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from einops import rearrange
from torchvision import models
import math
import torch
from torch import nn
class LanguageTransformer(nn.Module):
def __init__(self, vocab_size,
d_model, nhead,
num_encoder_layers, num_decoder_layers,
dim_feedforward, max_seq_length,
pos_dropout, trans_dropout):
super().__init__()
self.d_model = d_model
self.embed_tgt = nn.Embedding(vocab_size, d_model)
self.pos_enc = PositionalEncoding(d_model, pos_dropout, max_seq_length)
# self.learned_pos_enc = LearnedPositionalEncoding(d_model, pos_dropout, max_seq_length)
self.transformer = nn.Transformer(d_model, nhead,
num_encoder_layers, num_decoder_layers,
dim_feedforward, trans_dropout)
self.fc = nn.Linear(d_model, vocab_size)
def forward(self, src, tgt, src_key_padding_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None):
"""
Shape:
- src: (W, N, C)
- tgt: (T, N)
- src_key_padding_mask: (N, S)
- tgt_key_padding_mask: (N, T)
- memory_key_padding_mask: (N, S)
- output: (N, T, E)
"""
tgt_mask = self.gen_nopeek_mask(tgt.shape[0]).to(src.device)
src = self.pos_enc(src*math.sqrt(self.d_model))
# src = self.learned_pos_enc(src*math.sqrt(self.d_model))
tgt = self.pos_enc(self.embed_tgt(tgt) * math.sqrt(self.d_model))
output = self.transformer(src, tgt, tgt_mask=tgt_mask, src_key_padding_mask=src_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask)
# output = rearrange(output, 't n e -> n t e')
output = output.transpose(0, 1)
return self.fc(output)
def gen_nopeek_mask(self, length):
mask = (torch.triu(torch.ones(length, length)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def forward_encoder(self, src):
src = self.pos_enc(src*math.sqrt(self.d_model))
memory = self.transformer.encoder(src)
return memory
def forward_decoder(self, tgt, memory):
tgt_mask = self.gen_nopeek_mask(tgt.shape[0]).to(tgt.device)
tgt = self.pos_enc(self.embed_tgt(tgt) * math.sqrt(self.d_model))
output = self.transformer.decoder(tgt, memory, tgt_mask=tgt_mask)
# output = rearrange(output, 't n e -> n t e')
output = output.transpose(0, 1)
return self.fc(output), memory
def expand_memory(self, memory, beam_size):
memory = memory.repeat(1, beam_size, 1)
return memory
def get_memory(self, memory, i):
memory = memory[:, [i], :]
return memory
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=100):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
class LearnedPositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=100):
super(LearnedPositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
self.pos_embed = nn.Embedding(max_len, d_model)
self.layernorm = LayerNorm(d_model)
def forward(self, x):
seq_len = x.size(0)
pos = torch.arange(seq_len, dtype=torch.long, device=x.device)
pos = pos.unsqueeze(-1).expand(x.size()[:2])
x = x + self.pos_embed(pos)
return self.dropout(self.layernorm(x))
class LayerNorm(nn.Module):
"A layernorm module in the TF style (epsilon inside the square root)."
def __init__(self, d_model, variance_epsilon=1e-12):
super().__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
self.variance_epsilon = variance_epsilon
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.gamma * x + self.beta
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