64b800c6cf
Items completed: 1. Merged PR #2 (starlette/httpx deps) 2. Fixed async race condition in multimodal_ui.py 3. Wired TTSAdapter (ElevenLabs, Azure) in API routes 4. Moved super_big_brain.py from core/ to reasoning/ (backward compat shim) 5. Added API authentication middleware (Bearer token via FUSIONAGI_API_KEY) 6. Added async adapter interface (acomplete/acomplete_structured) 7. Migrated FastAPI on_event to lifespan (fixes 20 deprecation warnings) 8. Liquid Neural Networks (continuous-time adaptive weights) 9. Quantum-AI Hybrid compute backend (simulator + optimization) 10. Embodied Intelligence / Robotics bridge (actuator + sensor protocols) 11. Consciousness Engineering (formal self-model with introspection) 12. ASI Scoring Rubric (C/A/L/N/R self-assessment harness) 13. GPU integration tests for TensorFlow backend 14. Multi-stage production Dockerfile 15. Gitea CI/CD pipeline (lint, test matrix, Docker build) 16. API rate limiting middleware (per-IP sliding window) 17. OpenAPI docs cleanup (auth + rate limiting descriptions) 18. Benchmarking suite (decomposition, multi-path, recomposition, e2e) 19. Plugin system (head registry for custom heads) 427 tests passing, 0 ruff errors, 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
"""Tests for fusionagi.gpu.tensor_attention."""
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import pytest
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from fusionagi.gpu.backend import get_backend, reset_backend
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from fusionagi.gpu.tensor_attention import (
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attention_consensus,
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cross_claim_attention,
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)
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@pytest.fixture(autouse=True)
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def _use_numpy():
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reset_backend()
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get_backend(force="numpy")
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yield
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reset_backend()
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class TestAttentionConsensus:
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def test_empty(self):
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result = attention_consensus([], "query")
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assert result["head_scores"] == []
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assert result["consensus_score"] == 0.0
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def test_single_head(self):
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result = attention_consensus(
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[["the sky is blue"]],
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"what color is the sky",
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)
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assert len(result["head_scores"]) == 1
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assert isinstance(result["consensus_score"], float)
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def test_multiple_heads(self):
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result = attention_consensus(
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[
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["the sky is blue", "water is wet"],
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["security is important"],
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["cost should be minimized"],
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],
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"what should we do about the project",
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)
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assert len(result["head_scores"]) == 3
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assert 0.0 <= result["consensus_score"] <= 1.0
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def test_with_weights(self):
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result = attention_consensus(
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[["claim a"], ["claim b"]],
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"query",
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head_weights=[2.0, 0.5],
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)
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assert len(result["head_scores"]) == 2
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def test_empty_claims(self):
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result = attention_consensus(
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[[], []],
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"query",
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)
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assert len(result["head_scores"]) == 2
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assert result["head_scores"] == [0.0, 0.0]
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class TestCrossClaimAttention:
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def test_empty(self):
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result = cross_claim_attention([])
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assert result["similarity_matrix"] == []
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assert result["conflict_pairs"] == []
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def test_single(self):
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result = cross_claim_attention(["only one claim"])
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assert result["similarity_matrix"] == []
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def test_two_claims(self):
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result = cross_claim_attention(["claim one", "claim two"])
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assert len(result["similarity_matrix"]) == 2
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assert len(result["similarity_matrix"][0]) == 2
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def test_self_similarity_high(self):
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result = cross_claim_attention(["same text", "same text"])
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sim = result["similarity_matrix"]
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assert sim[0][0] > 0.9
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assert sim[1][1] > 0.9
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def test_conflict_detection(self):
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result = cross_claim_attention([
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"the project is very safe and reliable",
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"completely unrelated topic about food and cooking",
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])
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assert isinstance(result["conflict_pairs"], list)
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