Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,181 Bytes
69defc9 |
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 |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
class Transformer(nn.Module):
def __init__(self, n_tokens=None, n_layer=None, n_head=None, d_model=None, d_ff=None,
dropout=0.0, pad_idx=0):
super(Transformer, self).__init__()
from torch.nn import TransformerEncoder, TransformerEncoderLayer
# self.name = 'Transformer'
self.pos_encoder = PositionalEncoding(d_model, dropout)
encoder_layers = TransformerEncoderLayer(d_model, n_head, dim_feedforward=d_ff, dropout=dropout)
norm = nn.LayerNorm(d_model)
self.transformer_encoder = TransformerEncoder(encoder_layers, n_layer, norm=norm)
self.encoder = nn.Embedding(n_tokens, d_model, padding_idx=pad_idx)
self.d_model = d_model
self.decoder = nn.Linear(d_model, n_tokens)
self.init_weights()
def init_weights(self):
initrange = 0.1
self.encoder.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, src, src_mask, src_key_padding_mask=None):
src = self.encoder(src) * math.sqrt(self.d_model)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, src_mask,
src_key_padding_mask=src_key_padding_mask)
output = self.decoder(output)
return output
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
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) |