hacking sorcerer
nano nano miniGPT
hacking sorcerer
2025. 4. 28. 20:36
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import torch
import torch.nn as nn
import torch.nn.functional as F
class SelfAttentionHead(nn.Module):
def __init__(self, emb_dim, head_size, block_size):
super().__init__()
self.key = nn.Linear(emb_dim, head_size, bias=False)
self.query = nn.Linear(emb_dim, head_size, bias=False)
self.value = nn.Linear(emb_dim, head_size, bias=False)
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
self.dropout = nn.Dropout(0.1) # optional, for regularization
self.head_size = head_size
def forward(self, x):
B, T, C = x.shape
k = self.key(x) # (B, T, head_size)
q = self.query(x) # (B, T, head_size)
attn = (q @ k.transpose(-2, -1)) / (self.head_size ** 0.5) # (B, T, T)
attn = attn.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # masking
attn = F.softmax(attn, dim=-1) # attention scores
attn = self.dropout(attn)
v = self.value(x) # (B, T, head_size)
out = attn @ v # (B, T, head_size)
return out
class MultiHeadAttention(nn.Module):
def __init__(self, emb_dim, num_heads, block_size):
super().__init__()
self.heads = nn.ModuleList([SelfAttentionHead(emb_dim, emb_dim // num_heads, block_size) for _ in range(num_heads)])
self.proj = nn.Linear(emb_dim, emb_dim)
self.dropout = nn.Dropout(0.1)
def forward(self, x):
out = torch.cat([h(x) for h in self.heads], dim=-1)
out = self.proj(out)
out = self.dropout(out)
return out
class FeedForward(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.net = nn.Sequential(
nn.Linear(emb_dim, 4 * emb_dim),
nn.ReLU(),
nn.Linear(4 * emb_dim, emb_dim),
nn.Dropout(0.1),
)
def forward(self, x):
return self.net(x)
class TransformerBlock(nn.Module):
def __init__(self, emb_dim, num_heads, block_size):
super().__init__()
self.ln1 = nn.LayerNorm(emb_dim)
self.ln2 = nn.LayerNorm(emb_dim)
self.attn = MultiHeadAttention(emb_dim, num_heads, block_size)
self.ff = FeedForward(emb_dim)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.ff(self.ln2(x))
return x
class MiniGPT(nn.Module):
def __init__(self, vocab_size, block_size, emb_dim, n_layers, n_heads):
super().__init__()
self.token_embedding = nn.Embedding(vocab_size, emb_dim)
self.position_embedding = nn.Embedding(block_size, emb_dim)
self.blocks = nn.Sequential(*[TransformerBlock(emb_dim, n_heads, block_size) for _ in range(n_layers)])
self.ln_f = nn.LayerNorm(emb_dim) # final layer norm
self.lm_head = nn.Linear(emb_dim, vocab_size)
self.block_size = block_size
def forward(self, idx, targets=None):
B, T = idx.shape
assert T <= self.block_size, f"Cannot forward, sequence length {T} > block size {self.block_size}"
token_emb = self.token_embedding(idx) # (B, T, emb_dim)
pos_emb = self.position_embedding(torch.arange(T, device=idx.device)) # (T, emb_dim)
x = token_emb + pos_emb # (B, T, emb_dim)
x = self.blocks(x)
x = self.ln_f(x)
logits = self.lm_head(x) # (B, T, vocab_size)
if targets is None:
loss = None
else:
# Reshape logits and targets for cross-entropy
B, T, C = logits.shape
logits = logits.view(B*T, C)
targets = targets.view(B*T)
loss = F.cross_entropy(logits, targets)
return logits, loss
def generate(self, idx, max_new_tokens):
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.block_size:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] # focus only on last time step
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
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