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# code taken from https://towardsdatascience.com/how-to-code-the-transformer-in-pytorch-24db27c8f9ec
# and https://pytorch.org/tutorials/beginner/transformer_tutorial.html
import torch
import math
import copy
class Embedder(torch.nn.Module):
def __init__(self, vocab_size, d_model):
super().__init__()
self.embed = torch.nn.Embedding(vocab_size, d_model)
def forward(self, x):
return self.embed(x)
class PositionalEncoder(torch.nn.Module):
def __init__(self, d_model, dropout=0.1, max_seq_len=80):
super().__init__()
self.dropout = torch.nn.Dropout(p=dropout)
position = torch.arange(max_seq_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(max_seq_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe',
pe) # notifies PyTorch that this value should be saved like a model parameter but should not have gradients
def forward(self, x):
x = x + self.pe[:x.size(0)]
return self.dropout(x)
class MultiHeadAttention(torch.nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super().__init__()
self.d_model = d_model
self.d_k = d_model // heads
self.h = heads
self.q_linear = torch.nn.Linear(d_model, d_model)
self.v_linear = torch.nn.Linear(d_model, d_model)
self.k_linear = torch.nn.Linear(d_model, d_model)
self.dropout = torch.nn.Dropout(dropout)
self.out = torch.nn.Linear(d_model, d_model)
def forward(self, q, k, v, mask=None):
bs = q.size(0)
# perform linear operation and split into h heads
k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
# transpose to get dimensions bs * h * sl * d_model
k = k.transpose(1, 2)
q = q.transpose(1, 2)
v = v.transpose(1, 2)
# calculate attention using function we will define next
scores = attention(q, k, v, self.d_k, mask, self.dropout)
# concatenate heads and put through final linear layer
concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.d_model)
output = self.out(concat)
return output
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1e9)
scores = torch.nn.functional.softmax(scores, dim=-1)
if dropout is not None:
scores = dropout(scores)
output = torch.matmul(scores, v)
return output
class FeedForward(torch.nn.Module):
def __init__(self, d_model, d_ff=2048, dropout=0.1):
super().__init__()
# We set d_ff as a default to 2048
self.linear_1 = torch.nn.Linear(d_model, d_ff)
self.dropout = torch.nn.Dropout(dropout)
self.linear_2 = torch.nn.Linear(d_ff, d_model)
def forward(self, x):
x = self.dropout(torch.nn.functional.relu(self.linear_1(x)))
x = self.linear_2(x)
return x
class Norm(torch.nn.Module):
def __init__(self, d_model, eps=1e-6):
super().__init__()
self.size = d_model
# create two learnable parameters to calibrate normalization
self.alpha = torch.nn.Parameter(torch.ones(self.size))
self.bias = torch.nn.Parameter(torch.zeros(self.size))
self.eps = eps
def forward(self, x):
norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
return norm
# build an encoder layer with one multi-head attention layer and one # feed-forward layer
class EncoderLayer(torch.nn.Module):
def __init__(self, d_model, heads, dropout=0.1):
super().__init__()
self.norm_1 = Norm(d_model)
self.norm_2 = Norm(d_model)
self.attn = MultiHeadAttention(heads, d_model)
self.ff = FeedForward(d_model)
self.dropout_1 = torch.nn.Dropout(dropout)
self.dropout_2 = torch.nn.Dropout(dropout)
def forward(self, x, mask):
x2 = self.norm_1(x)
x = x + self.dropout_1(self.attn(x2, x2, x2, mask))
x2 = self.norm_2(x)
x = x + self.dropout_2(self.ff(x2))
return x
# build a decoder layer with two multi-head attention layers and
# one feed-forward layer
class DecoderLayer(torch.nn.Module):
def __init__(self, d_model, heads, dropout=0.1):
super().__init__()
self.norm_1 = Norm(d_model)
self.norm_2 = Norm(d_model)
self.norm_3 = Norm(d_model)
self.dropout_1 = torch.nn.Dropout(dropout)
self.dropout_2 = torch.nn.Dropout(dropout)
self.dropout_3 = torch.nn.Dropout(dropout)
self.attn_1 = MultiHeadAttention(heads, d_model)
self.attn_2 = MultiHeadAttention(heads, d_model)
self.ff = FeedForward(d_model)
def forward(self, x, e_outputs, src_mask, trg_mask):
x2 = self.norm_1(x)
x = x + self.dropout_1(self.attn_1(x2, x2, x2, trg_mask))
x2 = self.norm_2(x)
x = x + self.dropout_2(self.attn_2(x2, e_outputs, e_outputs,
src_mask))
x2 = self.norm_3(x)
x = x + self.dropout_3(self.ff(x2))
return x
# We can then build a convenient cloning function that can generate multiple layers:
def get_clones(module, N):
return torch.nn.ModuleList([copy.deepcopy(module) for i in range(N)])
class Encoder(torch.nn.Module):
def __init__(self, vocab_size, d_model, N, heads):
super().__init__()
self.N = N
self.embed = Embedder(vocab_size, d_model)
self.pe = PositionalEncoder(d_model)
self.layers = get_clones(EncoderLayer(d_model, heads), N)
self.norm = Norm(d_model)
def forward(self, src, mask):
x = self.embed(src)
x = self.pe(x)
for i in range(self.N):
x = self.layers[i](x, mask)
return self.norm(x)
class Decoder(torch.nn.Module):
def __init__(self, vocab_size, d_model, N, heads):
super().__init__()
self.N = N
self.embed = Embedder(vocab_size, d_model)
self.pe = PositionalEncoder(d_model)
self.layers = get_clones(DecoderLayer(d_model, heads), N)
self.norm = Norm(d_model)
def forward(self, trg, e_outputs, src_mask, trg_mask):
x = self.embed(trg)
x = self.pe(x)
for i in range(self.N):
x = self.layers[i](x, e_outputs, src_mask, trg_mask)
return self.norm(x)
class Transformer(torch.nn.Module):
def __init__(self, src_vocab, trg_vocab, d_model, N, heads):
super().__init__()
self.encoder = Encoder(src_vocab, d_model, N, heads)
self.decoder = Decoder(trg_vocab, d_model, N, heads)
self.out = torch.nn.Linear(d_model, trg_vocab)
def forward(self, src, trg, src_mask, trg_mask):
e_outputs = self.encoder(src, src_mask)
d_output = self.decoder(trg, e_outputs, src_mask, trg_mask)
output = self.out(d_output)
return output
# we don't perform softmax on the output as this will be handled
# automatically by our loss function |