Upload generate.py

generate.py

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+"""
+Inference script for nano GPT.
+
+Usage:
+    python generate.py --prompt "ROMEO:" --length 500 --temperature 0.8
+
+Loads best.pt (saved by train_standalone.py) and generates text.
+"""
+
+import argparse
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from dataclasses import dataclass
+
+
+@dataclass
+class GPTConfig:
+    block_size: int = 256
+    vocab_size: int = 65
+    n_layer: int = 4
+    n_head: int = 4
+    n_embd: int = 256
+    dropout: float = 0.0
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.register_buffer(
+            "bias",
+            torch.tril(torch.ones(config.block_size, config.block_size))
+                 .view(1, 1, config.block_size, config.block_size)
+        )
+
+    def forward(self, x):
+        B, T, C = x.size()
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        head_size = C // self.n_head
+        q = q.view(B, T, self.n_head, head_size).transpose(1, 2)
+        k = k.view(B, T, self.n_head, head_size).transpose(1, 2)
+        v = v.view(B, T, self.n_head, head_size).transpose(1, 2)
+        att = (q @ k.transpose(-2, -1)) * (1.0 / (head_size ** 0.5))
+        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float("-inf"))
+        att = F.softmax(att, dim=-1)
+        y = att @ v
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.c_proj(y)
+        return y
+
+
+class MLP(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.c_fc   = nn.Linear(config.n_embd, 4 * config.n_embd)
+        self.gelu   = nn.GELU()
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp  = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.config = config
+        self.transformer = nn.ModuleDict({
+            "wte": nn.Embedding(config.vocab_size, config.n_embd),
+            "wpe": nn.Embedding(config.block_size, config.n_embd),
+            "h":   nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            "ln_f": nn.LayerNorm(config.n_embd),
+        })
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.transformer.wte.weight = self.lm_head.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.size()
+        assert T <= self.config.block_size
+        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
+        x = 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), ignore_index=-1)
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        for _ in range(max_new_tokens):
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :]
+            if top_k is not None:
+                v, _ = torch.topk(logits, top_k, dim=-1)
+                logits[logits < v[:, [-1]]] = float("-inf")
+            probs = F.softmax(logits / temperature, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint", default="best.pt", help="Path to checkpoint")
+    parser.add_argument("--prompt", default="\n", help="Starting text")
+    parser.add_argument("--length", type=int, default=500, help="Tokens to generate")
+    parser.add_argument("--temperature", type=float, default=1.0, help="Sampling temperature")
+    parser.add_argument("--top_k", type=int, default=40, help="Top-k sampling")
+    parser.add_argument("--seed", type=int, default=1337, help="Random seed")
+    args = parser.parse_args()
+
+    torch.manual_seed(args.seed)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # Load checkpoint
+    ckpt = torch.load(args.checkpoint, map_location=device, weights_only=False)
+    config = ckpt["config"]
+    stoi = ckpt["stoi"]
+    itos = ckpt["itos"]
+
+    # Build model and load weights
+    model = GPT(config)
+    model.load_state_dict(ckpt["model_state_dict"])
+    model.to(device)
+    model.eval()
+
+    # Encode prompt
+    encode = lambda s: [stoi[c] for c in s]
+    decode = lambda l: "".join([itos[i] for i in l])
+
+    context = torch.tensor(encode(args.prompt), dtype=torch.long, device=device).unsqueeze(0)
+
+    # Generate
+    with torch.no_grad():
+        generated = model.generate(context, args.length, temperature=args.temperature, top_k=args.top_k)
+
+    print(decode(generated[0].tolist()))
+
+
+if __name__ == "__main__":
+    main()