# 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

```python
from fusionagi.gpu import get_backend, TensorBackend

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

## Dependencies

```toml
[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