File size: 6,882 Bytes
1dd09ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
import torch
import torch.nn as nn
import math
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Embeddings(nn.Module):
"""
Implements embeddings of the words and adds their positional encodings.
"""
def __init__(self, vocab_size, d_model, max_len = 50):
super(Embeddings, self).__init__()
self.d_model = d_model
self.dropout = nn.Dropout(0.1)
self.embed = nn.Embedding(vocab_size, d_model)
self.pe = self.create_positinal_encoding(max_len, self.d_model)
self.dropout = nn.Dropout(0.1)
def create_positinal_encoding(self, max_len, d_model):
pe = torch.zeros(max_len, d_model).to(device)
for pos in range(max_len): # for each position of the word
for i in range(0, d_model, 2): # for each dimension of the each position
pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))
pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))
pe = pe.unsqueeze(0) # include the batch size
return pe
def forward(self, encoded_words):
embedding = self.embed(encoded_words) * math.sqrt(self.d_model)
embedding += self.pe[:, :embedding.size(1)] # pe will automatically be expanded with the same batch size as encoded_words
embedding = self.dropout(embedding)
return embedding
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model):
super(MultiHeadAttention, self).__init__()
assert d_model % heads == 0
self.d_k = d_model // heads
self.heads = heads
self.dropout = nn.Dropout(0.1)
self.query = nn.Linear(d_model, d_model)
self.key = nn.Linear(d_model, d_model)
self.value = nn.Linear(d_model, d_model)
self.concat = nn.Linear(d_model, d_model)
def forward(self, query, key, value, mask):
"""
query, key, value of shape: (batch_size, max_len, 512)
mask of shape: (batch_size, 1, 1, max_words)
"""
# (batch_size, max_len, 512)
query = self.query(query)
key = self.key(key)
value = self.value(value)
# (batch_size, max_len, 512) --> (batch_size, max_len, h, d_k) --> (batch_size, h, max_len, d_k)
query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)
key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)
value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)
# (batch_size, h, max_len, d_k) matmul (batch_size, h, d_k, max_len) --> (batch_size, h, max_len, max_len)
scores = torch.matmul(query, key.permute(0,1,3,2)) / math.sqrt(query.size(-1))
scores = scores.masked_fill(mask == 0, -1e9) # (batch_size, h, max_len, max_len)
weights = F.softmax(scores, dim = -1) # (batch_size, h, max_len, max_len)
weights = self.dropout(weights)
# (batch_size, h, max_len, max_len) matmul (batch_size, h, max_len, d_k) --> (batch_size, h, max_len, d_k)
context = torch.matmul(weights, value)
# (batch_size, h, max_len, d_k) --> (batch_size, max_len, h, d_k) --> (batch_size, max_len, h * d_k)
context = context.permute(0,2,1,3).contiguous().view(context.shape[0], -1, self.heads * self.d_k)
# (batch_size, max_len, h * d_k)
interacted = self.concat(context)
return interacted
class FeedForward(nn.Module):
def __init__(self, d_model, middle_dim = 2048):
super(FeedForward, self).__init__()
self.fc1 = nn.Linear(d_model, middle_dim)
self.fc2 = nn.Linear(middle_dim, d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, x):
out = F.relu(self.fc1(x))
out = self.fc2(self.dropout(out))
return out
class EncoderLayer(nn.Module):
def __init__(self, d_model, heads):
super(EncoderLayer, self).__init__()
self.layernorm = nn.LayerNorm(d_model)
self.self_multihead = MultiHeadAttention(heads, d_model)
self.feed_forward = FeedForward(d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, embeddings, mask):
interacted = self.dropout(self.self_multihead(embeddings, embeddings, embeddings, mask))
interacted = self.layernorm(interacted + embeddings)
feed_forward_out = self.dropout(self.feed_forward(interacted))
encoded = self.layernorm(feed_forward_out + interacted)
return encoded
class DecoderLayer(nn.Module):
def __init__(self, d_model, heads):
super(DecoderLayer, self).__init__()
self.layernorm = nn.LayerNorm(d_model)
self.self_multihead = MultiHeadAttention(heads, d_model)
self.src_multihead = MultiHeadAttention(heads, d_model)
self.feed_forward = FeedForward(d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, embeddings, encoded, src_mask, target_mask):
query = self.dropout(self.self_multihead(embeddings, embeddings, embeddings, target_mask))
query = self.layernorm(query + embeddings)
interacted = self.dropout(self.src_multihead(query, encoded, encoded, src_mask))
interacted = self.layernorm(interacted + query)
feed_forward_out = self.dropout(self.feed_forward(interacted))
decoded = self.layernorm(feed_forward_out + interacted)
return decoded
class Transformer(nn.Module):
def __init__(self, d_model, heads, num_layers, word_map):
super(Transformer, self).__init__()
self.d_model = d_model
self.vocab_size = len(word_map)
self.embed = Embeddings(self.vocab_size, d_model)
self.encoder = nn.ModuleList([EncoderLayer(d_model, heads) for _ in range(num_layers)])
self.decoder = nn.ModuleList([DecoderLayer(d_model, heads) for _ in range(num_layers)])
self.logit = nn.Linear(d_model, self.vocab_size)
def encode(self, src_words, src_mask):
src_embeddings = self.embed(src_words)
for layer in self.encoder:
src_embeddings = layer(src_embeddings, src_mask)
return src_embeddings
def decode(self, target_words, target_mask, src_embeddings, src_mask):
tgt_embeddings = self.embed(target_words)
for layer in self.decoder:
tgt_embeddings = layer(tgt_embeddings, src_embeddings, src_mask, target_mask)
return tgt_embeddings
def forward(self, src_words, src_mask, target_words, target_mask):
encoded = self.encode(src_words, src_mask)
decoded = self.decode(target_words, target_mask, encoded, src_mask)
out = F.log_softmax(self.logit(decoded), dim = 2)
return out
|