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import torch
import torch.nn as nn
import torch.nn.functional as F
class SelfAttentionHead(nn.Module):
def __init__(self, head_size, n_embed, block_size, dropout=0.2) -> None:
super().__init__()
self.head_size = head_size
self.key = nn.Linear(n_embed, head_size, bias=False)
self.query = nn.Linear(n_embed, head_size, bias=False)
self.value = nn.Linear(n_embed, head_size, bias=False)
self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size)))
self.dropout = nn.Dropout(dropout)
def forward(self, x):
B, T, C = x.shape
k = self.key(x) # (B, T, C)
q = self.query(x) # (B, T, C)
wei = q @ k.transpose(-2, -1) * (C ** -0.5) # (B, T, C) @ (B, C, T) -> (B, T, T)
wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
wei = F.softmax(wei, dim=-1) # (B, T, T)
wei = self.dropout(wei)
v = self.value(x) # (B, T, C)
out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
return out
class MultiHeadAttention(nn.Module):
def __init__(self, num_heads, head_size, n_embed, block_size, dropout=0.2) -> None:
super().__init__()
self.heads = nn.ModuleList([SelfAttentionHead(head_size, n_embed, block_size) for _ in range(num_heads)])
# self.projection = nn.Linear(num_heads * head_size, n_embed)
self.projection = nn.Linear(n_embed, n_embed)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
out = torch.cat([h(x) for h in self.heads], dim=-1)
out = self.dropout(self.projection(out))
return out
class FeedForwardNet(nn.Module):
def __init__(self, n_embed, dropout=0.2) -> None:
super().__init__()
self.net = nn.Sequential(
nn.Linear(n_embed, 4 * n_embed),
nn.ReLU(),
nn.Linear(4 * n_embed, n_embed),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class DecoderBlock(nn.Module):
def __init__(self, n_embed, num_heads, block_size) -> None:
super().__init__()
head_size = n_embed // num_heads
self.sa_head = MultiHeadAttention(num_heads, head_size, n_embed, block_size)
self.ffn = FeedForwardNet(n_embed)
self.ln1 = nn.LayerNorm(n_embed)
self.ln2 = nn.LayerNorm(n_embed)
def forward(self, x):
x = x + self.sa_head(self.ln1(x))
x = x + self.ffn(self.ln2(x))
return x
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