sample.py

import json
import os
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from dataclasses import dataclass

@dataclass
class GPTConfig:
    block_size: int = 1024
    vocab_size: int = 50304
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768
    bias: bool = True

class LayerNorm(nn.Module):
    def __init__(self, ndim, bias):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
    def forward(self, input):
        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)

class CausalSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
        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()
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        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):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
    def forward(self, x):
        x = self.c_fc(x)
        x = F.gelu(x)
        x = self.c_proj(x)
        return x

class Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
        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 AttoGPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.transformer = nn.ModuleDict(dict(
            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 = LayerNorm(config.n_embd, bias=config.bias),
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

    def forward(self, idx):
        device = idx.device
        b, t = idx.size()
        pos = torch.arange(0, t, dtype=torch.long, device=device)
        tok_emb = self.transformer.wte(idx)
        pos_emb = self.transformer.wpe(pos)
        x = tok_emb + pos_emb
        for block in self.transformer.h:
            x = block(x)
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x)
        return logits

def load_model(path):
    with open(path, "r") as f:
        data = json.load(f)
    cfg = GPTConfig(**data["config"])
    model = AttoGPT(cfg)
    sd = {k: torch.tensor(v) for k, v in data["weights"].items()}
    model.load_state_dict(sd)
    itos = {int(k): v for k, v in data["vocab"].items()}
    stoi = {v: k for k, v in itos.items()}
    return model, itos, stoi, cfg

@torch.no_grad()
def generate(model, stoi, itos, block_size, prompt=" ", length=100, temperature=0.8):
    model.eval()
    tokens = [stoi.get(c, 0) for c in prompt]
    if not tokens: tokens = [0]
    idx = torch.tensor(tokens, dtype=torch.long).unsqueeze(0)
    for _ in range(length):
        idx_cond = idx if idx.size(1) <= block_size else idx[:, -block_size:]
        logits = model(idx_cond)
        logits = logits[:, -1, :] / temperature
        probs = F.softmax(logits, dim=-1)
        next_token = torch.multinomial(probs, num_samples=1)
        idx = torch.cat((idx, next_token), dim=1)
    return "".join(itos.get(t.item(), "?") for t in idx[0][len(tokens):])

if __name__ == "__main__":
    models_dir = "models"
    model_files = sorted([f for f in os.listdir(models_dir) if f.endswith(".json")])
    for filename in model_files:
        path = os.path.join(models_dir, filename)
        model, itos, stoi, cfg = load_model(path)
        print(f"\n{'='*60}\n  {filename}\n{'='*60}")
        for prompt in [" the ", " to be", " Ham"]:
            text = generate(model, stoi, itos, cfg.block_size, prompt.strip(), length=80)
            print(f'  prompt="{prompt.strip()}":\n    {text}\n')