Word2Vec源码解读（Pytorch版本）

1. 模型结构图
   
2. 模型实现
   Skip-gram模型

   # code by Tae Hwan Jung @graykode modified by 前行follow
   import numpy as np
   import torch
   import torch.nn as nn
   import torch.optim as optim
   import matplotlib.pyplot as plt
   
   def random_batch():
       random_inputs = []
       random_labels = []
       random_index = np.random.choice(range(len(skip_grams)), batch_size, replace=False)
   
       for i in random_index:
           random_inputs.append(np.eye(voc_size)[skip_grams[i][0]])  # target
           random_labels.append(skip_grams[i][1])  # context word
   
       return random_inputs, random_labels
   
   # Model
   class Word2Vec(nn.Module):
       def __init__(self):
           super(Word2Vec, self).__init__()
           # W and WT is not Traspose relationship
           self.W = nn.Linear(voc_size, embedding_size, bias=False) # voc_size > embedding_size Weight
           self.WT = nn.Linear(embedding_size, voc_size, bias=False) # embedding_size > voc_size Weight
   
       def forward(self, X):
           # X : [batch_size, voc_size]
           hidden_layer = self.W(X) # hidden_layer : [batch_size, embedding_size]
           output_layer = self.WT(hidden_layer) # output_layer : [batch_size, voc_size]
           return output_layer
   
   if __name__ == '__main__':
       batch_size = 2 # mini-batch size
       embedding_size = 2 # embedding size
   
       sentences = ["apple banana fruit", "banana orange fruit", "orange banana fruit",
                    "dog cat animal", "cat monkey animal", "monkey dog animal"]
   
       word_sequence = " ".join(sentences).split()
       word_list = " ".join(sentences).split()
       word_list = list(set(word_list))
       word_dict = {w: i for i, w in enumerate(word_list)}
       voc_size = len(word_list)
   
       # Make skip gram of one size window
       skip_grams = []
       for i in range(1, len(word_sequence) - 1):
           target = word_dict[word_sequence[i]]
           context = [word_dict[word_sequence[i - 1]], word_dict[word_sequence[i + 1]]]
           # 每个target（中心词）和左右窗口的每个词放到一个列表中
           for w in context:
               skip_grams.append([target, w])
   
       model = Word2Vec()
   
       criterion = nn.CrossEntropyLoss()
       optimizer = optim.Adam(model.parameters(), lr=0.001)
   
       # Training
       for epoch in range(5000):
           input_batch, target_batch = random_batch()
           input_batch = torch.Tensor(input_batch)
           target_batch = torch.LongTensor(target_batch)
   
           optimizer.zero_grad()
           output = model(input_batch)
   
           # output : [batch_size, voc_size], target_batch : [batch_size] (LongTensor, not one-hot)
           loss = criterion(output, target_batch)
           if (epoch + 1) % 1000 == 0:
               print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
   
           loss.backward()
           optimizer.step()
   
       for i, label in enumerate(word_list):
           W, WT = model.parameters()
           x, y = W[0][i].item(), W[1][i].item()
           plt.scatter(x, y)
           plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom')
       plt.show()
   
   

   运行结果：
   
   CBOW模型

   import numpy as np
   from torchtext.vocab import vocab
   from collections import Counter, OrderedDict
   from torch.utils.data import Dataset, DataLoader
   from torchtext.transforms import VocabTransform  # 注意：torchtext版本0.12+
   import torch
   from torch import nn
   from torch.nn import functional as F
   
   
   def get_text():
       sentence_list = [  # 假设这是全部的训练语料
           "nlp drives computer programs that translate text from one language to another",
           "nlp combines computational linguistics rule based modeling of human language with statistical",
           "nlp model respond to text or voice data and respond with text",
       ]
       return sentence_list
   
   
   class CbowDataSet(Dataset):
       def __init__(self, text_list, side_window=3):
           """
           构造Word2vec的CBOW采样Dataset
           :param text_list: 语料
           :param side_window: 单侧正例（构造背景词）采样数，总正例是：2 * side_window
           """
           super(CbowDataSet, self).__init__()
           self.side_window = side_window
           text_vocab, vocab_transform = self.reform_vocab(text_list)
           self.text_list = text_list  # 原始文本
           self.text_vocab = text_vocab  # torchtext的vocab
           self.vocab_transform = vocab_transform  # torchtext的vocab_transform
           self.cbow_data = self.generate_cbow()
   
       def __len__(self):
           return len(self.cbow_data)
   
       def __getitem__(self, idx):
           data_row = self.cbow_data[idx]
           return data_row[0], data_row[1]
   
       def reform_vocab(self, text_list):
           """根据语料构造torchtext的vocab"""
           total_word_list = []
           for _ in text_list:  # 将嵌套的列表([[xx,xx],[xx,xx]...])拉平 ([xx,xx,xx...])
               total_word_list += _.split(" ")
           counter = Counter(total_word_list)  # 统计计数
           sorted_by_freq_tuples = sorted(counter.items(), key=lambda x: x[1], reverse=True)  # 构造成可接受的格式：[(单词,num), ...]
           ordered_dict = OrderedDict(sorted_by_freq_tuples)
           # 开始构造 vocab
           special_token = ["", ""]  # 特殊字符
           text_vocab = vocab(ordered_dict, specials=special_token)  # 单词转token，specials里是特殊字符，可以为空
           text_vocab.set_default_index(0)
           vocab_transform = VocabTransform(text_vocab)
           return text_vocab, vocab_transform
   
       def generate_cbow(self):
           """生成CBOW的训练数据"""
           cbow_data = []
           for sentence in self.text_list:
               sentence_id_list = np.array(self.vocab_transform(sentence.split(' ')))
               for center_index in range(
                       self.side_window, len(sentence_id_list) - self.side_window):  # 防止前面或后面取不到足够的值，这是取index的上下界
                   pos_index = list(range(center_index - self.side_window, center_index + self.side_window + 1))
                   del pos_index[self.side_window]
                   cbow_data.append([sentence_id_list[center_index], sentence_id_list[pos_index]])
           return cbow_data
   
       def get_vocab_transform(self):
           return self.vocab_transform
   
       def get_vocab_size(self):
           return len(self.text_vocab)
   
   
   class Word2VecModel(nn.Module):
       def __init__(self, vocab_size, batch_size, word_embedding_size=100, hidden=64):
           """
           Word2vec模型CBOW实现
           :param vocab_size: 单词个数
           :param word_embedding_size: 每个词的词向量维度
           :param hidden: 隐层维度
           """
           super(Word2VecModel, self).__init__()
           self.vocab_size = vocab_size
           self.word_embedding_size = word_embedding_size
           self.hidden = hidden
           self.batch_size = batch_size
           self.word_embedding = nn.Embedding(self.vocab_size, self.word_embedding_size)  # token对应的embedding
           # 建模
           self.linear_in = nn.Linear(self.word_embedding_size, self.hidden)
           self.linear_out = nn.Linear(self.hidden, self.vocab_size)
   
       def forward(self, input_labels):
           around_embedding = self.word_embedding(input_labels)
           avg_around_embedding = torch.mean(around_embedding, dim=1)  # 1. 输入的词向量对应位置求平均
           in_emb = F.relu(self.linear_in(avg_around_embedding))  # 2. 过第一个linear，使用relu激活函数
           out_emb = F.log_softmax(self.linear_out(in_emb))  # 3. 过第二个linear，得到维度是：[batch_size, 单词总数]
           return out_emb
   
       def get_embedding(self, token_list: list):
           return self.word_embedding(torch.Tensor(token_list).long())
   
   
   def main():
       batch_size = 7
       sentence_list = get_text()
       cbow_data_set = CbowDataSet(sentence_list)  # 构造 DataSet
       data_loader = DataLoader(cbow_data_set, batch_size=batch_size, drop_last=True)  # 将DataSet封装成DataLoader
       # 开始训练
       model = Word2VecModel(cbow_data_set.get_vocab_size(), batch_size)
       optimizer = torch.optim.Adam(model.parameters())
       criterion = nn.CrossEntropyLoss()
       for _epoch_i in range(100):
           loss_list = []
           for center_token, back_token in data_loader:
               # 开始训练
               optimizer.zero_grad()
               model_out = model(back_token)
               loss = criterion(model_out, center_token)
               loss.backward()
               optimizer.step()
               loss_list.append(loss.item())
           print("训练中：", _epoch_i, "Loss:", np.sum(loss_list))
   
       # 最后测试一下
       # 得到: nlp can translate text from one language to another 的词向量
       sentence = "nlp can translate text from one language to another"
       vocab_transform = cbow_data_set.get_vocab_transform()
       sentence_ids = vocab_transform(sentence.split(' '))
       sentence_embedding = model.get_embedding(sentence_ids)
       print("这个是句向量的维度：", sentence_embedding.shape)
   
   
   if __name__ == '__main__':
       main()
   
   

3. Reference
   1. spik-gram源代码
   2. CBOW源代码