C.py

#!/usr/bin/env python3

# 5L_joint_sft.py — Joint AR+SAT Trainer with SFT Phase
# Merges 5L.py (Joint Model + Adaptive OOM) with 5apg.py (Robust Stream + SFT Phases)
# Features:
# - Joint AR + SAT training objective
# - Phase 1: Pretrain -> Phase 2: SFT (Chat/Instruction Tuning)
# - Adaptive OOM: Reduces Batch Size, then Block Size
# - Robust Data: Retries, JSONL, Chat Templates, Source Mixing
# - Chinchilla Scaling, Checkpoint Pruning, FP8/AMP support

from __future__ import annotations
import argparse, json, math, pathlib, random, time, os, sys
from contextlib import nullcontext
from typing import Dict, Any, List, Optional, Tuple
import torch
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

# ───────────────────────── Terminal Colors ─────────────────────────
class Colors:
    RESET = "\033[0m"
    BOLD = "\033[1m"
    DIM = "\033[2m"
    # Foreground
    RED = "\033[31m"
    GREEN = "\033[32m"
    YELLOW = "\033[33m"
    BLUE = "\033[34m"
    MAGENTA = "\033[35m"
    CYAN = "\033[36m"
    WHITE = "\033[37m"
    # Bright
    BRIGHT_GREEN = "\033[92m"
    BRIGHT_CYAN = "\033[96m"
    BRIGHT_YELLOW = "\033[93m"
    # Prompt color
    PROMPT = "\033[36m"  # Cyan for prompt
    GENERATED = "\033[0m"  # Default for generated

# ───────────────────────── 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
TOKENIZER_ID = os.environ.get("TOKENIZER_ID", "deepseek-ai/DeepSeek-V3.2-Exp")
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|>"})

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: Dict[str, Dict[str, int]] = {
    "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),
}

# Configuration
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
CKDIR = pathlib.Path("ckpts_joint")

# Defaults for SFT
DEFAULT_PRETRAIN_SOURCES = "cerebras/SlimPajama-627B"
DEFAULT_AFTER_SFT_SOURCES = "mlabonne/opc-sft-stage2-chat,HuggingFaceH4/ultrachat_200k"
DEFAULT_AFTER_SFT_BLOCK = 1122

# ───────────────────────── 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):
    """Prune old checkpoints for a specific phase."""
    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}")

# ───────────────────────── 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
    # Fallback for prompt/response pairs
    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):
    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 base == "json":
        data_files = {"train": config}
        ds = load_dataset("json", data_files=data_files, split="train", streaming=True, download_config=dc)
    else:
        ds = load_dataset(base, config, split="train", streaming=True, download_config=dc) if config else \
             load_dataset(base, split="train", streaming=True, download_config=dc)
    return iter(ds.shuffle(buffer_size=10_000, seed=seed))

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"):
    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)
            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)

# ───────────────────────── Relative positional bias (ALiBi) ─────────────────────────
def _alibi_slopes(n_heads: int):
    import math
    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 LowRankMHA(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 = h, d // h
        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 * r, d, bias=False)
        self.drop = nn.Dropout(0.1)

    def _proj(self, x):
        B, N, _ = x.shape
        return (x.view(B, N, self.h, self.dk).transpose(1, 2) @ self.U)

    def forward(self, x, mask=None, rel_bias_tokens=None, kv_cache=None, use_cache=False):
        q = self._proj(self.q(x))
        k_new = self._proj(self.k(x))
        v_new = self._proj(self.v(x))

        if kv_cache is None: k, v = k_new, v_new
        else:
            k, v = kv_cache
            if use_cache:
                k, v = torch.cat([k, k_new], dim=2), torch.cat([v, v_new], dim=2)

        att = (q @ k.transpose(-1, -2)) / math.sqrt(self.dk)
        if q.size(2) == k.size(2):
            if self.use_relpos and rel_bias_tokens is not None:
                att = att + alibi_bias(self.h, rel_bias_tokens)
        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 = LowRankMHA(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):
        n = x.size(1)
        if use_cache:
            y, new_kv = self.mha(self.ln1(x), mask, rel_bias_tokens=n if mask is not None else None, kv_cache=kv, use_cache=True)
            x = x + y + self.ff(self.ln2(x + y))
            return x, new_kv
        else:
            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):
        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)

    def forward(self, ids, mask, kv_caches=None, use_cache=False):
        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)
            new_kvs.append(kv_out)
        return self.ln(x), new_kvs

class ARHead(nn.Module):
    def __init__(self, d):
        super().__init__()
        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)

# ───────────────────────── Checkpoint helpers ─────────────────────────
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,
        **{k: v for k, v in meta.items() if k != "cfg"}
    }
    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:
    return sum(sum(p.numel() for p in m.parameters()) for m in modules if m is not None)

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
):
    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")
    )

    ce_tok = nn.CrossEntropyLoss(label_smoothing=0.1)
    ce_gate = nn.CrossEntropyLoss()

    pbar = tqdm(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
    
    now_wall = time.time()
    last_save_mono = time.monotonic() - (now_wall - (resume_wall_time or now_wall))

    print(f"[{phase_name}] Starting. Goal: {total_tokens_needed:,} tokens. Batch={BATCH}, Block={BLOCK}")

    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))
                
                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:
                if BATCH > 1:
                    print(f"\n[{phase_name} OOM] Reducing Batch: {BATCH} -> {BATCH - 1}")
                    BATCH -= 1
                else:
                    new_block = max(128, BLOCK // 2)
                    print(f"\n[{phase_name} OOM] Reducing Block: {BLOCK} -> {new_block}")
                    BLOCK = new_block
                
                batch_accum = []
                if DEV.type == "cuda": torch.cuda.empty_cache()
                steps_since_last_grow = 0
                continue
            raise

        step += 1
        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"
                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()})
                _prune_checkpoints(pathlib.Path(args.save_dir), phase_name, max_ckpts)
                last_save_mono = now_mono

        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()
    
    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()})
    
    return step, seen_tok, time.time()

# ───────────────────────── Main Orchestrator ─────────────────────────
def train(args):
    cfg = PRESETS[args.preset].copy()
    
    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).to(DEV)
    ar_h = ARHead(cfg["d"]).to(DEV)
    sat_h = SATHead(cfg["d"], mode="var").to(DEV)

    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 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}")

    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
    )

    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
        )

    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()})
    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):
    path = _resolve_ckpt(pathlib.Path(args.ckpt)) or pathlib.Path(args.ckpt)
    sd = torch.load(path, map_location="cpu", weights_only=False)
    cfg = sd["cfg"]
    
    core = Encoder(cfg).to(DEV)
    ar_h = ARHead(cfg["d"]).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"])
    
    # Encode prompt
    prompt_tokens = tok.encode(args.prompt)
    ids = torch.tensor([prompt_tokens], device=DEV)
    if ids.size(1) == 0: ids = torch.tensor([[EOS]], device=DEV)
    
    prompt_len = ids.size(1)
    
    print(f"Generating ({args.mode})...")
    start = time.time()
    
    # Print prompt in color
    sys.stdout.write(f"{Colors.PROMPT}{Colors.BOLD}{args.prompt}{Colors.RESET}")
    sys.stdout.flush()
    
    if args.mode == "ar":
        h, kvs = core(ids, causal_mask(ids.size(1)), use_cache=True)
        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)
            
            # Stream generated token in normal color
            new_tok = tok.decode([nxt.item()])
            sys.stdout.write(f"{Colors.GENERATED}{new_tok}")
            sys.stdout.flush()
            
            # Stop on EOS
            if nxt.item() == EOS:
                break
            
            h, kvs = core(ids[:, -1:], None, kv_caches=kvs, use_cache=True)
    else:
        added = 0
        while added < args.max_new:
            h = core(ids, sat_mask(ids.size(1)))
            logits_all, gate = sat_h(h[:, -SAT_BLOCK:])
            stride = 2 if (not args.var or gate is None) else (gate.softmax(-1).multinomial(1).item() + 1)
            
            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)
                ids = torch.cat([ids, nxt], 1)
                
                # Stream generated token in normal color
                new_tok = tok.decode([nxt.item()])
                sys.stdout.write(f"{Colors.GENERATED}{new_tok}")
                sys.stdout.flush()
                
                added += 1
                if added >= args.max_new: break
                if nxt.item() == EOS: break
            
            if nxt.item() == EOS: break

    # Final newline and stats
    print(f"\n{Colors.DIM}[{time.time()-start:.2f}s | {ids.size(1) - prompt_len} tokens generated]{Colors.RESET}")

# ───────────────────────── CLI ─────────────────────────
def main():
    ap = argparse.ArgumentParser()
    sub = ap.add_subparsers(dest="cmd", required=True)

    tr = sub.add_parser("train")
    tr.add_argument("--preset", choices=PRESETS, default="small")
    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("--save_every_sec", type=int, default=DEFAULT_SAVE_SEC)
    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("--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=1.0)
    inf.add_argument("--greedy", action="store_true")
    inf.add_argument("--top_k", type=int, default=0)
    inf.add_argument("--top_p", type=float, default=1.0)
    inf.add_argument("--min_p", type=float, default=0.0)
    inf.add_argument("--repetition_penalty", type=float, default=1.0)
    inf.add_argument("--presence_penalty", type=float, default=0.0)
    inf.add_argument("--frequency_penalty", type=float, default=0.0)
    inf.add_argument("--penalty_last_n", type=int, default=64)
    inf.add_argument("--var", action="store_true")

    args = ap.parse_args()
    if args.cmd == "train": train(args)
    else: infer(args)

if __name__ == "__main__":
    main()