File size: 2,463 Bytes
257d034 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import json
from pathlib import Path
import pytest
import torch
from safetensors import safe_open
from inference import DecisionModel, format_candidate
ROOT = Path(__file__).resolve().parent
def test_required_release_files_exist():
required = {
"README.md", "adapter_model.safetensors", "adapter_config.json",
"decision_head.safetensors", "tokenizer.json", "tokenizer_config.json",
"model.py", "inference.py", "requirements.txt", "example.py",
"calibration.json", "evaluation_report.json",
}
assert not required.difference(path.name for path in ROOT.iterdir())
def test_release_configs_are_portable():
adapter = json.loads((ROOT / "adapter_config.json").read_text(encoding="utf-8"))
calibration = json.loads((ROOT / "calibration.json").read_text(encoding="utf-8"))
assert adapter["base_model_name_or_path"] == "openbmb/MiniCPM5-2B-Base"
assert calibration["use_temperature"] is False
assert calibration["default_prediction"] == "raw"
def test_safetensors_are_readable_and_finite():
for name in ("adapter_model.safetensors", "decision_head.safetensors"):
with safe_open(ROOT / name, framework="pt", device="cpu") as handle:
keys = list(handle.keys())
assert keys
for key in keys:
assert torch.isfinite(handle.get_tensor(key)).all(), key
def test_probability_sum_with_stubbed_runtime(monkeypatch):
model = DecisionModel.__new__(DecisionModel)
model.device = torch.device("cpu")
model.max_length = 512
class Encoded(dict):
def to(self, _device):
return self
class Tokenizer:
def __call__(self, texts, **_kwargs):
n = len(texts)
return Encoded(input_ids=torch.ones((n, 2), dtype=torch.long), attention_mask=torch.ones((n, 2), dtype=torch.long))
class Scorer:
def __call__(self, input_ids, attention_mask):
del attention_mask
return torch.arange(input_ids.shape[0], dtype=torch.float32)
model.tokenizer = Tokenizer()
model.model = Scorer()
result = model.decide(state="s", question="q", options=["a", "b", "c"])
assert sum(result["probabilities"]) == pytest.approx(1.0, abs=1e-7)
assert result["choice"] == "c"
def test_prompt_format_is_stable():
text = format_candidate("state", "choice", "question", "option")
assert text.endswith("How well does this candidate answer the question?")
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