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model/simbot.py

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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)