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>
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
"""Tests for fusionagi.gpu.tensor_scoring and reasoning.gpu_scoring."""
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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_scoring import (
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gpu_score_hypotheses,
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gpu_score_claims_against_reference,
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)
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from fusionagi.reasoning.gpu_scoring import (
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generate_and_score_gpu,
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score_claims_gpu,
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deduplicate_claims_gpu,
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)
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from fusionagi.schemas.atomic import AtomicSemanticUnit, AtomicUnitType
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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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def _make_unit(content: str) -> AtomicSemanticUnit:
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return AtomicSemanticUnit(
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unit_id=f"u_{hash(content) % 10000}",
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content=content,
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type=AtomicUnitType.FACT,
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confidence=1.0,
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)
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class TestGPUScoreHypotheses:
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def test_empty(self):
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assert gpu_score_hypotheses([], []) == []
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def test_basic(self):
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units = [_make_unit("the sky is blue"), _make_unit("water is wet")]
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results = gpu_score_hypotheses(["the sky is blue"], units)
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assert len(results) == 1
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node, score = results[0]
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assert node.thought == "the sky is blue"
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assert 0.0 <= score <= 1.0
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def test_multiple_hypotheses(self):
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units = [_make_unit("python is great")]
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results = gpu_score_hypotheses(
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["python is great", "java is better", "rust is fast"],
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units,
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)
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assert len(results) == 3
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# Should be sorted by score descending
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scores = [s for _, s in results]
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assert scores == sorted(scores, reverse=True)
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def test_no_units(self):
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results = gpu_score_hypotheses(["test hypothesis"], [])
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assert len(results) == 1
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assert results[0][1] == 0.5
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def test_gpu_metadata(self):
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units = [_make_unit("test content")]
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results = gpu_score_hypotheses(["test content"], units)
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node, _ = results[0]
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assert node.metadata.get("gpu_scored") is True
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class TestGPUScoreClaimsAgainstReference:
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def test_empty(self):
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assert gpu_score_claims_against_reference([], "ref") == []
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def test_basic(self):
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scores = gpu_score_claims_against_reference(
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["claim one", "claim two"],
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"claim one reference",
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)
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assert len(scores) == 2
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assert all(isinstance(s, float) for s in scores)
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class TestReasoningGPUScoring:
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def test_generate_and_score_gpu(self):
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units = [_make_unit("hello world"), _make_unit("testing gpu")]
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results = generate_and_score_gpu(["hello world", "testing gpu"], units)
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assert len(results) == 2
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def test_score_claims_gpu(self):
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scores = score_claims_gpu(["test claim"], "reference text")
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assert len(scores) == 1
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assert isinstance(scores[0], float)
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def test_deduplicate_claims_gpu(self):
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groups = deduplicate_claims_gpu(["a", "b", "c"])
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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]
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