transformer-pytorch
Transformer model implemented with Pytorch
Attention is all you need-[Paper]
Architecture
Self-Attention
self_attention.py
class SelfAttention(nn.Module):
def __init__(self, embed_size, heads):
super(SelfAttention, self).__init__()
self.embed_size = embed_size
self.heads = heads
self.head_dim = embed_size // heads
assert (
self.head_dim * heads == embed_size
), "Embedding size needs to be divisible by heads"
self.values = nn.Linear(self.embed_size, self.embed_size, bias=False)
self.keys = nn.Linear(self.embed_size, self.embed_size, bias=False)
self.queries = nn.Linear(self.embed_size, self.embed_size, bias=False)
self.fc_out = nn.Linear(heads * self.head_dim, embed_size)
def forward(self, values, keys, query, mask):
# Get number of training examples
N = query.shape[0]
value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]
values = self.values(values)
keys = self.keys(keys)
queries = self.queries(query)
# Split the embedding into self.heads different pieces
# Multi head
# [N, len, embed_size] --> [N, len, heads, head_dim]
values = values.reshape(N, value_len, self.heads, self.head_dim)
keys = keys.reshape(N, key_len, self.heads, self.head_dim)
queries = queries.reshape(N, query_len, self.heads, self.head_dim)
# Einsum does matrix mult. for query*keys for each training example
# with every other training example, don't be confused by einsum
# it's just how I like doing matrix multiplication & bmm
energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys])
# queries shape: (N, query_len, heads, heads_dim),
# keys shape: (N, key_len, heads, heads_dim)
# energy: (N, heads, query_len, key_len)
# Mask padded indices so their weights become 0
if mask is not None:
energy = energy.masked_fill(mask == 0, float("-1e20"))
# Normalize energy values similarly to seq2seq + attention
# so that they sum to 1. Also divide by scaling factor for
# better stability
attention = torch.softmax(energy / (self.embed_size ** (1 / 2)), dim=3)
# attention shape: (N, heads, query_len, key_len)
out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape(
N, query_len, self.heads * self.head_dim
)
# attention shape: (N, heads, query_len, key_len)
# values shape: (N, value_len, heads, heads_dim)
# out after matrix multiply: (N, query_len, heads, head_dim), then
# we reshape and flatten the last two dimensions.
out = self.fc_out(out)
# Linear layer doesn't modify the shape, final shape will be
# (N, query_len, embed_size)
return out
Encoder Block
encoder_block.py
class EncoderBlock(nn.Module):
def __init__(self, embed_size, heads, dropout, forward_expansion):
super(EncoderBlock, self).__init__()
self.attention = SelfAttention(embed_size, heads)
self.norm1 = nn.LayerNorm(embed_size)
self.norm2 = nn.LayerNorm(embed_size)
self.feed_forward = nn.Sequential(
nn.Linear(embed_size, forward_expansion * embed_size),
nn.ReLU(),
nn.Linear(forward_expansion * embed_size, embed_size),
)
self.dropout = nn.Dropout(dropout)
def forward(self, value, key, query, mask):
attention = self.attention(value, key, query, mask)
# Add skip connection, run through normalization and finally dropout
x = self.dropout(self.norm1(attention + query))
forward = self.feed_forward(x)
out = self.dropout(self.norm2(forward + x))
return out
Encoder
encoder.py
class Encoder(nn.Module):
def __init__(
self,
src_vocab_size,
embed_size,
num_layers,
heads,
device,
forward_expansion,
dropout,
max_length,
):
super(Encoder, self).__init__()
self.embed_size = embed_size
self.device = device
self.word_embedding = nn.Embedding(src_vocab_size, embed_size)
self.position_embedding = nn.Embedding(max_length, embed_size)
self.layers = nn.ModuleList(
[
EncoderBlock(
embed_size,
heads,
dropout=dropout,
forward_expansion=forward_expansion,
)
for _ in range(num_layers)
]
)
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask):
N, seq_length = x.shape
positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
out = self.dropout(
(self.word_embedding(x) + self.position_embedding(positions))
)
# In the Encoder the query, key, value are all the same, it's in the
# decoder this will change. This might look a bit odd in this case.
for layer in self.layers:
out = layer(out, out, out, mask)
return out
Decoder Block
docoder_block.py
class DecoderBlock(nn.Module):
def __init__(self, embed_size, heads, forward_expansion, dropout, device):
super(DecoderBlock, self).__init__()
self.norm = nn.LayerNorm(embed_size)
self.attention = SelfAttention(embed_size, heads=heads)
self.transformer_block = EncoderBlock(
embed_size, heads, dropout, forward_expansion
)
self.dropout = nn.Dropout(dropout)
def forward(self, x, value, key, src_mask, trg_mask):
attention = self.attention(x, x, x, trg_mask)
query = self.dropout(self.norm(attention + x))
out = self.transformer_block(value, key, query, src_mask)
return out
Decoder
decoder.py
class Decoder(nn.Module):
def __init__(
self,
trg_vocab_size,
embed_size,
num_layers,
heads,
forward_expansion,
dropout,
device,
max_length,
):
super(Decoder, self).__init__()
self.device = device
self.word_embedding = nn.Embedding(trg_vocab_size, embed_size)
self.position_embedding = nn.Embedding(max_length, embed_size)
self.layers = nn.ModuleList(
[
DecoderBlock(embed_size, heads, forward_expansion, dropout, device)
for _ in range(num_layers)
]
)
self.dropout = nn.Dropout(dropout)
self.fc_out = nn.Linear(embed_size, trg_vocab_size)
def forward(self, x, enc_out, src_mask, trg_mask):
N, seq_length = x.shape
positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
x = self.dropout(
(self.word_embedding(x) + self.position_embedding(positions))
)
for layer in self.layers:
x = layer(x, enc_out, enc_out, src_mask, trg_mask)
out = self.fc_out(x)
return out
Transformer
transformer.py
class Transformer(nn.Module):
def __init__(
self,
src_vocab_size,
trg_vocab_size,
src_pad_idx,
trg_pad_idx,
embed_size=512,
num_layers=6,
forward_expansion=4,
heads=8,
dropout=0,
device="cpu",
max_length=100,
):
super(Transformer, self).__init__()
self.encoder = Encoder(
src_vocab_size,
embed_size,
num_layers,
heads,
device,
forward_expansion,
dropout,
max_length,
)
self.decoder = Decoder(
trg_vocab_size,
embed_size,
num_layers,
heads,
forward_expansion,
dropout,
device,
max_length,
)
self.src_pad_idx = src_pad_idx
self.trg_pad_idx = trg_pad_idx
self.device = device
def make_src_mask(self, src):
src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
# (N, 1, 1, src_len)
return src_mask.to(self.device)
def make_trg_mask(self, trg):
N, trg_len = trg.shape
trg_mask = torch.tril(torch.ones((trg_len, trg_len))).expand(
N, 1, trg_len, trg_len
)
return trg_mask.to(self.device)
def forward(self, src, trg):
src_mask = self.make_src_mask(src)
trg_mask = self.make_trg_mask(trg)
enc_src = self.encoder(src, src_mask)
out = self.decoder(trg, enc_src, src_mask, trg_mask)
return out
Authors
- Mingu Kang - Github
