Add n_tt_singlefile.py — Tenstorrent N300s training port

n_tt_singlefile.py

@@ -0,0 +1,1636 @@
+
+#!/usr/bin/env python3
+"""
+n_tt_singlefile.py
+
+Single-file AR+SAT trainer adapted for Tenstorrent training via TT-XLA / torch_xla.
+It keeps the same rough structure as the original script, but changes the runtime
+model so it can run on:
+- Tenstorrent TT-XLA (`--device_backend tt`)
+- CUDA
+- CPU
+
+Important TT notes:
+- Training is the primary target.
+- Inference remains available, but TT token-by-token generation is a bad fit for XLA
+  because sequence length changes trigger recompiles. The script therefore falls back
+  to CPU for `infer` unless `--force_tt_infer` is explicitly set.
+- On TT, CUDA AMP/GradScaler and `torch.compile(mode="reduce-overhead")` are disabled.
+- On TT, a manual token cross-entropy path is used instead of `nn.CrossEntropyLoss`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import pathlib
+import random
+import sys
+import time
+from contextlib import nullcontext
+from datetime import datetime, timedelta, timezone
+from typing import Dict, List, Optional, Tuple, Any
+
+# -----------------------------------------------------------------------------
+# Pre-parse runtime backend before importing torch_xla.
+# -----------------------------------------------------------------------------
+_pre = argparse.ArgumentParser(add_help=False)
+_pre.add_argument("cmd", nargs="?")
+_pre.add_argument("--device_backend", default=os.environ.get("AGI_DEVICE_BACKEND", "auto"))
+_pre.add_argument("--force_tt_infer", action="store_true")
+_pre_known, _ = _pre.parse_known_args()
+
+REQUESTED_BACKEND = (_pre_known.device_backend or "auto").lower()
+if REQUESTED_BACKEND == "tt":
+    os.environ.setdefault("PJRT_DEVICE", "TT")
+    os.environ.setdefault("XLA_STABLEHLO_COMPILE", "1")
+    os.environ.setdefault("TT_RUNTIME_TRACE_REGION_SIZE", os.environ.get("TT_RUNTIME_TRACE_REGION_SIZE", "10000000"))
+
+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
+
+TORCH_XLA_OK = False
+torch_xla = None
+xm = None
+xr = None
+xs = None
+
+try:
+    import torch_xla  # type: ignore
+    import torch_xla.core.xla_model as xm  # type: ignore
+    import torch_xla.runtime as xr  # type: ignore
+    import torch_xla.distributed.spmd as xs  # type: ignore
+
+    TORCH_XLA_OK = True
+except Exception:
+    TORCH_XLA_OK = False
+    torch_xla = None
+    xm = None
+    xr = None
+    xs = None
+
+# -----------------------------------------------------------------------------
+# Global runtime state
+# -----------------------------------------------------------------------------
+STATUS_FILE = "/workspace/status.json"
+
+BACKEND = "cpu"
+DEV = torch.device("cpu")
+TT_MESH = None
+
+hf_log.set_verbosity_error()
+
+if torch.cuda.is_available():
+    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|>"})
+
+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]] = {
+    "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 = 1
+SAT_BLOCK = 2
+LR_CORE, LR_HEAD = 5e-5, 2e-4
+EMIT_LAMBDA = 0.1
+DEFAULT_SAVE_SEC = 24 * 3600
+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
+
+_HOT_CFG_PATH = pathlib.Path("/workspace/hot_config.json")
+_hot_cache = {"mtime": 0.0, "data": {}}
+_MASK_CACHE: Dict[Tuple[str, str, int], torch.Tensor] = {}
+
+# -----------------------------------------------------------------------------
+# Cosmetic bits
+# -----------------------------------------------------------------------------
+class Colors:
+    RESET = "\033[0m"
+    BOLD = "\033[1m"
+    PROMPT = "\033[36m"
+    GEN = "\033[0m"
+    INFO = "\033[90m"
+    WARN = "\033[93m"
+
+
+class SafeProgress:
+    def __init__(self, total, initial=0, unit="tok"):
+        self.total, self.n, self.unit = total, initial, unit
+        self.last_print, self.postfix = initial, {}
+        self.start_time = time.time()
+
+    def update(self, n=1):
+        self.n += n
+        if self.n - self.last_print >= 1_000_000:
+            self._print()
+            self.last_print = self.n
+
+    def set_postfix(self, **kwargs):
+        self.postfix = kwargs
+
+    def _print(self):
+        elapsed = 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.")
+
+
+# -----------------------------------------------------------------------------
+# Runtime / backend helpers
+# -----------------------------------------------------------------------------
+def is_tt() -> bool:
+    return BACKEND == "tt"
+
+
+def is_cuda() -> bool:
+    return BACKEND == "cuda"
+
+
+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:
+        tz_name = "BST"
+        uk_time = utc_now + timedelta(hours=1)
+    else:
+        tz_name = "GMT"
+        uk_time = utc_now
+    return uk_time.strftime(f"%Y-%m-%d %H:%M:%S {tz_name}")
+
+
+def init_runtime(device_backend: str, *, for_infer: bool = False, force_tt_infer: bool = False):
+    global BACKEND, DEV, TT_MESH
+
+    device_backend = (device_backend or "auto").lower()
+
+    if device_backend == "auto":
+        if torch.cuda.is_available():
+            device_backend = "cuda"
+        else:
+            device_backend = "cpu"
+
+    if for_infer and device_backend == "tt" and not force_tt_infer:
+        print("[runtime] TT inference was requested, but token-by-token XLA generation is a recompile trap.")
+        print("[runtime] Falling back to CPU for infer. Training on TT is the intended path here.")
+        device_backend = "cpu"
+
+    if device_backend == "tt":
+        if not TORCH_XLA_OK:
+            raise RuntimeError(
+                "TT backend requested but torch_xla / TT-XLA is not available. "
+                "Use the TT-XLA image or install the pjrt-plugin-tt wheel."
+            )
+
+        try:
+            xr.set_device_type("TT")
+        except Exception:
+            pass
+
+        os.environ.setdefault("PJRT_DEVICE", "TT")
+        os.environ.setdefault("XLA_STABLEHLO_COMPILE", "1")
+
+        BACKEND = "tt"
+        DEV = torch_xla.device()
+        TT_MESH = None
+
+        try:
+            torch_xla.set_custom_compile_options({
+                "fp32_dest_acc_en": True,
+                "math_fidelity": "hifi4",
+            })
+        except Exception:
+            pass
+
+        print(f"[runtime] backend=tt device={DEV}")
+        return
+
+    if device_backend == "cuda":
+        if not torch.cuda.is_available():
+            raise RuntimeError("CUDA backend requested but CUDA is not available.")
+        BACKEND = "cuda"
+        DEV = torch.device("cuda")
+        TT_MESH = None
+        print(f"[runtime] backend=cuda device={DEV}")
+        return
+
+    BACKEND = "cpu"
+    DEV = torch.device("cpu")
+    TT_MESH = None
+    print(f"[runtime] backend=cpu device={DEV}")
+
+
+def tt_sync(wait: bool = True):
+    if is_tt():
+        torch_xla.sync(wait=wait)
+
+
+def maybe_clear_tt_cache():
+    if is_tt():
+        try:
+            xr.clear_computation_cache()
+        except Exception:
+            pass
+
+
+def optimizer_step_backend(optimizer: torch.optim.Optimizer):
+    if is_tt():
+        optimizer.step()
+        tt_sync(wait=True)
+    else:
+        optimizer.step()
+
+
+def state_dict_to_cpu(sd: Dict[str, Any]) -> Dict[str, Any]:
+    out: Dict[str, Any] = {}
+    for k, v in sd.items():
+        if torch.is_tensor(v):
+            out[k] = v.detach().cpu()
+        else:
+            out[k] = v
+    return out
+
+
+def rng_state():
+    if is_cuda():
+        try:
+            return torch.cuda.get_rng_state(DEV)
+        except TypeError:
+            return torch.cuda.get_rng_state()
+    return torch.get_rng_state()
+
+
+def _state_dict_cpu(module: nn.Module) -> Dict[str, torch.Tensor]:
+    raw = _strip_compiled_prefix(module.state_dict())
+    return state_dict_to_cpu(raw)
+
+
+def _state_dict_load(module: nn.Module, sd: Dict[str, torch.Tensor]):
+    module.load_state_dict(_strip_compiled_prefix(sd), strict=True)
+
+
+# -----------------------------------------------------------------------------
+# Status / utilities
+# -----------------------------------------------------------------------------
+def write_status(step, seen_tok, loss, batch, block, tok_per_sec, phase):
+    try:
+        with open(STATUS_FILE, "w") as f:
+            json.dump(
+                {
+                    "step": step,
+                    "seen_tok": seen_tok,
+                    "loss": float(loss) if loss is not None else None,
+                    "batch": batch,
+                    "block": block,
+                    "tok_per_sec": tok_per_sec,
+                    "phase": phase,
+                    "updated": time.time(),
+                    "target_tok": 35737600000,
+                    "backend": BACKEND,
+                },
+                f,
+            )
+    except Exception:
+        pass
+
+
+def show_status():
+    try:
+        with open(STATUS_FILE) as f:
+            s = json.load(f)
+        age = time.time() - s.get("updated", 0)
+        target = s.get("target_tok") or 35737600000
+        remaining = target - s.get("seen_tok", 0)
+        eta_sec = remaining / max(s.get("tok_per_sec", 1), 1)
+        eta_days = eta_sec / 86400
+        print(
+            f"Step: {s.get('step', '?'):,} | Tokens: {s.get('seen_tok', 0)/1e9:.2f}B / {target/1e9:.1f}B | "
+            f"Loss: {s.get('loss', 0):.4f}"
+        )
+        print(
+            f"Speed: {s.get('tok_per_sec', 0):.0f} tok/s | B={s.get('batch')} L={s.get('block')} | "
+            f"ETA: {eta_days:.1f} days | {age:.0f}s ago | backend={s.get('backend', '?')}"
+        )
+    except FileNotFoundError:
+        print("No status file. Training not running?")
+    except Exception as e:
+        print(f"Error: {e}")
+
+
+def get_hot_datasets(default):
+    try:
+        if _HOT_CFG_PATH.exists():
+            mt = _HOT_CFG_PATH.stat().st_mtime
+            if mt > _hot_cache["mtime"]:
+                _hot_cache["data"] = json.loads(_HOT_CFG_PATH.read_text())
+                _hot_cache["mtime"] = mt
+            cfg = _hot_cache["data"]
+            if "datasets" in cfg:
+                ds = cfg["datasets"]
+                if isinstance(ds, list):
+                    ds = ",".join(ds)
+                print(f"[HOT] Using: {ds[:60]}...")
+                return ds
+    except Exception as e:
+        print(f"[HOT] Error: {e}")
+    return default
+
+
+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("┌─────────────────────────────────────────┐")
+    print("│ TUNEABLE ATTENTION CONFIG               │")
+    print("├─────────────────────────────────────────┤")
+    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("└─────────────────────────────────────────┘")
+
+
+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=map_location)
+    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: Optional[int]):
+    if max_ckpts is None or max_ckpts <= 0:
+        return
+    try:
+        for tmp in save_dir.glob("*.pt.tmp"):
+            try:
+                tmp.unlink()
+                print(f"  [prune] cleaned stale tmp {tmp.name}")
+            except Exception:
+                pass
+        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 _strip_compiled_prefix(sd):
+    return {k.replace("_orig_mod.", ""): v for k, v in sd.items()}
+
+
+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")
+
+    if is_tt():
+        tt_sync(wait=True)
+
+    state = {
+        "core": _state_dict_cpu(core),
+        "ar": _state_dict_cpu(ar_h),
+        "sat": _state_dict_cpu(sat_h),
+        "opt": opt.state_dict(),
+        "scaler": scaler.state_dict() if hasattr(scaler, "state_dict") else {},
+        "cfg": meta.get("cfg"),
+        "tokenizer_id": TOKENIZER_ID,
+        "tie_weights": meta.get("tie_weights", False),
+        "backend": BACKEND,
+        **{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"],
+                "block_size": meta.get("block_size"),
+                "batch_size": meta.get("batch_size"),
+                "seen_tok": meta.get("seen_tok"),
+            }
+        )
+    )
+    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}")
+    _state_dict_load(core, ck["core"])
+    _state_dict_load(ar_h, ck["ar"])
+    _state_dict_load(sat_h, ck["sat"])
+    try:
+        opt.load_state_dict(ck["opt"])
+    except Exception:
+        pass
+    if ck.get("scaler"):
+        try:
+            scaler.load_state_dict(ck["scaler"])
+        except Exception:
+            pass
+    return ck.get("step", 0), ck.get("seen_tok", 0), ck.get("wall_time", time.time()), ck.get("block_size")
+
+
+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
+
+
+# -----------------------------------------------------------------------------
+# AMP helpers
+# -----------------------------------------------------------------------------
+try:
+    from torch.amp import autocast as _ac, GradScaler  # type: ignore
+except Exception:
+    from torch.cuda.amp import autocast as _ac, GradScaler  # type: ignore
+
+
+def _auto_amp_dtype():
+    if is_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):
+    if not (enabled and is_cuda()):
+        return nullcontext()
+    try:
+        return _ac(device_type="cuda", dtype=_auto_amp_dtype())
+    except TypeError:
+        return _ac(dtype=_auto_amp_dtype())
+
+
+# -----------------------------------------------------------------------------
+# Dataset stream helpers
+# -----------------------------------------------------------------------------
+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))
+
+    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)
+    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 % 2 == 0 and len(sources) > 1:
+                src_idx = (src_idx + 1) % len(sources)
+
+
+# -----------------------------------------------------------------------------
+# TT-friendly manual cross-entropy
+# -----------------------------------------------------------------------------
+def token_ce_loss(
+    logits: torch.Tensor,
+    targets: torch.Tensor,
+    *,
+    label_smoothing: float = 0.0,
+    ignore_index: int = -100,
+) -> torch.Tensor:
+    """
+    Manual token cross-entropy using log_softmax + gather.
+
+    This avoids `nn.CrossEntropyLoss`, which Tenstorrent's own training utilities
+    work around for language-model-style logits.
+    """
+    log_probs = F.log_softmax(logits, dim=-1)
+    valid = targets.ne(ignore_index)
+    safe_targets = torch.where(valid, targets, torch.zeros_like(targets))
+
+    nll = -log_probs.gather(dim=-1, index=safe_targets.unsqueeze(-1)).squeeze(-1)
+    if label_smoothing > 0.0:
+        smooth = -log_probs.mean(dim=-1)
+        per_tok = (1.0 - label_smoothing) * nll + label_smoothing * smooth
+    else:
+        per_tok = nll
+
+    per_tok = per_tok * valid.to(per_tok.dtype)
+    denom = valid.sum().clamp_min(1).to(per_tok.dtype)
+    return per_tok.sum() / denom
+
+
+# -----------------------------------------------------------------------------
+# ALiBi / masks
+# -----------------------------------------------------------------------------
+@torch._dynamo.disable
+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)
+
+
+@torch._dynamo.disable
+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
+
+
+def causal_mask(n):
+    key = (BACKEND, "causal", n)
+    if key not in _MASK_CACHE:
+        _MASK_CACHE[key] = torch.triu(torch.full((1, 1, n, n), float("-inf"), device=DEV), 1)
+    return _MASK_CACHE[key]
+
+
+def sat_mask(n, block=SAT_BLOCK):
+    key = (BACKEND, f"sat_{block}", n)
+    if key not in _MASK_CACHE:
+        idx = torch.arange(n, device=DEV)
+        grp = idx.unsqueeze(0) // block
+        allow = (grp.T == grp) | (grp.T > grp)
+        _MASK_CACHE[key] = torch.where(allow, 0.0, float("-inf")).unsqueeze(0).unsqueeze(0)
+    return _MASK_CACHE[key]
+
+
+def sat_mask_cached(new_len: int, cached_len: int, block=SAT_BLOCK):
+    total_len = cached_len + new_len
+    return torch.zeros((1, 1, new_len, total_len), device=DEV)
+
+
+# -----------------------------------------------------------------------------
+# Model
+# -----------------------------------------------------------------------------
+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).to(att.dtype)[:, :, -q.size(2):, :]
+        if mask is not None:
+            att = att + mask.to(att.dtype)
+
+        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 = nn.LayerNorm(d)
+        self.ln2 = 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
+        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)
+
+
+# -----------------------------------------------------------------------------
+# Train helpers
+# -----------------------------------------------------------------------------
+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 _move_model_for_backend(*mods, tt_bf16: bool):
+    for m in mods:
+        if is_tt():
+            if tt_bf16:
+                m.to(device=DEV, dtype=torch.bfloat16)
+            else:
+                m.to(device=DEV)
+        else:
+            m.to(device=DEV)
+
+
+def _losses_for_batch(args, core, ar_h, sat_h, ids):
+    tgt_ar = ids
+
+    h_ar = core(ids, causal_mask(ids.size(1)))
+    logits_ar = ar_h(h_ar)[:, :-1]
+    loss_ar = token_ce_loss(logits_ar, tgt_ar[:, 1:], label_smoothing=args.label_smoothing)
+
+    if args.ar_only:
+        return loss_ar, loss_ar, None
+
+    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 = token_ce_loss(logits_sat, tgt_sat, label_smoothing=args.label_smoothing)
+    if gate is not None:
+        loss_sat = loss_sat + EMIT_LAMBDA * F.cross_entropy(
+            gate,
+            torch.ones(ids.size(0), device=DEV, dtype=torch.long),
+        )
+    return loss_ar + loss_sat, loss_ar, loss_sat
+
+
+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,
+    )
+
+    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
+    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}")
+    print(f"[{phase_name}] AR_ONLY={args.ar_only}, TIE_WEIGHTS={tie_weights}, STREAMING={streaming}, backend={BACKEND}")
+
+    step_start_time = time.monotonic()
+    tok_per_sec_avg = 0.0
+
+    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, dtype=torch.long)
+        batch_accum = []
+
+        try:
+            if is_tt():
+                opt.zero_grad(set_to_none=True)
+                loss, loss_ar, loss_sat = _losses_for_batch(args, core, ar_h, sat_h, ids)
+                if args.max_grad_norm > 0:
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(list(core.parameters()) + list(ar_h.parameters()) + list(sat_h.parameters()), args.max_grad_norm)
+                else:
+                    loss.backward()
+                optimizer_step_backend(opt)
+                opt.zero_grad(set_to_none=True)
+                tt_sync(wait=True)
+                loss_value = float(loss.detach().cpu())
+            else:
+                with amp(args.amp):
+                    loss, loss_ar, loss_sat = _losses_for_batch(args, core, ar_h, sat_h, ids)
+                scaler.scale(loss).backward()
+                scaler.unscale_(opt)
+                if args.max_grad_norm > 0:
+                    nn.utils.clip_grad_norm_(list(core.parameters()) + list(ar_h.parameters()) + list(sat_h.parameters()), args.max_grad_norm)
+                scaler.step(opt)
+                scaler.update()
+                opt.zero_grad(set_to_none=True)
+                loss_value = float(loss.item())
+        except RuntimeError as e:
+            msg = str(e).lower()
+
+            if is_tt():
+                tt_sync(wait=True)
+                raise RuntimeError(
+                    f"TT training step failed. Keep shapes static and reduce --block / --batch_size manually. "
+                    f"Original error: {e}"
+                ) from e
+
+            if "out of memory" in msg or "cuda error" in msg:
+                batch_accum = []
+                opt.zero_grad(set_to_none=True)
+                if is_cuda():
+                    torch.cuda.empty_cache()
+                    torch.cuda.synchronize()
+                if BATCH > 1:
+                    print(f"\n[{phase_name} OOM] Reducing Batch: {BATCH} -> {BATCH - 1}")
+                    BATCH -= 1
+                    time.sleep(3)
+                    continue
+                if args.auto_grow:
+                    smaller = [b for b in grow_plan if b < BLOCK]
+                    new_block = smaller[-1] if smaller else max(128, BLOCK // 2)
+                else:
+                    new_block = max(128, BLOCK // 2)
+                print(f"\n[{phase_name} OOM] Reducing Block: {BLOCK} -> {new_block}")
+                BLOCK = new_block
+                time.sleep(3)
+                continue
+            raise
+
+        step += 1
+        toks_processed = BLOCK * BATCH
+        seen_tok += toks_processed
+        pbar.update(toks_processed)
+        pbar.set_postfix(loss=f"{loss_value:.3f}", B=BATCH, L=BLOCK)
+
+        step_elapsed = time.monotonic() - step_start_time
+        tok_per_sec_now = toks_processed / step_elapsed if step_elapsed > 0 else 0
+        tok_per_sec_avg = 0.9 * tok_per_sec_avg + 0.1 * tok_per_sec_now if tok_per_sec_avg > 0 else tok_per_sec_now
+        step_start_time = time.monotonic()
+
+        write_status(step, seen_tok, loss_value, BATCH, BLOCK, tok_per_sec_avg, phase_name)
+
+        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(),
+                        "tie_weights": tie_weights,
+                        "block_size": BLOCK,
+                        "batch_size": BATCH,
+                    },
+                )
+                _prune_checkpoints(pathlib.Path(args.save_dir), phase_name, max_ckpts)
+                last_save_mono = now_mono
+
+        if args.auto_grow and not is_tt():
+            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 is_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(),
+            "tie_weights": tie_weights,
+            "block_size": BLOCK,
+            "batch_size": BATCH,
+        },
+    )
+
+    return step, seen_tok, time.time()
+
+
+def train(args):
+    init_runtime(args.device_backend, for_infer=False)
+
+    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)
+    ar_h = ARHead(cfg["d"], tie_weights=tie_weights, embedding_weight=core.emb.weight if tie_weights else None)
+    sat_h = SATHead(cfg["d"], mode="var")
+
+    _move_model_for_backend(core, ar_h, sat_h, tt_bf16=args.tt_bf16)
+    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 is_cuda()))
+    start_step, seen_tok, last_wall, _resumed_block = 0, 0, None, None
+
+    if args.resume and not args.fresh:
+        start_step, seen_tok, last_wall, _resumed_block = load_ckpt(pathlib.Path(args.resume), core, ar_h, sat_h, opt, scaler)
+        print(f"Resumed from step {start_step}" + (f", block_size={_resumed_block}" if _resumed_block else ""))
+
+    if args.compile and is_cuda():
+        print("[torch.compile] Compiling model for CUDA...")
+        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.")
+    elif args.compile and is_tt():
+        print("[torch.compile] Ignored on TT backend. Use TT-XLA runtime instead.")
+
+    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,
+        (_resumed_block if _resumed_block and args.auto_grow else None) or 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,
+            "block_size": args.block or DEFAULT_BLOCK,
+            "batch_size": args.batch_size or DEFAULT_BATCH,
+        },
+    )
+    print("🎉 All Training Complete")
+
+
+# -----------------------------------------------------------------------------
+# Sampling / inference
+# -----------------------------------------------------------------------------
+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.to(logits.dtype))
+    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).to(probs.dtype)
+        )
+    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):
+    init_runtime(args.device_backend, for_infer=True, force_tt_infer=args.force_tt_infer)
+
+    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("┌─────────────────────────────────────────────────┐")
+    print(f"│ INFERENCE @ {uk_time:<35s} │")
+    print("├─────────────────────────────────────────────────┤")
+    print(f"│ Checkpoint: {ckpt_name:<35s} │")
+    print("└─────────────────────────────────────────────────┘")
+    print_expansion_info(cfg, tie_weights)
+
+    core = Encoder(cfg, tie_weights=tie_weights)
+    ar_h = ARHead(cfg["d"], tie_weights=tie_weights, embedding_weight=core.emb.weight if tie_weights else None)
+    sat_h = SATHead(cfg["d"])
+
+    core.load_state_dict(sd["core"])
+    ar_h.load_state_dict(sd["ar"])
+    sat_h.load_state_dict(sd["sat"])
+
+    if args.fp16 and is_cuda():
+        core.half()
+        ar_h.half()
+        sat_h.half()
+        print(f"{Colors.INFO}Using fp16 inference{Colors.RESET}")
+
+    _move_model_for_backend(core, ar_h, sat_h, tt_bf16=args.tt_bf16)
+    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, dtype=torch.long)
+    if ids.size(1) == 0:
+        ids = torch.tensor([[EOS]], device=DEV, dtype=torch.long)
+        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}) on {BACKEND}...{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)
+
+    if is_tt():
+        tt_sync(wait=True)
+
+    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].detach().cpu().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 | backend={BACKEND}]{Colors.RESET}")
+
+
+# -----------------------------------------------------------------------------
+# CLI
+# -----------------------------------------------------------------------------
+def main():
+    ap = argparse.ArgumentParser(description="AGILLM Expansion Ratio Testing - TT/CUDA/CPU single-file trainer")
+
+    ap.add_argument("--device_backend", choices=["auto", "cpu", "cuda", "tt"], default=REQUESTED_BACKEND)
+    ap.add_argument("--force_tt_infer", action="store_true", help="Allow TT inference even though it recompiles on changing sequence lengths")
+
+    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="CUDA only. TT ignores this.")
+    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("--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)
+
+    tr.add_argument("--tt_bf16", action="store_true", default=True, help="Use bf16 weights on TT (recommended)")
+    tr.add_argument("--no-tt-bf16", dest="tt_bf16", action="store_false")
+    tr.add_argument("--label_smoothing", type=float, default=0.1)
+    tr.add_argument("--max_grad_norm", type=float, default=1.0)
+
+    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")
+    inf.add_argument("--fp16", action="store_true", help="Use fp16 inference (CUDA only)")
+    inf.add_argument("--tt_bf16", action="store_true", default=True, help="Use bf16 weights on TT if TT infer is forced")
+    inf.add_argument("--no-tt-bf16", dest="tt_bf16", action="store_false")
+
+    st = sub.add_parser("status")
+
+    args = ap.parse_args()
+
+    if args.cmd == "train":
+        train(args)
+    elif args.cmd == "infer":
+        infer(args)
+    else:
+        show_status()
+
+
+if __name__ == "__main__":
+    main()