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2d0ef3b 2571402 2d0ef3b 2571402 2d0ef3b 2571402 2d0ef3b | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | from types import SimpleNamespace
import torch
import app.services.classifier_service as classifier_module
class _FakeTokenizer:
def __call__(self, sequence_pairs, padding, truncation, return_tensors):
batch_size = len(sequence_pairs)
return {
"input_ids": torch.ones((batch_size, 2), dtype=torch.long),
"attention_mask": torch.ones((batch_size, 2), dtype=torch.long),
}
class _FakeInferenceModel:
def __init__(self, logits: torch.Tensor, config: SimpleNamespace | None = None) -> None:
self._logits = logits
self.config = config or SimpleNamespace(
label2id={"CONTRADICTION": 0, "ENTAILMENT": 1},
id2label={0: "CONTRADICTION", 1: "ENTAILMENT"},
)
def __call__(self, **kwargs):
return SimpleNamespace(logits=self._logits)
class _FakeLoadModel:
def __init__(self) -> None:
self.config = SimpleNamespace(
label2id={"CONTRADICTION": 0, "ENTAILMENT": 1},
id2label={0: "CONTRADICTION", 1: "ENTAILMENT"},
)
def eval(self):
return self
def to(self, device):
return self
def test_classify_uses_runtime_candidate_labels(monkeypatch):
service = classifier_module.ClassifierService()
tokenizer = _FakeTokenizer()
model = _FakeInferenceModel(
logits=torch.tensor(
[
[3.2, 0.4], # finance -> low entailment
[0.3, 4.1], # sport -> highest entailment
[1.5, 1.9], # politics -> second-best entailment
]
)
)
monkeypatch.setattr(service, "_load_model", lambda: (tokenizer, model))
predicted = service.classify(
"This article discusses the latest football transfer strategy.",
["finance", "sport", "politics"],
)
assert predicted == "sport"
def test_classify_uses_task_specific_entailment_id_when_label_names_are_generic(monkeypatch):
service = classifier_module.ClassifierService()
tokenizer = _FakeTokenizer()
model = _FakeInferenceModel(
logits=torch.tensor(
[
[1.8, 0.3, 0.4], # finance -> low entailment
[0.4, 0.7, 3.7], # sport -> highest entailment
]
),
config=SimpleNamespace(
label2id={"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2},
id2label={0: "LABEL_0", 1: "LABEL_1", 2: "LABEL_2"},
task_specific_params={"zero-shot-classification": {"entailment_id": 2}},
num_labels=3,
),
)
monkeypatch.setattr(service, "_load_model", lambda: (tokenizer, model))
monkeypatch.setattr(classifier_module.settings, "classifier_entailment_label_id", None)
predicted = service.classify(
"The story is mostly about football transfers.",
["finance", "sport"],
)
assert predicted == "sport"
def test_classify_uses_explicit_entailment_id_setting_when_mapping_is_missing(monkeypatch):
service = classifier_module.ClassifierService()
tokenizer = _FakeTokenizer()
model = _FakeInferenceModel(
logits=torch.tensor(
[
[2.0, 0.3], # finance -> low entailment
[0.2, 3.4], # sport -> highest entailment
]
),
config=SimpleNamespace(
label2id={"NEGATIVE": 0, "POSITIVE": 1},
id2label={0: "NEGATIVE", 1: "POSITIVE"},
num_labels=2,
),
)
monkeypatch.setattr(service, "_load_model", lambda: (tokenizer, model))
monkeypatch.setattr(classifier_module.settings, "classifier_entailment_label_id", 1)
predicted = service.classify(
"The story is mostly about football transfers.",
["finance", "sport"],
)
assert predicted == "sport"
def test_classify_falls_back_to_mnli_entailment_index_for_generic_three_label_configs(monkeypatch):
service = classifier_module.ClassifierService()
tokenizer = _FakeTokenizer()
model = _FakeInferenceModel(
logits=torch.tensor(
[
[2.3, 0.6, 0.8], # finance -> low entailment
[0.4, 0.8, 3.9], # sport -> highest entailment
]
),
config=SimpleNamespace(
label2id={"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2},
id2label={0: "LABEL_0", 1: "LABEL_1", 2: "LABEL_2"},
num_labels=3,
),
)
monkeypatch.setattr(service, "_load_model", lambda: (tokenizer, model))
monkeypatch.setattr(classifier_module.settings, "classifier_entailment_label_id", None)
predicted = service.classify(
"The story is mostly about football transfers.",
["finance", "sport"],
)
assert predicted == "sport"
def test_classify_falls_back_to_mnli_entailment_index_for_missing_label_mapping(monkeypatch):
service = classifier_module.ClassifierService()
tokenizer = _FakeTokenizer()
model = _FakeInferenceModel(
logits=torch.tensor(
[
[1.8, 0.4, 0.5], # finance -> low entailment
[0.2, 0.5, 3.6], # sport -> highest entailment
]
),
config=SimpleNamespace(
label2id={},
id2label={},
num_labels=3,
),
)
monkeypatch.setattr(service, "_load_model", lambda: (tokenizer, model))
monkeypatch.setattr(classifier_module.settings, "classifier_entailment_label_id", None)
predicted = service.classify(
"The story is mostly about football transfers.",
["finance", "sport"],
)
assert predicted == "sport"
def test_model_quantization_falls_back_to_non_quantized_model(monkeypatch):
service = classifier_module.ClassifierService()
fake_model = _FakeLoadModel()
fake_tokenizer = object()
monkeypatch.setattr(
classifier_module.AutoTokenizer,
"from_pretrained",
lambda *args, **kwargs: fake_tokenizer,
)
monkeypatch.setattr(
classifier_module.AutoModelForSequenceClassification,
"from_pretrained",
lambda *args, **kwargs: fake_model,
)
monkeypatch.setattr(classifier_module.settings, "enable_model_quantization", True)
def _raise_quantization_error(*args, **kwargs):
raise RuntimeError("quantization backend unavailable")
monkeypatch.setattr(
classifier_module.torch.ao.quantization,
"quantize_dynamic",
_raise_quantization_error,
)
_, loaded_model = service._load_model()
assert loaded_model is fake_model
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