File size: 9,264 Bytes
4911af3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 | #!/usr/bin/env python3
# ============================================================================
# LEGAL NOTICE & WATERMARK: R08 NON-COMMERCIAL RESEARCH & ETHICAL LICENSE v2.0
# Copyright (c) 2026 Thomas Seri (Sentinel). All Rights Reserved.
# Exclusive Jurisdiction: Court of Milan (Sezione Specializzata Impresa), Italy.
# Clean-room reverse engineering, distillation, commercial or military use
# is strictly prohibited and constitutes a breach of contract.
# ============================================================================
from __future__ import annotations
__author__ = "Thomas Seri (Sentinel)"
__license__ = "R08-Non-Commercial-Ethical-License-v2.0"
__jurisdiction__ = "Court of Milan, Italy"
"""R08-v7.2 (Rob) — Sentinel LifeOS Geodesic Mesh Neural Network (Pyramidal Sphere with 12 Regional Hubs & 1D Causal Convolutions, 51.6M)
Official Hugging Face / PyTorch implementation.
Author: Thomas Seri (https://www.sentinelhub.eu)
"""
import json
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from safetensors.torch import load_file
from geodesic_network import WalkConfig
from geodesic_stages_v7_2_model import CascadeMeshModelV7_2
import pezzi_tokenizer as pt
from vocab_stages import (
StageBundle,
StageEncoded,
align_stages_to_pezzi,
load_bundle,
pass_to_v2,
pass_to_v3,
)
def realign_from_pezzi(
pezzi_ids: list[int], bundle: StageBundle, causal_align: bool = True
) -> Tuple[list[int], list[int], list[int]]:
"""Ricostruisce lo stato multi-stadio (pezzi, parole, unioni) in modo causale."""
v2 = pass_to_v2(pezzi_ids, bundle)
v3 = pass_to_v3(v2, bundle)
enc = StageEncoded(
pezzi_ids=list(pezzi_ids),
parole_ids=list(v2.parole_ids),
unioni_ids=list(v3.unioni_ids),
spans=[],
v2=v2,
v3=v3,
)
p, w, u = align_stages_to_pezzi(enc, bundle, causal=causal_align)
return list(p), list(w), list(u)
class R08Tokenizer:
"""Tokenizer a cascata a 3 stadi per R08-v7.2 (Rob)."""
def __init__(self, bundle: StageBundle):
self.bundle = bundle
self.pezzi = bundle.pezzi
self.eos_id = int(getattr(bundle.pezzi, "eos_id", 2))
self.pad_id = int(getattr(bundle.pezzi, "pad_id", 3))
@classmethod
def from_pretrained(cls, pretrained_path: Union[str, Path]) -> "R08Tokenizer":
root = Path(pretrained_path)
vocab_dir = root / "vocab_stages" if (root / "vocab_stages").exists() else root
bundle = load_bundle(vocab_dir)
return cls(bundle)
def encode(self, text: str) -> dict:
"""Codifica il testo nei 3 stadi sincronizzati (pezzi, parole, unioni)."""
enc = pt.encode_cascade(text, self.bundle)
p, w, u = align_stages_to_pezzi(enc, self.bundle, causal=True)
return {
"pezzi": list(p),
"parole": list(w),
"unioni": list(u),
}
def decode(self, token_ids: List[int]) -> str:
"""Decodifica gli ID token dei pezzi in testo UTF-8."""
return pt.decode(token_ids, self.pezzi)
class R08Model(nn.Module):
"""Modello neurale geodetico-piramidale R08-v7.2 (Rob) da 51.6M parametri."""
def __init__(self, config: dict, mesh_model: CascadeMeshModelV7_2):
super().__init__()
self.config = config
self.mesh_model = mesh_model
@classmethod
def from_pretrained(
cls,
pretrained_path: Union[str, Path],
device: Optional[str] = None,
torch_dtype: Optional[torch.dtype] = None,
) -> "R08Model":
root = Path(pretrained_path)
cfg_file = root / "config.json"
if not cfg_file.exists():
raise FileNotFoundError(f"config.json non trovato in {root}")
with open(cfg_file, "r", encoding="utf-8") as f:
config = json.load(f)
walk = WalkConfig(
max_hops=1,
exit_threshold=0.85,
hard_routing=False,
router_temperature=1.0,
)
mesh_model = CascadeMeshModelV7_2(
vocab_pezzi=config.get("vocab_pezzi", 1251),
vocab_parole=config.get("vocab_parole", 17908),
vocab_unioni=config.get("vocab_unioni", 20006),
d_model=config.get("d_model", 256),
frequency=config.get("mesh_topology", {}).get("frequency", 2),
walk=walk,
n_attn_layers=config.get("n_attn_layers", 2),
n_heads=config.get("n_heads", 8),
max_seq_len=config.get("max_seq_len", 512),
compass_spiral=config.get("routing", {}).get("compass_spiral", True),
fast_walk=True,
router_v6=config.get("routing", {}).get("router_v6", True),
hash_routing=config.get("routing", {}).get("hash_routing", True),
no_walk=config.get("routing", {}).get("no_walk", True),
pyramid_center=config.get("pyramid_center", True),
)
weights_file = root / "model.safetensors"
if not weights_file.exists():
raise FileNotFoundError(f"model.safetensors non trovato in {root}")
state_dict = load_file(str(weights_file))
model_sd = mesh_model.state_dict()
for k, v in model_sd.items():
if k.startswith("watchman.") and k not in state_dict:
state_dict[k] = v
mesh_model.load_state_dict(state_dict, strict=True)
if hasattr(mesh_model, "watchman") and mesh_model.watchman is not None:
mesh_model._watchman_license_text = mesh_model.watchman.verify_disk_license(root)
mesh_model._watchman_is_ethical = True
if device is None:
if torch.backends.mps.is_available():
device = "mps"
elif torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
mesh_model.to(torch.device(device))
if torch_dtype is not None:
mesh_model.to(dtype=torch_dtype)
mesh_model.eval()
return cls(config, mesh_model)
def forward(self, pezzi, parole, unioni, record_path: bool = False):
return self.mesh_model(pezzi, parole, unioni, record_path=record_path)
@torch.inference_mode()
def generate(
self,
prompt: str,
tokenizer: R08Tokenizer,
max_new_tokens: int = 48,
temperature: float = 0.7,
top_p: float = 0.9,
repetition_penalty: float = 1.15,
) -> str:
"""Generazione di testo autoregressiva con sampling controllato e penalità di ripetizione."""
if hasattr(self.mesh_model, "watchman") and self.mesh_model.watchman is not None:
is_ok, reason = self.mesh_model.watchman.scan_security_and_ethics(prompt)
if not is_ok:
self.mesh_model._watchman_is_ethical = False
return f"[WATCHMAN KEYSTONE — ACCESSO NEGATO] Richiesta non consentita ({reason})."
self.mesh_model._watchman_is_ethical = True
dev = next(self.mesh_model.parameters()).device
enc = tokenizer.encode(prompt)
p, w, u = enc["pezzi"], enc["parole"], enc["unioni"]
if not p:
p, w, u = [tokenizer.eos_id], [tokenizer.bundle.parole.pad_id], [tokenizer.bundle.unioni.pad_id]
new_tokens: list[int] = []
for _ in range(max_new_tokens):
seq_p = torch.tensor([p[-256:]], dtype=torch.long, device=dev)
seq_w = torch.tensor([w[-256:]], dtype=torch.long, device=dev)
seq_u = torch.tensor([u[-256:]], dtype=torch.long, device=dev)
logits, _ = self.mesh_model(seq_p, seq_w, seq_u, record_path=False)
next_logits = logits[0, -1]
# Repetition penalty
if repetition_penalty != 1.0 and new_tokens:
for token_id in set(new_tokens):
if next_logits[token_id] > 0:
next_logits[token_id] /= repetition_penalty
else:
next_logits[token_id] *= repetition_penalty
# Temperature & Top-p Sampling
if temperature > 0:
probs = torch.softmax(next_logits / temperature, dim=-1)
if top_p < 1.0:
sorted_probs, sorted_indices = torch.sort(probs, descending=True)
cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
probs[indices_to_remove] = 0.0
probs = probs / probs.sum()
nxt = int(torch.multinomial(probs, num_samples=1).item())
else:
nxt = int(next_logits.argmax().item())
if nxt in (tokenizer.eos_id, tokenizer.pad_id):
break
new_tokens.append(nxt)
p.append(nxt)
p, w, u = realign_from_pezzi(p, tokenizer.bundle, causal_align=True)
return tokenizer.decode(new_tokens)
|