overlay/scripts/chat.py

"""Interactive chat REPL for HYDRA.

Usage:
    python scripts/chat.py                  # auto-select best checkpoint
    python scripts/chat.py --ckpt PATH      # explicit checkpoint
    python scripts/chat.py --sft            # prefer sft_final.pt
    python scripts/chat.py --random         # skip ckpt, use random weights

HONESTY: model is ~7.5M params at d_model=256/n_layer=4. Expect incoherent
output. This REPL validates the *interface* — tokenizer roundtrip, generation
loop, stop-token handling, conversation history truncation. Coherent dialogue
is not a goal at this scale.

Slash commands:
    /reset   clear conversation history
    /quit    exit
    /temp X  set temperature (default 0.8)
    /topk K  set top-k (default 40)
    /topp P  set top-p (default 0.9)
    /max N   set max new tokens per turn (default 200)
    /rep R   set repetition penalty (default 1.1)
    /sys S   set a system prefix prepended to every turn
    /info    print current settings + checkpoint path
"""

from __future__ import annotations

import argparse
import os
import sys
import time
from dataclasses import asdict
from pathlib import Path

# Make repo root importable when invoked as `python scripts/chat.py`.
_REPO_ROOT = Path(__file__).resolve().parent.parent
if str(_REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(_REPO_ROOT))

import torch  # noqa: E402

from hydra.config import USE_MDLM, MDLM_MASK_ID  # noqa: E402
from hydra.mdlm_decode import mdlm_next_token_logits  # noqa: E402


def _next_token_logits(model, x: torch.Tensor) -> torch.Tensor:
    """Return next-token logits, branching on MDLM training mode.

    Audit 2026-05-09 issue #16: MDLM-trained models predict masked positions,
    not next tokens. Route through mdlm_next_token_logits if MDLM is on.
    """
    if USE_MDLM:
        mask_id = MDLM_MASK_ID
        if mask_id < 0:
            mask_id = int(getattr(model.config, "vocab_size", 0)) - 1
        return mdlm_next_token_logits(
            model,
            x,
            mask_id=mask_id,
            vocab_size=int(model.config.vocab_size),
        )
    out = model(x, targets=None)
    if out.dim() == 3:
        return out[:, -1, :].float()
    return out.float()


# Chat template — plain-text fallback (see .omc/chat_plan.md).
# If the SFT agent later reserves special tokens, redefine USER_TAG /
# ASSISTANT_TAG / END_TAG and the stop-string accordingly.
USER_TAG = "User:"
ASSISTANT_TAG = "Assistant:"
END_TAG = "\nUser:"  # stop-string matched on decoded output

CKPT_DIR = Path(os.path.expanduser("~/.cache/autoresearch/ckpts"))
CKPT_CANDIDATES_PRETRAIN = ["pretrain_final.pt", "latest.pt"]
CKPT_CANDIDATES_SFT = ["sft_final.pt"]


# ---------------------------------------------------------------------------
# Checkpoint resolution
# ---------------------------------------------------------------------------

def resolve_checkpoint(explicit: str | None, prefer_sft: bool) -> Path | None:
    """Return Path to checkpoint file, or None if nothing found.

    Order:
      1. `explicit` if provided and exists.
      2. If prefer_sft: sft_final.pt -> pretrain_final.pt -> latest.pt.
      3. Else: sft_final.pt (if exists) -> pretrain_final.pt -> latest.pt.
    """
    if explicit:
        p = Path(os.path.expanduser(explicit))
        if p.exists():
            return p
        print(f"[WARN] --ckpt {p} does not exist; falling through to auto-select.", file=sys.stderr)

    # Task spec: prefer sft_final.pt if it exists; otherwise pretrain_final.pt
    # then latest.pt. --sft just makes the preference explicit; it's already
    # the default behavior. We list SFT first in both orderings to honor the
    # spec, since the task description said "prefer sft if exists" by default.
    _ = prefer_sft  # reserved for future "pretrain-only" vs "sft-only" modes
    order = CKPT_CANDIDATES_SFT + CKPT_CANDIDATES_PRETRAIN
    for name in order:
        cand = CKPT_DIR / name
        if cand.exists():
            return cand
    return None


# ---------------------------------------------------------------------------
# Model + tokenizer loading
# ---------------------------------------------------------------------------

def load_model_and_tokenizer(ckpt_path: Path | None, device: torch.device):
    """Build model + tokenizer. If ckpt_path is None, random weights are used.

    Returns (model, tokenizer, meta) where meta is a dict with 'ckpt',
    'step', 'val_bpb' etc. for /info display.
    """
    from hydra.config import PostSemClawConfig
    from hydra.model import PostSemClawModel
    from prepare import Tokenizer

    tokenizer = Tokenizer.from_directory()
    vocab_size = tokenizer.get_vocab_size()
    print(f"[chat] Tokenizer loaded (vocab={vocab_size:,})")

    meta: dict = {"ckpt": str(ckpt_path) if ckpt_path else "<random>", "step": None, "val_bpb": None}

    # Build config. If checkpoint provides one, use it; else use env-var defaults.
    ckpt_state = None
    config_kwargs: dict = {}
    if ckpt_path is not None:
        print(f"[chat] Loading checkpoint: {ckpt_path}")
        ckpt_state = torch.load(ckpt_path, map_location=device, weights_only=False)
        cfg_dict = ckpt_state.get("config")
        if isinstance(cfg_dict, dict):
            # Filter to kwargs PostSemClawConfig actually accepts.
            allowed = set(PostSemClawConfig.__dataclass_fields__.keys())
            config_kwargs = {k: v for k, v in cfg_dict.items() if k in allowed}
        meta["step"] = ckpt_state.get("step")
        meta["val_bpb"] = ckpt_state.get("val_bpb") or ckpt_state.get("bpb")

    # Env-var defaults are applied by PostSemClawConfig field defaults; but the
    # training run builds the config explicitly from hydra.config module-level
    # constants. We mirror that here so the random-weights path aligns with
    # what train.py would instantiate for the same env.
    if not config_kwargs:
        from hydra.config import (  # noqa: E402
            D_MODEL, D_STATE, ENGRAM_KEY_DIM, ENGRAM_LAYER_IDX,
            ENGRAM_N_COLUMNS, EXPAND, HEADDIM, N_HEADS, N_LAYER,
        )
        from prepare import MAX_SEQ_LEN  # noqa: E402
        config_kwargs = dict(
            sequence_len=MAX_SEQ_LEN,
            vocab_size=vocab_size,
            n_layer=N_LAYER,
            d_model=D_MODEL,
            d_state=D_STATE,
            headdim=HEADDIM,
            n_heads=N_HEADS,
            expand=EXPAND,
            engram_n_columns=ENGRAM_N_COLUMNS,
            engram_key_dim=ENGRAM_KEY_DIM,
            engram_layer_idx=ENGRAM_LAYER_IDX,
        )

    # Build model on meta device then materialize — matches training.py path.
    with torch.device("meta"):
        model = PostSemClawModel(PostSemClawConfig(**config_kwargs))
    model.to_empty(device=device)
    model.init_weights()

    if ckpt_state is not None and "model_state_dict" in ckpt_state:
        # strict=False: the model has non-parameter buffers (SDR retina loaded
        # from npz, HTM Rust-side state, engram EMA stats) that may not be in
        # the state_dict. missing/unexpected-key warnings are expected and OK.
        missing, unexpected = model.load_state_dict(
            ckpt_state["model_state_dict"], strict=False
        )
        if missing:
            print(f"[chat] Note: {len(missing)} missing key(s) in state_dict (expected for HTM/SDR buffers).")
        if unexpected:
            print(f"[chat] Note: {len(unexpected)} unexpected key(s) in state_dict.")
    elif ckpt_path is None:
        print("[chat] [WARN] NO CHECKPOINT — using random weights. Output will be gibberish.", file=sys.stderr)

    model.eval()
    return model, tokenizer, meta


# ---------------------------------------------------------------------------
# Generation
# ---------------------------------------------------------------------------

def generate_stream(
    model,
    tokenizer,
    prompt_ids: list[int],
    *,
    max_new_tokens: int,
    temperature: float,
    top_k: int,
    top_p: float,
    repetition_penalty: float,
    stop_strings: tuple[str, ...],
    max_seq_len: int,
    device: torch.device,
    rep_window: int = 64,
):
    """Yield decoded-text chunks as tokens are generated.

    Truncates `prompt_ids` to the last `max_seq_len` tokens if needed. Stops
    early when any `stop_strings` substring appears in the newly-decoded
    continuation.
    """
    from scripts.sample_utils import sample_token

    # Truncate prompt to window.
    if len(prompt_ids) > max_seq_len:
        prompt_ids = prompt_ids[-max_seq_len:]

    ctx = torch.tensor([prompt_ids], device=device, dtype=torch.long)
    generated: list[int] = []
    # Track already-streamed byte length so we can detect when the decoded
    # string has grown (BPE tokens may decode to multi-char strings mid-merge).
    streamed_chars = 0
    accumulated_text = ""

    autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)

    for _ in range(max_new_tokens):
        with torch.no_grad(), autocast_ctx:
            # Audit 2026-05-09 #16: route through MDLM contract if active.
            last_logits = _next_token_logits(model, ctx)[0]

        recent = generated[-rep_window:] if generated else None
        next_id = sample_token(
            last_logits,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            repetition_penalty=repetition_penalty,
            recent_tokens=recent,
        )
        generated.append(next_id)

        # Decode everything so-far then diff — BPE decoding is not token-local,
        # so a per-token decode can drop bytes.
        new_text = tokenizer.decode(generated)
        delta = new_text[streamed_chars:]
        if delta:
            streamed_chars = len(new_text)
            accumulated_text = new_text
            yield delta

        # Stop-string check.
        hit_stop = any(s and s in accumulated_text for s in stop_strings)
        if hit_stop:
            break

        # Advance context. If we've filled the window, drop oldest token.
        ctx = torch.cat([ctx, torch.tensor([[next_id]], device=device, dtype=torch.long)], dim=1)
        if ctx.size(1) > max_seq_len:
            ctx = ctx[:, -max_seq_len:]

    # Final accumulated text is also returned for history tracking.
    return accumulated_text  # noqa: B901  (generator return for history)


def _consume_stream_with_print(stream_gen):
    """Iterate a generator, print each chunk, return the full text.

    Replacement for a naïve list(stream) since `generate_stream` is a generator
    that yields then returns the final text.
    """
    collected = []
    try:
        while True:
            chunk = next(stream_gen)
            collected.append(chunk)
            sys.stdout.write(chunk)
            sys.stdout.flush()
    except StopIteration as stop:
        # stop.value holds the return value of the generator.
        final = stop.value
        if final is not None:
            return final
        return "".join(collected)


# ---------------------------------------------------------------------------
# REPL
# ---------------------------------------------------------------------------

def build_prompt(system: str, history: list[tuple[str, str]], user_msg: str) -> str:
    """Assemble the text prompt fed to the tokenizer."""
    parts: list[str] = []
    if system:
        parts.append(system.rstrip() + "\n")
    for u, a in history:
        parts.append(f"{USER_TAG} {u}\n{ASSISTANT_TAG} {a}\n")
    parts.append(f"{USER_TAG} {user_msg}\n{ASSISTANT_TAG}")
    return "".join(parts)


def run_repl(
    model,
    tokenizer,
    meta: dict,
    *,
    device: torch.device,
    max_seq_len: int,
) -> None:
    settings = {
        "temperature": float(os.environ.get("HYDRA_CHAT_TEMP", "0.8")),
        "top_k": int(os.environ.get("HYDRA_CHAT_TOPK", "40")),
        "top_p": float(os.environ.get("HYDRA_CHAT_TOPP", "0.9")),
        "max_new_tokens": int(os.environ.get("HYDRA_CHAT_MAX", "200")),
        "repetition_penalty": float(os.environ.get("HYDRA_CHAT_REP", "1.1")),
        "system": os.environ.get("HYDRA_CHAT_SYSTEM", ""),
    }
    history: list[tuple[str, str]] = []

    print()
    print("=" * 60)
    print("HYDRA chat REPL")
    print(f"  checkpoint: {meta['ckpt']}")
    if meta.get("step") is not None:
        print(f"  step: {meta['step']}")
    if meta.get("val_bpb") is not None:
        print(f"  val_bpb: {meta['val_bpb']}")
    print("  type /info for settings, /quit to exit")
    print("=" * 60)
    print()

    while True:
        try:
            line = input(f"{USER_TAG} ")
        except (EOFError, KeyboardInterrupt):
            print()
            return

        line = line.rstrip()
        if not line:
            continue

        if line.startswith("/"):
            cmd, *rest = line.split(maxsplit=1)
            arg = rest[0] if rest else ""
            if cmd == "/quit" or cmd == "/exit":
                return
            elif cmd == "/reset":
                history = []
                print("[reset]")
                continue
            elif cmd == "/info":
                print(f"[info] ckpt={meta['ckpt']} settings={settings} history_turns={len(history)}")
                continue
            elif cmd == "/temp":
                try:
                    settings["temperature"] = float(arg)
                    print(f"[temp={settings['temperature']}]")
                except ValueError:
                    print(f"[err] /temp needs a float, got {arg!r}")
                continue
            elif cmd == "/topk":
                try:
                    settings["top_k"] = int(arg)
                    print(f"[topk={settings['top_k']}]")
                except ValueError:
                    print(f"[err] /topk needs an int, got {arg!r}")
                continue
            elif cmd == "/topp":
                try:
                    settings["top_p"] = float(arg)
                    print(f"[topp={settings['top_p']}]")
                except ValueError:
                    print(f"[err] /topp needs a float, got {arg!r}")
                continue
            elif cmd == "/max":
                try:
                    settings["max_new_tokens"] = int(arg)
                    print(f"[max={settings['max_new_tokens']}]")
                except ValueError:
                    print(f"[err] /max needs an int, got {arg!r}")
                continue
            elif cmd == "/rep":
                try:
                    settings["repetition_penalty"] = float(arg)
                    print(f"[rep={settings['repetition_penalty']}]")
                except ValueError:
                    print(f"[err] /rep needs a float, got {arg!r}")
                continue
            elif cmd == "/sys":
                settings["system"] = arg
                print(f"[sys set, {len(arg)} chars]")
                continue
            else:
                print(f"[err] unknown command {cmd!r}. Try /info /reset /quit.")
                continue

        # Normal chat turn.
        prompt_text = build_prompt(settings["system"], history, line)
        prompt_ids = tokenizer.encode(prompt_text)

        sys.stdout.write(f"{ASSISTANT_TAG} ")
        sys.stdout.flush()

        stream = generate_stream(
            model, tokenizer, prompt_ids,
            max_new_tokens=settings["max_new_tokens"],
            temperature=settings["temperature"],
            top_k=settings["top_k"],
            top_p=settings["top_p"],
            repetition_penalty=settings["repetition_penalty"],
            stop_strings=(END_TAG,),
            max_seq_len=max_seq_len,
            device=device,
        )
        response_text = _consume_stream_with_print(stream)
        if not response_text.endswith("\n"):
            sys.stdout.write("\n")
            sys.stdout.flush()

        # Strip trailing stop marker from the remembered history.
        clean = response_text
        if END_TAG in clean:
            clean = clean.split(END_TAG, 1)[0]
        clean = clean.strip()
        history.append((line, clean))


# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------

def _parse_args(argv: list[str] | None = None) -> argparse.Namespace:
    p = argparse.ArgumentParser(description="HYDRA chat REPL")
    p.add_argument("--ckpt", type=str, default=None,
                   help="Path to checkpoint (.pt). If omitted, auto-select.")
    p.add_argument("--sft", action="store_true",
                   help="Prefer an SFT checkpoint if available.")
    p.add_argument("--random", action="store_true",
                   help="Skip checkpoint load; use random weights.")
    p.add_argument("--device", type=str, default=None,
                   help="Torch device (default: cuda if available else cpu).")
    return p.parse_args(argv)


def main(argv: list[str] | None = None) -> int:
    args = _parse_args(argv)

    if args.device:
        device = torch.device(args.device)
    elif torch.cuda.is_available():
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")
        print("[chat] [WARN] CUDA not available; HYDRA's HTM/Mamba kernels may fail on CPU.", file=sys.stderr)

    ckpt_path: Path | None
    if args.random:
        ckpt_path = None
    else:
        ckpt_path = resolve_checkpoint(args.ckpt, args.sft)

    t0 = time.time()
    model, tokenizer, meta = load_model_and_tokenizer(ckpt_path, device)
    dt = time.time() - t0
    print(f"[chat] Model ready in {dt:.1f}s on {device}")

    from prepare import MAX_SEQ_LEN
    run_repl(model, tokenizer, meta, device=device, max_seq_len=MAX_SEQ_LEN)
    return 0


if __name__ == "__main__":
    sys.exit(main())