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# model.py
import torch
import torch.nn as nn
import math
class SelfAttention(nn.Module):
def __init__(self, embed_dim, num_heads):
super().__init__()
assert embed_dim % num_heads == 0
self.head_dim = embed_dim // num_heads
self.num_heads = num_heads
self.query = nn.Linear(embed_dim, embed_dim)
self.key = nn.Linear(embed_dim, embed_dim)
self.value = nn.Linear(embed_dim, embed_dim)
self.out_proj = nn.Linear(embed_dim, embed_dim)
def forward(self, x):
B, T, C = x.size()
q = self.query(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2) # (B, heads, T, head_dim)
k = self.key(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
v = self.value(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
scores = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim) # (B, heads, T, T)
mask = torch.tril(torch.ones(T, T)).to(x.device)
scores = scores.masked_fill(mask == 0, float('-inf'))
attn = torch.softmax(scores, dim=-1)
out = attn @ v # (B, heads, T, head_dim)
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.out_proj(out)
class TransformerBlock(nn.Module):
def __init__(self, embed_dim, num_heads):
super().__init__()
self.attn = SelfAttention(embed_dim, num_heads)
self.ln1 = nn.LayerNorm(embed_dim)
self.ff = nn.Sequential(
nn.Linear(embed_dim, embed_dim * 4),
nn.GELU(),
nn.Linear(embed_dim * 4, embed_dim)
)
self.ln2 = nn.LayerNorm(embed_dim)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.ff(self.ln2(x))
return x
class TinyTransformer(nn.Module):
def __init__(self, vocab_size, max_len, embed_dim=128, num_heads=2, num_layers=1):
super().__init__()
self.token_embed = nn.Embedding(vocab_size, embed_dim)
self.pos_embed = nn.Parameter(torch.zeros(1, max_len, embed_dim))
self.blocks = nn.ModuleList([
TransformerBlock(embed_dim, num_heads) for _ in range(num_layers)
])
self.ln_final = nn.LayerNorm(embed_dim)
self.head = nn.Linear(embed_dim, vocab_size)
def forward(self, x):
B, T = x.size()
tok_emb = self.token_embed(x) # (B, T, C)
pos_emb = self.pos_embed[:, :T, :] # (1, T, C)
x = tok_emb + pos_emb # (B, T, C)
for block in self.blocks:
x = block(x)
x = self.ln_final(x)
logits = self.head(x) # (B, T, vocab_size)
return logits
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