training/fable_router_common.py

"""Pure contract and provenance helpers for the Fable expert-router campaign.

This module intentionally has no Torch dependency.  It is shared by local
unit tests, the Colab/Kaggle entry points, and the GPU structural smoke.
"""
from __future__ import annotations

import hashlib
import json
import struct
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Iterable


EXPECTED_PROJECTIONS = {
    "gate_proj.weight": (512, 2048),
    "up_proj.weight": (512, 2048),
    "down_proj.weight": (2048, 512),
}

DTYPE_BYTES = {"BF16": 2, "F16": 2}


def sha256(path: Path, chunk_size: int = 8 * 1024 * 1024) -> str:
    digest = hashlib.sha256()
    with path.open("rb") as handle:
        while chunk := handle.read(chunk_size):
            digest.update(chunk)
    return digest.hexdigest()


def canonical_json_sha256(value: Any) -> str:
    encoded = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8")
    return hashlib.sha256(encoded).hexdigest()


def read_json(path: Path) -> dict[str, Any]:
    payload = json.loads(path.read_text(encoding="utf-8"))
    if not isinstance(payload, dict):
        raise ValueError(f"expected a JSON object: {path}")
    return payload


def safetensors_header(path: Path) -> tuple[int, dict[str, Any]]:
    with path.open("rb") as handle:
        raw_size = handle.read(8)
        if len(raw_size) != 8:
            raise ValueError(f"truncated Safetensors size header: {path}")
        size = struct.unpack("<Q", raw_size)[0]
        if size < 2 or size > 256 * 1024 * 1024:
            raise ValueError(f"implausible Safetensors header size {size}: {path}")
        raw_header = handle.read(size)
        if len(raw_header) != size:
            raise ValueError(f"truncated Safetensors JSON header: {path}")
    header = json.loads(raw_header)
    if not isinstance(header, dict):
        raise ValueError(f"Safetensors header is not an object: {path}")
    return 8 + size, header


@dataclass(frozen=True)
class BankValidation:
    bank_id: str
    tensor_count: int
    payload_bytes: int
    dtypes: tuple[str, ...]
    layers: tuple[int, ...]

    def as_dict(self) -> dict[str, Any]:
        return {
            "bankId": self.bank_id,
            "tensorCount": self.tensor_count,
            "payloadBytes": self.payload_bytes,
            "dtypes": list(self.dtypes),
            "layers": list(self.layers),
        }


def selected_expert_ids(bank: dict[str, Any], layer: int) -> list[int]:
    raw = bank.get("selectedExperts", {}).get(str(layer))
    if not isinstance(raw, list):
        raise ValueError(f"bank {bank.get('id')} has no selected expert list for layer {layer}")
    ids = [int(item) for item in raw]
    if len(ids) != 32 or len(set(ids)) != 32:
        raise ValueError(f"bank {bank.get('id')} layer {layer} must select 32 unique experts")
    return ids


def expected_tensor_names(bank: dict[str, Any]) -> Iterable[tuple[int, int, str, tuple[int, int]]]:
    for layer in range(30):
        for expert in selected_expert_ids(bank, layer):
            for projection, shape in EXPECTED_PROJECTIONS.items():
                yield layer, expert, f"model.layers.{layer}.mlp.experts.{expert}.{projection}", shape


def validate_bank_header_entries(
    header: dict[str, Any], file_payload: int, bank: dict[str, Any]
) -> BankValidation:
    tensor_entries = {key: value for key, value in header.items() if key != "__metadata__"}
    expected = list(expected_tensor_names(bank))
    expected_names = {row[2] for row in expected}
    actual_names = set(tensor_entries)
    missing = sorted(expected_names - actual_names)
    extra = sorted(actual_names - expected_names)
    if missing or extra:
        raise ValueError(
            f"bank tensor identity mismatch: missing={missing[:3]} ({len(missing)}), "
            f"extra={extra[:3]} ({len(extra)})"
        )

    dtypes: set[str] = set()
    payload_bytes = 0
    intervals: list[tuple[int, int, str]] = []
    for layer, expert, name, expected_shape in expected:
        entry = tensor_entries[name]
        shape = tuple(int(item) for item in entry.get("shape", []))
        if shape != expected_shape:
            raise ValueError(
                f"{name} has shape {shape}, expected {expected_shape} "
                f"(layer={layer}, expert={expert})"
            )
        dtype = str(entry.get("dtype"))
        if dtype not in DTYPE_BYTES:
            raise ValueError(f"{name} has unsupported training dtype {dtype}")
        offsets = entry.get("data_offsets")
        if not isinstance(offsets, list) or len(offsets) != 2:
            raise ValueError(f"{name} has malformed data offsets")
        start, end = map(int, offsets)
        expected_bytes = expected_shape[0] * expected_shape[1] * DTYPE_BYTES[dtype]
        if start < 0 or end <= start or end - start != expected_bytes:
            raise ValueError(
                f"{name} has invalid data offsets {offsets}; expected {expected_bytes} bytes"
            )
        intervals.append((start, end, name))
        payload_bytes += end - start
        dtypes.add(dtype)

    # Safetensors does not require tensor entries (or our selected-expert
    # manifest) to be ordered by their payload offsets.  Validate the physical
    # layout independently from semantic tensor identity.
    previous_end = 0
    for start, end, name in sorted(intervals):
        if start != previous_end:
            relation = "overlap" if start < previous_end else "gap"
            raise ValueError(
                f"bank payload has a {relation} before {name}: "
                f"expected offset {previous_end}, found {start}"
            )
        previous_end = end

    if previous_end != file_payload or payload_bytes != file_payload:
        raise ValueError(
            f"bank payload coverage mismatch: last={previous_end}, summed={payload_bytes}, "
            f"file={file_payload}"
        )
    return BankValidation(
        bank_id=str(bank["id"]),
        tensor_count=len(expected),
        payload_bytes=payload_bytes,
        dtypes=tuple(sorted(dtypes)),
        layers=tuple(range(30)),
    )


def validate_bank_header(bank_path: Path, bank: dict[str, Any]) -> BankValidation:
    data_start, header = safetensors_header(bank_path)
    return validate_bank_header_entries(header, bank_path.stat().st_size - data_start, bank)


def validate_curriculum_row(row: dict[str, Any], expected_split: str) -> None:
    if row.get("schema") != "AutonomaFableRouterRecord.v2":
        raise ValueError(f"unexpected curriculum schema: {row.get('schema')}")
    if row.get("split") != expected_split:
        raise ValueError(f"row split {row.get('split')} does not match {expected_split}")
    if row.get("lane") not in {
        "host_preservation",
        "verified_expert",
        "interaction_pattern",
    }:
        raise ValueError(f"unexpected curriculum lane: {row.get('lane')}")
    messages = row.get("messages")
    if not isinstance(messages, list) or not messages:
        raise ValueError("curriculum row has no messages")
    if not any(message.get("role") == "assistant" for message in messages if isinstance(message, dict)):
        raise ValueError("curriculum row has no assistant target")


def iter_jsonl(path: Path) -> Iterable[dict[str, Any]]:
    with path.open("r", encoding="utf-8") as handle:
        for line_number, line in enumerate(handle, 1):
            try:
                payload = json.loads(line)
            except json.JSONDecodeError as exc:
                raise ValueError(f"invalid JSONL at {path}:{line_number}: {exc}") from exc
            if not isinstance(payload, dict):
                raise ValueError(f"non-object JSONL row at {path}:{line_number}")
            yield payload


def verify_file(path: Path, expected_bytes: int, expected_sha256: str) -> dict[str, Any]:
    actual_bytes = path.stat().st_size
    actual_sha256 = sha256(path)
    if actual_bytes != expected_bytes or actual_sha256 != expected_sha256:
        raise ValueError(
            f"artifact mismatch for {path}: bytes={actual_bytes}/{expected_bytes}, "
            f"sha256={actual_sha256}/{expected_sha256}"
        )
    return {"path": str(path), "bytes": actual_bytes, "sha256": actual_sha256}