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import torch
import torch.nn as nn
import math
class CausalSelfAttention(nn.Module):
def __init__(self, d_model, n_heads, block_size):
super().__init__()
self.n_heads = n_heads
self.key = nn.Linear(d_model, d_model)
self.query = nn.Linear(d_model, d_model)
self.value = nn.Linear(d_model, d_model)
self.proj = nn.Linear(d_model, d_model)
self.register_buffer(
"mask",
torch.tril(torch.ones(block_size, block_size))
)
def forward(self, x):
B, T, C = x.size()
H = self.n_heads
k = self.key(x).view(B, T, H, C // H).transpose(1, 2)
q = self.query(x).view(B, T, H, C // H).transpose(1, 2)
v = self.value(x).view(B, T, H, C // H).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) / math.sqrt(C // H)
att = att.masked_fill(self.mask[:T, :T] == 0, float("-inf"))
att = torch.softmax(att, dim=-1)
out = att @ v
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.proj(out)
class Block(nn.Module):
def __init__(self, d_model, n_heads, block_size):
super().__init__()
self.ln1 = nn.LayerNorm(d_model)
self.attn = CausalSelfAttention(d_model, n_heads, block_size)
self.ln2 = nn.LayerNorm(d_model)
self.mlp = nn.Sequential(
nn.Linear(d_model, 4 * d_model),
nn.GELU(),
nn.Linear(4 * d_model, d_model)
)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class SIMGPT(nn.Module):
def __init__(self, vocab_size, block_size, n_layers, n_heads, d_model):
super().__init__()
self.token_emb = nn.Embedding(vocab_size, d_model)
self.pos_emb = nn.Embedding(block_size, d_model)
self.blocks = nn.Sequential(*[
Block(d_model, n_heads, block_size) for _ in range(n_layers)
])
self.ln = nn.LayerNorm(d_model)
self.head = nn.Linear(d_model, vocab_size)
def forward(self, idx):
B, T = idx.shape
pos = torch.arange(0, T, device=idx.device)
x = self.token_emb(idx) + self.pos_emb(pos)
x = self.blocks(x)
x = self.ln(x)
return self.head(x)
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