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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 steps
-
- def make_batch():
- 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 2i
- sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
- return torch.FloatTensor(sinusoid_table)
-
- def get_attn_pad_mask(seq_q, seq_k):
- # print(seq_q)
- batch_size, len_q = seq_q.size()
- batch_size, len_k = seq_k.size()
- # eq(zero) is PAD token
- pad_attn_mask = seq_k.data.eq(0).unsqueeze(1) # batch_size x 1 x len_k(=len_q), one is masking
- return pad_attn_mask.expand(batch_size, len_q, len_k) # batch_size x len_q x len_k
-
- def 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
-
- 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
-
- 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]
-
- 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)
-
- 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,V
- enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]
- return enc_outputs, attn
-
- 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
-
- 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
-
- 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
-
- 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
-
- def greedy_decoder(model, enc_input, start_symbol):
- """
- For simplicity, a Greedy Decoder is Beam search when K=1. This is necessary for inference as we don't know the
- target sequence input. Therefore we try to generate the target input word by word, then feed it into the transformer.
- Starting Reference: http://nlp.seas.harvard.edu/2018/04/03/attention.html#greedy-decoding
- :param model: Transformer Model
- :param enc_input: The encoder input
- :param start_symbol: The start symbol. In this example it is 'S' which corresponds to index 4
- :return: The target input
- """
- enc_outputs, enc_self_attns = model.encoder(enc_input)
- dec_input = torch.zeros(1, 5).type_as(enc_input.data)
- next_symbol = start_symbol
- for i in range(0, 5):
- dec_input[0][i] = next_symbol
- dec_outputs, _, _ = model.decoder(dec_input, enc_input, enc_outputs)
- projected = model.projection(dec_outputs)
- prob = projected.squeeze(0).max(dim=-1, keepdim=False)[1]
- next_word = prob.data[i]
- next_symbol = next_word.item()
- return dec_input
-
- 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()
-
- if __name__ == '__main__':
- sentences = ['ich mochte ein bier P', 'S i want a beer', 'i want a beer E']
- # Transformer Parameters
- # Padding Should be Zero index
- src_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 source
- tgt_len = 5 # length of target
-
- d_model = 512 # Embedding Size
- d_ff = 2048 # FeedForward dimension
- d_k = d_v = 64 # dimension of K(=Q), V
- n_layers = 6 # number of Encoder of Decoder Layer
- n_heads = 8 # number of heads in Multi-Head Attention
-
- model = Transformer()
-
- criterion = nn.CrossEntropyLoss()
- optimizer = optim.Adam(model.parameters(), lr=0.001)
-
- enc_inputs, dec_inputs, target_batch = make_batch()
-
- 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()
-
- # Test
- greedy_dec_input = greedy_decoder(model, enc_inputs, start_symbol=tgt_vocab["S"])
- predict, _, _, _ = model(enc_inputs, greedy_dec_input)
- 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)
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