Add model code (CharGPT).

model.py

@@ -0,0 +1,114 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, n_embd, n_head):
+        super().__init__()
+        assert n_embd % n_head == 0
+        self.n_head = n_head
+        self.head_dim = n_embd // n_head
+        self.c_attn = nn.Linear(n_embd, 3 * n_embd, bias=False)
+        self.c_proj = nn.Linear(n_embd, n_embd, bias=False)
+
+    def forward(self, x):
+        B, T, C = x.size()
+        q, k, v = self.c_attn(x).split(self.head_dim * self.n_head, dim=2)
+        q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        att = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        att = att.transpose(1, 2).contiguous().view(B, T, C)
+        return self.c_proj(att)
+
+
+class MLP(nn.Module):
+    def __init__(self, n_embd, n_inner):
+        super().__init__()
+        self.c_fc = nn.Linear(n_embd, n_inner, bias=False)
+        self.c_proj = nn.Linear(n_inner, n_embd, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x)))
+
+
+class Block(nn.Module):
+    def __init__(self, n_embd, n_head):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(n_embd)
+        self.attn = CausalSelfAttention(n_embd, n_head)
+        self.ln_2 = nn.LayerNorm(n_embd)
+        self.mlp = MLP(n_embd, 4 * n_embd)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class CharGPT(nn.Module):
+    """Character-level causal transformer (nanoGPT-style). No bias in
+    attention / FFN / lm_head; LayerNorm carries the affine bias."""
+
+    def __init__(self, vocab_size, block_size, n_layer, n_head, n_embd):
+        super().__init__()
+        self.block_size = block_size
+        self.transformer = nn.ModuleDict({
+            "wte": nn.Embedding(vocab_size, n_embd),
+            "wpe": nn.Embedding(block_size, n_embd),
+            "drop": nn.Dropout(0.0),
+            "h": nn.ModuleList([Block(n_embd, n_head) for _ in range(n_layer)]),
+            "ln_f": nn.LayerNorm(n_embd),
+        })
+        self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.size()
+        assert T <= self.block_size, f"block size {self.block_size} < {T}"
+        pos = torch.arange(0, T, device=idx.device)
+        x = self.transformer["drop"](
+            self.transformer["wte"](idx) + self.transformer["wpe"](pos))
+        for block in self.transformer["h"]:
+            x = block(x)
+        x = self.transformer["ln_f"](x)
+        logits = self.lm_head(x)
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)),
+                                   targets.view(-1))
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=0.8, top_k=40):
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -self.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            if top_k:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = float("-inf")
+            probs = F.softmax(logits, dim=-1)
+            idx = torch.cat((idx, torch.multinomial(probs, num_samples=1)), dim=1)
+        return idx
+
+
+def from_config(config):
+    return CharGPT(
+        vocab_size=config["vocab_size"],
+        block_size=config["block_size"],
+        n_layer=config["n_layer"],
+        n_head=config["n_head"],
+        n_embd=config["n_embd"],
+    )
+
+
+if __name__ == "__main__":
+    import json
+    cfg = json.load(open("config.json"))
+    m = from_config(cfg)
+    print("params:", sum(p.numel() for p in m.parameters()))
+    # tiny smoke test
+    x = torch.randint(0, cfg["vocab_size"], (1, 32))
+    logits, loss = m(x, x)
+    print("smoke loss:", loss.item())