validate.py

#!/usr/bin/env python3
"""Validate RTX PRO 4000 benchmark JSONL rows."""

from __future__ import annotations

import argparse
import json
import re
import sys
from pathlib import Path
from typing import Any


REQUIRED = [
    "variant",
    "vram_gb",
    "memory_type",
    "bus_width_bit",
    "bandwidth_gbps",
    "tdp_w",
    "pcie_lanes",
    "gpu_count",
    "model",
    "params_b",
    "quant",
    "backend",
    "backend_version",
    "driver_version",
    "os",
    "context_len",
    "concurrency",
    "tok_s_prefill",
    "tok_s_decode",
    "vram_used_gb",
    "power_w",
    "thinking",
    "source",
    "submitter",
    "date",
    "notes",
]

ENUMS = {
    "variant": {"desktop", "sff", "laptop"},
    "memory_type": {"gddr7", "gddr6"},
    "quant": {"gguf-q4_k_m", "gguf-q8_0", "nvfp4", "fp8", "bf16", "other"},
    "backend": {"llama.cpp", "vllm", "tensorrt-llm", "other"},
    "thinking": {"on", "off", "n/a"},
    "source": {"owner-measured"},
}

VARIANT_DISCRIMINATORS = {
    "desktop": {
        "bandwidth_gbps": 672,
        "tdp_w": 140,
        "pcie_lanes": 16,
    },
    "sff": {
        "bandwidth_gbps": 432,
        "tdp_w": 70,
        "pcie_lanes": 8,
    },
    "laptop": {
        "vram_gb": 16,
        "bandwidth_gbps": 896,
    },
}

VARIANT_METADATA = {
    "desktop": {
        "vram_gb": 24,
        "memory_type": "gddr7",
        "bus_width_bit": 192,
    },
    "sff": {
        "vram_gb": 24,
        "memory_type": "gddr7",
        "bus_width_bit": 192,
    },
    "laptop": {
        "vram_gb": 16,
        "memory_type": "gddr7",
        "bus_width_bit": 256,
    },
}

NUMERIC = {
    "vram_gb",
    "bandwidth_gbps",
    "tdp_w",
    "params_b",
    "tok_s_prefill",
    "tok_s_decode",
    "vram_used_gb",
    "power_w",
}

INTEGER = {"gpu_count", "bus_width_bit", "pcie_lanes", "context_len", "concurrency"}


def row_errors(row: dict[str, Any], line_no: int) -> list[str]:
    errors: list[str] = []

    for field in REQUIRED:
        if field not in row:
            errors.append(f"line {line_no}: missing required field {field}")

    for field in row:
        if field not in REQUIRED:
            errors.append(f"line {line_no}: unexpected field {field}")

    for field, allowed in ENUMS.items():
        value = row.get(field)
        if value not in allowed:
            errors.append(f"line {line_no}: {field}={value!r} not in {sorted(allowed)}")

    # tok_s_prefill / power_w may be null (JSON null) on concurrency>1 rows where they are
    # NOT separately measured - the row's headline is tok_s_decode (the aggregate). A null is
    # the honest sentinel there; an arbitrary borrowed single-stream value would be misleading.
    nullable_numeric = {"tok_s_prefill", "power_w"}
    for field in NUMERIC:
        value = row.get(field)
        if value is None and field in nullable_numeric:
            continue
        if not isinstance(value, (int, float)) or isinstance(value, bool):
            errors.append(f"line {line_no}: {field} must be numeric")
        elif value < 0:
            errors.append(f"line {line_no}: {field} must be >= 0")

    for field in INTEGER:
        value = row.get(field)
        if not isinstance(value, int) or isinstance(value, bool):
            errors.append(f"line {line_no}: {field} must be an integer")
        elif value < 1:
            errors.append(f"line {line_no}: {field} must be >= 1")

    for field in ("model", "backend_version", "driver_version", "os", "submitter", "notes"):
        value = row.get(field)
        if not isinstance(value, str) or not value.strip():
            errors.append(f"line {line_no}: {field} must be a non-empty string")

    date_value = row.get("date")
    if not isinstance(date_value, str) or not re.fullmatch(r"\d{4}-\d{2}-\d{2}", date_value):
        errors.append(f"line {line_no}: date must be YYYY-MM-DD")

    variant = row.get("variant")
    discriminator_expected = VARIANT_DISCRIMINATORS.get(variant)
    if discriminator_expected:
        for field, expected_value in discriminator_expected.items():
            actual = row.get(field)
            if actual != expected_value:
                errors.append(
                    f"line {line_no}: {variant} discriminator requires {field}={expected_value}, got {actual!r}"
                )

    metadata_expected = VARIANT_METADATA.get(variant)
    if metadata_expected:
        for field, expected_value in metadata_expected.items():
            actual = row.get(field)
            if actual != expected_value:
                errors.append(
                    f"line {line_no}: {variant} metadata expects {field}={expected_value}, got {actual!r}"
                )

    if row.get("source") != "owner-measured":
        errors.append(f"line {line_no}: source must be owner-measured")

    return errors


def validate(path: Path) -> tuple[int, list[str]]:
    errors: list[str] = []
    count = 0

    try:
        lines = path.read_text(encoding="utf-8").splitlines()
    except OSError as exc:
        return 0, [f"{path}: {exc}"]

    for line_no, line in enumerate(lines, start=1):
        if not line.strip():
            continue
        try:
            row = json.loads(line)
        except json.JSONDecodeError as exc:
            errors.append(f"line {line_no}: invalid JSON: {exc}")
            continue
        if not isinstance(row, dict):
            errors.append(f"line {line_no}: row must be a JSON object")
            continue
        count += 1
        errors.extend(row_errors(row, line_no))

    if count == 0:
        errors.append(f"{path}: no rows found")
    return count, errors


def main() -> int:
    parser = argparse.ArgumentParser(description="Validate benchmark JSONL rows")
    parser.add_argument("path", nargs="?", default="data/results.jsonl")
    args = parser.parse_args()

    count, errors = validate(Path(args.path))
    if errors:
        for error in errors:
            print(error, file=sys.stderr)
        return 1
    print(f"OK: {count} rows valid")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())