wehub-resource-sync
259 lines
14 KiB
Plaintext
259 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"# code by Tae Hwan Jung(Jeff Jung) @graykode, Derek Miller @dmmiller612\n",
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"# Reference : https://github.com/jadore801120/attention-is-all-you-need-pytorch\n",
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"# https://github.com/JayParks/transformer\n",
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"import numpy as np\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# S: Symbol that shows starting of decoding input\n",
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"# E: Symbol that shows starting of decoding output\n",
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"# P: Symbol that will fill in blank sequence if current batch data size is short than time steps\n",
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"\n",
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"def make_batch(sentences):\n",
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" input_batch = [[src_vocab[n] for n in sentences[0].split()]]\n",
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" output_batch = [[tgt_vocab[n] for n in sentences[1].split()]]\n",
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" target_batch = [[tgt_vocab[n] for n in sentences[2].split()]]\n",
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" return torch.LongTensor(input_batch), torch.LongTensor(output_batch), torch.LongTensor(target_batch)\n",
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"\n",
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"def get_sinusoid_encoding_table(n_position, d_model):\n",
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" def cal_angle(position, hid_idx):\n",
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" return position / np.power(10000, 2 * (hid_idx // 2) / d_model)\n",
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" def get_posi_angle_vec(position):\n",
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" return [cal_angle(position, hid_j) for hid_j in range(d_model)]\n",
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"\n",
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" sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])\n",
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" sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i\n",
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" sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1\n",
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" return torch.FloatTensor(sinusoid_table)\n",
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"\n",
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"def get_attn_pad_mask(seq_q, seq_k):\n",
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" batch_size, len_q = seq_q.size()\n",
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" batch_size, len_k = seq_k.size()\n",
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" # eq(zero) is PAD token\n",
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" pad_attn_mask = seq_k.data.eq(0).unsqueeze(1) # batch_size x 1 x len_k(=len_q), one is masking\n",
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" return pad_attn_mask.expand(batch_size, len_q, len_k) # batch_size x len_q x len_k\n",
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"\n",
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"def get_attn_subsequent_mask(seq):\n",
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" attn_shape = [seq.size(0), seq.size(1), seq.size(1)]\n",
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" subsequent_mask = np.triu(np.ones(attn_shape), k=1)\n",
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" subsequent_mask = torch.from_numpy(subsequent_mask).byte()\n",
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" return subsequent_mask\n",
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"\n",
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"class ScaledDotProductAttention(nn.Module):\n",
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" def __init__(self):\n",
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" super(ScaledDotProductAttention, self).__init__()\n",
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"\n",
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" def forward(self, Q, K, V, attn_mask):\n",
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" 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)]\n",
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" scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.\n",
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" attn = nn.Softmax(dim=-1)(scores)\n",
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" context = torch.matmul(attn, V)\n",
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" return context, attn\n",
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"\n",
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"class MultiHeadAttention(nn.Module):\n",
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" def __init__(self):\n",
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" super(MultiHeadAttention, self).__init__()\n",
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" self.W_Q = nn.Linear(d_model, d_k * n_heads)\n",
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" self.W_K = nn.Linear(d_model, d_k * n_heads)\n",
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" self.W_V = nn.Linear(d_model, d_v * n_heads)\n",
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" self.linear = nn.Linear(n_heads * d_v, d_model)\n",
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" self.layer_norm = nn.LayerNorm(d_model)\n",
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"\n",
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" def forward(self, Q, K, V, attn_mask):\n",
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" # 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]\n",
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" residual, batch_size = Q, Q.size(0)\n",
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" # (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)\n",
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" 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]\n",
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" 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]\n",
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" 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]\n",
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"\n",
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" 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]\n",
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"\n",
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" # 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)]\n",
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" context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)\n",
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" 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]\n",
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" output = self.linear(context)\n",
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" return self.layer_norm(output + residual), attn # output: [batch_size x len_q x d_model]\n",
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"\n",
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"class PoswiseFeedForwardNet(nn.Module):\n",
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" def __init__(self):\n",
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" super(PoswiseFeedForwardNet, self).__init__()\n",
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" self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)\n",
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" self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)\n",
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" self.layer_norm = nn.LayerNorm(d_model)\n",
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"\n",
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" def forward(self, inputs):\n",
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" residual = inputs # inputs : [batch_size, len_q, d_model]\n",
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" output = nn.ReLU()(self.conv1(inputs.transpose(1, 2)))\n",
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" output = self.conv2(output).transpose(1, 2)\n",
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" return self.layer_norm(output + residual)\n",
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"\n",
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"class EncoderLayer(nn.Module):\n",
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" def __init__(self):\n",
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" super(EncoderLayer, self).__init__()\n",
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" self.enc_self_attn = MultiHeadAttention()\n",
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" self.pos_ffn = PoswiseFeedForwardNet()\n",
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"\n",
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" def forward(self, enc_inputs, enc_self_attn_mask):\n",
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" enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,V\n",
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" enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]\n",
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" return enc_outputs, attn\n",
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"\n",
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"class DecoderLayer(nn.Module):\n",
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" def __init__(self):\n",
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" super(DecoderLayer, self).__init__()\n",
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" self.dec_self_attn = MultiHeadAttention()\n",
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" self.dec_enc_attn = MultiHeadAttention()\n",
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" self.pos_ffn = PoswiseFeedForwardNet()\n",
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"\n",
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" def forward(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):\n",
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" dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_self_attn_mask)\n",
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" dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)\n",
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" dec_outputs = self.pos_ffn(dec_outputs)\n",
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" return dec_outputs, dec_self_attn, dec_enc_attn\n",
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"\n",
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"class Encoder(nn.Module):\n",
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" def __init__(self):\n",
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" super(Encoder, self).__init__()\n",
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" self.src_emb = nn.Embedding(src_vocab_size, d_model)\n",
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" self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(src_len+1, d_model),freeze=True)\n",
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" self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])\n",
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"\n",
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" def forward(self, enc_inputs): # enc_inputs : [batch_size x source_len]\n",
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" enc_outputs = self.src_emb(enc_inputs) + self.pos_emb(torch.LongTensor([[1,2,3,4,0]]))\n",
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" enc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs)\n",
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" enc_self_attns = []\n",
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" for layer in self.layers:\n",
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" enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask)\n",
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" enc_self_attns.append(enc_self_attn)\n",
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" return enc_outputs, enc_self_attns\n",
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"\n",
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"class Decoder(nn.Module):\n",
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" def __init__(self):\n",
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" super(Decoder, self).__init__()\n",
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" self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)\n",
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" self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(tgt_len+1, d_model),freeze=True)\n",
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" self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])\n",
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"\n",
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" def forward(self, dec_inputs, enc_inputs, enc_outputs): # dec_inputs : [batch_size x target_len]\n",
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" dec_outputs = self.tgt_emb(dec_inputs) + self.pos_emb(torch.LongTensor([[5,1,2,3,4]]))\n",
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" dec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs)\n",
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" dec_self_attn_subsequent_mask = get_attn_subsequent_mask(dec_inputs)\n",
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" dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequent_mask), 0)\n",
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"\n",
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" dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs)\n",
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"\n",
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" dec_self_attns, dec_enc_attns = [], []\n",
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" for layer in self.layers:\n",
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" dec_outputs, dec_self_attn, dec_enc_attn = layer(dec_outputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask)\n",
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" dec_self_attns.append(dec_self_attn)\n",
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" dec_enc_attns.append(dec_enc_attn)\n",
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" return dec_outputs, dec_self_attns, dec_enc_attns\n",
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"\n",
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"class Transformer(nn.Module):\n",
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" def __init__(self):\n",
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" super(Transformer, self).__init__()\n",
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" self.encoder = Encoder()\n",
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" self.decoder = Decoder()\n",
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" self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False)\n",
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" def forward(self, enc_inputs, dec_inputs):\n",
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" enc_outputs, enc_self_attns = self.encoder(enc_inputs)\n",
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" dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs, enc_outputs)\n",
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" dec_logits = self.projection(dec_outputs) # dec_logits : [batch_size x src_vocab_size x tgt_vocab_size]\n",
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" return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns\n",
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"\n",
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"def showgraph(attn):\n",
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" attn = attn[-1].squeeze(0)[0]\n",
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" attn = attn.squeeze(0).data.numpy()\n",
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" fig = plt.figure(figsize=(n_heads, n_heads)) # [n_heads, n_heads]\n",
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" ax = fig.add_subplot(1, 1, 1)\n",
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" ax.matshow(attn, cmap='viridis')\n",
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" ax.set_xticklabels(['']+sentences[0].split(), fontdict={'fontsize': 14}, rotation=90)\n",
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" ax.set_yticklabels(['']+sentences[2].split(), fontdict={'fontsize': 14})\n",
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" plt.show()\n",
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"\n",
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"if __name__ == '__main__':\n",
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" sentences = ['ich mochte ein bier P', 'S i want a beer', 'i want a beer E']\n",
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"\n",
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" # Transformer Parameters\n",
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" # Padding Should be Zero\n",
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" src_vocab = {'P': 0, 'ich': 1, 'mochte': 2, 'ein': 3, 'bier': 4}\n",
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" src_vocab_size = len(src_vocab)\n",
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"\n",
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" tgt_vocab = {'P': 0, 'i': 1, 'want': 2, 'a': 3, 'beer': 4, 'S': 5, 'E': 6}\n",
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" number_dict = {i: w for i, w in enumerate(tgt_vocab)}\n",
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" tgt_vocab_size = len(tgt_vocab)\n",
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"\n",
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" src_len = 5 # length of source\n",
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" tgt_len = 5 # length of target\n",
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"\n",
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" d_model = 512 # Embedding Size\n",
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" d_ff = 2048 # FeedForward dimension\n",
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" d_k = d_v = 64 # dimension of K(=Q), V\n",
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" n_layers = 6 # number of Encoder of Decoder Layer\n",
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" n_heads = 8 # number of heads in Multi-Head Attention\n",
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"\n",
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" model = Transformer()\n",
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"\n",
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" criterion = nn.CrossEntropyLoss()\n",
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" optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
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"\n",
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" enc_inputs, dec_inputs, target_batch = make_batch(sentences)\n",
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"\n",
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" for epoch in range(20):\n",
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" optimizer.zero_grad()\n",
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" outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)\n",
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" loss = criterion(outputs, target_batch.contiguous().view(-1))\n",
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" print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" # Test\n",
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" predict, _, _, _ = model(enc_inputs, dec_inputs)\n",
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" predict = predict.data.max(1, keepdim=True)[1]\n",
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" print(sentences[0], '->', [number_dict[n.item()] for n in predict.squeeze()])\n",
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"\n",
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" print('first head of last state enc_self_attns')\n",
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" showgraph(enc_self_attns)\n",
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"\n",
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" print('first head of last state dec_self_attns')\n",
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" showgraph(dec_self_attns)\n",
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"\n",
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" print('first head of last state dec_enc_attns')\n",
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" showgraph(dec_enc_attns)"
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],
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"outputs": [],
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"execution_count": null
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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} |