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>
106 lines
3.3 KiB
Python
106 lines
3.3 KiB
Python
"""GPU-accelerated scoring integration for reasoning pipeline.
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Provides drop-in GPU replacements for CPU scoring functions used in
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multi_path.py and consensus_engine.py. Automatically falls back to
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CPU when GPU is not available.
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"""
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from __future__ import annotations
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from typing import Callable
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from fusionagi._logger import logger
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from fusionagi.reasoning.tot import ThoughtNode
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from fusionagi.schemas.atomic import AtomicSemanticUnit, AtomicUnitType
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def generate_and_score_gpu(
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hypotheses: list[str],
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units: list[AtomicSemanticUnit],
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score_fn: Callable[[ThoughtNode, list[AtomicSemanticUnit]], float] | None = None,
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) -> list[tuple[ThoughtNode, float]]:
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"""GPU-accelerated hypothesis scoring, drop-in for generate_and_score_parallel.
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Uses GPU tensor operations for batched scoring when available,
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falling back to the original CPU implementation.
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Args:
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hypotheses: List of hypothesis texts.
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units: Atomic semantic units for context.
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score_fn: Optional custom scoring function (overrides GPU scoring).
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Returns:
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List of (ThoughtNode, score) tuples sorted by score descending.
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"""
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if score_fn is not None:
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from fusionagi.reasoning.multi_path import generate_and_score_parallel
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return generate_and_score_parallel(hypotheses, units, score_fn)
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try:
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from fusionagi.gpu.tensor_scoring import gpu_score_hypotheses
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results = gpu_score_hypotheses(hypotheses, units)
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logger.debug(
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"GPU scoring used for hypotheses",
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extra={"count": len(hypotheses), "backend": "gpu"},
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)
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return results
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except ImportError:
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from fusionagi.reasoning.multi_path import generate_and_score_parallel
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logger.debug("GPU not available, using CPU scoring")
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return generate_and_score_parallel(hypotheses, units)
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def score_claims_gpu(
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claims: list[str],
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reference: str,
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) -> list[float]:
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"""Score claims against a reference using GPU when available.
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Args:
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claims: List of claim texts.
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reference: Reference text.
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Returns:
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List of scores for each claim.
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"""
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try:
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from fusionagi.gpu.tensor_scoring import gpu_score_claims_against_reference
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return gpu_score_claims_against_reference(claims, reference)
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except ImportError:
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from fusionagi.reasoning.multi_path import _score_consistency
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scores: list[float] = []
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for claim in claims:
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node = ThoughtNode(thought=claim, trace=[claim])
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unit = AtomicSemanticUnit(
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unit_id="ref", content=reference, type=AtomicUnitType.FACT, confidence=1.0
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)
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scores.append(_score_consistency(node, [unit]))
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return scores
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def deduplicate_claims_gpu(
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claims: list[str],
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threshold: float = 0.85,
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) -> list[list[int]]:
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"""GPU-accelerated claim deduplication.
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Args:
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claims: List of claim texts.
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threshold: Similarity threshold for grouping.
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Returns:
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List of groups (each group is a list of indices).
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"""
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try:
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from fusionagi.gpu.tensor_similarity import deduplicate_claims
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return deduplicate_claims(claims, threshold)
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except ImportError:
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groups: list[list[int]] = [[i] for i in range(len(claims))]
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return groups
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