fa71f973a6
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- New fusionagi/gpu/ module with TensorBackend protocol abstraction - TensorFlowBackend: GPU-accelerated ops with TensorCore mixed-precision - NumPyBackend: CPU fallback (always available, no extra deps) - Auto-selects best available backend at runtime - GPU-accelerated operations: - Cosine similarity matrix (batched, XLA-compiled) - Multi-head attention for consensus scoring - Batch hypothesis scoring on GPU - Semantic similarity search (pairwise, nearest-neighbor, deduplication) - New TensorFlowAdapter (fusionagi/adapters/): - LLMAdapter for local TF/Keras model inference - TensorCore mixed-precision support - GPU-accelerated embedding synthesis fallback - Reasoning pipeline integration: - gpu_scoring.py: drop-in GPU replacement for multi_path scoring - Super Big Brain: use_gpu config flag, GPU scoring when available - Memory integration: - gpu_search.py: GPU-accelerated semantic search for SemanticGraphMemory - Self-improvement integration: - gpu_training.py: gradient-based heuristic weight optimization - Reflective memory training loop with loss tracking - Dependencies: gpu extra (tensorflow>=2.16, numpy>=1.26) - 64 new tests (276 total), all passing - Architecture spec: docs/gpu_tensorcore_integration.md 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 reset_backend, get_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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