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源码来自于：
https://github.com/graykode/nlp-tutorial/blob/master/5-1.Transformer/Transformer.ipynb

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• DASOU讲AI：Transformer代码(源码Pytorch版本)从零解读(Pytorch版本）
  https://www.bilibili.com/video/BV1dR4y1E7aL
• <Transformer从零详细解读(可能是你见过最通俗易懂的讲解)>
  https://www.bilibili.com/video/BV1Di4y1c7Zm/

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# %%
# code by Tae Hwan Jung(Jeff Jung) @graykode, Derek Miller @dmmiller612
# Reference : https://github.com/jadore801120/attention-is-all-you-need-pytorch
#           https://github.com/JayParks/transformer
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt# S: Symbol that shows starting of decoding input
# E: Symbol that shows starting of decoding output
# P: Symbol that will fill in blank sequence if current batch data size is short than time stepsdef make_batch(sentences):input_batch = [[src_vocab[n] for n in sentences[0].split()]]output_batch = [[tgt_vocab[n] for n in sentences[1].split()]]target_batch = [[tgt_vocab[n] for n in sentences[2].split()]]return torch.LongTensor(input_batch), torch.LongTensor(output_batch), torch.LongTensor(target_batch)def get_sinusoid_encoding_table(n_position, d_model):def cal_angle(position, hid_idx):return position / np.power(10000, 2 * (hid_idx // 2) / d_model)def get_posi_angle_vec(position):return [cal_angle(position, hid_j) for hid_j in range(d_model)]sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2isinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1return torch.FloatTensor(sinusoid_table)def get_attn_pad_mask(seq_q, seq_k):batch_size, len_q = seq_q.size()batch_size, len_k = seq_k.size()# eq(zero) is PAD tokenpad_attn_mask = seq_k.data.eq(0).unsqueeze(1)  # batch_size x 1 x len_k(=len_q), one is maskingreturn pad_attn_mask.expand(batch_size, len_q, len_k)  # batch_size x len_q x len_kdef get_attn_subsequent_mask(seq):attn_shape = [seq.size(0), seq.size(1), seq.size(1)]subsequent_mask = np.triu(np.ones(attn_shape), k=1)subsequent_mask = torch.from_numpy(subsequent_mask).byte()return subsequent_mask

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ScaledDotProductAttention

class ScaledDotProductAttention(nn.Module):def __init__(self):super(ScaledDotProductAttention, self).__init__()def forward(self, Q, K, V, attn_mask):scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.attn = nn.Softmax(dim=-1)(scores)context = torch.matmul(attn, V)return context, attn

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MultiHeadAttention

class MultiHeadAttention(nn.Module):def __init__(self):super(MultiHeadAttention, self).__init__()self.W_Q = nn.Linear(d_model, d_k * n_heads)self.W_K = nn.Linear(d_model, d_k * n_heads)self.W_V = nn.Linear(d_model, d_v * n_heads)self.linear = nn.Linear(n_heads * d_v, d_model)self.layer_norm = nn.LayerNorm(d_model)def forward(self, Q, K, V, attn_mask):# q: [batch_size x len_q x d_model], k: [batch_size x len_k x d_model], v: [batch_size x len_k x d_model]residual, batch_size = Q, Q.size(0)# (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # q_s: [batch_size x n_heads x len_q x d_k]k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # k_s: [batch_size x n_heads x len_k x d_k]v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2)  # v_s: [batch_size x n_heads x len_k x d_v]attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size x n_heads x len_q x len_k]# context: [batch_size x n_heads x len_q x d_v], attn: [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]output = self.linear(context)return self.layer_norm(output + residual), attn # output: [batch_size x len_q x d_model]

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PoswiseFeedForwardNet

class PoswiseFeedForwardNet(nn.Module):def __init__(self):super(PoswiseFeedForwardNet, self).__init__()self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)self.layer_norm = nn.LayerNorm(d_model)def forward(self, inputs):residual = inputs # inputs : [batch_size, len_q, d_model]output = nn.ReLU()(self.conv1(inputs.transpose(1, 2)))output = self.conv2(output).transpose(1, 2)return self.layer_norm(output + residual)

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EncoderLayer

class EncoderLayer(nn.Module):def __init__(self):super(EncoderLayer, self).__init__()self.enc_self_attn = MultiHeadAttention()self.pos_ffn = PoswiseFeedForwardNet()def forward(self, enc_inputs, enc_self_attn_mask):enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,Venc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]return enc_outputs, attn

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DecoderLayer

class DecoderLayer(nn.Module):def __init__(self):super(DecoderLayer, self).__init__()self.dec_self_attn = MultiHeadAttention()self.dec_enc_attn = MultiHeadAttention()self.pos_ffn = PoswiseFeedForwardNet()def forward(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_self_attn_mask)dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)dec_outputs = self.pos_ffn(dec_outputs)return dec_outputs, dec_self_attn, dec_enc_attn

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Encoder

class Encoder(nn.Module):def __init__(self):super(Encoder, self).__init__()self.src_emb = nn.Embedding(src_vocab_size, d_model)self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(src_len+1, d_model),freeze=True)self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])def forward(self, enc_inputs): # enc_inputs : [batch_size x source_len]enc_outputs = self.src_emb(enc_inputs) + self.pos_emb(torch.LongTensor([[1,2,3,4,0]]))enc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs)enc_self_attns = []for layer in self.layers:enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask)enc_self_attns.append(enc_self_attn)return enc_outputs, enc_self_attns

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Decoder

class Decoder(nn.Module):def __init__(self):super(Decoder, self).__init__()self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(tgt_len+1, d_model),freeze=True)self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])def forward(self, dec_inputs, enc_inputs, enc_outputs): # dec_inputs : [batch_size x target_len]dec_outputs = self.tgt_emb(dec_inputs) + self.pos_emb(torch.LongTensor([[5,1,2,3,4]]))dec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs)dec_self_attn_subsequent_mask = get_attn_subsequent_mask(dec_inputs)dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequent_mask), 0)dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs)dec_self_attns, dec_enc_attns = [], []for layer in self.layers:dec_outputs, dec_self_attn, dec_enc_attn = layer(dec_outputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask)dec_self_attns.append(dec_self_attn)dec_enc_attns.append(dec_enc_attn)return dec_outputs, dec_self_attns, dec_enc_attns

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Transformer

class Transformer(nn.Module):def __init__(self):super(Transformer, self).__init__()self.encoder = Encoder()self.decoder = Decoder()self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False)def forward(self, enc_inputs, dec_inputs):enc_outputs, enc_self_attns = self.encoder(enc_inputs)dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs, enc_outputs)dec_logits = self.projection(dec_outputs) # dec_logits : [batch_size x src_vocab_size x tgt_vocab_size]return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns

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showgraph

def showgraph(attn):attn = attn[-1].squeeze(0)[0]attn = attn.squeeze(0).data.numpy()fig = plt.figure(figsize=(n_heads, n_heads)) # [n_heads, n_heads]ax = fig.add_subplot(1, 1, 1)ax.matshow(attn, cmap='viridis')ax.set_xticklabels(['']+sentences[0].split(), fontdict={'fontsize': 14}, rotation=90)ax.set_yticklabels(['']+sentences[2].split(), fontdict={'fontsize': 14})plt.show()

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main

if __name__ == '__main__':sentences = ['ich mochte ein bier P', 'S i want a beer', 'i want a beer E']# Transformer Parameters# Padding Should be Zerosrc_vocab = {'P': 0, 'ich': 1, 'mochte': 2, 'ein': 3, 'bier': 4}src_vocab_size = len(src_vocab)tgt_vocab = {'P': 0, 'i': 1, 'want': 2, 'a': 3, 'beer': 4, 'S': 5, 'E': 6}number_dict = {i: w for i, w in enumerate(tgt_vocab)}tgt_vocab_size = len(tgt_vocab)src_len = 5 # length of sourcetgt_len = 5 # length of targetd_model = 512  # Embedding Sized_ff = 2048  # FeedForward dimensiond_k = d_v = 64  # dimension of K(=Q), Vn_layers = 6  # number of Encoder of Decoder Layern_heads = 8  # number of heads in Multi-Head Attentionmodel = Transformer()criterion = nn.CrossEntropyLoss()optimizer = optim.Adam(model.parameters(), lr=0.001)enc_inputs, dec_inputs, target_batch = make_batch(sentences)for epoch in range(20):optimizer.zero_grad()outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)loss = criterion(outputs, target_batch.contiguous().view(-1))print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))loss.backward()optimizer.step()# Testpredict, _, _, _ = model(enc_inputs, dec_inputs)predict = predict.data.max(1, keepdim=True)[1]print(sentences[0], '->', [number_dict[n.item()] for n in predict.squeeze()])print('first head of last state enc_self_attns')showgraph(enc_self_attns)print('first head of last state dec_self_attns')showgraph(dec_self_attns)print('first head of last state dec_enc_attns')showgraph(dec_enc_attns)