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48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
"""Synthetic scaled dot product attention using published RuVector packages.
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Requires ruvector-attention==0.2.0, ruvector==0.1.3 and numpy==2.2.6.
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This validates attention math, not model training or text generation.
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"""
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from __future__ import annotations
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from importlib.metadata import version
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import numpy as np
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from ruvector_attention import attend
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def main() -> None:
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assert version("ruvector-attention") == "0.2.0"
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assert version("ruvector") == "0.1.3"
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query = np.array([1.0, 0.0], dtype=np.float32)
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candidates = np.array([[1.0, 0.0], [0.0, 1.0], [-1.0, 0.0]], dtype=np.float32)
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result = attend(query, candidates)
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# Independent float64 reference; subtracting max prevents exp overflow.
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logits = candidates.astype(np.float64) @ query.astype(np.float64) / np.sqrt(2.0)
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reference = np.exp(logits - logits.max())
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reference /= reference.sum()
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np.testing.assert_allclose(result.weights, reference, rtol=1e-6, atol=1e-7)
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np.testing.assert_allclose(result.blended, reference @ candidates, rtol=1e-6, atol=1e-7)
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assert result.blended.shape == (2,)
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assert np.isclose(result.weights.sum(), 1.0)
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assert int(np.argmax(result.weights)) == 0
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rejected = 0
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for bad_query in ([1.0], [float("nan"), 0.0]):
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try:
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attend(bad_query, candidates)
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except ValueError:
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rejected += 1
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else:
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raise AssertionError("invalid input was accepted")
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assert rejected == 2
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print("ruvector-attention=0.2.0 ruvector=0.1.3")
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print("weights=" + ",".join(f"{weight:.6f}" for weight in result.weights))
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print("blended=" + ",".join(f"{coordinate:.6f}" for coordinate in result.blended))
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print("PASS: native attention matches reference; invalid inputs rejected")
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if __name__ == "__main__":
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main()
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