feat: evaluate pinned local embedding models on CPU
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"""Reproducible CPU smoke evaluation; no application database or remote inference service."""
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import argparse
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import hashlib
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import importlib.metadata
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import json
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import math
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import platform
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import statistics
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import sys
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from dataclasses import asdict
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from pathlib import Path
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from time import perf_counter
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from app.services.embedding import LOCAL_MODELS, EmbeddingError, LocalEmbeddingProvider
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def load_dataset(path: Path) -> tuple[dict, str]:
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with path.open("rb") as source:
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raw = source.read(2_000_001)
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if len(raw) > 2_000_000:
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raise ValueError("评估输入不得超过 2MB")
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data = json.loads(raw)
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if not isinstance(data, dict):
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raise ValueError("评估输入须为 JSON 对象")
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documents, queries = data["documents"], data["queries"]
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if not isinstance(documents, list) or not isinstance(queries, list):
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raise ValueError("documents 与 queries 须为列表")
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if not 1 <= len(documents) <= 1000 or not 1 <= len(queries) <= 1000:
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raise ValueError("文档和问题各须为 1–1000 条")
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for rows in [documents, queries]:
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if any(not isinstance(row, dict) for row in rows):
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raise ValueError("每条记录须为对象")
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ids = [row["id"] for row in rows]
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if any(not isinstance(x, str) or not x or len(x) > 100 for x in ids) or len(
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set(ids)
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) != len(ids):
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raise ValueError("每组 ID 必须非空、唯一且最多 100 字符")
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if any(
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not isinstance(row["text"], str) or not row["text"].strip() or len(row["text"]) > 10000
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for row in rows
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):
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raise ValueError("每条文本须为非空且最多 10000 字符")
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ids = {row["id"] for row in documents}
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for query in queries:
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relevant = query["relevant_ids"]
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if (
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not isinstance(relevant, list)
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or not relevant
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or any(not isinstance(x, str) for x in relevant)
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or len(set(relevant)) != len(relevant)
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or not set(relevant) <= ids
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):
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raise ValueError("相关文档标注必须非空、唯一并引用现有文档")
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if not isinstance(data["dataset_id"], str) or not isinstance(data["purpose"], str):
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raise ValueError("必须填写数据集 ID 与用途")
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return data, hashlib.sha256(raw).hexdigest()
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def rank_and_measure(documents, queries, document_vectors, query_vectors, *, top_k=3):
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if not documents or not queries or top_k < 1:
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raise ValueError("评估须有文档、问题且 top_k 为正数")
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if len(document_vectors) != len(documents) or len(query_vectors) != len(queries):
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raise ValueError("评估向量数量与输入不一致")
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rows = []
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for query, vector in zip(queries, query_vectors, strict=True):
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ranked = sorted(
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[
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(doc["id"], math.fsum(x * y for x, y in zip(vector, embedding, strict=True)))
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for doc, embedding in zip(documents, document_vectors, strict=True)
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],
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key=lambda pair: (-pair[1], pair[0]),
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)
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relevant = set(query["relevant_ids"])
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first = next(
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(rank for rank, (id_, _) in enumerate(ranked[:top_k], 1) if id_ in relevant), None
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)
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rows.append(
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{
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"query_id": query["id"],
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"top_results": [
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{"id": id_, "score": round(score, 8)} for id_, score in ranked[:top_k]
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],
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"hit_at_1": int(ranked[0][0] in relevant),
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"recall_at_k": len({id_ for id_, _ in ranked[:top_k]} & relevant) / len(relevant),
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"reciprocal_rank_at_k": 1 / first if first else 0,
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}
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)
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return {
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"top_k": top_k,
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"hit_at_1": statistics.mean(row["hit_at_1"] for row in rows),
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"recall_at_k": statistics.mean(row["recall_at_k"] for row in rows),
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"mrr_at_k": statistics.mean(row["reciprocal_rank_at_k"] for row in rows),
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"per_query": rows,
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}
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def measure(call, repeats):
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call() # Warm this exact input shape before taking the recorded samples.
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timings = []
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result = None
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for _ in range(repeats):
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start = perf_counter()
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result = call()
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timings.append((perf_counter() - start) * 1000)
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return result, {
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"samples_ms": [round(value, 3) for value in timings],
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"median_ms": round(statistics.median(timings), 3),
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}
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def main():
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parser = argparse.ArgumentParser(
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description="本地 CPU Embedding 实测；合成结果不能用于论文结论"
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)
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parser.add_argument("--model", choices=LOCAL_MODELS, required=True)
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parser.add_argument(
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"--dataset", type=Path, default=Path(__file__).with_name("synthetic_zh.json")
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)
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parser.add_argument("--batch-size", type=int, default=8)
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parser.add_argument("--threads", type=int, default=2)
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parser.add_argument("--repeats", type=int, default=5)
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parser.add_argument("--cache-dir", type=Path)
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parser.add_argument(
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"--download", action="store_true", help="允许下载固定 revision 的公开模型文件"
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)
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parser.add_argument("--output", type=Path)
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args = parser.parse_args()
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if sys.platform != "linux":
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parser.error("当前 RSS 测量仅支持 Linux；Provider 可在其他支持的平台单独使用")
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import resource
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if not 1 <= args.repeats <= 20:
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parser.error("repeats 必须为 1–20")
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dataset, digest = load_dataset(args.dataset)
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print("初始化固定版本的 CPU 模型。", file=sys.stderr, flush=True)
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started = perf_counter()
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provider = LocalEmbeddingProvider(
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LOCAL_MODELS[args.model],
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batch_size=args.batch_size,
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threads=args.threads,
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cache_dir=args.cache_dir,
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offline=not args.download,
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)
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initialization_seconds = perf_counter() - started
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print("模型就绪，执行预热和编码测量。", file=sys.stderr, flush=True)
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provider.encode(["预热测试"], query=True)
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corpus = [row["text"] for row in dataset["documents"]]
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queries = [row["text"] for row in dataset["queries"]]
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documents, corpus_time = measure(lambda: provider.encode(corpus), args.repeats)
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vectors, query_time = measure(lambda: provider.encode(queries, query=True), args.repeats)
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_, single_time = measure(lambda: provider.encode([queries[0]], query=True), args.repeats)
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long_text = "合成测试资料仅用于测量本地编码资源，不表示任何真实景区信息。" * 12
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_, long_time = measure(lambda: provider.encode([long_text] * args.batch_size), args.repeats)
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try:
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provider.encode(["超长测试" * 500])
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except EmbeddingError:
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length_guard = True
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else:
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raise AssertionError("超长输入未被拒绝")
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import torch
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report = {
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"schema_version": 1,
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"evaluation_kind": "synthetic_smoke"
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if dataset["purpose"] == "synthetic_smoke_not_domain_benchmark"
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else "provided_dataset_not_independently_verified",
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"dataset": {
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"id": dataset["dataset_id"],
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"purpose": dataset["purpose"],
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"sha256": digest,
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"documents": len(corpus),
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"queries": len(queries),
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},
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"embedding_spec": asdict(provider.spec),
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"space_id": provider.spec.space_id,
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"runtime": {
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"python": platform.python_version(),
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"torch": torch.__version__,
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"transformers": importlib.metadata.version("transformers"),
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"huggingface_hub": importlib.metadata.version("huggingface_hub"),
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"numpy": importlib.metadata.version("numpy"),
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"tokenizers": importlib.metadata.version("tokenizers"),
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"safetensors": importlib.metadata.version("safetensors"),
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"device": "cpu",
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"threads": torch.get_num_threads(),
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"interop_threads": torch.get_num_interop_threads(),
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"batch_size": args.batch_size,
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"repeats": args.repeats,
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"warmup_per_shape": 1,
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},
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"initialization_seconds": round(initialization_seconds, 3),
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"timings": {
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"corpus_encode": corpus_time,
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"query_batch_encode": query_time,
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"single_query_encode": single_time,
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"long_text_batch_encode": long_time,
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},
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"long_text_characters": len(long_text),
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"long_text_tokens": len(provider.tokenizer(long_text)["input_ids"]),
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"peak_process_rss_mib": round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024, 1),
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"token_overflow_rejected": length_guard,
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"retrieval": rank_and_measure(dataset["documents"], dataset["queries"], documents, vectors),
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"limitations": [
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"合成小样本仅检查流程，不能代表那拉提领域质量或正式实验分数",
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"计时为本机预热后的编码，包含分词/补齐/归一化/结果转换，不含 API、数据库与网络请求",
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"峰值 RSS 是整个独立进程的高水位，包含解释器和模型；初始化包含缓存检查/下载与首次模型加载",
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],
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}
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encoded = json.dumps(report, ensure_ascii=False, indent=2) + "\n"
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if args.output:
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args.output.parent.mkdir(parents=True, exist_ok=True)
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args.output.write_text(encoded, encoding="utf-8")
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args.output.chmod(0o600)
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print("评估完成，报告已写入指定本机位置。", file=sys.stderr)
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else:
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print(encoded, end="")
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if __name__ == "__main__":
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main()
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