132 lines
5.0 KiB
Python
132 lines
5.0 KiB
Python
"""soak harness 纯函数层测试(M2 设计 §8;T10/T11)。
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语料还原器与不变量断言函数是纯函数,用真实 schema 的二次构造微型库测试
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(真实场景优先: schema 与 data/soak 实库逐字一致)。
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"""
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import json
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import sqlite3
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import pytest
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from tools.soak.corpus import (
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assemble_frame_messages,
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chs_image_messages,
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load_replay_payloads,
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load_trace_chains,
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)
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from tools.soak.scenarios import weighted_mix
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def _mini_harness_db(path):
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"""predictions 表 schema 与 data/soak/harness.db 逐字一致的微型库。"""
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conn = sqlite3.connect(path)
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conn.execute(
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"CREATE TABLE predictions (run_id TEXT, timestamp TEXT, video_id TEXT,"
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" question_id TEXT, task_type TEXT, prediction TEXT, answer TEXT, evidence TEXT,"
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" reasoning TEXT, steps_used INTEGER, prompt_tokens INTEGER,"
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" completion_tokens INTEGER, stop_reason TEXT, steps_json TEXT)"
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)
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steps = [
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{"thought": f"思考{i}", "tool_call": {"tool": "view_node", "args": {"i": i}},
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"tool_output": f"输出{i}" * 50}
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for i in range(3)
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]
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conn.execute(
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"INSERT INTO predictions VALUES ('r1','t','vid1','q1','synopsis','A','A','','',3,1,1,'stop',?)",
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(json.dumps(steps, ensure_ascii=False),),
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)
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conn.execute(
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"INSERT INTO predictions VALUES ('r1','t','vid2','q2','synopsis','B','B','','',0,1,1,'stop','[]')"
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)
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conn.commit()
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conn.close()
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def _mini_telemetry_db(path):
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conn = sqlite3.connect(path)
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conn.execute(
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"CREATE TABLE llm_calls (call_id TEXT PRIMARY KEY, parent_call_id TEXT,"
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" session_id TEXT, model_name TEXT, provider TEXT, messages TEXT, response TEXT,"
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" thinking TEXT, prompt_tokens INTEGER, completion_tokens INTEGER, latency_ms INTEGER,"
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" ttft_ms REAL, max_inter_token_ms REAL, cache_hit INTEGER, error TEXT, created_at TEXT)"
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)
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msgs = json.dumps([{"role": "user", "content": "回放负载"}])
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conn.execute(
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"INSERT INTO llm_calls VALUES ('c1',NULL,'s1','m','p',?,'r','',1,1,1,NULL,NULL,0,NULL,'t')",
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(msgs,),
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)
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conn.execute(
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"INSERT INTO llm_calls VALUES ('c2',NULL,'s1','m','p','not-json','r','',1,1,1,NULL,NULL,0,NULL,'t')"
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)
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conn.commit()
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conn.close()
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class TestTraceChains:
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def test_cumulative_chain_shapes(self, tmp_path):
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db = tmp_path / "h.db"
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_mini_harness_db(db)
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chains = load_trace_chains(db)
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assert len(chains) == 1 # steps_used=0 的行剔除
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chain = chains[0]
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assert len(chain) == 3 # 每步一个累积快照
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# 快照单调增长: system+user 起步,每步追加 assistant+user
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assert [len(snap) for snap in chain] == [4, 6, 8]
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first = chain[0]
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assert first[0]["role"] == "system" and first[1]["role"] == "user"
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assert first[2]["role"] == "assistant" and "思考0" in first[2]["content"]
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assert first[3]["role"] == "user" and "输出0" in first[3]["content"]
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# 后续快照是前缀扩展(累积语义)
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assert chain[1][:4] == chain[0]
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def test_max_steps_cap(self, tmp_path):
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db = tmp_path / "h.db"
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_mini_harness_db(db)
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assert [len(s) for s in load_trace_chains(db, max_steps=2)[0]] == [4, 6]
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class TestReplayPayloads:
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def test_loads_and_skips_unparseable(self, tmp_path):
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db = tmp_path / "t.db"
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_mini_telemetry_db(db)
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payloads = load_replay_payloads(db)
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assert len(payloads) == 1 # 坏 JSON 行剔除
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assert payloads[0][0]["content"] == "回放负载"
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class TestImageAssembly:
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def test_frame_messages_two_tiers(self, tmp_path):
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vid = tmp_path / "vid1" / "frames"
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vid.mkdir(parents=True)
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for i in range(6):
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(vid / f"f{i}.jpg").write_bytes(b"\xff\xd8fakejpeg" + bytes([i]))
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msgs = assemble_frame_messages(tmp_path, n_frames=4, rng=lambda: 0.0)
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parts = msgs[0]["content"]
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images = [p for p in parts if p["type"] == "image_url"]
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assert len(images) == 4
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assert images[0]["image_url"]["url"].startswith("data:image/jpeg;base64,")
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def test_chs_single_image_with_instruction(self, tmp_path):
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(tmp_path / "chs_0001.jpg").write_bytes(b"\xff\xd8fake")
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msgs = chs_image_messages(tmp_path / "chs_0001.jpg")
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parts = msgs[0]["content"]
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assert any(p["type"] == "image_url" for p in parts)
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text = next(p["text"] for p in parts if p["type"] == "text")
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assert "JSON" in text # 固定结构化指令
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class TestWeightedMix:
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def test_ratio_approximation(self):
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import random
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rng = random.Random(42).random
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weights = {"P1": 0.1, "P2": 0.2, "P3": 0.5, "P5": 0.2}
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picks = [weighted_mix(weights, rng) for _ in range(10_000)]
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for key, w in weights.items():
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assert picks.count(key) / 10_000 == pytest.approx(w, abs=0.05)
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def test_invalid_weights_rejected(self):
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with pytest.raises(ValueError):
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weighted_mix({"P1": 0.0}, lambda: 0.5)
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