhất và điểm softmax của nó.
   3. Ghi lại mã token và điểm số.
   4. Chuẩn bị token hiện tại làm đầu vào bộ giải mã tiếp theo.
6. Trả về collections token của “từ và điểm số.

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

Đánh giá văn bản của tôi

Bây giờ, chúng ta đã xác định được phương pháp giải mã, chúng ta có thể viết các hàm để đánh giá chuỗi đầu vào của câu. evaluate , hàm quản lý quy trình xử lý câu đầu vào ở cấp độ thấp. Trước tiên, chúng tôi định dạng câu dưới dạng một loạt chỉ mục từ đầu vào với batch_size==1. Chúng tôi thực hiện điều này bằng cách chuyển đổi các từ trong câu thành các chỉ mục tương ứng của chúng và chuyển đổi các kích thước để chuẩn bị tensor cho các mô hình của chúng tôi. Chúng tôi cũng tạo một lengths tensor chứa độ dài của câu đầu vào. Trong trường hợp này, lengths là vô hướng vì chúng tôi chỉ đánh giá một câu tại một thời điểm (batch_size==1). Tiếp theo, chúng ta thu được tensor câu trả lời đã được giải mã bằng cách sử dụng GreedySearchDecoder đối tượng (searcher). Cuối cùng, chúng tôi chuyển đổi chỉ mục của phản hồi thành các “từ” và trả về danh sách các từ được giải mã.

evaluateInput đóng vai trò là giao diện người dùng cho chatbot của chúng tôi. Khi được gọi, một trường văn bản đầu vào sẽ xuất hiện để chúng ta có thể nhập câu truy vấn của mình. Sau khi nhập câu đầu vào và nhấn Enter, văn bản của chúng tôi được chuẩn hóa giống như dữ liệu huấn luyện của chúng tôi và cuối cùng được đưa vào hàm evaluate để thu được câu đầu ra được giải mã. Chúng tôi lặp lại quá trình này để có thể tiếp tục trò chuyện với bot của mình cho đến khi nhập “q” hoặc “quit”.

Cuối cùng, nếu một câu được nhập có chứa một từ không có trong từ vựng, chúng tôi sẽ xử lý vấn đề này một cách khéo léo bằng cách in thông báo lỗi và nhắc người dùng nhập một câu khác.

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.")

Chạy Model

Cuối cùng, đã đến lúc chạy mô hình của chúng ta!

Bất kể chúng ta muốn huấn luyện hay thử nghiệm mô hình chatbot, chúng ta đều phải khởi tạo các mô hình mã hóa và giải mã riêng lẻ. Trong khối tiếp theo, chúng tôi đặt các cấu hình mong muốn của mình, chọn bắt đầu lại từ đầu hoặc đặt điểm kiểm tra để tải từ đó, đồng thời xây dựng và khởi tạo các mô hình. Hãy thoải mái thử nghiệm với các cấu hình mô hình khác nhau để tối ưu hóa hiệu suất.

# 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

Sample code tải từ checkpoint:

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!')

Out:

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

Chạy Training

Chạy khối sau nếu bạn muốn huấn luyện mô hình.

Đầu tiên, chúng tôi đặt tham số huấn luyện, sau đó khởi tạo trình tối ưu hóa và cuối cùng chúng tôi gọi hàm trainIters để chạy các vòng lặp huấn luyện của mình.

# 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)

Out:

Building optimizers ...
Starting Training!
Initializing ...
Training...
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Chạy Evaluation

Để trò chuyện với mô hình của bạn, hãy chạy khối sau.

# 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)

Phần kết luận

Đó là tất cả cho bài viết này, thưa các bạn. Xin chúc mừng, giờ bạn đã biết các nguyên tắc cơ bản để xây dựng mô hình chatbot tổng quát! Nếu quan tâm, bạn có thể thử điều chỉnh hành vi của chatbot bằng cách điều chỉnh mô hình và các tham số huấn luyện cũng như tùy chỉnh dữ liệu mà bạn huấn luyện mô hình trên đó.

Hãy xem các hướng dẫn khác để biết thêm các ứng dụng deep learning thú vị trong PyTorch!

Tổng thời gian chạy script: (5 phút 52,653 giây)