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>
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
"""Tests for fusionagi.gpu.tensor_similarity."""
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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_similarity import (
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pairwise_text_similarity,
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deduplicate_claims,
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nearest_neighbors,
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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 TestPairwiseTextSimilarity:
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def test_basic(self):
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sim = pairwise_text_similarity(["hello world"], ["hello world"])
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assert sim.shape == (1, 1)
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assert sim[0, 0] > 0.9
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def test_different_texts(self):
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sim = pairwise_text_similarity(["hello world"], ["completely different text"])
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assert sim.shape == (1, 1)
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assert sim[0, 0] < 1.0
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def test_multi(self):
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sim = pairwise_text_similarity(
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["cat", "dog"],
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["car", "bike", "train"],
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)
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assert sim.shape == (2, 3)
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class TestDeduplicateClaims:
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def test_empty(self):
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assert deduplicate_claims([]) == []
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def test_single(self):
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groups = deduplicate_claims(["one claim"])
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assert groups == [[0]]
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def test_identical(self):
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groups = deduplicate_claims(
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["the sky is blue", "the sky is blue"],
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threshold=0.9,
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)
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assert len(groups) == 1
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assert sorted(groups[0]) == [0, 1]
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def test_different(self):
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groups = deduplicate_claims(
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["the sky is blue", "python is a programming language"],
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threshold=0.99,
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)
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assert len(groups) == 2
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def test_all_indices_covered(self):
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claims = ["a", "b", "c", "d"]
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groups = deduplicate_claims(claims, threshold=0.99)
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all_indices = sorted(idx for group in groups for idx in group)
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assert all_indices == [0, 1, 2, 3]
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class TestNearestNeighbors:
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def test_empty_query(self):
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result = nearest_neighbors([], ["corpus text"])
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assert result == []
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def test_empty_corpus(self):
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result = nearest_neighbors(["query"], [])
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assert result == [[]]
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def test_basic(self):
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result = nearest_neighbors(
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["hello world"],
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["hello world", "goodbye moon", "hello planet"],
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top_k=2,
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)
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assert len(result) == 1
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assert len(result[0]) == 2
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# Each result is (index, score)
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assert isinstance(result[0][0], tuple)
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assert isinstance(result[0][0][0], int)
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assert isinstance(result[0][0][1], float)
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def test_top_k_limit(self):
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corpus = [f"text {i}" for i in range(20)]
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result = nearest_neighbors(["text 5"], corpus, top_k=3)
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assert len(result[0]) == 3
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