nano_eval.py

#!/usr/bin/env python3
"""Evaluate a SentenceTransformer model on NanoMIRACL (NDCG@10).

This mirrors the NanoBEIR evaluation style from sentence-transformers, adapted to
the NanoMIRACL layout (configs: corpus/queries/qrels, splits: NanoMIRACL-<lang>).
"""

from __future__ import annotations

import argparse
import glob
import json
import logging
import os
import time
from collections.abc import Callable, Sequence
from typing import Any, cast

import numpy as np
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
from sentence_transformers.similarity_functions import SimilarityFunction
from sentence_transformers.util import is_datasets_available
from torch import Tensor
from tqdm import tqdm

DEFAULT_DATASET_PATH = "output/NanoMIRACL-fixed"
DEFAULT_DATASET_ID = "hotchpotch/NanoMIRACL"

LANGS = [
    "ar",
    "bn",
    "de",
    "en",
    "es",
    "fa",
    "fi",
    "fr",
    "hi",
    "id",
    "ja",
    "ko",
    "ru",
    "sw",
    "te",
    "th",
    "yo",
    "zh",
]

ALIASES = {
    "jp": "ja",
}

logger = logging.getLogger(__name__)


def _normalize_lang(name: str) -> str:
    key = name.lower()
    return ALIASES.get(key, key)


def _split_name(lang: str) -> str:
    return f"NanoMIRACL-{lang}"


def _human_readable(lang: str) -> str:
    return f"NanoMIRACL-{lang}"


class NanoMiraclEvaluator(SentenceEvaluator):
    """Evaluate a model on NanoMIRACL across languages."""

    information_retrieval_class = InformationRetrievalEvaluator

    def __init__(
        self,
        dataset_names: list[str] | None = None,
        dataset_path: str | None = DEFAULT_DATASET_PATH,
        dataset_id: str | None = None,
        mrr_at_k: list[int] | None = None,
        ndcg_at_k: list[int] | None = None,
        accuracy_at_k: list[int] | None = None,
        precision_recall_at_k: list[int] | None = None,
        map_at_k: list[int] | None = None,
        show_progress_bar: bool = False,
        batch_size: int = 32,
        write_csv: bool = True,
        truncate_dim: int | None = None,
        score_functions: dict[str, Callable[[Tensor, Tensor], Tensor]] | None = None,
        main_score_function: str | SimilarityFunction | None = None,
        aggregate_fn: Callable[[list[float]], float] = np.mean,
        aggregate_key: str = "mean",
        query_prompts: str | dict[str, str] | None = None,
        corpus_prompts: str | dict[str, str] | None = None,
        write_predictions: bool = False,
        ndcg_only: bool = True,
    ) -> None:
        super().__init__()

        if dataset_names is None:
            dataset_names = LANGS
        self.dataset_names = [_normalize_lang(name) for name in dataset_names]
        self.dataset_id = dataset_id
        self.dataset_path = dataset_path
        self.aggregate_fn = aggregate_fn
        self.aggregate_key = aggregate_key
        self.write_csv = write_csv

        self.query_prompts = self._normalize_prompts(query_prompts)
        self.corpus_prompts = self._normalize_prompts(corpus_prompts)

        self.show_progress_bar = show_progress_bar
        self.score_functions = score_functions or {}
        self.score_function_names = sorted(self.score_functions.keys())
        self.main_score_function = main_score_function
        self.truncate_dim = truncate_dim
        self.name = f"NanoMIRACL_{aggregate_key}"
        if self.truncate_dim:
            self.name += f"_{self.truncate_dim}"

        self.ndcg_only = ndcg_only
        self.mrr_at_k = mrr_at_k or [10]
        self.ndcg_at_k = ndcg_at_k or [10]
        if ndcg_only:
            self.accuracy_at_k = [10]
            self.precision_recall_at_k = [10]
            self.map_at_k = [10]
        else:
            self.accuracy_at_k = accuracy_at_k or [1, 3, 5, 10]
            self.precision_recall_at_k = precision_recall_at_k or [1, 3, 5, 10]
            self.map_at_k = map_at_k or [100]

        self._validate_dataset_names()
        self._validate_prompts()

        ir_kwargs = {
            "mrr_at_k": self.mrr_at_k,
            "ndcg_at_k": self.ndcg_at_k,
            "accuracy_at_k": self.accuracy_at_k,
            "precision_recall_at_k": self.precision_recall_at_k,
            "map_at_k": self.map_at_k,
            "show_progress_bar": show_progress_bar,
            "batch_size": batch_size,
            "write_csv": write_csv,
            "truncate_dim": truncate_dim,
            "score_functions": score_functions,
            "main_score_function": main_score_function,
            "write_predictions": write_predictions,
        }

        self.evaluators = [
            self._load_dataset(name, **ir_kwargs)
            for name in tqdm(self.dataset_names, desc="Loading NanoMIRACL", leave=False)
        ]

        self.csv_file = f"NanoMIRACL_evaluation_{aggregate_key}_results.csv"
        self.csv_headers = ["epoch", "steps"]
        self._append_csv_headers(self.score_function_names)

    def _normalize_prompts(self, prompts: str | dict[str, str] | None) -> dict[str, str] | None:
        if prompts is None:
            return None
        if isinstance(prompts, str):
            return {name: prompts for name in self.dataset_names}
        normalized: dict[str, str] = {}
        for key, value in prompts.items():
            normalized[_normalize_lang(key)] = value
        return normalized

    def _append_csv_headers(self, score_function_names):
        for score_name in score_function_names:
            for k in self.accuracy_at_k:
                self.csv_headers.append(f"{score_name}-Accuracy@{k}")
            for k in self.precision_recall_at_k:
                self.csv_headers.append(f"{score_name}-Precision@{k}")
                self.csv_headers.append(f"{score_name}-Recall@{k}")
            for k in self.mrr_at_k:
                self.csv_headers.append(f"{score_name}-MRR@{k}")
            for k in self.ndcg_at_k:
                self.csv_headers.append(f"{score_name}-NDCG@{k}")
            for k in self.map_at_k:
                self.csv_headers.append(f"{score_name}-MAP@{k}")

    def _find_files(self, config: str, split: str) -> list[str]:
        if not self.dataset_path:
            raise ValueError("dataset_path is required when loading from local parquet files.")
        pattern = os.path.join(self.dataset_path, config, f"{split}-*.parquet")
        files = sorted(glob.glob(pattern))
        if not files:
            raise FileNotFoundError(f"No parquet files found for {config}/{split} under {self.dataset_path}")
        return files

    def _load_dataset(self, lang: str, **ir_kwargs) -> InformationRetrievalEvaluator:
        if not is_datasets_available():
            raise ValueError("datasets is required; install via `pip install datasets`.")

        from datasets import load_dataset

        split_name = _split_name(lang)
        t0 = time.perf_counter()
        if self.dataset_id:
            corpus_ds = load_dataset(self.dataset_id, "corpus", split=split_name)
            queries_ds = load_dataset(self.dataset_id, "queries", split=split_name)
            qrels_ds = load_dataset(self.dataset_id, "qrels", split=split_name)
        else:
            corpus_ds = load_dataset(
                "parquet",
                data_files=self._find_files("corpus", split_name),
                split="train",
            )
            queries_ds = load_dataset(
                "parquet",
                data_files=self._find_files("queries", split_name),
                split="train",
            )
            qrels_ds = load_dataset(
                "parquet",
                data_files=self._find_files("qrels", split_name),
                split="train",
            )
        logger.info("[NanoMIRACL] loaded datasets for %s in %.2fs", lang, time.perf_counter() - t0)

        corpus_dict = {}
        t1 = time.perf_counter()
        for sample in corpus_ds:
            row = cast(dict[str, Any], sample)
            text = row.get("text")
            if text:
                corpus_dict[row["_id"]] = text

        queries_dict = {}
        for sample in queries_ds:
            row = cast(dict[str, Any], sample)
            text = row.get("text")
            if text:
                queries_dict[row["_id"]] = text

        qrels_dict: dict[str, set[str]] = {}
        for sample in qrels_ds:
            row = cast(dict[str, Any], sample)
            qid = row["query-id"]
            cids = row["corpus-id"]
            if isinstance(cids, list):
                qrels_dict.setdefault(qid, set()).update(cids)
            else:
                qrels_dict.setdefault(qid, set()).add(cids)

        logger.info(
            "[NanoMIRACL] materialized dicts for %s in %.2fs (corpus=%d, queries=%d, qrels=%d)",
            lang,
            time.perf_counter() - t1,
            len(corpus_dict),
            len(queries_dict),
            len(qrels_dict),
        )

        if self.query_prompts is not None:
            ir_kwargs["query_prompt"] = self.query_prompts.get(lang, None)
        if self.corpus_prompts is not None:
            ir_kwargs["corpus_prompt"] = self.corpus_prompts.get(lang, None)

        evaluator = InformationRetrievalEvaluator(
            queries_dict,
            corpus_dict,
            qrels_dict,
            name=_human_readable(lang),
            **ir_kwargs,
        )
        return evaluator

    def _validate_dataset_names(self) -> None:
        valid = set(LANGS)
        missing = [name for name in self.dataset_names if name not in valid]
        if missing:
            raise ValueError(f"Invalid language(s): {missing}. Valid: {sorted(valid)}")

    def _validate_prompts(self) -> None:
        error_msg = ""
        if self.query_prompts is not None:
            missing = [lang for lang in self.dataset_names if lang not in self.query_prompts]
            if missing:
                error_msg += f"Missing query prompts for: {missing}\n"
        if self.corpus_prompts is not None:
            missing = [lang for lang in self.dataset_names if lang not in self.corpus_prompts]
            if missing:
                error_msg += f"Missing corpus prompts for: {missing}\n"
        if error_msg:
            raise ValueError(error_msg.strip())

    def __call__(
        self,
        model: SentenceTransformer,
        output_path: str | None = None,
        epoch: int = -1,
        steps: int = -1,
        *args,
        **kwargs,
    ) -> dict[str, float]:
        per_metric_agg: dict[str, list[float]] = {}
        per_dataset: dict[str, float] = {}

        if self.score_functions is None:
            self.score_functions = {model.similarity_fn_name: model.similarity}
            self.score_function_names = [model.similarity_fn_name]
            self._append_csv_headers(self.score_function_names)

        for evaluator in tqdm(self.evaluators, desc="Evaluating NanoMIRACL", disable=not self.show_progress_bar):
            logger.info("Evaluating %s", evaluator.name)
            results = evaluator(model, output_path, epoch, steps)
            for key, value in results.items():
                per_dataset[key] = value

                if "_" in key:
                    _, metric_name = key.split("_", 1)
                else:
                    metric_name = key
                per_metric_agg.setdefault(metric_name, []).append(value)

        agg_results = {
            f"{self.name}_{metric}": self.aggregate_fn(vals)
            for metric, vals in per_metric_agg.items()
        }

        if not self.primary_metric:
            main_score_fn = self.main_score_function
            main = None if main_score_fn is None else str(main_score_fn)
            ndcg_target = f"ndcg@{max(self.ndcg_at_k)}"
            candidates = [k for k in agg_results if k.endswith(ndcg_target)]
            if main:
                preferred = [k for k in candidates if main in k]
                if preferred:
                    self.primary_metric = preferred[0]
            if not self.primary_metric and candidates:
                self.primary_metric = candidates[0]

        if self.primary_metric and self.primary_metric in agg_results:
            logger.info("Primary %s: %.4f", self.primary_metric, agg_results[self.primary_metric])

        per_dataset.update(agg_results)
        if self.ndcg_only:
            per_dataset = {k: v for k, v in per_dataset.items() if "ndcg@10" in k}
        return per_dataset


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Evaluate a model on NanoMIRACL")
    parser.add_argument("--model-path", required=True, help="Path or HF id for SentenceTransformer model")
    parser.add_argument(
        "--dataset-path",
        default=DEFAULT_DATASET_PATH,
        help="Local NanoMIRACL dataset root (or HF dataset id if the path does not exist).",
    )
    parser.add_argument(
        "--dataset-id",
        default=DEFAULT_DATASET_ID,
        help="Hugging Face dataset id (overrides --dataset-path).",
    )
    parser.add_argument("--langs", nargs="*", default=None, help="Languages (default: all)")
    parser.add_argument("--batch-size", type=int, default=128, help="Eval batch size")
    parser.add_argument("--output", default=None, help="Optional JSON output path for metrics")
    parser.add_argument("--show-progress", action="store_true", help="Show per-language tqdm during eval")
    parser.add_argument(
        "--no-autocast",
        action="store_true",
        help="Disable torch.autocast (default: enabled on CUDA with bf16 if available)",
    )
    parser.add_argument(
        "--autocast-dtype",
        choices=["bf16", "fp16"],
        default="bf16",
        help="autocast dtype (bf16 or fp16)",
    )
    parser.add_argument("--query-prompt", default=None, help="Prefix applied to queries")
    parser.add_argument("--corpus-prompt", default=None, help="Prefix applied to corpus/passages")
    parser.add_argument(
        "--all-metrics",
        action="store_true",
        help="Return all metrics (default: ndcg@10 only)",
    )
    parser.add_argument(
        "--trust-remote-code",
        action="store_true",
        help="Pass trust_remote_code=True to SentenceTransformer (needed for some HF models)",
    )
    return parser.parse_args()


def _infer_dataset_id(dataset_path: str | None) -> str | None:
    if dataset_path is None:
        return None
    if os.path.exists(dataset_path):
        return None
    if "/" in dataset_path:
        return dataset_path
    raise FileNotFoundError(
        f"dataset-path '{dataset_path}' not found and does not look like a HF dataset id (expected 'namespace/name')."
    )


def main(argv: Sequence[str] | None = None) -> None:
    args = parse_args()
    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")

    langs = args.langs or LANGS
    langs = [_normalize_lang(lang) for lang in langs]

    model = SentenceTransformer(args.model_path, prompts=None, trust_remote_code=args.trust_remote_code)
    model.eval()

    dataset_id = args.dataset_id
    dataset_path = args.dataset_path
    if dataset_id is None:
        inferred = _infer_dataset_id(dataset_path)
        if inferred is not None:
            dataset_id = inferred
            dataset_path = None

    evaluator = NanoMiraclEvaluator(
        dataset_names=langs,
        dataset_path=dataset_path,
        dataset_id=dataset_id,
        batch_size=args.batch_size,
        show_progress_bar=args.show_progress,
        write_csv=False,
        query_prompts=args.query_prompt if args.query_prompt else None,
        corpus_prompts=args.corpus_prompt if args.corpus_prompt else None,
        ndcg_only=not args.all_metrics,
    )

    use_autocast = not args.no_autocast
    autocast_dtype = {"bf16": "bfloat16", "fp16": "float16"}[args.autocast_dtype]
    autocast_ctx = None
    if use_autocast:
        import torch

        device_type = "cuda" if torch.cuda.is_available() else "cpu"
        autocast_ctx = torch.autocast(device_type=device_type, dtype=getattr(torch, autocast_dtype))

    if autocast_ctx:
        with autocast_ctx:
            results = evaluator(model)
    else:
        results = evaluator(model)

    score_fn = model.similarity_fn_name
    ndcg_key_suffix = f"{score_fn}_ndcg@10"

    per_lang = {}
    for lang in evaluator.dataset_names:
        key = f"{_split_name(lang)}_{ndcg_key_suffix}"
        if key in results:
            per_lang[lang] = results[key]

    avg = float(np.mean(list(per_lang.values()))) if per_lang else float("nan")

    print("NanoMIRACL Evaluation (NDCG@10)")
    print(f"Model: {args.model_path}")
    for lang in evaluator.dataset_names:
        val = per_lang.get(lang)
        if val is None:
            continue
        print(f"{_split_name(lang)}_{ndcg_key_suffix}: {val:.4f}")
    print(f"NanoMIRACL_mean_{ndcg_key_suffix}: {avg:.4f}")

    if args.output:
        payload = {"model": args.model_path, "avg": avg, "per_lang": per_lang, "metrics": results}
        with open(args.output, "w", encoding="utf-8") as f:
            json.dump(payload, f, ensure_ascii=False, indent=2)


if __name__ == "__main__":
    main()