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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import importlib.util
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from pathlib import Path
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import torch
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HERE = Path(__file__).parent
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def load(name):
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spec = importlib.util.spec_from_file_location(name, HERE / f"{name}.py")
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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def test_sesame_tag_categories_are_explicit():
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sesame = load("run_sesame")
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assert sesame.category("hello <laughs> there") == "laugh"
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assert sesame.category("hello <giggle> there") == "giggle"
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assert sesame.category("hello <sighs> there") == "sigh"
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assert sesame.category("hello there") == "neutral"
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def test_orpheus_collator_masks_label_padding():
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orpheus = load("run_orpheus")
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rows = [
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{"input_ids": [1, 2], "labels": [1, 2], "attention_mask": [1, 1]},
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{"input_ids": [3], "labels": [3], "attention_mask": [1]},
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]
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batch = orpheus.PadCollator(9)(rows)
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assert batch["input_ids"].tolist() == [[1, 2], [3, 9]]
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assert batch["labels"].tolist() == [[1, 2], [3, -100]]
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assert batch["attention_mask"].tolist() == [[1, 1], [1, 0]]
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def test_sesame_collator_stacks_all_model_inputs():
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sesame = load("run_sesame")
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rows = [{"input_ids": torch.tensor([1, 2]), "labels": torch.tensor([3, 4])}] * 2
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batch = sesame.TensorCollator()(rows)
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assert batch["input_ids"].shape == (2, 2)
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assert batch["labels"].shape == (2, 2)
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def test_sha256_is_stable(tmp_path):
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analysis = load("analyze_campaign")
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path = tmp_path / "artifact"
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path.write_bytes(b"experiment-8-6")
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assert analysis.sha256(path) == "b07a691b33e493299473b6323258c9d643b2981d6de43e8c8adc3c4edc222d15"
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