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3217baa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | """Fourth GPT model definition and inference using PyTorch (CPU)."""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import json
import os
import re
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x):
norm = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
return x * norm * self.weight
class TransformerBlock(nn.Module):
def __init__(self, n_embd, n_head):
super().__init__()
self.n_head = n_head
self.head_dim = n_embd // n_head
self.norm1 = RMSNorm(n_embd)
self.wq = nn.Linear(n_embd, n_embd, bias=False)
self.wk = nn.Linear(n_embd, n_embd, bias=False)
self.wv = nn.Linear(n_embd, n_embd, bias=False)
self.wo = nn.Linear(n_embd, n_embd, bias=False)
self.norm2 = RMSNorm(n_embd)
self.mlp_fc1 = nn.Linear(n_embd, 4 * n_embd, bias=False)
self.mlp_fc2 = nn.Linear(4 * n_embd, n_embd, bias=False)
def forward(self, x, mask):
B, T, _ = x.shape
xn = self.norm1(x)
q = self.wq(xn).reshape(B, T, self.n_head, self.head_dim).transpose(1, 2)
k = self.wk(xn).reshape(B, T, self.n_head, self.head_dim).transpose(1, 2)
v = self.wv(xn).reshape(B, T, self.n_head, self.head_dim).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
att = att + mask
att = F.softmax(att, dim=-1)
out = (att @ v).transpose(1, 2).reshape(B, T, -1)
x = x + self.wo(out)
xn2 = self.norm2(x)
h = F.relu(self.mlp_fc1(xn2))
x = x + self.mlp_fc2(h)
return x
class GPT(nn.Module):
def __init__(self, vocab_size, n_layer, n_embd, block_size, n_head):
super().__init__()
self.block_size = block_size
self.wte = nn.Embedding(vocab_size, n_embd)
self.wpe = nn.Embedding(block_size, n_embd)
self.ln_pre = RMSNorm(n_embd)
self.layers = nn.ModuleList([TransformerBlock(n_embd, n_head) for _ in range(n_layer)])
self.ln_post = RMSNorm(n_embd)
self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
def forward(self, tokens):
B, T = tokens.shape
x = self.wte(tokens) + self.wpe(torch.arange(T, device=tokens.device))
x = self.ln_pre(x)
mask = torch.triu(torch.full((T, T), -1e9, device=tokens.device), diagonal=1)
for layer in self.layers:
x = layer(x, mask)
x = self.ln_post(x)
return self.lm_head(x)
class FourthModel:
"""Wraps the GPT model with tokenizer and generation logic."""
def __init__(self, checkpoint_dir=None):
if checkpoint_dir is None:
checkpoint_dir = os.path.join(os.path.dirname(__file__) or ".", "model_weights")
self.checkpoint_dir = checkpoint_dir
self.model = None
self.stoi = None
self.itos = None
self.bos = None
self.config = None
def load(self):
config_path = os.path.join(self.checkpoint_dir, "config.json")
with open(config_path) as f:
self.config = json.load(f)
self.stoi = self.config["stoi"]
self.bos = self.config["bos"]
self.itos = {int(i): c for c, i in self.stoi.items()}
self.itos[self.bos] = ""
self.model = GPT(
vocab_size=self.config["vocab_size"],
n_layer=self.config["n_layer"],
n_embd=self.config["n_embd"],
block_size=self.config["block_size"],
n_head=self.config["n_head"],
)
# Load weights — try PyTorch format first, fall back to npz
pt_path = os.path.join(self.checkpoint_dir, "weights.pt")
npz_path = os.path.join(self.checkpoint_dir, "weights.npz")
if os.path.exists(pt_path):
state_dict = torch.load(pt_path, map_location="cpu", weights_only=True)
else:
import numpy as np
npz = np.load(npz_path)
state_dict = {k: torch.tensor(npz[k]) for k in npz.files}
self.model.load_state_dict(state_dict)
self.model.eval()
nparams = sum(p.numel() for p in self.model.parameters())
print(f"Loaded model: {nparams} params, vocab={self.config['vocab_size']}")
@torch.no_grad()
def generate(self, prompt: str, max_tokens: int = 128, temperature: float = 0.7) -> str:
"""Generate a response to a prompt."""
clean = re.sub(r'[^a-z |]', '', prompt.lower().strip())
clean = re.sub(r' +', ' ', clean).strip()
if not clean.endswith("|"):
clean += "|"
block_size = self.config["block_size"]
tokens = [self.bos] + [self.stoi.get(c, self.bos) for c in clean]
for _ in range(min(max_tokens, block_size - len(tokens))):
x = torch.tensor([tokens[-block_size:]], dtype=torch.long)
logits = self.model(x)
logits = logits[0, -1] / temperature
probs = F.softmax(logits, dim=-1)
tok = torch.multinomial(probs, 1).item()
if tok == self.bos:
break
tokens.append(tok)
full = "".join(self.itos.get(t, "?") for t in tokens[1:])
parts = full.split("|", 1)
return parts[1] if len(parts) > 1 else full
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