Updated transformer greedy

5-1.Transformer/Transformer(Greedy_decoder)-Torch.py

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+'''
+  code by Tae Hwan Jung(Jeff Jung) @graykode
+  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
+from torch.autograd import Variable
+import matplotlib.pyplot as plt
+
+dtype = torch.FloatTensor
+# 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
+sentences = ['ich mochte ein bier P', 'S i want a beer', 'i want a beer E']
+
+# Transformer Parameters
+src_vocab = {w: i for i, w in enumerate(sentences[0].split())}
+src_vocab_size = len(src_vocab)
+tgt_vocab = {w: i for i, w in enumerate(set((sentences[1]+' '+sentences[2]).split()))}
+number_dict = {i: w for i, w in enumerate(set((sentences[1]+' '+sentences[2]).split()))}
+tgt_vocab_size = len(tgt_vocab)
+
+src_len = 5
+tgt_len = 5
+
+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
+
+
+def 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 Variable(torch.LongTensor(input_batch)), Variable(torch.LongTensor(output_batch)), Variable(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):
+    batch_size, len_q = seq_q.size()
+    batch_size, len_k = seq_k.size()
+    pad_attn_mask = seq_k.data.eq(0).unsqueeze(1)  # batch_size x 1 x len_k(=len_q)
+    return pad_attn_mask.expand(batch_size, len_q, len_k)  # batch_size x len_q x len_k
+
+
+class ScaledDotProductAttention(nn.Module):
+
+    def __init__(self):
+        super(ScaledDotProductAttention, self).__init__()
+
+    def forward(self, Q, K, V, attn_mask=None):
+        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)]
+        if attn_mask is not None:
+            scores.masked_fill_(attn_mask, -1e9)
+        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)
+
+    def forward(self, Q, K, V, attn_mask=None):
+        # 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]
+
+        if attn_mask is not None: # attn_mask : [batch_size x len_q x len_k]
+            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=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 = nn.Linear(n_heads * d_v, d_model)(context)
+        return nn.LayerNorm(d_model)(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)
+
+    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 nn.LayerNorm(d_model)(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_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs) # 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, enc_attn_mask, dec_attn_mask=None):
+        dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_attn_mask)
+        dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, 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([[1,2,3,4,5]]))
+        enc_self_attns = []
+        for layer in self.layers:
+            enc_outputs, enc_self_attn = layer(enc_outputs)
+            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_attn_mask=None): # dec_inputs : [batch_size x target_len]
+        dec_outputs = self.tgt_emb(dec_inputs) + self.pos_emb(torch.LongTensor([[1,2,3,4,5]]))
+        dec_enc_attn_pad_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,
+                                                             enc_attn_mask=dec_enc_attn_pad_mask,
+                                                             dec_attn_mask=dec_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, decoder_mask=None):
+        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, decoder_mask)
+        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
+    """
+    memory, attention = model.encoder(enc_input)
+    dec_input = torch.ones(1, 5).fill_(0).type_as(enc_input.data)
+    dec_mask = torch.from_numpy(np.triu(np.ones((1, 5, 5)), 1).astype('uint8')) == 0
+    next_symbol = start_symbol
+    for i in range(0, 5):
+        dec_input[0][i] = next_symbol
+        out = model.decoder(Variable(dec_input), enc_input, memory, dec_mask)
+        projected = model.projection(out[0])
+        prob = projected.view(-1, projected.size(-1))
+        prob = prob.data.max(1, keepdim=True)[1]
+        next_word = prob.data[i]
+        next_symbol = next_word[0]
+    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__':
+
+    model = Transformer()
+
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+    for epoch in range(100):
+        optimizer.zero_grad()
+        enc_inputs, dec_inputs, target_batch = make_batch(sentences)
+        outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)
+        loss = criterion(outputs, target_batch.contiguous().view(-1))
+        if (epoch + 1) % 20 == 0:
+            print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
+        loss.backward()
+        optimizer.step()
+
+
+    # Test
+    greedy_dec_input = greedy_decoder(model, enc_inputs, start_symbol=4)
+    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)