openjev/formatting.py

from __future__ import annotations
import json
import torch


def encode_piece(tok, text: str) -> list[int]:
    return tok(text, add_special_tokens=False).input_ids


def state_to_text(state) -> str:
    if isinstance(state, str):
        return state
    return json.dumps(state, ensure_ascii=False, sort_keys=True, separators=(",", ":"))


def _clip_head_tail(ids: list[int], limit: int) -> list[int]:
    """Deterministically keep evidence from both ends of a long field."""
    if limit <= 0:
        return []
    if len(ids) <= limit:
        return ids
    head = (limit + 1) // 2
    tail = limit - head
    return ids[:head] + (ids[-tail:] if tail else [])


def _assemble(tok, state_ids, q_ids, option_ids, max_length: int):
    ids = [tok.bos_token_id] if tok.bos_token_id is not None else []
    ids += encode_piece(tok, "State:\n")
    ids += state_ids
    ids += encode_piece(tok, "\n\nQuestion:\n")
    ids += q_ids
    ids += encode_piece(tok, "\n\nOptions:\n")

    option_positions = []
    option_spans = []
    for idx, opt_ids in enumerate(option_ids):
        ids += encode_piece(tok, f"- [{idx}] ")
        span_start = len(ids)
        ids += opt_ids
        span_end = len(ids)
        option_spans.append((span_start, span_end))
        # Represent each option by its final semantic token, after it has seen
        # the full option text but before the newline delimiter.
        option_positions.append(len(ids) - 1)
        ids += encode_piece(tok, "\n")

    ids += encode_piece(tok, "\nDecision:")
    decide_position = len(ids) - 1
    if len(ids) > max_length:
        return None
    return ids, option_positions, decide_position, option_spans


def pack_question(tok, state, question: dict, max_length: int):
    """Pack a decision example without dropping long states.

    Priority order is structural markers/options/question first, then state.
    Long state is head+tail truncated. If question/options themselves are huge,
    they are bounded as a second-stage fallback. Returning None is reserved for
    structurally impossible cases (e.g. too many options for max_length).
    """
    if max_length < 32:
        return None

    state_ids = encode_piece(tok, state_to_text(state))
    q_ids = encode_piece(tok, str(question["instruction"]))
    option_ids = [encode_piece(tok, str(opt["text"])) for opt in question["options"]]
    if not option_ids or any(not x for x in option_ids):
        return None

    was_truncated = False

    # First discover how much room remains for the state while preserving the
    # complete question and option text.
    probe = _assemble(tok, [], q_ids, option_ids, max_length=10**9)
    if probe is None:
        return None
    structural_len = len(probe[0])
    if structural_len <= max_length:
        state_budget = max_length - structural_len
        was_truncated = len(state_ids) > state_budget
        packed = _assemble(tok, _clip_head_tail(state_ids, state_budget), q_ids, option_ids, max_length)
    else:
        was_truncated = True
        # Extremely verbose question/tool schemas: cap semantic fields rather
        # than dropping the sample. Typical training examples never hit this.
        nopt = len(option_ids)
        q_cap = min(len(q_ids), max(16, max_length // 8))
        # Start modestly; then shrink until the structural representation fits.
        opt_cap = max(8, min(96, max_length // max(8, nopt * 2)))
        q_fit = _clip_head_tail(q_ids, q_cap)
        opts_fit = [_clip_head_tail(x, opt_cap) for x in option_ids]
        packed = _assemble(tok, [], q_fit, opts_fit, max_length)
        while packed is None and (q_cap > 8 or opt_cap > 4):
            q_cap = max(8, q_cap // 2)
            opt_cap = max(4, opt_cap // 2)
            q_fit = _clip_head_tail(q_ids, q_cap)
            opts_fit = [_clip_head_tail(x, opt_cap) for x in option_ids]
            packed = _assemble(tok, [], q_fit, opts_fit, max_length)

        if packed is not None:
            # If the shrunken question/options leave room, fill it with state.
            base_len = len(packed[0])
            state_budget = max(0, max_length - base_len)
            was_truncated = was_truncated or len(state_ids) > state_budget
            packed = _assemble(tok, _clip_head_tail(state_ids, state_budget), q_fit, opts_fit, max_length)

    if packed is None:
        return None
    ids, option_positions, decide_position, option_spans = packed
    return {
        "input_ids": ids,
        "option_positions": option_positions,
        "option_spans": option_spans,
        "decide_position": decide_position,
        "target": question.get("target_distribution"),
        "was_truncated": was_truncated,
    }


def collate_packed(tok, packed: list[dict]) -> dict[str, torch.Tensor]:
    if not packed:
        raise RuntimeError("No packable examples")
    bsz = len(packed)
    max_seq = max(len(x["input_ids"]) for x in packed)
    max_opts = max(len(x["option_positions"]) for x in packed)
    pad_id = tok.pad_token_id if tok.pad_token_id is not None else 0
    input_ids = torch.full((bsz, max_seq), pad_id, dtype=torch.long)
    attention_mask = torch.zeros((bsz, max_seq), dtype=torch.long)
    option_positions = torch.zeros((bsz, max_opts), dtype=torch.long)
    option_mask = torch.zeros((bsz, max_opts), dtype=torch.bool)
    decide_positions = torch.zeros((bsz,), dtype=torch.long)
    targets = torch.zeros((bsz, max_opts), dtype=torch.float32)
    option_starts = torch.zeros((bsz, max_opts), dtype=torch.long)
    option_ends = torch.zeros((bsz, max_opts), dtype=torch.long)
    has_targets = all(x.get("target") is not None for x in packed)
    for i, x in enumerate(packed):
        n = len(x["input_ids"])
        m = len(x["option_positions"])
        input_ids[i, :n] = torch.tensor(x["input_ids"], dtype=torch.long)
        attention_mask[i, :n] = 1
        option_positions[i, :m] = torch.tensor(x["option_positions"], dtype=torch.long)
        spans = x.get("option_spans") or [(int(v), int(v)+1) for v in x["option_positions"]]
        option_starts[i, :m] = torch.tensor([a for a, _ in spans], dtype=torch.long)
        option_ends[i, :m] = torch.tensor([b for _, b in spans], dtype=torch.long)
        option_mask[i, :m] = True
        decide_positions[i] = x["decide_position"]
        if has_targets:
            targets[i, :m] = torch.tensor(x["target"], dtype=torch.float32)
    out = {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "option_positions": option_positions,
        "option_starts": option_starts,
        "option_ends": option_ends,
        "option_mask": option_mask,
        "decide_positions": decide_positions,
    }
    if has_targets:
        out["targets"] = targets
    return out


def pack_shared_request(tok, state, questions: list[dict], max_length: int):
    """Pack one common state prefix plus causal question suffixes.

    Every question sees exactly the same serialized/clipped state. This is the
    representation needed for safe KV-prefix reuse at inference time.
    """
    if max_length < 32 or not questions:
        return None
    bos = [tok.bos_token_id] if tok.bos_token_id is not None else []
    state_marker = encode_piece(tok, "State:\n")
    state_ids = encode_piece(tok, state_to_text(state))

    suffixes = []
    max_suffix_len = 0
    for question in questions:
        q_ids = encode_piece(tok, str(question["instruction"]))
        option_ids = [encode_piece(tok, str(opt["text"])) for opt in question["options"]]
        if not option_ids or any(not x for x in option_ids):
            return None
        ids = encode_piece(tok, "\n\nQuestion:\n") + q_ids + encode_piece(tok, "\n\nOptions:\n")
        option_positions = []
        for idx, opt_ids in enumerate(option_ids):
            ids += encode_piece(tok, f"- [{idx}] ")
            ids += opt_ids
            option_positions.append(len(ids) - 1)
            ids += encode_piece(tok, "\n")
        ids += encode_piece(tok, "\nDecision:")
        decide_position = len(ids) - 1
        suffixes.append({
            "input_ids": ids,
            "option_positions": option_positions,
            "decide_position": decide_position,
        })
        max_suffix_len = max(max_suffix_len, len(ids))

    fixed_prefix_len = len(bos) + len(state_marker)
    state_budget = max_length - fixed_prefix_len - max_suffix_len
    if state_budget < 0:
        return None
    clipped = _clip_head_tail(state_ids, state_budget)
    was_truncated = len(clipped) < len(state_ids)
    prefix_ids = bos + state_marker + clipped
    for s in suffixes:
        s["was_truncated"] = was_truncated
        if len(prefix_ids) + len(s["input_ids"]) > max_length:
            return None
    return {"prefix_ids": prefix_ids, "suffixes": suffixes, "was_truncated": was_truncated}