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layers.py
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layers.py
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"""https://github.com/bentrevett/pytorch-seq2seq/blob/master/6%20-%20Attention%20is%20All%20You%20Need.ipynb"""
import torch
import torch.nn as nn
from utils import get_device
device = get_device()
class Encoder(nn.Module):
def __init__(
self,
input_dim,
hid_dim,
n_layers,
n_heads,
pf_dim,
dropout,
device,
max_length=100,
):
super().__init__()
self.device = device
self.tok_embedding = nn.Embedding(input_dim, hid_dim)
self.pos_embedding = nn.Embedding(max_length, hid_dim)
self.layers = nn.ModuleList(
[
EncoderLayer(hid_dim, n_heads, pf_dim, dropout, device)
for _ in range(n_layers)
]
)
self.dropout = nn.Dropout(dropout)
self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)
def forward(self, src, src_mask):
# src = [batch size, src len]
# src_mask = [batch size, 1, 1, src len]
batch_size = src.shape[0]
src_len = src.shape[1]
pos = (
torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)
)
# pos = [batch size, src len]
src = self.dropout(
(self.tok_embedding(src) * self.scale) + self.pos_embedding(pos)
)
# src = [batch size, src len, hid dim]
for layer in self.layers:
src = layer(src, src_mask)
# src = [batch size, src len, hid dim]
return src
class EncoderLayer(nn.Module):
def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
super().__init__()
self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
self.ff_layer_norm = nn.LayerNorm(hid_dim)
self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
self.positionwise_feedforward = PositionwiseFeedforwardLayer(
hid_dim, pf_dim, dropout
)
self.dropout = nn.Dropout(dropout)
def forward(self, src, src_mask):
# src = [batch size, src len, hid dim]
# src_mask = [batch size, 1, 1, src len]
# self attention
_src, _ = self.self_attention(src, src, src, src_mask)
# dropout, residual connection and layer norm
src = self.self_attn_layer_norm(src + self.dropout(_src))
# src = [batch size, src len, hid dim]
# positionwise feedforward
_src = self.positionwise_feedforward(src)
# dropout, residual and layer norm
src = self.ff_layer_norm(src + self.dropout(_src))
# src = [batch size, src len, hid dim]
return src
class MultiHeadAttentionLayer(nn.Module):
def __init__(self, hid_dim, n_heads, dropout, device):
super().__init__()
assert hid_dim % n_heads == 0
self.hid_dim = hid_dim
self.n_heads = n_heads
self.head_dim = hid_dim // n_heads
self.fc_q = nn.Linear(hid_dim, hid_dim)
self.fc_k = nn.Linear(hid_dim, hid_dim)
self.fc_v = nn.Linear(hid_dim, hid_dim)
self.fc_o = nn.Linear(hid_dim, hid_dim)
self.dropout = nn.Dropout(dropout)
self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)
def forward(self, query, key, value, mask=None):
batch_size = query.shape[0]
# query = [batch size, query len, hid dim]
# key = [batch size, key len, hid dim]
# value = [batch size, value len, hid dim]
Q = self.fc_q(query)
K = self.fc_k(key)
V = self.fc_v(value)
# Q = [batch size, query len, hid dim]
# K = [batch size, key len, hid dim]
# V = [batch size, value len, hid dim]
Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
# Q = [batch size, n heads, query len, head dim]
# K = [batch size, n heads, key len, head dim]
# V = [batch size, n heads, value len, head dim]
energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale
# energy = [batch size, n heads, query len, key len]
if mask is not None:
energy = energy.masked_fill(mask == 0, -1e10)
attention = torch.softmax(energy, dim=-1)
# attention = [batch size, n heads, query len, key len]
x = torch.matmul(self.dropout(attention), V)
# x = [batch size, n heads, query len, head dim]
x = x.permute(0, 2, 1, 3).contiguous()
# x = [batch size, query len, n heads, head dim]
x = x.view(batch_size, -1, self.hid_dim)
# x = [batch size, query len, hid dim]
x = self.fc_o(x)
# x = [batch size, query len, hid dim]
return x, attention
class PositionwiseFeedforwardLayer(nn.Module):
def __init__(self, hid_dim, pf_dim, dropout):
super().__init__()
self.fc_1 = nn.Linear(hid_dim, pf_dim)
self.fc_2 = nn.Linear(pf_dim, hid_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
# x = [batch size, seq len, hid dim]
x = self.dropout(torch.relu(self.fc_1(x)))
# x = [batch size, seq len, pf dim]
x = self.fc_2(x)
# x = [batch size, seq len, hid dim]
return x
class Decoder(nn.Module):
def __init__(
self,
output_dim,
hid_dim,
n_layers,
n_heads,
pf_dim,
dropout,
device,
max_length=100,
):
super().__init__()
self.device = device
self.tok_embedding = nn.Embedding(output_dim, hid_dim)
self.pos_embedding = nn.Embedding(max_length, hid_dim)
self.layers = nn.ModuleList(
[
DecoderLayer(hid_dim, n_heads, pf_dim, dropout, device)
for _ in range(n_layers)
]
)
self.fc_out = nn.Linear(hid_dim, output_dim)
self.dropout = nn.Dropout(dropout)
self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)
def forward(self, trg, enc_src, trg_mask, src_mask):
# trg = [batch size, trg len]
# enc_src = [batch size, src len, hid dim]
# trg_mask = [batch size, 1, trg len, trg len]
# src_mask = [batch size, 1, 1, src len]
batch_size = trg.shape[0]
trg_len = trg.shape[1]
pos = (
torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)
)
# pos = [batch size, trg len]
trg = self.dropout(
(self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)
)
# trg = [batch size, trg len, hid dim]
for layer in self.layers:
trg, attention = layer(trg, enc_src, trg_mask, src_mask)
# trg = [batch size, trg len, hid dim]
# attention = [batch size, n heads, trg len, src len]
output = self.fc_out(trg)
# output = [batch size, trg len, output dim]
return output, attention
class DecoderLayer(nn.Module):
def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
super().__init__()
self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
self.enc_attn_layer_norm = nn.LayerNorm(hid_dim)
self.ff_layer_norm = nn.LayerNorm(hid_dim)
self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
self.encoder_attention = MultiHeadAttentionLayer(
hid_dim, n_heads, dropout, device
)
self.positionwise_feedforward = PositionwiseFeedforwardLayer(
hid_dim, pf_dim, dropout
)
self.dropout = nn.Dropout(dropout)
def forward(self, trg, enc_src, trg_mask, src_mask):
# trg = [batch size, trg len, hid dim]
# enc_src = [batch size, src len, hid dim]
# trg_mask = [batch size, 1, trg len, trg len]
# src_mask = [batch size, 1, 1, src len]
# self attention
_trg, _ = self.self_attention(trg, trg, trg, trg_mask)
# dropout, residual connection and layer norm
trg = self.self_attn_layer_norm(trg + self.dropout(_trg))
# trg = [batch size, trg len, hid dim]
# encoder attention
_trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)
# dropout, residual connection and layer norm
trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))
# trg = [batch size, trg len, hid dim]
# positionwise feedforward
_trg = self.positionwise_feedforward(trg)
# dropout, residual and layer norm
trg = self.ff_layer_norm(trg + self.dropout(_trg))
# trg = [batch size, trg len, hid dim]
# attention = [batch size, n heads, trg len, src len]
return trg, attention