iomgaa
6bdb802f01
- Add all claude skills (brainstorming, commit, debugging, TDD, etc.) - Add claude hooks (pre-commit-guard, post-edit-quality) - Add research templates (experiment plan, research brief, etc.) - Add claude tools (arxiv/semantic_scholar/openalex fetch, wiki, exa) - Add TRM4 reference implementation as algorithm fidelity baseline - Add research-wiki content (plans, index, graph, query_pack) - Update .gitignore to exclude .graphify_version runtime state
277 lines
9.0 KiB
Python
277 lines
9.0 KiB
Python
"""
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配置管理模块单元测试
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====================
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覆盖: YAML 加载、.env 覆盖、CLI 覆盖、缺字段报错、优先级验证、embed_dim 一致性。
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"""
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from __future__ import annotations
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from pathlib import Path
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import pytest
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import yaml
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from video_tree_trm.config import Config, TreeConfig
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# ---------------------------------------------------------------------------
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# Fixtures
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# ---------------------------------------------------------------------------
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_FULL_YAML = {
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"tree": {
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"max_paragraphs_per_l2": 5,
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"l1_segment_duration": 600.0,
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"l2_clip_duration": 20.0,
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"l3_fps": 1.0,
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"l2_representative_frames": 3,
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"cache_dir": "cache/trees",
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},
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"embed": {
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"backend": "local",
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"model_name": "test-model",
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"embed_dim": 2560,
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"device": "cpu",
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"api_key": "",
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"api_url": "",
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},
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"llm": {
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"backend": "qwen",
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"api_key": "yaml-llm-key",
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"model": "qwen-plus",
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"api_url": "https://example.com/llm",
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"max_tokens": 256,
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"temperature": 0.1,
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},
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"vlm": {
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"backend": "qwen",
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"api_key": "yaml-vlm-key",
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"model": "qwen-vl-plus",
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"api_url": "https://example.com/vlm",
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"max_tokens": 256,
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"temperature": 0.1,
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},
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"retriever": {
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"embed_dim": 2560,
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"num_heads": 4,
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"L_layers": 2,
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"L_cycles": 4,
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"max_rounds": 5,
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"ffn_expansion": 2.0,
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"checkpoint": None,
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},
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"train": {
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"lr": 1e-4,
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"weight_decay": 1e-5,
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"batch_size": 1,
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"max_epochs_phase1": 30,
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"max_epochs_phase2": 20,
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"nav_loss_weight": 1.0,
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"act_loss_weight": 0.1,
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"act_lambda_step": 0.1,
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"act_gamma": 0.9,
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"eval_interval": 5,
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"save_dir": "checkpoints",
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"dataset": "longbench",
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"dataset_path": "data/longbench",
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},
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}
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@pytest.fixture()
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def yaml_path(tmp_path: Path) -> Path:
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"""创建完整配置的临时 YAML 文件。"""
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p = tmp_path / "config" / "default.yaml"
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p.parent.mkdir(parents=True, exist_ok=True)
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with open(p, "w", encoding="utf-8") as f:
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yaml.dump(_FULL_YAML, f, allow_unicode=True)
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return p
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@pytest.fixture()
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def env_path(tmp_path: Path) -> Path:
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"""创建临时 .env 文件。"""
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p = tmp_path / ".env"
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p.write_text(
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"LLM_API_KEY=env-llm-key\n"
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"LLM_MODEL=env-llm-model\n"
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"LLM_API_URL=https://env.example.com/llm\n"
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"VLM_API_KEY=env-vlm-key\n"
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"VLM_MODEL=env-vlm-model\n"
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"VLM_API_URL=https://env.example.com/vlm\n"
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"EMBED_BACKEND=remote\n"
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"EMBED_MODEL=env-embed-model\n"
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"EMBED_API_KEY=env-embed-key\n"
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"EMBED_API_URL=https://env.example.com/embed\n"
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)
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return p
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# ---------------------------------------------------------------------------
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# 测试: YAML 加载
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# ---------------------------------------------------------------------------
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class TestYAMLLoad:
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"""YAML 基础加载测试。"""
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def test_load_full_yaml(self, yaml_path: Path) -> None:
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"""完整 YAML 应成功加载所有字段。"""
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cfg = Config.load(
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str(yaml_path), env_path=str(yaml_path.parent / ".env.nonexist")
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)
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assert isinstance(cfg.tree, TreeConfig)
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assert cfg.tree.max_paragraphs_per_l2 == 5
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assert cfg.tree.l1_segment_duration == 600.0
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assert cfg.embed.embed_dim == 2560
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assert cfg.retriever.checkpoint is None
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assert cfg.train.dataset == "longbench"
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def test_file_not_found(self, tmp_path: Path) -> None:
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"""不存在的 YAML 应抛出 FileNotFoundError。"""
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with pytest.raises(FileNotFoundError, match="配置文件不存在"):
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Config.load(str(tmp_path / "nonexist.yaml"))
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# ---------------------------------------------------------------------------
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# 测试: 缺字段报错
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# ---------------------------------------------------------------------------
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class TestMissingField:
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"""缺少必需字段时应抛出 TypeError。"""
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def test_missing_tree_field(self, tmp_path: Path) -> None:
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"""tree 节缺少字段应报 TypeError。"""
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bad_yaml = _FULL_YAML.copy()
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bad_yaml = {
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k: (v.copy() if isinstance(v, dict) else v) for k, v in _FULL_YAML.items()
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}
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del bad_yaml["tree"]["cache_dir"]
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p = tmp_path / "bad.yaml"
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with open(p, "w") as f:
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yaml.dump(bad_yaml, f)
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with pytest.raises(TypeError):
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Config.load(str(p), env_path=str(tmp_path / ".env.nonexist"))
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def test_missing_section(self, tmp_path: Path) -> None:
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"""缺少整个配置节应报 TypeError。"""
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bad_yaml = {k: v for k, v in _FULL_YAML.items() if k != "train"}
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p = tmp_path / "bad2.yaml"
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with open(p, "w") as f:
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yaml.dump(bad_yaml, f)
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with pytest.raises(TypeError):
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Config.load(str(p), env_path=str(tmp_path / ".env.nonexist"))
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# ---------------------------------------------------------------------------
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# 测试: .env 覆盖
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# ---------------------------------------------------------------------------
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class TestEnvOverride:
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""".env 文件应覆盖 YAML 中的 api_key。"""
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def test_env_overrides_api_keys(self, yaml_path: Path, env_path: Path) -> None:
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"""api_key/model/api_url 应优先使用 .env 中的值。"""
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cfg = Config.load(str(yaml_path), env_path=str(env_path))
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assert cfg.llm.api_key == "env-llm-key"
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assert cfg.llm.model == "env-llm-model"
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assert cfg.llm.api_url == "https://env.example.com/llm"
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assert cfg.vlm.api_key == "env-vlm-key"
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assert cfg.vlm.model == "env-vlm-model"
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assert cfg.vlm.api_url == "https://env.example.com/vlm"
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def test_env_overrides_embed(self, yaml_path: Path, env_path: Path) -> None:
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"""embed 相关字段应优先使用 .env 中的值。"""
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cfg = Config.load(str(yaml_path), env_path=str(env_path))
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assert cfg.embed.backend == "remote"
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assert cfg.embed.model_name == "env-embed-model"
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assert cfg.embed.api_key == "env-embed-key"
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assert cfg.embed.api_url == "https://env.example.com/embed"
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def test_yaml_fallback_when_no_env(self, yaml_path: Path) -> None:
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"""无 .env 时应使用 YAML 中的值。"""
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cfg = Config.load(
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str(yaml_path), env_path=str(yaml_path.parent / ".env.nonexist")
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)
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assert cfg.llm.api_key == "yaml-llm-key"
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assert cfg.vlm.api_key == "yaml-vlm-key"
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# ---------------------------------------------------------------------------
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# 测试: CLI 覆盖
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# ---------------------------------------------------------------------------
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class TestCLIOverride:
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"""CLI args 应覆盖 YAML 和 .env 的值。"""
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def test_cli_overrides_yaml(self, yaml_path: Path) -> None:
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"""CLI 点路径覆盖应生效。"""
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cfg = Config.load(
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str(yaml_path),
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cli_args={"retriever.num_heads": 8, "train.lr": 0.001},
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env_path=str(yaml_path.parent / ".env.nonexist"),
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)
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assert cfg.retriever.num_heads == 8
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assert cfg.train.lr == 0.001
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def test_cli_overrides_env(self, yaml_path: Path, env_path: Path) -> None:
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"""CLI 应覆盖 .env 中的 api_key。"""
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cfg = Config.load(
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str(yaml_path),
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cli_args={"llm.api_key": "cli-key"},
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env_path=str(env_path),
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)
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assert cfg.llm.api_key == "cli-key"
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# ---------------------------------------------------------------------------
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# 测试: 优先级
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# ---------------------------------------------------------------------------
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class TestPriority:
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"""三层优先级: CLI > .env > YAML。"""
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def test_full_priority_chain(self, yaml_path: Path, env_path: Path) -> None:
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"""CLI > .env > YAML 的完整优先级链。"""
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cfg = Config.load(
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str(yaml_path),
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cli_args={"llm.api_key": "cli-key"},
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env_path=str(env_path),
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)
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# CLI 覆盖 .env
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assert cfg.llm.api_key == "cli-key"
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# .env 覆盖 YAML（vlm 未被 CLI 覆盖）
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assert cfg.vlm.api_key == "env-vlm-key"
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# ---------------------------------------------------------------------------
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# 测试: embed_dim 一致性校验
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# ---------------------------------------------------------------------------
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class TestEmbedDimConsistency:
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"""embed.embed_dim 与 retriever.embed_dim 必须一致。"""
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def test_inconsistent_embed_dim(self, tmp_path: Path) -> None:
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"""embed_dim 不一致应抛出 ValueError。"""
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bad_yaml = {
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k: (v.copy() if isinstance(v, dict) else v) for k, v in _FULL_YAML.items()
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}
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bad_yaml["retriever"] = bad_yaml["retriever"].copy()
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bad_yaml["retriever"]["embed_dim"] = 512 # 与 embed.embed_dim=768 不一致
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p = tmp_path / "bad_dim.yaml"
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with open(p, "w") as f:
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yaml.dump(bad_yaml, f)
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with pytest.raises(ValueError, match="不一致"):
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Config.load(str(p), env_path=str(tmp_path / ".env.nonexist"))
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