Upload model.py with huggingface_hub

model.py

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+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from huggingface_hub import PyTorchModelHubMixin
+
+class Head(nn.Module):
+    """Single head of self-attention."""
+    def __init__(self, head_size, n_embd, block_size, dropout):
+        super().__init__()
+        self.key = nn.Linear(n_embd, head_size, bias=False)
+        self.query = nn.Linear(n_embd, head_size, bias=False)
+        self.value = nn.Linear(n_embd, 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, hs)
+        q = self.query(x)  # (B, T, hs)
+        wei = q @ k.transpose(-2, -1) * k.shape[-1]**-0.5  # (B, T, T)
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
+        wei = F.softmax(wei, dim=-1)
+        wei = self.dropout(wei)
+        v = self.value(x)  # (B, T, hs)
+        out = wei @ v      # (B, T, hs)
+        return out
+
+class MultiHeadAttention(nn.Module):
+    """Multiple heads in parallel."""
+    def __init__(self, num_heads, head_size, n_embd, block_size, dropout):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(head_size, n_embd, block_size, dropout) for _ in range(num_heads)])
+        self.proj = nn.Linear(head_size * num_heads, n_embd)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)  # (B, T, C)
+        out = self.dropout(self.proj(out))
+        return out
+
+class FeedForward(nn.Module):
+    """Simple FFN."""
+    def __init__(self, n_embd, dropout):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),
+            nn.ReLU(),
+            nn.Linear(4 * n_embd, n_embd),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+class Block(nn.Module):
+    """Transformer block."""
+    def __init__(self, n_embd, n_head, block_size, dropout):
+        super().__init__()
+        head_size = n_embd // n_head
+        self.sa = MultiHeadAttention(n_head, head_size, n_embd, block_size, dropout)
+        self.ffwd = FeedForward(n_embd, dropout)
+        self.ln1 = nn.LayerNorm(n_embd)
+        self.ln2 = nn.LayerNorm(n_embd)
+
+    def forward(self, x):
+        x = x + self.sa(self.ln1(x))
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+class GPT(nn.Module, PyTorchModelHubMixin):
+    """Full decoder-only GPT."""
+    def __init__(self, vocab_size, n_embd=384, n_head=6, n_layer=6, block_size=256, dropout=0.2):
+        super().__init__()
+        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
+        self.position_embedding_table = nn.Embedding(block_size, n_embd)
+        self.blocks = nn.Sequential(*[Block(n_embd, n_head, block_size, dropout) for _ in range(n_layer)])
+        self.ln_f = nn.LayerNorm(n_embd)
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+        self.block_size = block_size
+        print(f"Model created with {sum(p.numel() for p in self.parameters())/1e6:.1f}M params")
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        tok_emb = self.token_embedding_table(idx)  # (B, T, C)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))  # (T, C)
+        x = tok_emb + pos_emb  # (B, T, C)
+        x = self.blocks(x)     # (B, T, C)
+        x = self.ln_f(x)       # (B, T, C)
+        logits = self.lm_head(x)  # (B, T, V)
+
+        if targets is None:
+            loss = None
+        else:
+            B, T, V = logits.shape
+            logits = logits.view(B * T, V)
+            targets = targets.view(B * T)
+            loss = F.cross_entropy(logits, targets)
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0):
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -self.block_size:]  # crop
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx