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import math
import torch
import torch.nn as nn
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEncoding, self).__init__()
# create a matrix of [max_len, d_model] filled with zeros
pe = torch.zeros(max_len, d_model)
# Create a column vector of positions [0, 1, 2, ..., max_len -1]
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
# Calculate the "division term" for the cos/sin math
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
# Fill even indices (0, 2, 4, ...) with Sine
pe[:, 0::2] = torch.sin(position * div_term)
# Fill odd indices (1, 3, 5, ...) with Cosine
pe[:, 1::2] = torch.cos(position * div_term)
# Add a batch dimension [1, max_len, d_model]
pe = pe.unsqueeze(0)
# register buffer ensures this is saved with the model but not trained
self.register_buffer('pe', pe)
def forward(self, x):
# x is word embeddings [Batch, Seq_len, d-model]
# We simply add the positional vectors to the word vectors
x = x + self.pe[:, :x.size(1)]
return x
class TransformerBlock(nn.Module):
def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1):
super(TransformerBlock, self).__init__()
# Multihead Attention: How many different "perspectives" the model has (num_heads)
self.attention = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True)
# Layer Normalization
self.norm1 = nn.LayerNorm(embed_dim)
self.norm2 = nn.LayerNorm(embed_dim)
# Feed Forward Network
self.ff = nn.Sequential(
nn.Linear(embed_dim, ff_dim),
nn.ReLU(),
nn.Linear(ff_dim, embed_dim)
)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
# x shape: [batch, seq_len, embed_dim]
# Attention (Residual + Norm)
attn_output, _ = self.attention(x, x, x)
x = self.norm1(x + self.dropout(attn_output))
# Feed Forward (Residual Connection +_Norm)
ff_output = self.ff(x)
x = self.norm2(x + self.dropout(ff_output))
return x
class TransformerSentimentModel(nn.Module):
def __init__(self, vocab_size, embed_dim, num_heads, ff_dim, num_layers, output_dim, max_len=300):
super(TransformerSentimentModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim)
self.pos_encoding = PositionalEncoding(embed_dim, max_len)
self.blocks = nn.ModuleList([
TransformerBlock(embed_dim, num_heads, ff_dim) for _ in range(num_layers)
])
self.dropout = nn.Dropout(0.5)
self.fc = nn.Linear(embed_dim, output_dim)
def forward(self, input_ids):
# x: [batch, seq_len]
x = self.embedding(input_ids) # [batch, seq_len, embed_dim]
x = self.pos_encoding(x)
for block in self.blocks:
x = block(x)
x = x[:, 0, :]
return self.fc(self.dropout(x)) |