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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self,
emb_dim,
hid_dim,
n_layers,
kernel_size,
dropout,
device,
max_length = 512):
super().__init__()
assert kernel_size % 2 == 1, "Kernel size must be odd!"
self.device = device
self.scale = torch.sqrt(torch.FloatTensor([0.5])).to(device)
# self.tok_embedding = nn.Embedding(input_dim, emb_dim)
self.pos_embedding = nn.Embedding(max_length, emb_dim)
self.emb2hid = nn.Linear(emb_dim, hid_dim)
self.hid2emb = nn.Linear(hid_dim, emb_dim)
self.convs = nn.ModuleList([nn.Conv1d(in_channels = hid_dim,
out_channels = 2 * hid_dim,
kernel_size = kernel_size,
padding = (kernel_size - 1) // 2)
for _ in range(n_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, src):
#src = [batch size, src len]
src = src.transpose(0, 1)
batch_size = src.shape[0]
src_len = src.shape[1]
device = src.device
#create position tensor
pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(device)
#pos = [0, 1, 2, 3, ..., src len - 1]
#pos = [batch size, src len]
#embed tokens and positions
# tok_embedded = self.tok_embedding(src)
tok_embedded = src
pos_embedded = self.pos_embedding(pos)
#tok_embedded = pos_embedded = [batch size, src len, emb dim]
#combine embeddings by elementwise summing
embedded = self.dropout(tok_embedded + pos_embedded)
#embedded = [batch size, src len, emb dim]
#pass embedded through linear layer to convert from emb dim to hid dim
conv_input = self.emb2hid(embedded)
#conv_input = [batch size, src len, hid dim]
#permute for convolutional layer
conv_input = conv_input.permute(0, 2, 1)
#conv_input = [batch size, hid dim, src len]
#begin convolutional blocks...
for i, conv in enumerate(self.convs):
#pass through convolutional layer
conved = conv(self.dropout(conv_input))
#conved = [batch size, 2 * hid dim, src len]
#pass through GLU activation function
conved = F.glu(conved, dim = 1)
#conved = [batch size, hid dim, src len]
#apply residual connection
conved = (conved + conv_input) * self.scale
#conved = [batch size, hid dim, src len]
#set conv_input to conved for next loop iteration
conv_input = conved
#...end convolutional blocks
#permute and convert back to emb dim
conved = self.hid2emb(conved.permute(0, 2, 1))
#conved = [batch size, src len, emb dim]
#elementwise sum output (conved) and input (embedded) to be used for attention
combined = (conved + embedded) * self.scale
#combined = [batch size, src len, emb dim]
return conved, combined
class Decoder(nn.Module):
def __init__(self,
output_dim,
emb_dim,
hid_dim,
n_layers,
kernel_size,
dropout,
trg_pad_idx,
device,
max_length = 512):
super().__init__()
self.kernel_size = kernel_size
self.trg_pad_idx = trg_pad_idx
self.device = device
self.scale = torch.sqrt(torch.FloatTensor([0.5])).to(device)
self.tok_embedding = nn.Embedding(output_dim, emb_dim)
self.pos_embedding = nn.Embedding(max_length, emb_dim)
self.emb2hid = nn.Linear(emb_dim, hid_dim)
self.hid2emb = nn.Linear(hid_dim, emb_dim)
self.attn_hid2emb = nn.Linear(hid_dim, emb_dim)
self.attn_emb2hid = nn.Linear(emb_dim, hid_dim)
self.fc_out = nn.Linear(emb_dim, output_dim)
self.convs = nn.ModuleList([nn.Conv1d(in_channels = hid_dim,
out_channels = 2 * hid_dim,
kernel_size = kernel_size)
for _ in range(n_layers)])
self.dropout = nn.Dropout(dropout)
def calculate_attention(self, embedded, conved, encoder_conved, encoder_combined):
#embedded = [batch size, trg len, emb dim]
#conved = [batch size, hid dim, trg len]
#encoder_conved = encoder_combined = [batch size, src len, emb dim]
#permute and convert back to emb dim
conved_emb = self.attn_hid2emb(conved.permute(0, 2, 1))
#conved_emb = [batch size, trg len, emb dim]
combined = (conved_emb + embedded) * self.scale
#combined = [batch size, trg len, emb dim]
energy = torch.matmul(combined, encoder_conved.permute(0, 2, 1))
#energy = [batch size, trg len, src len]
attention = F.softmax(energy, dim=2)
#attention = [batch size, trg len, src len]
attended_encoding = torch.matmul(attention, encoder_combined)
#attended_encoding = [batch size, trg len, emd dim]
#convert from emb dim -> hid dim
attended_encoding = self.attn_emb2hid(attended_encoding)
#attended_encoding = [batch size, trg len, hid dim]
#apply residual connection
attended_combined = (conved + attended_encoding.permute(0, 2, 1)) * self.scale
#attended_combined = [batch size, hid dim, trg len]
return attention, attended_combined
def forward(self, trg, encoder_conved, encoder_combined):
#trg = [batch size, trg len]
#encoder_conved = encoder_combined = [batch size, src len, emb dim]
trg = trg.transpose(0, 1)
batch_size = trg.shape[0]
trg_len = trg.shape[1]
device = trg.device
#create position tensor
pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(device)
#pos = [batch size, trg len]
#embed tokens and positions
tok_embedded = self.tok_embedding(trg)
pos_embedded = self.pos_embedding(pos)
#tok_embedded = [batch size, trg len, emb dim]
#pos_embedded = [batch size, trg len, emb dim]
#combine embeddings by elementwise summing
embedded = self.dropout(tok_embedded + pos_embedded)
#embedded = [batch size, trg len, emb dim]
#pass embedded through linear layer to go through emb dim -> hid dim
conv_input = self.emb2hid(embedded)
#conv_input = [batch size, trg len, hid dim]
#permute for convolutional layer
conv_input = conv_input.permute(0, 2, 1)
#conv_input = [batch size, hid dim, trg len]
batch_size = conv_input.shape[0]
hid_dim = conv_input.shape[1]
for i, conv in enumerate(self.convs):
#apply dropout
conv_input = self.dropout(conv_input)
#need to pad so decoder can't "cheat"
padding = torch.zeros(batch_size,
hid_dim,
self.kernel_size - 1).fill_(self.trg_pad_idx).to(device)
padded_conv_input = torch.cat((padding, conv_input), dim = 2)
#padded_conv_input = [batch size, hid dim, trg len + kernel size - 1]
#pass through convolutional layer
conved = conv(padded_conv_input)
#conved = [batch size, 2 * hid dim, trg len]
#pass through GLU activation function
conved = F.glu(conved, dim = 1)
#conved = [batch size, hid dim, trg len]
#calculate attention
attention, conved = self.calculate_attention(embedded,
conved,
encoder_conved,
encoder_combined)
#attention = [batch size, trg len, src len]
#apply residual connection
conved = (conved + conv_input) * self.scale
#conved = [batch size, hid dim, trg len]
#set conv_input to conved for next loop iteration
conv_input = conved
conved = self.hid2emb(conved.permute(0, 2, 1))
#conved = [batch size, trg len, emb dim]
output = self.fc_out(self.dropout(conved))
#output = [batch size, trg len, output dim]
return output, attention
class ConvSeq2Seq(nn.Module):
def __init__(self, vocab_size, emb_dim, hid_dim, enc_layers, dec_layers, enc_kernel_size, dec_kernel_size, enc_max_length, dec_max_length, dropout, pad_idx, device):
super().__init__()
enc = Encoder(emb_dim, hid_dim, enc_layers, enc_kernel_size, dropout, device, enc_max_length)
dec = Decoder(vocab_size, emb_dim, hid_dim, dec_layers, dec_kernel_size, dropout, pad_idx, device, dec_max_length)
self.encoder = enc
self.decoder = dec
def forward_encoder(self, src):
encoder_conved, encoder_combined = self.encoder(src)
return encoder_conved, encoder_combined
def forward_decoder(self, trg, memory):
encoder_conved, encoder_combined = memory
output, attention = self.decoder(trg, encoder_conved, encoder_combined)
return output, (encoder_conved, encoder_combined)
def forward(self, src, trg):
#src = [batch size, src len]
#trg = [batch size, trg len - 1] (<eos> token sliced off the end)
#calculate z^u (encoder_conved) and (z^u + e) (encoder_combined)
#encoder_conved is output from final encoder conv. block
#encoder_combined is encoder_conved plus (elementwise) src embedding plus
# positional embeddings
encoder_conved, encoder_combined = self.encoder(src)
#encoder_conved = [batch size, src len, emb dim]
#encoder_combined = [batch size, src len, emb dim]
#calculate predictions of next words
#output is a batch of predictions for each word in the trg sentence
#attention a batch of attention scores across the src sentence for
# each word in the trg sentence
output, attention = self.decoder(trg, encoder_conved, encoder_combined)
#output = [batch size, trg len - 1, output dim]
#attention = [batch size, trg len - 1, src len]
return output#, attention
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