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>
57 lines
1.3 KiB
Python
57 lines
1.3 KiB
Python
"""GPU-accelerated tensor operations for FusionAGI.
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Auto-selects the best available backend:
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- TensorFlow with TensorCore/mixed-precision (when installed)
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- NumPy CPU fallback (always available)
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Install GPU support: pip install fusionagi[gpu]
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"""
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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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from fusionagi.gpu.tensor_attention import (
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attention_consensus,
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cross_claim_attention,
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)
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from fusionagi.gpu.tensor_scoring import (
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gpu_score_claims_against_reference,
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gpu_score_hypotheses,
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)
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from fusionagi.gpu.tensor_similarity import (
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deduplicate_claims,
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nearest_neighbors,
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pairwise_text_similarity,
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)
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from fusionagi.gpu.training import (
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TrainingConfig,
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TrainingResult,
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optimize_heuristic_weights,
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prepare_training_pairs,
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run_gpu_training,
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)
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__all__ = [
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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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"deduplicate_claims",
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"nearest_neighbors",
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"pairwise_text_similarity",
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"attention_consensus",
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"cross_claim_attention",
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"gpu_score_claims_against_reference",
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"gpu_score_hypotheses",
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"TrainingConfig",
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"TrainingResult",
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"optimize_heuristic_weights",
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"prepare_training_pairs",
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"run_gpu_training",
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]
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