iomgaa
8a69a54078
Address review: replace bare KeyError with a contextual ValueError invariant check in _grade_unit (fail-loud, no catch/skip/fallback); tighten _drop_orphan_pairs to require exactly one original + one mirror with no extra illegal-role records (total==2); clarify InferenceResult docstring to unit-grained semantics. Add tests for missing-prediction descriptive error and extra-illegal-role pair drop.
366 lines
14 KiB
Python
366 lines
14 KiB
Python
"""inference pair-level 双向 AND 聚合单元测试（Task 6）。
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覆盖 question-gen v3 Phase 1 Task 6 的核心契约：
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- 逐题推理不变：每条 GeneratedQuestion 照常各答一次、per-question prediction
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仍逐题落 predictions 表（保留逐题溯源）。
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- pair 按 pair_id 收齐 original + mirror 后合成 1 条 unit-level 记录，
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pair 正确 = (P.pred==P.answer) AND (Q.pred==Q.answer)（双向 AND）。
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- InferenceResult.total / correct / per_task_type 全部按 unit 粒度
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（single 计 1，pair 计 1）。
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- 孤儿 pair（收不齐 2 条）→ 告警 + 剔除该 unit、不计入 total（不静默）。
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"""
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from __future__ import annotations
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import json
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from typing import Any
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from unittest.mock import AsyncMock
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import pytest
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from loguru import logger
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from app.harness.inference import _aggregate_results, run_inference
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from app.harness.log import HarnessLog
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from core.types import GeneratedQuestion, LLMResponse
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# ── 测试基础设施 ──────────────────────────────────────────────────
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def _make_question(
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question_id: str,
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*,
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task_type: str = "Action Reasoning",
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answer: str = "B",
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pair_id: str | None = None,
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question_role: str = "single",
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flip_axis: str | None = None,
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video_id: str = "v1",
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) -> GeneratedQuestion:
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"""构造测试题目；pair_id 非空时视为孪生对成员。"""
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return GeneratedQuestion(
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question_id=question_id,
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video_id=video_id,
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task_type=task_type,
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question="测试问题",
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options=("A. 选项A", "B. 选项B", "C. 选项C", "D. 选项D"),
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answer=answer,
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source_nodes=("L1_001",),
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difficulty="medium",
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pair_id=pair_id,
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question_role=question_role,
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flip_axis=flip_axis if pair_id else None,
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)
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def _make_record(
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question_id: str,
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*,
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prediction: str | None,
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answer: str = "B",
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task_type: str = "Action Reasoning",
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steps_used: int = 2,
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prompt_tokens: int = 100,
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completion_tokens: int = 50,
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stop_reason: str = "finished",
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) -> dict[str, Any]:
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"""构造与题目匹配的 prediction record（键与 _run_single_question 一致）。"""
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return {
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"question_id": question_id,
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"prediction": prediction,
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"answer": answer,
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"task_type": task_type,
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"steps_used": steps_used,
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"stop_reason": stop_reason,
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}
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def _make_llm_response(answer: str = "B") -> LLMResponse:
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"""构造 submit_answer 场景的 LLMResponse。"""
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content = json.dumps(
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{
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"reflect": {"observation": "找到答案"},
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"plan": {"next_step": "提交"},
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"action": {
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"tool": "submit_answer",
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"args": {"answer": answer, "evidence": "证据", "reasoning": "推理"},
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},
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}
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)
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return LLMResponse(
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content=content,
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thinking="思考",
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model="test-model",
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provider="test",
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prompt_tokens=100,
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completion_tokens=50,
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latency_ms=200,
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ttft_ms=30.0,
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max_inter_token_ms=5.0,
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cache_hit=False,
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call_id="test-call-001",
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)
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async def _stub_tool_dispatch(
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tool_name: str, args: dict[str, Any], *, context: dict[str, Any]
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) -> str:
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"""测试用工具调度函数。"""
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if tool_name == "submit_answer":
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return "答案已提交"
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raise ValueError(f"未知工具: {tool_name}")
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def _stub_prompt_builder(qa: GeneratedQuestion) -> tuple[str, str]:
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"""测试用 prompt 构建函数。"""
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return "系统提示词", f"用户问题: {qa.question}"
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@pytest.fixture
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def harness_log(tmp_path: Any, request: Any) -> HarnessLog:
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"""创建临时 HarnessLog 实例。"""
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db_path = str(tmp_path / f"harness_{id(request)}.db")
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log = HarnessLog(db_path, "test-run")
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yield log
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log.close()
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# ── unit 粒度聚合（_aggregate_results 直测） ─────────────────────────
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class TestUnitLevelAggregation:
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"""_aggregate_results 按 unit 粒度聚合测试。"""
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def test_single_units_counted_per_question(self) -> None:
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"""全 single：total = single 数，correct 逐题判定。"""
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questions = [
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_make_question("s1", answer="B"),
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_make_question("s2", answer="A"),
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]
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records = [
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_make_record("s1", prediction="B", answer="B"),
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_make_record("s2", prediction="C", answer="A"),
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]
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result = _aggregate_results(records, questions, "run-single")
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assert result.total == 2
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assert result.correct == 1
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assert abs(result.accuracy - 0.5) < 1e-9
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def test_pair_both_correct_is_one_correct_unit(self) -> None:
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"""pair 两题皆对 → 1 个 unit、correct=1。"""
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questions = [
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_make_question("po", answer="B", pair_id="p", question_role="pair_original"),
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_make_question("pm", answer="A", pair_id="p", question_role="pair_mirror"),
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]
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records = [
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_make_record("po", prediction="B", answer="B"),
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_make_record("pm", prediction="A", answer="A"),
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]
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result = _aggregate_results(records, questions, "run-pair-ok")
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assert result.total == 1
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assert result.correct == 1
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assert result.accuracy == 1.0
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def test_pair_one_wrong_fails_by_and(self) -> None:
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"""pair 一题错 → 双向 AND 判 unit 错，correct=0。"""
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questions = [
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_make_question("po", answer="B", pair_id="p", question_role="pair_original"),
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_make_question("pm", answer="A", pair_id="p", question_role="pair_mirror"),
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]
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records = [
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_make_record("po", prediction="B", answer="B"), # 对
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_make_record("pm", prediction="D", answer="A"), # 错
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]
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result = _aggregate_results(records, questions, "run-pair-half")
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assert result.total == 1
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assert result.correct == 0
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assert result.accuracy == 0.0
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def test_mixed_single_and_pair_unit_total(self) -> None:
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"""single + pair 混合：total = single 数 + pair 数（pair 计 1）。"""
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questions = [
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_make_question("s1", answer="B"),
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_make_question("po", answer="B", pair_id="p", question_role="pair_original"),
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_make_question("pm", answer="A", pair_id="p", question_role="pair_mirror"),
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]
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records = [
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_make_record("s1", prediction="B", answer="B"),
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_make_record("po", prediction="B", answer="B"),
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_make_record("pm", prediction="A", answer="A"),
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]
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result = _aggregate_results(records, questions, "run-mixed")
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assert result.total == 2 # 1 single + 1 pair
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assert result.correct == 2
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def test_per_task_type_by_unit(self) -> None:
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"""per_task_type 按 unit 计数：pair 归入其 task_type 计 1 个 unit。"""
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questions = [
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_make_question("s1", task_type="SP", answer="B"),
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_make_question(
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"po", task_type="AR", answer="B", pair_id="p", question_role="pair_original"
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),
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_make_question(
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"pm", task_type="AR", answer="A", pair_id="p", question_role="pair_mirror"
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),
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]
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records = [
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_make_record("s1", prediction="B", answer="B", task_type="SP"),
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_make_record("po", prediction="B", answer="B", task_type="AR"),
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_make_record("pm", prediction="C", answer="A", task_type="AR"), # pair 错
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]
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result = _aggregate_results(records, questions, "run-tt")
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assert result.per_task_type["AR"]["total"] == 1 # pair 计 1 个 unit
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assert result.per_task_type["AR"]["correct"] == 0
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assert result.per_task_type["SP"]["total"] == 1
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assert result.per_task_type["SP"]["correct"] == 1
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def test_orphan_pair_dropped_and_warned(self) -> None:
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"""孤儿 pair（收不齐 2 条）→ 告警 + 剔除、不计入 total。"""
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questions = [
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_make_question("s1", answer="B"),
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_make_question(
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"po", answer="B", pair_id="orphan", question_role="pair_original"
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), # 缺 mirror
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]
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records = [
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_make_record("s1", prediction="B", answer="B"),
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_make_record("po", prediction="B", answer="B"),
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]
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captured: list[str] = []
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sink_id = logger.add(captured.append, level="WARNING", format="{message}")
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try:
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result = _aggregate_results(records, questions, "run-orphan")
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finally:
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logger.remove(sink_id)
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assert result.total == 1 # 仅 single，孤儿 pair 被剔除
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assert result.correct == 1
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assert any("orphan" in msg for msg in captured), "孤儿 pair 未告警（静默）"
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def test_pair_with_extra_illegal_role_dropped(self) -> None:
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"""pair_id 下混入额外非法 role 记录（total>2）→ 整对剔除、不计入 total。"""
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questions = [
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_make_question("s1", answer="B"),
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_make_question("po", answer="B", pair_id="p", question_role="pair_original"),
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_make_question("pm", answer="A", pair_id="p", question_role="pair_mirror"),
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# 共享 pair_id 的额外非法记录（重复 original 角色）
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_make_question("px", answer="C", pair_id="p", question_role="pair_original"),
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]
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records = [
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_make_record("s1", prediction="B", answer="B"),
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_make_record("po", prediction="B", answer="B"),
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_make_record("pm", prediction="A", answer="A"),
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_make_record("px", prediction="C", answer="C"),
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]
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captured: list[str] = []
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sink_id = logger.add(captured.append, level="WARNING", format="{message}")
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try:
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result = _aggregate_results(records, questions, "run-illegal-role")
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finally:
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logger.remove(sink_id)
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assert result.total == 1 # 仅 single 存活，非法配对整对剔除
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assert result.correct == 1
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assert any("total=3" in msg for msg in captured), "非法配对未告警（静默）"
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def test_unit_missing_prediction_raises_descriptive_error(self) -> None:
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"""unit 缺 prediction → 描述性 ValueError（fail-loud，非静默、非裸 KeyError）。
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故意破坏聚合不变量（questions 含 s2 但 records 无 s2），验证带上下文报错。
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"""
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questions = [
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_make_question("s1", answer="B"),
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_make_question("s2", answer="A"),
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]
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records = [_make_record("s1", prediction="B", answer="B")] # 缺 s2 的 record
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with pytest.raises(ValueError, match=r"缺 prediction.*聚合不变量被破坏"):
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_aggregate_results(records, questions, "run-broken-invariant")
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def test_empty_records_returns_zero(self) -> None:
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"""空 records/questions → 零值结果。"""
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result = _aggregate_results([], [], "run-empty")
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assert result.total == 0
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assert result.correct == 0
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assert result.accuracy == 0.0
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assert result.per_task_type == {}
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def test_token_and_steps_span_all_records(self) -> None:
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"""token/steps 诊断字段覆盖全部 record（含 pair 两条）。"""
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questions = [
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_make_question("po", answer="B", pair_id="p", question_role="pair_original"),
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_make_question("pm", answer="A", pair_id="p", question_role="pair_mirror"),
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]
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records = [
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_make_record("po", prediction="B", answer="B", steps_used=3, prompt_tokens=100),
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_make_record("pm", prediction="A", answer="A", steps_used=1, prompt_tokens=200),
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]
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result = _aggregate_results(records, questions, "run-diag")
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assert result.token_usage["prompt_tokens"] == 300
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assert abs(result.steps_mean - 2.0) < 1e-9 # (3+1)/2 record 粒度
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# ── run_inference 端到端：逐题溯源 + pair 聚合 ──────────────────────
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class TestRunInferencePairEndToEnd:
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"""run_inference pair 端到端：逐题落库不变 + unit 级聚合。"""
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@pytest.mark.asyncio
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async def test_pair_predictions_persisted_per_question(self, harness_log: HarnessLog) -> None:
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"""pair 两题各自逐题落 predictions（保留逐题溯源），聚合按 unit。"""
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llm = AsyncMock()
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llm.chat.return_value = _make_llm_response(answer="B")
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questions = [
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_make_question("po", answer="B", pair_id="p", question_role="pair_original"),
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_make_question("pm", answer="B", pair_id="p", question_role="pair_mirror"),
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]
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result = await run_inference(
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questions,
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llm=llm,
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tool_dispatch_fn=_stub_tool_dispatch,
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prompt_builder=_stub_prompt_builder,
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log=harness_log,
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run_id="run-pair-e2e",
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concurrency=2,
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max_steps=10,
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skill_mode="auto",
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)
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# unit 级：1 个 pair unit，两题皆对 → correct=1
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assert result.total == 1
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assert result.correct == 1
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# 逐题溯源：predictions 表两条 record 都在
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rows = harness_log.query("SELECT * FROM predictions WHERE run_id = ?", ("test-run",))
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qids = {r["question_id"] for r in rows}
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assert qids == {"po", "pm"}
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@pytest.mark.asyncio
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async def test_orphan_pair_excluded_single_survives(self, harness_log: HarnessLog) -> None:
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"""run_inference 中孤儿 pair 被剔除、single 仍计入。"""
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llm = AsyncMock()
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llm.chat.return_value = _make_llm_response(answer="B")
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questions = [
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_make_question("s1", answer="B"),
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_make_question("po", answer="B", pair_id="orphan", question_role="pair_original"),
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]
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result = await run_inference(
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questions,
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llm=llm,
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tool_dispatch_fn=_stub_tool_dispatch,
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prompt_builder=_stub_prompt_builder,
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log=harness_log,
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run_id="run-orphan-e2e",
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concurrency=2,
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max_steps=10,
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skill_mode="auto",
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)
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assert result.total == 1 # single 存活，孤儿剔除
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# 逐题溯源：孤儿题仍逐题落库（推理不变）
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rows = harness_log.query("SELECT * FROM predictions WHERE run_id = ?", ("test-run",))
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assert {r["question_id"] for r in rows} == {"s1", "po"}
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