Add training script with tokenizer fix

n.py

@@ -0,0 +1,1149 @@
+#!/usr/bin/env python3
+
+# n.py — Joint AR+SAT Trainer with Expansion Ratio Testing
+# Enhanced inference: checkpoint name, tok/s, UK time
+
+from __future__ import annotations
+import argparse, json, math, pathlib, random, time, os, sys, threading, hashlib
+from pathlib import Path
+from contextlib import nullcontext
+from typing import Dict, Any, List, Optional, Tuple
+from datetime import datetime, timezone
+import torch
+
+# SafeProgress - Claude-safe progress (discrete lines, not single growing line)
+class SafeProgress:
+    def __init__(self, total, initial=0, unit="tok", print_every=500):
+        self.total, self.n, self.unit = total, initial, unit
+        self.last_print, self.postfix = initial, {}
+        self.start_time = __import__('time').time()
+    def update(self, n=1):
+        self.n += n
+        if self.n - self.last_print >= 1000000:  # print every ~1M tokens
+            self._print(); self.last_print = self.n
+    def set_postfix(self, **kwargs): self.postfix = kwargs
+    def _print(self):
+        elapsed = __import__('time').time() - self.start_time
+        rate = self.n / elapsed if elapsed > 0 else 0
+        pct = 100 * self.n / self.total if self.total > 0 else 0
+        pf = ' '.join(f"{k}={v}" for k,v in self.postfix.items())
+        print(f"[{pct:.1f}%] {self.n:,}/{self.total:,} {self.unit} | {rate:.0f} tok/s | {pf}")
+    def close(self): self._print(); print("Done.")
+
+import torch.nn as nn
+import torch.nn.functional as F
+from datasets import load_dataset, DownloadConfig
+from transformers import AutoTokenizer, logging as hf_log
+# from tqdm.auto import tqdm  # DISABLED - kills Claude context
+
+# ─────────────────────────────── HOT DATASET LOADING ───────────────────────────────
+HOT_CONFIG_PATH = Path("/workspace/hot_config.json")
+_hot_config_cache = {"mtime": 0, "data": {}}
+
+def get_hot_config() -> dict:
+    """Load hot_config.json with caching, return empty dict if missing"""
+    try:
+        if HOT_CONFIG_PATH.exists():
+            mtime = HOT_CONFIG_PATH.stat().st_mtime
+            if mtime > _hot_config_cache["mtime"]:
+                with open(HOT_CONFIG_PATH) as f:
+                    _hot_config_cache["data"] = json.load(f)
+                _hot_config_cache["mtime"] = mtime
+        return _hot_config_cache["data"]
+    except Exception as e:
+        print(f"[hot_config] Error loading: {e}")
+        return {}
+
+def get_hot_datasets(default_sources: str) -> str:
+    """Get datasets from hot_config if present, else use default"""
+    cfg = get_hot_config()
+    if "datasets" in cfg and cfg["datasets"]:
+        hot_ds = cfg["datasets"]
+        if isinstance(hot_ds, list):
+            hot_ds = ",".join(hot_ds)
+        print(f"[hot_config] Using hot datasets: {hot_ds}")
+        return hot_ds
+    return default_sources
+
+
+# DISABLED: # Auto-rotating log to prevent context-window suicide
+# DISABLED: try:
+# DISABLED:     from rotating_log import install_rotating_log
+# DISABLED:     install_rotating_log()
+# DISABLED: except ImportError:
+# pass  # Running without rotation
+
+# ───────────────────────── ANSI Colors ─────────────────────────
+class Colors:
+    RESET = "\033[0m"
+    BOLD = "\033[1m"
+    PROMPT = "\033[36m"
+    GEN = "\033[0m"
+    INFO = "\033[90m"
+    WARN = "\033[93m"
+
+# ───────────────────────── Globals ─────────────────────────
+hf_log.set_verbosity_error()
+DEV = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+torch.backends.cuda.matmul.allow_tf32 = True
+try:
+    torch.set_float32_matmul_precision("high")
+except Exception:
+    pass
+
+TOKENIZER_ID = os.environ.get("TOKENIZER_ID", "deepseek-ai/DeepSeek-V3.2")
+tok = AutoTokenizer.from_pretrained(TOKENIZER_ID, use_fast=True, trust_remote_code=True)
+if tok.pad_token is None:
+    tok.add_special_tokens({"pad_token": "<|pad|>"})
+
+# ─── Fix tokenizer Ġ/▁ mismatch ───
+# The DeepSeek-V3.2 vocab uses Ġ (U+0120) for space-prefixed tokens,
+# but some transformers versions set the Metaspace pre-tokenizer to use
+# ▁ (U+2581) instead, causing encode/decode to lose all spaces.
+def _fix_tokenizer_space_mismatch(tokenizer):
+    try:
+        import json as _json
+        from tokenizers import Tokenizer as _Tokenizer
+        bt = tokenizer.backend_tokenizer
+        tj = _json.loads(bt.to_str())
+        pre = tj.get("pre_tokenizer", {})
+        needs_fix = (pre.get("type") == "Metaspace" and pre.get("replacement") == "\u2581")
+        if not needs_fix:
+            return
+        # Check if vocab actually uses Ġ (U+0120) for spaces
+        vocab = tj.get("model", {}).get("vocab", {})
+        has_gpt2_space = any(k.startswith("\u0120") for k in list(vocab.keys())[:500])
+        if not has_gpt2_space:
+            return
+        # Patch pre_tokenizer: ▁ -> Ġ
+        tj["pre_tokenizer"]["replacement"] = "\u0120"
+        # Patch decoder: ▁ -> Ġ in Replace step
+        for step in tj.get("decoder", {}).get("decoders", []):
+            if step.get("type") == "Replace":
+                pat = step.get("pattern", {})
+                if pat.get("String") == "\u2581":
+                    pat["String"] = "\u0120"
+        # Rebuild backend tokenizer
+        fixed = _Tokenizer.from_str(_json.dumps(tj))
+        tokenizer.backend_tokenizer = fixed
+        # Verify fix
+        test_ids = tokenizer.encode("hello world")
+        test_dec = tokenizer.decode(test_ids, skip_special_tokens=True)
+        if "hello world" in test_dec:
+            print("[tokenizer] Fixed Ġ/▁ space mismatch")
+        else:
+            print(f"[tokenizer] WARNING: fix applied but decode test failed: {repr(test_dec)}")
+    except Exception as e:
+        print(f"[tokenizer] Could not fix space mismatch: {e}")
+
+_fix_tokenizer_space_mismatch(tok)
+
+VOCAB, EOS = (
+    max(tok.get_vocab().values()) + 1,
+    tok.eos_token_id if tok.eos_token_id is not None else tok.sep_token_id
+)
+
+# ───────────────────────── PRESETS ─────────────────────────
+PRESETS: Dict[str, Dict[str, int]] = {
+    "femto_1x":  dict(d=16, layers=1, heads=1, rank=16),
+    "femto_12x": dict(d=16, layers=1, heads=1, rank=192),
+    "femto_24x": dict(d=16, layers=1, heads=1, rank=384),
+    "pico_1x":   dict(d=32, layers=1, heads=2, rank=16),
+    "pico_3x":   dict(d=32, layers=1, heads=2, rank=48),
+    "pico_6x":   dict(d=32, layers=1, heads=2, rank=96),
+    "pico_12x":  dict(d=32, layers=1, heads=2, rank=192),
+    "pico_24x":  dict(d=32, layers=1, heads=2, rank=384),
+    "pico_48x":  dict(d=32, layers=1, heads=2, rank=768),
+    "nano_1x":   dict(d=64,  layers=2, heads=4, rank=16),
+    "nano_3x":   dict(d=64,  layers=2, heads=4, rank=48),
+    "nano_6x":   dict(d=64,  layers=2, heads=4, rank=96),
+    "nano_12x":  dict(d=64,  layers=2, heads=4, rank=192),
+    "nano_24x":  dict(d=64,  layers=2, heads=4, rank=384),
+    "nano_48x":  dict(d=64,  layers=2, heads=4, rank=768),
+    "nano_96x":  dict(d=64,  layers=2, heads=4, rank=1536),
+    "micro_3x":  dict(d=128, layers=4, heads=8, rank=48),
+    "micro_6x":  dict(d=128, layers=4, heads=8, rank=96),
+    "micro_12x": dict(d=128, layers=4, heads=8, rank=192),
+    "micro_24x": dict(d=128, layers=4, heads=8, rank=384),
+    "small":     dict(d=512, layers=8,  heads=16, rank=64),
+    "smallx2":   dict(d=512, layers=16, heads=16, rank=64),
+    "base":      dict(d=768, layers=12, heads=24, rank=96),
+    "base18":    dict(d=768, layers=18, heads=24, rank=96),
+    "large":     dict(d=1024, layers=24, heads=16, rank=128),
+}
+
+DEFAULT_BLOCK = 1122
+DEFAULT_BATCH = 4
+SAT_BLOCK = 2
+LR_CORE, LR_HEAD = 5e-5, 2e-4
+EMIT_LAMBDA = 0.1
+DEFAULT_SAVE_SEC = 24 * 3600
+DEFAULT_DELTA_STEPS = 500      # lightweight weight-only save every N steps
+DEFAULT_MAX_DELTAS = 5         # keep last N deltas (older pruned after full save)
+CKDIR = pathlib.Path("ckpts_expansion")
+
+DEFAULT_PRETRAIN_SOURCES = "OpenTransformer/goddess-crawl,OpenTransformer/agillm-crawl-data,OpenTransformer/web-crawl-2026,OpenTransformer/web-crawl-clean-v2,OpenTransformer/scraped-web-data,OpenTransformer/turbo-crawl,OpenTransformer/sft-data-clean,OpenTransformer/web-crawl-v1"
+DEFAULT_AFTER_SFT_SOURCES = "mlabonne/opc-sft-stage2-chat,HuggingFaceH4/ultrachat_200k"
+DEFAULT_AFTER_SFT_BLOCK = 1122
+
+# ───────────────────────── UK Time Helper ─────────────────────────
+def get_uk_time() -> str:
+    utc_now = datetime.now(timezone.utc)
+    year = utc_now.year
+    march_last = datetime(year, 3, 31, 1, 0, tzinfo=timezone.utc)
+    while march_last.weekday() != 6:
+        march_last = march_last.replace(day=march_last.day - 1)
+    oct_last = datetime(year, 10, 31, 1, 0, tzinfo=timezone.utc)
+    while oct_last.weekday() != 6:
+        oct_last = oct_last.replace(day=oct_last.day - 1)
+    if march_last <= utc_now < oct_last:
+        uk_offset = 1
+        tz_name = "BST"
+    else:
+        uk_offset = 0
+        tz_name = "GMT"
+    from datetime import timedelta
+    uk_time = utc_now + timedelta(hours=uk_offset)
+    return uk_time.strftime(f'%Y-%m-%d %H:%M:%S {tz_name}')
+
+# ───────────────────────── Utilities ─────────────────────────
+def rng_state():
+    if DEV.type == "cuda":
+        try:
+            return torch.cuda.get_rng_state(DEV)
+        except TypeError:
+            return torch.cuda.get_rng_state()
+    return torch.get_rng_state()
+
+def _is_probably_ckpt(path: pathlib.Path) -> bool:
+    try:
+        return path.is_file() and path.suffix == ".pt" and not path.name.endswith(".pt.tmp") and path.stat().st_size > (1<<20)
+    except Exception:
+        return False
+
+def _resolve_ckpt(path: pathlib.Path) -> pathlib.Path | None:
+    try:
+        if path.is_dir():
+            cands = sorted([p for p in path.glob("*.pt") if _is_probably_ckpt(p)],
+                           key=lambda p: p.stat().st_mtime, reverse=True)
+            return cands[0] if cands else None
+        if path.suffix == ".tmp":
+            solid = path.with_suffix("")
+            return solid if _is_probably_ckpt(solid) else _resolve_ckpt(path.parent)
+        return path if _is_probably_ckpt(path) else _resolve_ckpt(path.parent)
+    except Exception:
+        return None
+
+def _try_load(path: pathlib.Path, map_location="cpu"):
+    try:
+        return torch.load(path, map_location="cpu")
+    except Exception as e:
+        print(f"[ckpt-skip] {path} not usable: {e}")
+        return None
+
+def _prune_checkpoints(save_dir: pathlib.Path, phase_name: str, max_ckpts: int):
+    if max_ckpts is None or max_ckpts <= 0:
+        return
+    try:
+        pattern = f"{phase_name}_step*.pt"
+        ckpts = sorted(
+            [p for p in save_dir.glob(pattern) if _is_probably_ckpt(p)],
+            key=lambda p: p.stat().st_mtime
+        )
+        excess = len(ckpts) - max_ckpts
+        if excess > 0:
+            for p in ckpts[:excess]:
+                try:
+                    p.unlink()
+                    print(f"  [prune] deleted old {p.name}")
+                except Exception:
+                    pass
+    except Exception as e:
+        print(f"[ckpt-prune] error: {e}")
+
+def print_expansion_info(cfg: dict, tie_weights: bool = False):
+    d_k = cfg["d"] // cfg["heads"]
+    rank = cfg["rank"]
+    ratio = rank / d_k
+    regime = "COMPRESSION" if ratio < 1 else ("IDENTITY" if ratio == 1 else "EXPANSION")
+    tie_str = "YES" if tie_weights else "NO"
+    print(f"┌─────────────────────────────────────────┐")
+    print(f"│ TUNEABLE ATTENTION CONFIG               │")
+    print(f"├─────────────────────────────────────────┤")
+    print(f"│ d_model: {cfg['d']:4d}  heads: {cfg['heads']:2d}  d_k: {d_k:3d}     │")
+    print(f"│ layers: {cfg['layers']:4d}  tie_weights: {tie_str:3s}          │")
+    print(f"│ rank: {rank:4d}  ratio: {ratio:.1f}x  [{regime:11s}] │")
+    print(f"└─────────────────────────────────────────┘")
+
+# ───────────────────────── AMP helper ─────────────────────────
+try:
+    from torch.amp import autocast as _ac, GradScaler
+except ImportError:
+    from torch.cuda.amp import autocast as _ac, GradScaler
+
+def _auto_amp_dtype():
+    if DEV.type == "cuda":
+        try:
+            if torch.cuda.is_bf16_supported(): return torch.bfloat16
+            return torch.float16
+        except Exception: return torch.float16
+    return torch.float32
+
+def amp(enabled: bool):
+    return nullcontext() if not (enabled and DEV.type == "cuda") else _ac(device_type="cuda", dtype=_auto_amp_dtype())
+
+# ───────────────────────── Chat & Data Stream ─────────────────────────
+def _coerce_role(r: str) -> str:
+    r = (r or "").lower()
+    if r in {"user", "human", "customer"}: return "user"
+    if r in {"assistant", "gpt", "bot"}: return "assistant"
+    if r in {"system", "context"}: return "system"
+    return r or "user"
+
+def _render_chat_text_from_ex(ex: dict, messages_key: str, add_generation_prompt: bool) -> Optional[str]:
+    msgs = ex.get(messages_key)
+    if msgs is None:
+        for alt in ("conversations", "dialog", "turns"):
+            if isinstance(ex.get(alt), list):
+                msgs = ex[alt]; break
+    if isinstance(msgs, list) and msgs and isinstance(msgs[0], dict):
+        try:
+            norm = []
+            for m in msgs:
+                role = _coerce_role(m.get("role", "")); content = m.get("content", m.get("text", ""))
+                if not isinstance(content, str): continue
+                norm.append({"role": role, "content": content})
+            if not norm: return None
+            return tok.apply_chat_template(norm, tokenize=False, add_generation_prompt=add_generation_prompt)
+        except Exception: return None
+    for a, b in (("prompt", "response"), ("instruction", "output"), ("question", "answer")):
+        if isinstance(ex.get(a), str) and isinstance(ex.get(b), str):
+            return f"User: {ex[a]}\nAssistant: {ex[b]}"
+    return None
+
+def _open_stream_one(ds_name: str, seed: int, streaming: bool = True):
+    dc = DownloadConfig(max_retries=5, use_etag=True, resume_download=True)
+    if ":" in ds_name: base, config = ds_name.split(":", 1)
+    else: base, config = ds_name, None
+    if not streaming:
+        print(f"[download] Downloading {ds_name} (non-streaming)...")
+    if base == "json":
+        data_files = {"train": config}
+        ds = load_dataset("json", data_files=data_files, split="train", streaming=streaming, download_config=dc)
+    else:
+        ds = load_dataset(base, config, split="train", streaming=streaming, download_config=dc) if config else \
+             load_dataset(base, split="train", streaming=streaming, download_config=dc)
+    if streaming:
+        return iter(ds.shuffle(buffer_size=1000, seed=seed))
+    else:
+        print(f"[download] Got {len(ds):,} examples. Shuffling...")
+        ds = ds.shuffle(seed=seed)
+        return iter(ds)
+
+def token_stream(ds_names: str, target: int, seed: int = 42,
+                 chat: bool = False, chat_messages_key: str = "messages",
+                 sft_add_generation_prompt: bool = False, dataset_field_text: str = "text",
+                 streaming: bool = True):
+    ds_names = get_hot_datasets(ds_names)  # HOT LOAD
+    sources = [s.strip() for s in ds_names.split(",") if s.strip()]
+    if not sources: return
+    src_idx = 0; emitted = 0; it = None; attempts = 0; backoff_base = 2.0
+    while emitted < target:
+        try:
+            if it is None: it = _open_stream_one(sources[src_idx], seed, streaming=streaming)
+            ex = next(it)
+            text = None
+            if isinstance(ex, dict):
+                if chat:
+                    text = _render_chat_text_from_ex(ex, chat_messages_key, sft_add_generation_prompt)
+                if text is None:
+                    if dataset_field_text and isinstance(ex.get(dataset_field_text), str):
+                        text = ex[dataset_field_text]
+                    elif isinstance(ex.get("text"), str):
+                        text = ex["text"]
+            if not isinstance(text, str):
+                attempts = 0; continue
+            enc = tok.encode(text)
+            if EOS is not None and (len(enc) == 0 or enc[-1] != EOS):
+                enc = enc + [EOS]
+            for t in enc:
+                yield t
+                emitted += 1
+                if emitted >= target: return
+            attempts = 0
+        except StopIteration:
+            it = None; src_idx = (src_idx + 1) % len(sources)
+        except Exception as e:
+            attempts += 1
+            sleep_s = min(60.0, backoff_base ** min(attempts, 6))
+            print(f"[stream-retry] {sources[src_idx]} error: {type(e).__name__}, sleeping {sleep_s:.1f}s")
+            time.sleep(sleep_s); it = None
+            if attempts % 5 == 0 and len(sources) > 1:
+                src_idx = (src_idx + 1) % len(sources)
+
+# ───────────────────────── ALiBi ─────────────────────────
+def _alibi_slopes(n_heads: int):
+    def pow2slopes(n):
+        start = 2 ** (-2 ** -(math.log2(n) - 3))
+        ratio = start
+        return [start * (ratio ** i) for i in range(n)]
+    if math.log2(n_heads).is_integer(): vals = pow2slopes(n_heads)
+    else:
+        closest = 2 ** math.floor(math.log2(n_heads))
+        vals = pow2slopes(closest)
+        extra = pow2slopes(2 * closest)
+        vals += extra[0::2][: n_heads - closest]
+    return torch.tensor(vals, device=DEV).view(1, n_heads, 1, 1)
+
+def alibi_bias(n_heads: int, n_tokens: int):
+    i = torch.arange(n_tokens, device=DEV).view(1, 1, n_tokens, 1)
+    j = torch.arange(n_tokens, device=DEV).view(1, 1, 1, n_tokens)
+    dist = (j - i).clamp_min(0) 
+    return -_alibi_slopes(n_heads) * dist
+
+# ───────────────────────── Model components ─────────────────────────
+class TuneableAttentionMHA(nn.Module):
+    def __init__(self, d: int, h: int, r: int, use_relpos: bool = True):
+        super().__init__()
+        assert d % h == 0
+        self.h, self.dk, self.r = h, d // h, r
+        self.use_relpos = use_relpos
+        self.q = nn.Linear(d, d, bias=False)
+        self.k = nn.Linear(d, d, bias=False)
+        self.v = nn.Linear(d, d, bias=False)
+        self.U = nn.Parameter(torch.randn(self.dk, r))
+        nn.init.orthogonal_(self.U)
+        self.proj = nn.Linear(h * self.dk, d, bias=False)
+        self.drop = nn.Dropout(0.1)
+
+    def _proj_qk(self, x):
+        B, N, _ = x.shape
+        return (x.view(B, N, self.h, self.dk).transpose(1, 2) @ self.U)
+    
+    def _reshape_v(self, x):
+        B, N, _ = x.shape
+        return x.view(B, N, self.h, self.dk).transpose(1, 2)
+
+    def forward(self, x, mask=None, rel_bias_tokens=None, kv_cache=None, use_cache=False):
+        q = self._proj_qk(self.q(x))
+        k_new = self._proj_qk(self.k(x))
+        v_new = self._reshape_v(self.v(x))
+        if kv_cache is None: 
+            k, v = k_new, v_new
+        else:
+            k_cached, v_cached = kv_cache
+            if use_cache:
+                k = torch.cat([k_cached, k_new], dim=2)
+                v = torch.cat([v_cached, v_new], dim=2)
+            else:
+                k, v = k_new, v_new
+        att = (q @ k.transpose(-1, -2)) / math.sqrt(self.dk)
+        if self.use_relpos and rel_bias_tokens is not None:
+            att = att + alibi_bias(self.h, rel_bias_tokens)[:, :, -q.size(2):, :]
+        if mask is not None: 
+            att = att + mask
+        z = (att.softmax(-1) @ v).transpose(1, 2).reshape(x.size(0), x.size(1), -1)
+        out = self.drop(self.proj(z))
+        return (out, (k, v)) if use_cache else out
+
+
+class Block(nn.Module):
+    def __init__(self, d: int, h: int, r: int):
+        super().__init__()
+        self.ln1, self.ln2 = nn.LayerNorm(d), nn.LayerNorm(d)
+        self.mha = TuneableAttentionMHA(d, h, r)
+        self.ff = nn.Sequential(nn.Linear(d, 4 * d), nn.ReLU(), nn.Linear(4 * d, d))
+
+    def forward(self, x, mask, kv=None, use_cache=False, total_seq_len=None):
+        if use_cache:
+            y, new_kv = self.mha(self.ln1(x), mask, rel_bias_tokens=total_seq_len, kv_cache=kv, use_cache=True)
+            x = x + y + self.ff(self.ln2(x + y))
+            return x, new_kv
+        else:
+            n = x.size(1)
+            x = x + self.mha(self.ln1(x), mask, rel_bias_tokens=n)
+            return x + self.ff(self.ln2(x))
+
+
+class Encoder(nn.Module):
+    def __init__(self, cfg, tie_weights: bool = False):
+        super().__init__()
+        d, l, h, r = cfg["d"], cfg["layers"], cfg["heads"], cfg["rank"]
+        self.emb = nn.Embedding(VOCAB, d)
+        self.blocks = nn.ModuleList([Block(d, h, r) for _ in range(l)])
+        self.ln = nn.LayerNorm(d)
+        self.tie_weights = tie_weights
+
+    def forward(self, ids, mask, kv_caches=None, use_cache=False, total_seq_len=None):
+        x = self.emb(ids)
+        if not use_cache:
+            for blk in self.blocks: 
+                x = blk(x, mask)
+            return self.ln(x)
+        new_kvs = []
+        for i, blk in enumerate(self.blocks):
+            kv = kv_caches[i] if kv_caches else None
+            x, kv_out = blk(x, mask, kv, use_cache=True, total_seq_len=total_seq_len)
+            new_kvs.append(kv_out)
+        return self.ln(x), new_kvs
+
+
+class ARHead(nn.Module):
+    def __init__(self, d, tie_weights: bool = False, embedding_weight: nn.Parameter = None):
+        super().__init__()
+        self.tie_weights = tie_weights
+        if tie_weights and embedding_weight is not None:
+            self.proj = nn.Linear(d, VOCAB, bias=False)
+            self.proj.weight = embedding_weight
+        else:
+            self.proj = nn.Linear(d, VOCAB)
+    
+    def forward(self, h): 
+        return self.proj(h)
+
+
+class SATHead(nn.Module):
+    def __init__(self, d, mode="var"):
+        super().__init__()
+        self.proj = nn.Linear(d, VOCAB)
+        self.gate = nn.Linear(d, 2) if mode == "var" else None
+    def forward(self, h_last):
+        return self.proj(h_last), (self.gate(h_last[:, 0]) if self.gate else None)
+
+
+# ───────────────────────── Masks ─────────────────────────
+def causal_mask(n):
+    return torch.triu(torch.full((1, 1, n, n), float("-inf"), device=DEV), 1)
+
+def sat_mask(n, block=SAT_BLOCK):
+    idx = torch.arange(n, device=DEV)
+    grp = idx.unsqueeze(0) // block
+    allow = (grp.T == grp) | (grp.T > grp)
+    return torch.where(allow, 0.0, float("-inf")).unsqueeze(0).unsqueeze(0)
+
+def sat_mask_cached(new_len: int, cached_len: int, block=SAT_BLOCK):
+    total_len = cached_len + new_len
+    mask = torch.zeros((1, 1, new_len, total_len), device=DEV)
+    return mask
+
+
+# ───────────────────────── Checkpoint helpers ─────────────────────────
+
+# ───────────────────────── Delta Checkpoints (weight-only, async) ─────────────────────────
+_delta_lock = threading.Lock()
+_delta_thread: Optional[threading.Thread] = None
+
+def _sha256_file(path: pathlib.Path) -> str:
+    """Compute SHA256 of a file for integrity verification."""
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 20), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+def _do_delta_save(tensors: dict, path: pathlib.Path, meta: dict):
+    """Background worker: write weight-only checkpoint + checksum."""
+    try:
+        path.parent.mkdir(exist_ok=True, parents=True)
+        tmp = path.with_suffix(path.suffix + ".dtmp")
+        torch.save({"weights": tensors, **meta}, tmp, _use_new_zipfile_serialization=False)
+        digest = _sha256_file(tmp)
+        tmp.replace(path)
+        # Write sidecar checksum
+        path.with_suffix(".sha256").write_text(f"{digest}  {path.name}\n")
+        print(f"  [delta] saved {path.name} ({digest[:12]}...)")
+    except Exception as e:
+        print(f"  [delta] FAILED {path.name}: {e}")
+
+def save_delta(core, ar_h, sat_h, step: int, seen_tok: int, save_dir: pathlib.Path, phase_name: str):
+    """Save weight-only delta in background thread. Non-blocking."""
+    global _delta_thread
+    # Wait for any previous delta write to finish
+    if _delta_thread is not None and _delta_thread.is_alive():
+        _delta_thread.join(timeout=60)
+    # Snapshot weights to CPU (detach from GPU graph)
+    with _delta_lock:
+        tensors = {
+            "core": {k: v.detach().cpu() for k, v in core.state_dict().items()},
+            "ar":   {k: v.detach().cpu() for k, v in ar_h.state_dict().items()},
+            "sat":  {k: v.detach().cpu() for k, v in sat_h.state_dict().items()},
+        }
+    meta = {"step": step, "seen_tok": seen_tok, "wall_time": time.time(), "delta": True}
+    path = save_dir / f"{phase_name}_delta_step{step:08d}.pt"
+    _delta_thread = threading.Thread(target=_do_delta_save, args=(tensors, path, meta), daemon=True)
+    _delta_thread.start()
+
+def _prune_deltas(save_dir: pathlib.Path, phase_name: str, max_deltas: int):
+    """Keep only the most recent max_deltas delta files."""
+    if max_deltas is None or max_deltas <= 0:
+        return
+    try:
+        pattern = f"{phase_name}_delta_step*.pt"
+        deltas = sorted(
+            [p for p in save_dir.glob(pattern) if p.stat().st_size > 0],
+            key=lambda p: p.stat().st_mtime
+        )
+        excess = len(deltas) - max_deltas
+        if excess > 0:
+            for p in deltas[:excess]:
+                try:
+                    p.unlink()
+                    sha = p.with_suffix(".sha256")
+                    if sha.exists(): sha.unlink()
+                    print(f"  [delta-prune] deleted {p.name}")
+                except Exception:
+                    pass
+    except Exception as e:
+        print(f"  [delta-prune] error: {e}")
+
+def load_delta(path: pathlib.Path, core, ar_h, sat_h):
+    """Load weight-only delta. Returns (step, seen_tok) or raises."""
+    # Verify checksum if sidecar exists
+    sha_path = path.with_suffix(".sha256")
+    if sha_path.exists():
+        expected = sha_path.read_text().split()[0]
+        actual = _sha256_file(path)
+        if expected != actual:
+            raise ValueError(f"Checksum mismatch for {path.name}: expected {expected[:12]}... got {actual[:12]}...")
+        print(f"  [delta] checksum OK for {path.name}")
+    ck = torch.load(path, map_location="cpu", weights_only=False)
+    if not ck.get("delta"):
+        raise ValueError(f"{path.name} is not a delta checkpoint")
+    core.load_state_dict(ck["weights"]["core"])
+    ar_h.load_state_dict(ck["weights"]["ar"])
+    sat_h.load_state_dict(ck["weights"]["sat"])
+    return ck.get("step", 0), ck.get("seen_tok", 0)
+
+def _flush_delta():
+    """Wait for any in-flight delta save to complete."""
+    global _delta_thread
+    if _delta_thread is not None and _delta_thread.is_alive():
+        print("  [delta] flushing in-flight write...")
+        _delta_thread.join(timeout=120)
+
+def save_ckpt(path: pathlib.Path, core, ar_h, sat_h, opt, scaler, meta):
+    path.parent.mkdir(exist_ok=True, parents=True)
+    tmp = path.with_suffix(path.suffix + ".tmp")
+    state = {
+        "core": core.state_dict(), "ar": ar_h.state_dict(), "sat": sat_h.state_dict(),
+        "opt": opt.state_dict(), "scaler": scaler.state_dict(),
+        "cfg": meta.get("cfg"), "tokenizer_id": TOKENIZER_ID,
+        "tie_weights": meta.get("tie_weights", False),
+        **{k: v for k, v in meta.items() if k not in ("cfg", "tie_weights")}
+    }
+    torch.save(state, tmp, _use_new_zipfile_serialization=False)
+    tmp.replace(path)
+    (path.parent / "latest.json").write_text(json.dumps({"path": str(path), "step": meta["step"]}))
+    print(f"\n✓ saved checkpoint {path.name}")
+
+def load_ckpt(path, core, ar_h, sat_h, opt, scaler):
+    p = _resolve_ckpt(path) or path
+    ck = _try_load(p, map_location="cpu")
+    if ck is None: raise FileNotFoundError(f"No valid checkpoint at {p}")
+    core.load_state_dict(ck["core"])
+    ar_h.load_state_dict(ck["ar"])
+    sat_h.load_state_dict(ck["sat"])
+    opt.load_state_dict(ck["opt"])
+    scaler.load_state_dict(ck["scaler"])
+    return ck.get("step", 0), ck.get("seen_tok", 0), ck.get("wall_time", time.time())
+
+def _safe_load_any(path: pathlib.Path, tgt: nn.Module, key: str | None = None):
+    p = _resolve_ckpt(path) or path
+    if not p.exists(): return 0
+    ck = _try_load(p, map_location="cpu")
+    if ck is None: return 0
+    sd = ck.get(key, ck) if key else ck
+    if isinstance(sd, dict) and "state_dict" in sd: sd = sd["state_dict"]
+    tgt_sd = tgt.state_dict()
+    filt = {k: v for k, v in sd.items() if k in tgt_sd and v.shape == tgt_sd[k].shape}
+    if filt: tgt.load_state_dict(filt, strict=False)
+    return len(filt)
+
+def infer_cfg_from_ckpt(path: pathlib.Path):
+    p = _resolve_ckpt(path) or path
+    if not p.exists(): return None
+    sd = _try_load(p, map_location="cpu")
+    if sd is None: return None
+    if "cfg" in sd: return dict(sd["cfg"])
+    return None
+
+
+# ───────────────────────���─ Training Logic ─────────────────────────
+def _parse_grow_plan(s: str) -> List[int]:
+    return sorted(set([int(x.strip()) for x in s.split(",") if x.strip() and int(x.strip()) >= 128]))
+
+def _count_enabled_params(*modules) -> int:
+    seen_data_ptrs = set()
+    total = 0
+    for m in modules:
+        if m is None:
+            continue
+        for p in m.parameters():
+            if p.data_ptr() not in seen_data_ptrs:
+                seen_data_ptrs.add(p.data_ptr())
+                total += p.numel()
+    return total
+
+def _phase_freeze(core: nn.Module, *, freeze_core: bool, unfreeze_ln: bool, train_emb: bool):
+    for p in core.parameters(): p.requires_grad = not freeze_core
+    if freeze_core:
+        if unfreeze_ln:
+            for blk in core.blocks:
+                for p in blk.ln1.parameters(): p.requires_grad = True
+                for p in blk.ln2.parameters(): p.requires_grad = True
+            for p in core.ln.parameters(): p.requires_grad = True
+        if train_emb:
+            for p in core.emb.parameters(): p.requires_grad = True
+
+def _train_phase(
+    args, phase_name: str,
+    core, ar_h, sat_h, opt, scaler,
+    start_step, seen_tok, resume_wall_time,
+    cfg, source, steps, block_size, batch_size,
+    chat_cfg: dict,
+    max_ckpts: int,
+    target_tokens_override: Optional[int] = None,
+    tie_weights: bool = False,
+    streaming: bool = True
+):
+    BLOCK = block_size
+    BATCH = batch_size
+    if target_tokens_override is not None:
+        target_tokens = target_tokens_override
+    else:
+        ratio = 51.2 if args.chilla_max_double else 25
+        param_count = _count_enabled_params(core, ar_h, sat_h)
+        target_tokens = int(ratio * param_count)
+    if steps:
+        phase_target_tokens = steps * BLOCK * BATCH
+        total_tokens_needed = seen_tok + phase_target_tokens
+    else:
+        total_tokens_needed = target_tokens
+        if total_tokens_needed <= seen_tok:
+            print(f"[{phase_name}] target {total_tokens_needed} already reached.")
+            return start_step, seen_tok, resume_wall_time
+    stream = token_stream(
+        source, total_tokens_needed, seed=42,
+        chat=chat_cfg.get("chat", False),
+        chat_messages_key=chat_cfg.get("key", "messages"),
+        sft_add_generation_prompt=chat_cfg.get("gen_prompt", False),
+        dataset_field_text=chat_cfg.get("text_field", "text"),
+        streaming=streaming
+    )
+    ce_tok = nn.CrossEntropyLoss(label_smoothing=0.1)
+    ce_gate = nn.CrossEntropyLoss()
+    pbar = SafeProgress(total=total_tokens_needed, initial=seen_tok, unit="tok")
+    grow_plan = _parse_grow_plan(args.grow_plan) if args.auto_grow else []
+    buf: list[int] = []
+    batch_accum: list[list[int]] = []
+    step = start_step
+    steps_since_last_grow = 0
+    oom_retries = 0
+    MAX_OOM_RETRIES = 2
+    now_wall = time.time()
+    last_save_mono = time.monotonic() - (now_wall - (resume_wall_time or now_wall))
+    last_delta_step = start_step
+    print(f"[{phase_name}] Starting. Goal: {total_tokens_needed:,} tokens. Batch={BATCH}, Block={BLOCK}")
+    print(f"[{phase_name}] AR_ONLY={args.ar_only}, TIE_WEIGHTS={tie_weights}, STREAMING={streaming}")
+    while seen_tok < total_tokens_needed:
+        try:
+            while len(buf) < BLOCK:
+                buf.append(next(stream))
+        except StopIteration:
+            break
+        seq = buf[:BLOCK]
+        buf = buf[BLOCK:]
+        batch_accum.append(seq)
+        if len(batch_accum) < BATCH:
+            continue
+        ids = torch.tensor(batch_accum, device=DEV)
+        batch_accum = []
+        tgt_ar = ids.clone()
+        try:
+            with amp(args.amp):
+                h_ar = core(ids, causal_mask(ids.size(1)))
+                logits_ar = ar_h(h_ar)[:, :-1]
+                loss_ar = ce_tok(logits_ar.reshape(-1, VOCAB), tgt_ar[:, 1:].reshape(-1))
+                if args.ar_only:
+                    loss = loss_ar
+                else:
+                    h_sat = core(ids, sat_mask(ids.size(1)))
+                    logits_sat, gate = sat_h(h_sat[:, -SAT_BLOCK:])
+                    tgt_sat = ids[:, 1:SAT_BLOCK+1]
+                    loss_sat = ce_tok(logits_sat.reshape(-1, VOCAB), tgt_sat.reshape(-1))
+                    if gate is not None:
+                        loss_sat += EMIT_LAMBDA * ce_gate(gate, torch.ones(ids.size(0), device=DEV, dtype=torch.long))
+                    loss = loss_ar + loss_sat
+            scaler.scale(loss).backward()
+            scaler.unscale_(opt)
+            nn.utils.clip_grad_norm_(core.parameters(), 1.0)
+            scaler.step(opt)
+            scaler.update()
+            opt.zero_grad(set_to_none=True)
+        except RuntimeError as e:
+            msg = str(e).lower()
+            if "out of memory" in msg or "cuda error" in msg:
+                batch_accum = []
+                opt.zero_grad(set_to_none=True)
+                if DEV.type == "cuda":
+                    torch.cuda.empty_cache()
+                    torch.cuda.synchronize()
+                oom_retries += 1
+                if oom_retries <= MAX_OOM_RETRIES:
+                    print(f"\n[{phase_name} OOM] Retry {oom_retries}/{MAX_OOM_RETRIES} at Batch={BATCH}, clearing VRAM...")
+                    time.sleep(2)
+                    continue
+                oom_retries = 0
+                if BATCH > 1:
+                    print(f"\n[{phase_name} OOM] Reducing Batch: {BATCH} -> {BATCH - 1} (after {MAX_OOM_RETRIES} retries)")
+                    BATCH -= 1
+                    time.sleep(2)
+                else:
+                    new_block = max(128, BLOCK // 2)
+                    print(f"\n[{phase_name} OOM] Reducing Block: {BLOCK} -> {new_block}")
+                    BLOCK = new_block
+                    time.sleep(2)
+                steps_since_last_grow = 0
+                continue
+            raise
+        step += 1
+        oom_retries = 0
+        toks_processed = BLOCK * BATCH
+        seen_tok += toks_processed
+        pbar.update(toks_processed)
+        pbar.set_postfix(loss=f"{loss.item():.3f}", B=BATCH, L=BLOCK)
+        if args.save_every_sec > 0:
+            now_mono = time.monotonic()
+            if now_mono - last_save_mono >= args.save_every_sec:
+                ck_name = f"{phase_name}_step{step:08d}.pt"
+                _flush_delta()  # wait for any in-flight delta before full save
+                _prune_checkpoints(pathlib.Path(args.save_dir), phase_name, max_ckpts)
+                save_ckpt(pathlib.Path(args.save_dir) / ck_name, core, ar_h, sat_h, opt, scaler,
+                          meta={"cfg": cfg, "step": step, "seen_tok": seen_tok, "wall_time": time.time(), "tie_weights": tie_weights})
+                last_save_mono = now_mono
+                # Prune old deltas after a full save (they're superseded)
+                _prune_deltas(pathlib.Path(args.save_dir), phase_name, args.delta_max_keep)
+                last_delta_step = step  # reset delta counter after full save
+        # ── Delta checkpoint (step-based, weight-only, async) ──
+        if args.delta_every_steps > 0 and (step - last_delta_step) >= args.delta_every_steps:
+            _prune_deltas(pathlib.Path(args.save_dir), phase_name, args.delta_max_keep)
+            save_delta(core, ar_h, sat_h, step, seen_tok, pathlib.Path(args.save_dir), phase_name)
+            last_delta_step = step
+        if args.auto_grow:
+            steps_since_last_grow += 1
+            if steps_since_last_grow >= args.grow_every_steps:
+                steps_since_last_grow = 0
+                try:
+                    idx = grow_plan.index(BLOCK)
+                    if idx + 1 < len(grow_plan):
+                        BLOCK = grow_plan[idx + 1]
+                        print(f"[{phase_name} Grow] Block -> {BLOCK}")
+                        if DEV.type == "cuda": torch.cuda.empty_cache()
+                except ValueError:
+                    grow_plan = sorted(set(grow_plan + [BLOCK]))
+    pbar.close()
+    _flush_delta()  # ensure any in-flight delta completes before final save
+    save_ckpt(pathlib.Path(args.save_dir) / f"{phase_name}_final.pt", core, ar_h, sat_h, opt, scaler,
+              meta={"cfg": cfg, "step": step, "seen_tok": seen_tok, "wall_time": time.time(), "tie_weights": tie_weights})
+    return step, seen_tok, time.time()
+
+
+# ───────────────────────── Main Orchestrator ─────────────────────────
+def train(args):
+    cfg = PRESETS[args.preset].copy()
+    tie_weights = args.tie_weights
+    print_expansion_info(cfg, tie_weights)
+    if not args.fresh:
+        src_probe = pathlib.Path(args.warmstart_from) if args.warmstart_from else pathlib.Path(args.save_dir) / "final.pt"
+        prev_cfg = infer_cfg_from_ckpt(src_probe)
+    else: prev_cfg = None
+    if prev_cfg:
+        cfg.update({k: v for k, v in prev_cfg.items() if k in cfg})
+        if args.x2 and prev_cfg.get("layers"): cfg["layers"] = max(cfg["layers"], prev_cfg["layers"] * 2)
+    if args.rank: cfg["rank"] = args.rank
+    if args.x2 and not prev_cfg: cfg["layers"] *= 2
+    print(f"Config: {cfg}")
+    core = Encoder(cfg, tie_weights=tie_weights).to(DEV)
+    ar_h = ARHead(cfg["d"], tie_weights=tie_weights, embedding_weight=core.emb.weight if tie_weights else None).to(DEV)
+    sat_h = SATHead(cfg["d"], mode="var").to(DEV)
+    total_params = _count_enabled_params(core, ar_h, sat_h)
+    print(f"Total parameters: {total_params:,}")
+    if tie_weights:
+        print(f"{Colors.WARN}[weight-tying] Embedding and LM head share weights{Colors.RESET}")
+    if not args.fresh:
+        src = pathlib.Path(args.warmstart_from) if args.warmstart_from else pathlib.Path(args.save_dir) / "final.pt"
+        src = _resolve_ckpt(src)
+        if src:
+            loaded = _safe_load_any(src, core, key="core")
+            _safe_load_any(src, ar_h, key="ar")
+            _safe_load_any(src, sat_h, key="sat")
+            if loaded: print(f"Warm-start loaded from {src}")
+    _phase_freeze(core, freeze_core=args.freeze_core, unfreeze_ln=args.unfreeze_ln, train_emb=args.train_emb)
+    opt = torch.optim.AdamW([
+        {"params": [p for p in core.parameters() if p.requires_grad], "lr": args.lr_core},
+        {"params": ar_h.parameters(), "lr": args.lr_head},
+        {"params": sat_h.parameters(), "lr": args.lr_head},
+    ])
+    scaler = GradScaler(enabled=(args.amp and DEV.type == "cuda"))
+    start_step, seen_tok, last_wall = 0, 0, None
+    if args.resume_delta and not args.fresh:
+        delta_step, delta_tok = load_delta(pathlib.Path(args.resume_delta), core, ar_h, sat_h)
+        start_step, seen_tok, last_wall = delta_step, delta_tok, None
+        print(f"Resumed from DELTA at step {start_step} (optimizer state reset — momentum rebuilds in ~100 steps)")
+    elif args.resume and not args.fresh:
+        start_step, seen_tok, last_wall = load_ckpt(pathlib.Path(args.resume), core, ar_h, sat_h, opt, scaler)
+        print(f"Resumed from step {start_step}")
+    # torch.compile AFTER loading checkpoint (key names differ)
+    if args.compile:
+        print("[torch.compile] Compiling model...")
+        core = torch.compile(core, mode="reduce-overhead")
+        ar_h = torch.compile(ar_h, mode="reduce-overhead")
+        sat_h = torch.compile(sat_h, mode="reduce-overhead")
+        print("[torch.compile] Done.")
+    step, seen_tok, last_wall = _train_phase(
+        args, "pretrain", core, ar_h, sat_h, opt, scaler,
+        start_step, seen_tok, last_wall, cfg,
+        args.source, args.steps, 
+        args.block or DEFAULT_BLOCK, 
+        args.batch_size or DEFAULT_BATCH,
+        chat_cfg={"chat": args.chat, "key": args.chat_messages_key, "gen_prompt": args.sft_add_generation_prompt, "text_field": args.dataset_field_text},
+        max_ckpts=args.max_ckpts,
+        target_tokens_override=args.target_tokens,
+        tie_weights=tie_weights
+    )
+    if (not args.after_sft_source) and (args.after_sft_steps and args.after_sft_steps > 0):
+        args.after_sft_source = DEFAULT_AFTER_SFT_SOURCES
+        args.after_sft_chat = True
+        if args.after_sft_add_generation_prompt is None: args.after_sft_add_generation_prompt = True
+        if not args.after_sft_block: args.after_sft_block = DEFAULT_AFTER_SFT_BLOCK
+    if args.after_sft_source and args.after_sft_steps and args.after_sft_steps > 0:
+        print("\n[Orchestrator] Starting Post-Pretraining SFT Phase...")
+        _phase_freeze(core, 
+                      freeze_core=args.after_sft_freeze_core, 
+                      unfreeze_ln=args.after_sft_unfreeze_ln, 
+                      train_emb=args.after_sft_train_emb)
+        opt = torch.optim.AdamW([
+            {"params": [p for p in core.parameters() if p.requires_grad], "lr": args.after_sft_lr_core or args.lr_core},
+            {"params": ar_h.parameters(), "lr": args.after_sft_lr_head or args.lr_head},
+            {"params": sat_h.parameters(), "lr": args.after_sft_lr_head or args.lr_head},
+        ])
+        step, seen_tok, last_wall = _train_phase(
+            args, "sft", core, ar_h, sat_h, opt, scaler,
+            step, seen_tok, last_wall, cfg,
+            args.after_sft_source, args.after_sft_steps,
+            args.after_sft_block or DEFAULT_AFTER_SFT_BLOCK,
+            args.batch_size or DEFAULT_BATCH,
+            chat_cfg={
+                "chat": args.after_sft_chat, 
+                "key": args.after_sft_chat_messages_key,
+                "gen_prompt": args.after_sft_add_generation_prompt if args.after_sft_add_generation_prompt is not None else args.sft_add_generation_prompt,
+                "text_field": args.after_sft_dataset_field_text
+            },
+            max_ckpts=args.max_ckpts,
+            target_tokens_override=None,
+            tie_weights=tie_weights,
+            streaming=False
+        )
+    save_ckpt(pathlib.Path(args.save_dir) / "final.pt", core, ar_h, sat_h, opt, scaler,
+              meta={"cfg": cfg, "step": step, "seen_tok": seen_tok, "wall_time": time.time(), "tie_weights": tie_weights})
+    print("🎉 All Training Complete")
+
+
+# ───────────────────────── Sampling ─────────────────────────
+def _apply_penalties(logits, ids, n, rep_p, pres_p, freq_p):
+    if ids.numel() == 0: return logits
+    hist = ids[0, -n:].long() if n > 0 else ids[0].long()
+    uniq, counts = torch.unique(hist, return_counts=True)
+    if pres_p or freq_p:
+        logits[..., uniq] -= (pres_p + freq_p * counts.float())
+    if rep_p != 1.0:
+        sel = logits[..., uniq]
+        logits[..., uniq] = torch.where(sel > 0, sel / rep_p, sel * rep_p)
+    return logits
+
+def _sample(logits, T, top_k, top_p, min_p, greedy):
+    if greedy: return logits.argmax(-1, keepdim=True)
+    probs = (logits / max(T, 1e-8)).softmax(-1)
+    if top_k:
+        v, i = torch.topk(probs, min(top_k, probs.size(-1)))
+        probs = torch.zeros_like(probs).scatter_(-1, i, v)
+    if top_p < 1.0:
+        s_probs, s_idx = torch.sort(probs, descending=True, dim=-1)
+        probs = torch.zeros_like(probs).scatter_(-1, s_idx, s_probs * (torch.cumsum(s_probs, -1) <= top_p).float())
+    if min_p > 0: probs[probs < min_p] = 0
+    if probs.sum() == 0: return logits.argmax(-1, keepdim=True)
+    return probs.div_(probs.sum()).multinomial(1)
+
+@torch.no_grad()
+def infer(args):
+    if args.mode == "ar":
+        if args.temperature is None: args.temperature = 0.7
+        if args.top_k is None: args.top_k = 0
+        if args.repetition_penalty is None: args.repetition_penalty = 1.3
+        if args.presence_penalty is None: args.presence_penalty = 0.0
+        if args.frequency_penalty is None: args.frequency_penalty = 0.3
+        if args.penalty_last_n is None: args.penalty_last_n = 128
+        if args.var is None: args.var = False
+    else:
+        if args.temperature is None: args.temperature = 0.5
+        if args.top_k is None: args.top_k = 30
+        if args.repetition_penalty is None: args.repetition_penalty = 2.0
+        if args.presence_penalty is None: args.presence_penalty = 0.6
+        if args.frequency_penalty is None: args.frequency_penalty = 1.0
+        if args.penalty_last_n is None: args.penalty_last_n = 200
+        if args.var is None: args.var = True
+    path = _resolve_ckpt(pathlib.Path(args.ckpt)) or pathlib.Path(args.ckpt)
+    sd = torch.load(path, map_location="cpu")
+    cfg = sd["cfg"]
+    tie_weights = sd.get("tie_weights", False)
+    uk_time = get_uk_time()
+    ckpt_name = path.name
+    print(f"┌─────────────────────────────────────────────────┐")
+    print(f"│ INFERENCE @ {uk_time:<35s} │")
+    print(f"├─────────────────────────────────────────────────┤")
+    print(f"│ Checkpoint: {ckpt_name:<35s} │")
+    print(f"└─────────────────────────────────────────────────┘")
+    print_expansion_info(cfg, tie_weights)
+    core = Encoder(cfg, tie_weights=tie_weights).to(DEV)
+    ar_h = ARHead(cfg["d"], tie_weights=tie_weights, embedding_weight=core.emb.weight if tie_weights else None).to(DEV)
+    sat_h = SATHead(cfg["d"]).to(DEV)
+    core.load_state_dict(sd["core"])
+    ar_h.load_state_dict(sd["ar"])
+    sat_h.load_state_dict(sd["sat"])
+    core.eval()
+    ar_h.eval()
+    sat_h.eval()
+    total_params = _count_enabled_params(core, ar_h, sat_h)
+    if total_params >= 1_000_000_000:
+        param_str = f"{total_params / 1_000_000_000:.2f}B"
+    elif total_params >= 1_000_000:
+        param_str = f"{total_params / 1_000_000:.2f}M"
+    elif total_params >= 1_000:
+        param_str = f"{total_params / 1_000:.2f}K"
+    else:
+        param_str = f"{total_params}"
+    print(f"Model size: {param_str} parameters ({total_params:,})")
+    prompt_tokens = tok.encode(args.prompt)
+    prompt_len = len(prompt_tokens)
+    ids = torch.tensor([prompt_tokens], device=DEV)
+    if ids.size(1) == 0: 
+        ids = torch.tensor([[EOS]], device=DEV)
+        prompt_len = 1
+    mode_str = args.mode
+    if args.mode == "sat":
+        mode_str = f"sat-{'var' if args.var else 'fixed'}"
+    print(f"{Colors.INFO}Generating ({mode_str})...{Colors.RESET}")
+    start = time.time()
+    if args.mode == "ar":
+        h, kvs = core(ids, causal_mask(ids.size(1)), use_cache=True, total_seq_len=ids.size(1))
+        for _ in range(args.max_new):
+            logits = ar_h(h)[:, -1]
+            logits = _apply_penalties(logits, ids, args.penalty_last_n, args.repetition_penalty, args.presence_penalty, args.frequency_penalty)
+            nxt = _sample(logits, args.temperature, args.top_k, args.top_p, args.min_p, args.greedy)
+            ids = torch.cat([ids, nxt], 1)
+            h, kvs = core(ids[:, -1:], None, kv_caches=kvs, use_cache=True, total_seq_len=ids.size(1))
+    else:
+        cached_len = ids.size(1)
+        h, kvs = core(ids, sat_mask(ids.size(1)), use_cache=True, total_seq_len=cached_len)
+        added = 0
+        while added < args.max_new:
+            logits_all, gate = sat_h(h[:, -SAT_BLOCK:])
+            stride = SAT_BLOCK if (not args.var or gate is None) else (gate.softmax(-1).multinomial(1).item() + 1)
+            new_tokens = []
+            for i in range(int(stride)):
+                logits = logits_all[:, i]
+                logits = _apply_penalties(logits, ids, args.penalty_last_n, args.repetition_penalty, args.presence_penalty, args.frequency_penalty)
+                nxt = _sample(logits, args.temperature, args.top_k, args.top_p, args.min_p, args.greedy)
+                new_tokens.append(nxt)
+                ids = torch.cat([ids, nxt], 1)
+                added += 1
+                if added >= args.max_new: break
+            if added >= args.max_new: break
+            new_ids = torch.cat(new_tokens, dim=1)
+            mask = sat_mask_cached(new_ids.size(1), cached_len)
+            h, kvs = core(new_ids, mask, kv_caches=kvs, use_cache=True, total_seq_len=ids.size(1))
+            cached_len = ids.size(1)
+    elapsed = time.time() - start
+    gen_tokens = len(ids[0]) - prompt_len
+    tok_per_sec = gen_tokens / elapsed if elapsed > 0 else 0
+    all_tokens = ids[0].tolist()
+    prompt_text = tok.decode(all_tokens[:prompt_len], skip_special_tokens=True)
+    gen_text = tok.decode(all_tokens[prompt_len:], skip_special_tokens=True)
+    print(f"{Colors.PROMPT}{prompt_text}{Colors.RESET}{gen_text}")
+    print(f"{Colors.INFO}[{elapsed:.2f}s | {gen_tokens} tokens | {tok_per_sec:.1f} tok/s]{Colors.RESET}")
+
+
+# ───────────────────────── CLI ─────────────────────────
+def main():
+    ap = argparse.ArgumentParser(description="AGILLM Expansion Ratio Testing")
+    sub = ap.add_subparsers(dest="cmd", required=True)
+    tr = sub.add_parser("train")
+    tr.add_argument("--preset", choices=PRESETS.keys(), default="nano_3x")
+    tr.add_argument("--rank", type=int)
+    tr.add_argument("--block", type=int, default=DEFAULT_BLOCK)
+    tr.add_argument("--batch_size", type=int, default=DEFAULT_BATCH)
+    tr.add_argument("--source", default=DEFAULT_PRETRAIN_SOURCES)
+    tr.add_argument("--target_tokens", type=int)
+    tr.add_argument("--steps", type=int)
+    tr.add_argument("--amp", action="store_true")
+    tr.add_argument("--compile", action="store_true", help="Use torch.compile for speedup")
+    tr.add_argument("--save_every_sec", type=int, default=DEFAULT_SAVE_SEC)
+    tr.add_argument("--delta_every_steps", type=int, default=DEFAULT_DELTA_STEPS, help="Weight-only delta save every N steps (0=off)")
+    tr.add_argument("--delta_max_keep", type=int, default=DEFAULT_MAX_DELTAS, help="Max delta checkpoints to keep")
+    tr.add_argument("--resume_delta", type=str, help="Resume from a delta (weight-only, no optimizer state)")
+    tr.add_argument("--save_dir", default=str(CKDIR))
+    tr.add_argument("--resume", type=str)
+    tr.add_argument("--x2", action="store_true")
+    tr.add_argument("--warmstart_from", type=str)
+    tr.add_argument("--fresh", action="store_true")
+    tr.add_argument("--max_ckpts", type=int, default=None)
+    tr.add_argument("--chilla_max_double", action="store_true")
+    tr.add_argument("--tie_weights", action="store_true")
+    tr.add_argument("--ar_only", action="store_true")
+    tr.add_argument("--freeze_core", action="store_true")
+    tr.add_argument("--unfreeze_ln", action="store_true")
+    tr.add_argument("--train_emb", action="store_true")
+    tr.add_argument("--lr_core", type=float, default=LR_CORE)
+    tr.add_argument("--lr_head", type=float, default=LR_HEAD)
+    tr.add_argument("--chat", action="store_true")
+    tr.add_argument("--chat_messages_key", default="messages")
+    tr.add_argument("--dataset_field_text", default="text")
+    tr.add_argument("--sft_add_generation_prompt", action="store_true")
+    tr.add_argument("--auto_grow", action="store_true")
+    tr.add_argument("--grow_plan", default="576,640,768,896,1024,1122")
+    tr.add_argument("--grow_every_steps", type=int, default=50000)
+    tr.add_argument("--after_sft_source", default="")
+    tr.add_argument("--after_sft_steps", type=int, default=0)
+    tr.add_argument("--after_sft_chat", action="store_true")
+    tr.add_argument("--after_sft_chat_messages_key", default="messages")
+    tr.add_argument("--after_sft_dataset_field_text", default="text")
+    tr.add_argument("--after_sft_add_generation_prompt", type=bool, default=None)
+    tr.add_argument("--after_sft_block", type=int, default=0)
+    tr.add_argument("--after_sft_freeze_core", action="store_true")
+    tr.add_argument("--after_sft_unfreeze_ln", action="store_true")
+    tr.add_argument("--after_sft_train_emb", action="store_true")
+    tr.add_argument("--after_sft_lr_core", type=float, default=0.0)
+    tr.add_argument("--after_sft_lr_head", type=float, default=0.0)
+    inf = sub.add_parser("infer")
+    inf.add_argument("--mode", choices=["ar", "sat"], required=True)
+    inf.add_argument("--ckpt", required=True)
+    inf.add_argument("--prompt", required=True)
+    inf.add_argument("--max_new", type=int, default=120)
+    inf.add_argument("--temperature", type=float, default=None)
+    inf.add_argument("--greedy", action="store_true")
+    inf.add_argument("--top_k", type=int, default=None)
+    inf.add_argument("--top_p", type=float, default=0.9)
+    inf.add_argument("--min_p", type=float, default=0.0)
+    inf.add_argument("--repetition_penalty", type=float, default=None)
+    inf.add_argument("--presence_penalty", type=float, default=None)
+    inf.add_argument("--frequency_penalty", type=float, default=None)
+    inf.add_argument("--penalty_last_n", type=int, default=None)
+    inf.add_argument("--var", action="store_true", default=None)
+    inf.add_argument("--no-var", dest="var", action="store_false")
+    args = ap.parse_args()
+    if args.cmd == "train": train(args)
+    else: infer(args)
+
+
+if __name__ == "__main__":
+    main()