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>
87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
"""GPU-accelerated semantic search for memory subsystems.
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Provides vector similarity search using GPU-accelerated embeddings
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for SemanticGraphMemory and EpisodicMemory.
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"""
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from __future__ import annotations
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from typing import Any
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from fusionagi._logger import logger
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from fusionagi.schemas.atomic import AtomicSemanticUnit
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def semantic_search(
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query: str,
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units: list[AtomicSemanticUnit],
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top_k: int = 10,
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) -> list[tuple[AtomicSemanticUnit, float]]:
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"""Search atomic semantic units by vector similarity using GPU.
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Args:
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query: Query text to search for.
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units: List of atomic semantic units to search within.
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top_k: Number of top results to return.
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Returns:
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List of (unit, similarity_score) tuples sorted by score descending.
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"""
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if not units:
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return []
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try:
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from fusionagi.gpu.tensor_similarity import nearest_neighbors
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corpus = [u.content for u in units]
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results = nearest_neighbors([query], corpus, top_k=top_k)
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if not results or not results[0]:
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return []
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return [(units[idx], score) for idx, score in results[0] if idx < len(units)]
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except ImportError:
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return _cpu_fallback_search(query, units, top_k)
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def _cpu_fallback_search(
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query: str,
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units: list[AtomicSemanticUnit],
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top_k: int,
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) -> list[tuple[AtomicSemanticUnit, float]]:
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"""CPU fallback: simple word-overlap similarity."""
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query_words = set(query.lower().split())
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scored: list[tuple[AtomicSemanticUnit, float]] = []
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for unit in units:
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unit_words = set(unit.content.lower().split())
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if not unit_words:
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continue
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overlap = len(query_words & unit_words)
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score = overlap / max(len(query_words | unit_words), 1)
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scored.append((unit, score))
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scored.sort(key=lambda x: x[1], reverse=True)
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return scored[:top_k]
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def batch_embed_units(
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units: list[AtomicSemanticUnit],
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) -> Any:
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"""Embed a batch of atomic semantic units using GPU.
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Args:
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units: Units to embed.
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Returns:
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Embedding tensor (backend-specific type).
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"""
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try:
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from fusionagi.gpu.backend import get_backend
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be = get_backend()
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texts = [u.content for u in units]
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return be.embed_texts(texts)
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except ImportError:
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logger.debug("GPU not available for batch embedding")
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return None
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