FusionAGI/docs/gpu_tensorcore_integration.md

GPU / TensorCore Integration — Architecture Spec

Overview

FusionAGI integrates GPU-accelerated compute via TensorFlow, CUDA TensorCores, and JAX to transform reasoning, similarity scoring, consensus, and training from CPU-bound symbolic operations into massively parallel tensor operations.

Design Principles

1. Optional dependency — GPU support is an extra (pip install fusionagi[gpu]). All GPU-accelerated code paths have CPU fallbacks.
2. Module boundary — GPU compute lives in fusionagi/gpu/ (new module). Other modules import from fusionagi.gpu only when GPU acceleration is needed.
3. Backend abstraction — TensorBackend protocol abstracts TensorFlow, JAX, and pure-NumPy backends. The system auto-selects the best available backend.

Module: fusionagi/gpu/

fusionagi/gpu/
├── __init__.py           # Public API, auto-detection
├── backend.py            # TensorBackend protocol + backend registry
├── tensorflow_ops.py     # TF/TensorCore similarity, attention, scoring
├── tensor_similarity.py  # GPU-accelerated embedding similarity
├── tensor_attention.py   # Multi-head attention for consensus
├── tensor_scoring.py     # Batch hypothesis scoring on GPU
└── training.py           # GPU-accelerated training loop for self-improvement

Integration Points

1. Reasoning Pipeline (reasoning/)

Current: multi_path.py scores hypotheses sequentially with word-overlap heuristics. GPU: Batch embed hypotheses → cosine similarity matrix on GPU → parallel scoring.

Current: consensus_engine.py uses Jaccard word overlap for similarity. GPU: Dense embedding vectors + GPU cosine similarity for semantic matching.

2. Super Big Brain (core/super_big_brain.py)

Current: generate_and_score_parallel uses ThreadPoolExecutor. GPU: Tensor-parallel scoring with batched dot-products on TensorCore.

3. Memory Subsystem (memory/)

Current: semantic_graph.py is pure Python dict/adjacency list. GPU: Vector similarity search via GPU-accelerated embedding lookup.

4. Self-Improvement (self_improvement/)

Current: AutoTrainer suggests heuristic updates, no actual neural training. GPU: GPU-backed fine-tuning loops, gradient-based heuristic optimization.

5. Adapter Layer (adapters/)

New: TensorFlowAdapter — local model inference via TF/Keras with TensorCore.

Data Flow

User Prompt
  │
  ▼
Decomposition (CPU — symbolic)
  │
  ▼
Embedding (GPU — TF/TensorCore)
  │
  ├──► Similarity Matrix (GPU — batched cosine)
  │         │
  │         ▼
  │    Consensus Scoring (GPU — attention)
  │
  ├──► Hypothesis Scoring (GPU — batched inference)
  │
  ▼
Recomposition (CPU — symbolic + GPU scores)
  │
  ▼
Final Response

Backend Selection

from fusionagi.gpu import get_backend, TensorBackend

backend: TensorBackend = get_backend()  # Auto-selects best available
# Returns: TensorFlowBackend > NumPyBackend (fallback)

Dependencies

[project.optional-dependencies]
gpu = ["tensorflow>=2.16", "numpy>=1.26"]

TensorFlow 2.16+ includes:

• TensorCore (FP16/BF16 mixed-precision) via tf.keras.mixed_precision
• XLA compilation for GPU kernel fusion
• tf.linalg for batched linear algebra
• TensorRT integration for inference optimization