Notes

Certainly! Below is the complete implementation of the simplified Transformer model with all the missing components filled in:

```python
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_seq_length):
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.max_seq_length = max_seq_length
self.positional_encodings = self.generate_encodings()

def generate_encodings(self):
pe = torch.zeros(self.max_seq_length, self.d_model)
position = torch.arange(0, self.max_seq_length, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, self.d_model, 2).float() * (-math.log(10000.0) / self.d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
return pe.unsqueeze(0)

def forward(self, x):
return x + self.positional_encodings[:, :x.size(1)]

class MultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.d_model = d_model
self.num_heads = num_heads
self.head_dim = d_model // num_heads

self.q_linear = nn.Linear(d_model, d_model)
self.k_linear = nn.Linear(d_model, d_model)
self.v_linear = nn.Linear(d_model, d_model)

self.final_linear = nn.Linear(d_model, d_model)

def forward(self, query, key, value, mask=None):
batch_size = query.size(0)

query = self.q_linear(query)
key = self.k_linear(key)
value = self.v_linear(value)

query = query.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
key = key.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
value = value.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)

scores = torch.matmul(query, key.transpose(-2, -1))
if mask is not None:
scores = scores.masked_fill(mask == 0, float("-1e20"))

attention = F.softmax(scores, dim=-1)
weighted_sum = torch.matmul(attention, value)

weighted_sum = weighted_sum.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
output = self.final_linear(weighted_sum)
return output

class PositionwiseFeedForward(nn.Module):
def __init__(self, d_model, hidden_dim, drop_prob):
super(PositionwiseFeedForward, self).__init__()
self.linear1 = nn.Linear(d_model, hidden_dim)
self.linear2 = nn.Linear(hidden_dim, d_model)
self.dropout = nn.Dropout(drop_prob)

def forward(self, x):
x = self.linear1(x)
x = F.relu(x)
x = self.dropout(x)
x = self.linear2(x)
return x

class TransformerLayer(nn.Module):
def __init__(self, d_model, num_heads, ffn_hidden_dim, drop_prob):
super(TransformerLayer, self).__init__()
self.multihead_attention = MultiHeadAttention(d_model, num_heads)
self.layer_norm1 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(drop_prob)

self.feedforward = PositionwiseFeedForward(d_model, ffn_hidden_dim, drop_prob)
self.layer_norm2 = nn.LayerNorm(d_model)
self.dropout2 = nn.Dropout(drop_prob)

def forward(self, x, mask):
attention_output = self.multihead_attention(x, x, x, mask)
x = x + self.dropout1(attention_output)
x = self.layer_norm1(x)

feedforward_output = self.feedforward(x)
x = x + self.dropout2(feedforward_output)
x = self.layer_norm2(x)
return x

class Transformer(nn.Module):
def __init__(self, num_layers, d_model, num_heads, ffn_hidden_dim, input_vocab_size,
output_vocab_size, max_seq_length, drop_prob):
super(Transformer, self).__init__()
self.num_layers = num_layers
self.encoder = nn.Embedding(input_vocab_size, d_model)
self.positional_encoding = PositionalEncoding(d_model, max_seq_length)
self.transformer_layers = nn.ModuleList([
TransformerLayer(d_model, num_heads, ffn_hidden_dim, drop_prob) for _ in range(num_layers)
])
self.decoder = nn.Linear(d_model, output_vocab_size)

def forward(self, source, target, source_mask):
source_embedding = self.encoder(source) + self.positional_encoding(source)
x = source_embedding

for layer in self.transformer_layers:
x = layer(x, source_mask)

logits = self.decoder(x)
return logits

# Instantiate the Transformer model
num_layers = 6
d_model = 512
num_heads = 8
ffn_hidden_dim = 2048
input_vocab_size = len(input_vocab)
output_vocab_size = len(output_vocab)
max_seq_length = 300
drop_prob = 0.1

transformer = Transformer(num_layers, d_model, num_heads, ffn_hidden_dim, input_vocab_size,
output_vocab_size, max_seq_length, drop_prob)

# Move the model to GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transform