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>
130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
"""Tests for fusionagi.gpu backend, similarity, attention, scoring, and training."""
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import pytest
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import numpy as np
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from fusionagi.gpu.backend import (
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DeviceType,
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NumPyBackend,
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TensorBackend,
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get_backend,
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reset_backend,
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)
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@pytest.fixture(autouse=True)
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def _reset():
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"""Reset backend singleton between tests."""
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reset_backend()
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yield
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reset_backend()
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class TestNumPyBackend:
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"""Tests for NumPyBackend (CPU fallback)."""
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def test_name(self):
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be = NumPyBackend()
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assert be.name == "numpy"
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def test_device(self):
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be = NumPyBackend()
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assert be.device == DeviceType.CPU
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def test_gpu_available(self):
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be = NumPyBackend()
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assert be.gpu_available() is False
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def test_embed_texts_shape(self):
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be = NumPyBackend()
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emb = be.embed_texts(["hello world", "foo bar baz"])
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assert emb.shape == (2, 256)
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def test_embed_texts_normalized(self):
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be = NumPyBackend()
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emb = be.embed_texts(["some text here"])
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norm = np.linalg.norm(emb[0])
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assert abs(norm - 1.0) < 1e-5
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def test_embed_texts_deterministic(self):
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be = NumPyBackend()
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emb1 = be.embed_texts(["hello world"])
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emb2 = be.embed_texts(["hello world"])
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np.testing.assert_array_almost_equal(emb1, emb2)
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def test_cosine_similarity_matrix_shape(self):
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be = NumPyBackend()
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a = be.embed_texts(["hello", "world"])
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b = be.embed_texts(["foo", "bar", "baz"])
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sim = be.cosine_similarity_matrix(a, b)
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assert sim.shape == (2, 3)
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def test_cosine_similarity_self(self):
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be = NumPyBackend()
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emb = be.embed_texts(["test sentence"])
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sim = be.cosine_similarity_matrix(emb, emb)
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assert abs(sim[0, 0] - 1.0) < 1e-5
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def test_batch_score_shape(self):
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be = NumPyBackend()
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hyp = be.embed_texts(["h1", "h2", "h3"])
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ref = be.embed_texts(["reference"])[0]
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scores = be.batch_score(hyp, ref)
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assert scores.shape == (3,)
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def test_batch_score_with_weights(self):
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be = NumPyBackend()
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hyp = be.embed_texts(["h1", "h2"])
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ref = be.embed_texts(["reference"])[0]
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weights = np.ones(256, dtype=np.float32)
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scores = be.batch_score(hyp, ref, weights)
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assert scores.shape == (2,)
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def test_multi_head_attention_shape(self):
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be = NumPyBackend()
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q = be.embed_texts(["query1", "query2"])
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k = be.embed_texts(["key1", "key2", "key3"])
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v = be.embed_texts(["val1", "val2", "val3"])
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out = be.multi_head_attention(q, k, v, num_heads=4)
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assert out.shape[0] == 2
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def test_to_numpy_roundtrip(self):
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be = NumPyBackend()
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arr = np.array([1.0, 2.0, 3.0])
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tensor = be.from_numpy(arr)
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result = be.to_numpy(tensor)
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np.testing.assert_array_equal(arr, result)
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def test_device_summary(self):
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be = NumPyBackend()
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summary = be.device_summary()
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assert summary["backend"] == "numpy"
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assert summary["device"] == "cpu"
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def test_enable_mixed_precision_noop(self):
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be = NumPyBackend()
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be.enable_mixed_precision()
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class TestGetBackend:
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"""Tests for backend auto-selection."""
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def test_force_numpy(self):
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be = get_backend(force="numpy")
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assert be.name == "numpy"
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def test_default_returns_backend(self):
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be = get_backend()
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assert isinstance(be, TensorBackend)
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def test_cached_singleton(self):
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be1 = get_backend(force="numpy")
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be2 = get_backend()
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assert be1 is be2
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def test_reset_clears_cache(self):
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be1 = get_backend(force="numpy")
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reset_backend()
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be2 = get_backend(force="numpy")
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assert be1 is not be2
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