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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=512, dropout=0.1):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))
pe = torch.zeros(max_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)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
class GEM(nn.Module):
def __init__(self, vocab_size, d_model, n_heads, d_ff, n_layers, dropout=0.1):
super(GEM, self).__init__()
self.embedding = nn.Embedding(vocab_size, d_model)
self.positional_encoding = PositionalEncoding(d_model, dropout=dropout)
encoder_layers = nn.TransformerEncoderLayer(d_model, n_heads, d_ff, dropout, batch_first=True)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, n_layers)
self.fc_out = nn.Linear(d_model, vocab_size)
self.d_model = d_model
def forward(self, input_ids, attention_mask=None):
x = self.embedding(input_ids) * math.sqrt(self.d_model)
x = self.positional_encoding(x)
if attention_mask is not None:
# Ensure attention_mask is in the shape (batch_size, sequence_length)
# Convert attention_mask to (batch_size, sequence_length) format
attention_mask = attention_mask.bool() # Ensure it's a boolean tensor
x = self.transformer_encoder(x, src_key_padding_mask=attention_mask)
else:
x = self.transformer_encoder(x)
x = self.fc_out(x)
return x
def generate(self, input_ids, max_length, temperature=1.0):
self.eval()
with torch.no_grad():
for _ in range(max_length - input_ids.size(1)):
outputs = self(input_ids)
next_token_logits = outputs[:, -1, :] / temperature
next_token = torch.multinomial(F.softmax(next_token_logits, dim=-1), num_samples=1)
input_ids = torch.cat([input_ids, next_token], dim=-1)
return input_ids
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